Published on in Vol 26 (2024)

This is a member publication of National University of Singapore

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/58892, first published .
AI-Based Noninvasive Blood Glucose Monitoring: Scoping Review

AI-Based Noninvasive Blood Glucose Monitoring: Scoping Review

AI-Based Noninvasive Blood Glucose Monitoring: Scoping Review

Review

1Department of Nursing, Ng Teng Fong General Hospital, Singapore, Singapore

2Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

3Research Unit of Health Sciences and Technology, University of Oulu, Oulu, Finland

4Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore

Corresponding Author:

Han Shi Jocelyn Chew, BSN, PhD

Alice Lee Centre for Nursing Studies

Yong Loo Lin School of Medicine

National University of Singapore

10 Medical Drive

Singapore, 117597

Singapore

Phone: 65 65168687

Email: jocelyn.chew.hs@nus.edu.sg


Background: Current blood glucose monitoring (BGM) methods are often invasive and require repetitive pricking of a finger to obtain blood samples, predisposing individuals to pain, discomfort, and infection. Noninvasive blood glucose monitoring (NIBGM) is ideal for minimizing discomfort, reducing the risk of infection, and increasing convenience.

Objective: This review aimed to map the use cases of artificial intelligence (AI) in NIBGM.

Methods: A systematic scoping review was conducted according to the Arksey O’Malley five-step framework. Eight electronic databases (CINAHL, Embase, PubMed, Web of Science, Scopus, The Cochrane-Central Library, ACM Digital Library, and IEEE Xplore) were searched from inception until February 8, 2023. Study selection was conducted by 2 independent reviewers, descriptive analysis was conducted, and findings were presented narratively. Study characteristics (author, country, type of publication, study design, population characteristics, mean age, types of noninvasive techniques used, and application, as well as characteristics of the BGM systems) were extracted independently and cross-checked by 2 investigators. Methodological quality appraisal was conducted using the Checklist for assessment of medical AI.

Results: A total of 33 papers were included, representing studies from Asia, the United States, Europe, the Middle East, and Africa published between 2005 and 2023. Most studies used optical techniques (n=19, 58%) to estimate blood glucose levels (n=27, 82%). Others used electrochemical sensors (n=4), imaging (n=2), mixed techniques (n=2), and tissue impedance (n=1). Accuracy ranged from 35.56% to 94.23% and Clarke error grid (A+B) ranged from 86.91% to 100%. The most popular machine learning algorithm used was random forest (n=10) and the most popular deep learning model was the artificial neural network (n=6). The mean overall checklist for assessment of medical AI score on the included papers was 33.5 (SD 3.09), suggesting an average of medium quality. The studies reviewed demonstrate that some AI techniques can accurately predict glucose levels from noninvasive sources while enhancing comfort and ease of use for patients. However, the overall range of accuracy was wide due to the heterogeneity of models and input data.

Conclusions: Efforts are needed to standardize and regulate the use of AI technologies in BGM, as well as develop consensus guidelines and protocols to ensure the quality and safety of AI-assisted monitoring systems. The use of AI for NIBGM is a promising area of research that has the potential to revolutionize diabetes management.

J Med Internet Res 2024;26:e58892

doi:10.2196/58892

Keywords



According to the International Diabetes Federation, around 537 million adults aged 20-79 years were diagnosed with diabetes in 2021, a number that has been projected to increase to 783 million in 2045 [IDF Diabetes Atlas 2021. Belgium. International Diabetes Federation; 2021. 1]. Chronic diabetes mellitus (DM) leads to many severe complications, including stroke, blindness, ulcers, kidney failure, and vascular damage [Duckworth W, Abraira C, Moritz T. Glucose control and vascular complications in veterans with type 2 diabetes. J Vasc Surg. Apr 2009;49(4):1084. [CrossRef]2]. DM management places a massive burden on health care expenditure, which has more than quadrupled to at least US $966 billion over the last 15 years [IDF Diabetes Atlas 2021. Belgium. International Diabetes Federation; 2021. 1]. The most common and possibly life-threatening complication of DM is hypoglycemia [Lee A, Juraschek SP, Windham BG, Lee CJ, Sharrett AR, Coresh J, et al. Severe hypoglycemia and risk of falls in type 2 diabetes: the atherosclerosis risk in communities (ARIC) study. Diabetes Care. Sep 2020;43(9):2060-2065. [FREE Full text] [CrossRef] [Medline]3], where common symptoms include autonomic (anxiety, tremors, palpitations, and diaphoresis) and neuroglycopenic (blurred vision, dizziness, headache, and loss of consciousness) manifestations [Nakhleh A, Shehadeh N. Hypoglycemia in diabetes: an update on pathophysiology, treatment, and prevention. World J Diabetes. Dec 15, 2021;12(12):2036-2049. [FREE Full text] [CrossRef] [Medline]4]. Therefore, individuals with DM are often advised to monitor their blood glucose levels regularly to detect and manage abnormalities [Nakhleh A, Shehadeh N. Hypoglycemia in diabetes: an update on pathophysiology, treatment, and prevention. World J Diabetes. Dec 15, 2021;12(12):2036-2049. [FREE Full text] [CrossRef] [Medline]4]. However, current blood glucose monitoring (BGM) methods are often invasive and require repetitive pricking of a finger to obtain blood samples, predisposing individuals to pain, discomfort, and infection [Rodbard D. Continuous glucose monitoring: a review of recent studies demonstrating improved glycemic outcomes. Diabetes Technol Ther. Jun 2017;19(S3):S25-S37. [FREE Full text] [CrossRef] [Medline]5]. The threshold for the onset of hypoglycemia also differs among patients (ie, typically higher in patients with uncontrolled diabetes), indicating the need for personalized BGM strategies [Nakhleh A, Shehadeh N. Hypoglycemia in diabetes: an update on pathophysiology, treatment, and prevention. World J Diabetes. Dec 15, 2021;12(12):2036-2049. [FREE Full text] [CrossRef] [Medline]4].

Besides invasive BGM techniques, minimally invasive and noninvasive techniques have been developed. The most common minimally invasive method adopts the glucose-oxidase principle where a wire-based sensor is inserted in the subcutaneous layer of the skin [Lane JE, Shivers JP, Zisser H. Continuous glucose monitors: current status and future developments. Curr Opin Endocrinol Diabetes Obes. Apr 2013;20(2):106-111. [CrossRef] [Medline]6]. It involves a calibration process that measures the current signal from the interstitial fluid rather than from the blood [McGarraugh G. The chemistry of commercial continuous glucose monitors. Diabetes Technol Ther. Jun 2009;11 Suppl 1(S1):S17-S24. [CrossRef] [Medline]7]. However, frequent calibration is required to maintain sensor accuracy by using traditional invasive fingerpick samples as a reference [Rossetti P, Bondia J, Vehí J, Fanelli CG. Estimating plasma glucose from interstitial glucose: the issue of calibration algorithms in commercial continuous glucose monitoring devices. Sensors (Basel). 2010;10(12):10936-10952. [FREE Full text] [CrossRef] [Medline]8]. Recent flash glucose monitoring uses factory calibration which does not require calibration by the user but this method requires frequent replacement of the needle electrode every 1-2 weeks [Bailey T, Bode BW, Christiansen MP, Klaff LJ, Alva S. The performance and usability of a factory-calibrated flash glucose monitoring system. Diabetes Technol Ther. Nov 2015;17(11):787-794. [FREE Full text] [CrossRef] [Medline]9].

Noninvasive blood glucose monitoring (NIBGM) is ideal for minimizing discomfort, reducing the risk of infection, and increasing convenience. The latest advancements include near-infrared spectroscopy (NIRS), photoplethysmography (PPG), Raman spectroscopy (RS), photoacoustic signals, and biosensors, like saliva and tears [Agrawal H, Jain P, Joshi AM. Machine learning models for non-invasive glucose measurement: towards diabetes management in smart healthcare. Health Technol (Berl). 2022;12(5):955-970. [FREE Full text] [CrossRef] [Medline]10]. As noninvasive methods do not directly detect blood glucose levels from blood samples, artificial intelligence (AI) could be used to estimate and predict blood glucose levels based on specific features selected. The use of AI could also facilitate the personalized BGM to inform treatment options, including insulin initiation and titration [Nimri R, Battelino T, Laffel LM, Slover RH, Schatz D, Weinzimer SA, et al. Insulin dose optimization using an automated artificial intelligence-based decision support system in youths with type 1 diabetes. Nat Med. Sep 2020;26(9):1380-1384. [CrossRef] [Medline]11,Kavakiotis I, Tsave O, Salifoglou A, Maglaveras N, Vlahavas I, Chouvarda I. Machine learning and data mining methods in diabetes research. Comput Struct Biotechnol J. 2017;15:104-116. [FREE Full text] [CrossRef] [Medline]12]. Although AI algorithms have been widely used in various health care settings including decision support systems and warning systems for hypoglycemia in patients with T1DM, little is known regarding the applicability of noninvasive methods [Woldaregay AZ, Årsand E, Botsis T, Albers D, Mamykina L, Hartvigsen G. Data-driven blood glucose pattern classification and anomalies detection: machine-learning applications in type 1 diabetes. J Med Internet Res. May 01, 2019;21(5):e11030. [FREE Full text] [CrossRef] [Medline]13,Contreras I, Vehi J. Artificial intelligence for diabetes management and decision support: literature review. J Med Internet Res. May 30, 2018;20(5):e10775. [FREE Full text] [CrossRef] [Medline]14].

Several studies have explored noninvasive methods for measuring and monitoring blood glucose levels in patients with and those without DM [Bolla AS, Priefer R. Blood glucose monitoring—an overview of current and future non-invasive devices. Diabetes Metab Syndr. 2020;14(5):739-751. [CrossRef] [Medline]15-Shokrekhodaei M, Cistola DP, Roberts RC, Quinones S. Non-invasive glucose monitoring using optical sensor and machine learning techniques for diabetes applications. IEEE Access. 2021;9:73029-73045. [FREE Full text] [CrossRef] [Medline]17]. However, these reviews did not cover the use of machine learning (ML) systems often embedded in these devices, nor did they perform a comprehensive analysis of the accuracy of the devices. Similarly, some reviews have focused on the use of AI approaches for diabetes diagnosis and management using optical sensors [Shokrekhodaei M, Cistola DP, Roberts RC, Quinones S. Non-invasive glucose monitoring using optical sensor and machine learning techniques for diabetes applications. IEEE Access. 2021;9:73029-73045. [FREE Full text] [CrossRef] [Medline]17] and breath analysis [Puntambekar V. MedTech internship diaries 2018. Natl Med J India. 2020;33(1):59. [CrossRef]18]. While these reviews present a comprehensive analysis of the available and used ML models, they often only cover one method of data collection, such as optical sensors. Two reviews focused on heart rate variability analysis [Gusev M, Poposka L, Spasevski G, Kostoska M, Koteska B, Simjanoska M, et al. Noninvasive glucose measurement using machine learning and neural network methods and correlation with heart rate variability. J Sens. Jan 06, 2020;2020:1-13. [CrossRef]19,Swapna G, Soman K, Vinayakumar R. Deep Learning Techniques for Biomedical and Health Informatics. New York. Springer; 2019. 20] while another review focused on both electrocardiography (ECG) and PPG signals [Zanelli S, Ammi M, Hallab M, El Yacoubi MA. Diabetes detection and management through photoplethysmographic and electrocardiographic signals analysis: a systematic review. Sensors (Basel). Jun 29, 2022;22(13):4890. [FREE Full text] [CrossRef] [Medline]21]. Furthermore, another review focused on detailing an overview of ML and AI techniques in the field of DM detection and self-management but not on NIBGM [Chaki J, Ganesh ST, Cidham S, Theertan SA. Machine learning and artificial intelligence based diabetes mellitus detection and self-management: a systematic review. J King Saud Univ Comp Inf Sci. 2022;34(6):3204-3225. [CrossRef]22]. Therefore, a comprehensive review of the existing literature is needed to understand the current status of the use of AI in NIBGM. Given the novelty of using AI in NIBGM systems, evidence on the accuracy and effectiveness of such technologies is limited. Thus, we conducted a scoping review to rapidly map the key concepts and evidence regarding the use of AI for continuous NIBGM. Our findings would scope the available evidence on this topic, and identify the existing research gaps to inform the value and direction of conducting a full systematic review [Levac D, Colquhoun H, O'Brien KK. Scoping studies: advancing the methodology. Implement Sci. 2010;5(1):69. [FREE Full text] [CrossRef] [Medline]23].


Overview

This scoping review was conducted using Arksey and O’Malley’s five-step framework and reported according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta‐Analyses extension for Scoping Reviews) guidelines (

Multimedia Appendix 1

PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta‐Analyses extension for Scoping Reviews) checklist.

DOCX File , 17 KBMultimedia Appendix 1) [Arksey H, O'Malley L. Scoping studies: towards a methodological framework. Int J Soc Res Meth. 2005;8(1):19-32. [CrossRef]24].

Step 1: Identifying the Research Question

Our primary research question was as follows: (1) what are the use cases of AI-assisted noninvasive BGM systems? Our secondary research question was as follows: (2) what are the AI models developed for noninvasive BGM?

Step 2: Identifying Relevant Studies

A comprehensive literature search was conducted on 08 February 2023 across 8 major databases, namely CINAHL, Embase, PubMed, Web of Science, Scopus, The Cochrane-Central Library, ACM Digital Library, and IEEE Xplore. The following key terms were used in the search: “glucose monitoring”; “monitoring glucose”; “artificial intelligence”; “computer heuristics”; “fuzzy logic”; “knowledge bases”; “machine learning”; “natural language processing”; “neural networks”; and “sentiment analysis” (

Multimedia Appendix 2

Specific search strategy for each database.

DOCX File , 15 KBMultimedia Appendix 2). Content experts were consulted and previous reviews on similar topics were hand searched for additional relevant studies.

Step 3: Study Selection and Methodological Quality Assessment

After the search was completed, duplicate studies were identified and removed. The remaining papers had their abstracts screened in a double-blinded, independent manner by 2 investigators (PZC and HSJC) according to the following inclusion criteria: prospective and retrospective primary studies that described the use of AI for continuous noninvasive BGM among human participants and written primarily in English. Papers were excluded if they: (1) were reviews or gray literature, (2) did not involve AI in the continuous NIBGM, (3) involved nonhuman participants, or (4) were primarily non–English-language papers. Disputes were resolved via discussion and consensus by the 2 investigators. Both investigators (PZC and HSJC) then combed the relevant journals, bibliography, and conference submissions to identify more relevant papers, and all papers then underwent a full-text sieve based on the inclusion and exclusion criteria.

The methodological quality appraisal of each study was conducted independently by 2 reviewers (PZC and EJ) using the Checklist for assessment of medical AI (ChAMAI) [Cabitza F, Campagner A. The need to separate the wheat from the chaff in medical informatics: introducing a comprehensive checklist for the (self)-assessment of medical AI studies. Int J Med Inform. Sep 2021;153:104510. [FREE Full text] [CrossRef] [Medline]25]. Reviewers rated each included study on 30 items representing 6 domains namely problem understanding, data understanding, data preparation, modeling, validation, and deployment. Each item (bolded to represent having a high priority) received a rating of OK (adequately addressed), mR (sufficient but improvable), or MR (inadequately addressed, corresponding to a score of 2, 1, and 0, respectively. Items on a low-priority (not bolded) received half the scores and the maximum total score is 50 [Zhou Y, Ge Y, Shi X, Wu K, Chen W, Ding Y, et al. Machine learning predictive models for acute pancreatitis: a systematic review. Int J Med Inform. Jan 2022;157:104641. [FREE Full text] [CrossRef] [Medline]26]. Overall scores indicate the study quality to be low (0-19.5), medium (20-34.5), or high (35-50). Discrepancies were resolved by a third reviewer (HSJC).

Step 4: Data Charting

Data were extracted independently and cross-checked by 2 investigators (PZC and HSJC). Any disputes were resolved via discussion and consensus. Study characteristics extracted included the author names, country, type of publication, study design, population characteristics, mean age, types of noninvasive techniques used, and application, as well as characteristics of the BGM systems (use of AI, AI type, AI features, types of data imputed, technology used, dataset, validation, proportion of training and testing dataset, and metrics used).


Step 5: Collating, Summarizing, and Reporting the Results

Our initial search yielded 1270 studies. After removing duplicated citations, screening through 848 titles and abstracts, and 65 full-text papers, 33 papers were included in this scoping review (Figure 1).

Figure 1. PRISMA-ScR flowchart. PRISMA-ScR: Preferred Reporting Items for Systematic Reviews and Meta‐Analyses extension for Scoping Reviews.

Study Characteristics

Most of the included studies were conducted in Asia (n=21, 64%) [Agrawal H, Jain P, Joshi AM. Machine learning models for non-invasive glucose measurement: towards diabetes management in smart healthcare. Health Technol (Berl). 2022;12(5):955-970. [FREE Full text] [CrossRef] [Medline]10,Sumaiya J, Hasan MR, Hossain E. Noninvasive blood glucose measurement using live video by smartphone. 2020. Presented at: 2020 IEEE 8th R10 Humanitarian Technology Conference (R10-HTC); December 01, 2020; Kuching, Malaysia. [CrossRef]27-Ali MS, Shoumy NJ, Khatun S, Kamarudin LM. Non-invasive blood glucose measurement performance analysis through UWB imaging. 2016. Presented at: 2016 3rd International Conference on Electronic Design (ICED); August 11, 2016; Phuket, Thailand. [CrossRef]46], were peer-reviewed journal papers (n=24, 73%) [Agrawal H, Jain P, Joshi AM. Machine learning models for non-invasive glucose measurement: towards diabetes management in smart healthcare. Health Technol (Berl). 2022;12(5):955-970. [FREE Full text] [CrossRef] [Medline]10,Guo D, Zhang D, Zhang L, Lu G. Non-invasive blood glucose monitoring for diabetics by means of breath signal analysis. Sens Actuators B. 2012;173:106-113. [CrossRef]30-Zhu J, Zhou Y, Huang J, Zhou A, Chen Z. Noninvasive blood glucose concentration measurement based on conservation of energy metabolism and machine learning. Sensors (Basel). Oct 21, 2021;21(21):6989. [FREE Full text] [CrossRef] [Medline]41,Alarcón-Paredes A, Francisco-García V, Guzmán-Guzmán I, Cantillo-Negrete J, Cuevas-Valencia R, Alonso-Silverio G. An IoT-based non-invasive glucose level monitoring system using raspberry pi. Appl Sci. Jul 28, 2019;9(15):3046. [CrossRef]47-Valero M, Pola P, Falaiye O, Ingram KH, Zhao L, Shahriar H, et al. Development of a noninvasive blood glucose monitoring system prototype: pilot study. JMIR Form Res. Aug 26, 2022;6(8):e38664. [FREE Full text] [CrossRef] [Medline]57], were prospective cohort studies (n=20, 61%) [Guo D, Zhang D, Zhang L, Lu G. Non-invasive blood glucose monitoring for diabetics by means of breath signal analysis. Sens Actuators B. 2012;173:106-113. [CrossRef]30-Liu WJ, Huang A, Wang P, Chu C. PbFG: physique-based fuzzy granular modeling for non-invasive blood glucose monitoring. Inf Sci. 2019;497:56-76. [CrossRef]33,Nie Z, Rong M, Li K. Blood glucose prediction based on imagingphotoplethysmography in combination with machine learning. Biomed Signal Process Control. 2023;79:104179. [CrossRef]35-Rajeshwaran K, Thirunavukkarasu T, Pooja S, Rajeshkumar S. Machine learning based non-invasive glucose observation for diabetes. 2022. Presented at: 2022 Smart Technologies, Communication and Robotics (STCR); December 11, 2022; Sathyamangalam, India. [CrossRef]43,Alarcón-Paredes A, Francisco-García V, Guzmán-Guzmán I, Cantillo-Negrete J, Cuevas-Valencia R, Alonso-Silverio G. An IoT-based non-invasive glucose level monitoring system using raspberry pi. Appl Sci. Jul 28, 2019;9(15):3046. [CrossRef]47,Bent B, Cho PJ, Wittmann A, Thacker C, Muppidi S, Snyder M, et al. Non-invasive wearables for remote monitoring of HbA1c and glucose variability: proof of concept. BMJ Open Diabetes Res Care. Jun 2021;9(1):e002027. [FREE Full text] [CrossRef] [Medline]49,Enejder AMK, Scecina TG, Oh J, Hunter M, Shih W, Sasic S, et al. Raman spectroscopy for noninvasive glucose measurements. J Biomed Opt. 2005;10(3):031114. [FREE Full text] [CrossRef] [Medline]51-Jain P, Joshi AM, Mohanty SP. iGLU: an intelligent device for accurate noninvasive blood glucose-level monitoring in smart healthcare. IEEE Consumer Electron Mag. 2020;9(1):35-42. [CrossRef]53,Valero M, Pola P, Falaiye O, Ingram KH, Zhao L, Shahriar H, et al. Development of a noninvasive blood glucose monitoring system prototype: pilot study. JMIR Form Res. Aug 26, 2022;6(8):e38664. [FREE Full text] [CrossRef] [Medline]57,Francisco-García V, Guzmán-Guzmán IP, Salgado-Rivera R, Alarcón-Paredes A. Non-invasive glucose level estimation: a comparison of regression models using the MFCC as feature extractor. In: Pattern Recognition. Cham. Springer International Publishing; 2019:206-215.58], and used optical (eg, near-infrared and PPG) techniques (n=19, 58%) [Agrawal H, Jain P, Joshi AM. Machine learning models for non-invasive glucose measurement: towards diabetes management in smart healthcare. Health Technol (Berl). 2022;12(5):955-970. [FREE Full text] [CrossRef] [Medline]10,Sumaiya J, Hasan MR, Hossain E. Noninvasive blood glucose measurement using live video by smartphone. 2020. Presented at: 2020 IEEE 8th R10 Humanitarian Technology Conference (R10-HTC); December 01, 2020; Kuching, Malaysia. [CrossRef]27,Nanayakkara ND, Munasingha SC, Ruwanpathirana GP. Non-invasive blood glucose monitoring using a hybrid technique. 2018. Presented at: Moratuwa Engineering Research Conference (MERCon)/4th International Multidisciplinary Engineering Research Conference; June 01, 2018; Katubedda, Sri Lanka. [CrossRef]29,Habbu S, Dale M, Ghongade R. Estimation of blood glucose by non-invasive method using photoplethysmography. Sādhanā. May 6, 2019;44(6):135. [CrossRef]31,Liu WJ, Huang A, Wang P, Chu C. PbFG: physique-based fuzzy granular modeling for non-invasive blood glucose monitoring. Inf Sci. 2019;497:56-76. [CrossRef]33,Nie Z, Rong M, Li K. Blood glucose prediction based on imagingphotoplethysmography in combination with machine learning. Biomed Signal Process Control. 2023;79:104179. [CrossRef]35-Segman YJ. Device and method for noninvasive glucose assessment. J Diabetes Sci Technol. Nov 2018;12(6):1159-1168. [FREE Full text] [CrossRef] [Medline]37,Yu Y, Huang J, Zhu J, Liang S. An accurate noninvasive blood glucose measurement system using portable near-infrared spectrometer and transfer learning framework. IEEE Sens J. 2021;21(3):3506-3519. [CrossRef]39,Zhang G, Mei Z, Zhang Y, Ma X, Lo B, Chen D, et al. A noninvasive blood glucose monitoring system based on smartphone PPG signal processing and machine learning. IEEE Trans Ind Inf. 2020;16(11):7209-7218. [CrossRef]40,Manurung BE, Munggaran HR, Ramadhan GF, Koesoema AP. Non-invasive blood glucose monitoring using near-infrared spectroscopy based on internet of things using machine learning. 2019. Presented at: 7th IEEE Region10 Humanitarian Technology Conference (IEEE R10 HTC); November 12, 2019; Depok, West Java, Indonesia. [CrossRef]42,Abubeker KM, Baskar S. A machine learning strategy for internet-of-things-enabled diabetic prediction to mitigate pneumonia risk. 2022. Presented at: 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO); October 12, 2022; Noida, India. [CrossRef]44,Krishnan SH, Vinupritha P, Kathirvelu D. Non-invasive glucose monitoring using machine learning. 2020. Presented at: 2020 International Conference on Communication and Signal Processing (ICCSP); July 28, 2020; Chennai, India. [CrossRef]45,Alarcón-Paredes A, Francisco-García V, Guzmán-Guzmán I, Cantillo-Negrete J, Cuevas-Valencia R, Alonso-Silverio G. An IoT-based non-invasive glucose level monitoring system using raspberry pi. Appl Sci. Jul 28, 2019;9(15):3046. [CrossRef]47,Enejder AMK, Scecina TG, Oh J, Hunter M, Shih W, Sasic S, et al. Raman spectroscopy for noninvasive glucose measurements. J Biomed Opt. 2005;10(3):031114. [FREE Full text] [CrossRef] [Medline]51,Jain P, Joshi AM, Mohanty SP. iGLU: an intelligent device for accurate noninvasive blood glucose-level monitoring in smart healthcare. IEEE Consumer Electron Mag. 2020;9(1):35-42. [CrossRef]53,Monte-Moreno E. Non-invasive estimate of blood glucose and blood pressure from a photoplethysmograph by means of machine learning techniques. Artif Intell Med. Oct 2011;53(2):127-138. [CrossRef] [Medline]56-Francisco-García V, Guzmán-Guzmán IP, Salgado-Rivera R, Alarcón-Paredes A. Non-invasive glucose level estimation: a comparison of regression models using the MFCC as feature extractor. In: Pattern Recognition. Cham. Springer International Publishing; 2019:206-215.58] to estimate blood glucose levels (n=27, 82%) [Sumaiya J, Hasan MR, Hossain E. Noninvasive blood glucose measurement using live video by smartphone. 2020. Presented at: 2020 IEEE 8th R10 Humanitarian Technology Conference (R10-HTC); December 01, 2020; Kuching, Malaysia. [CrossRef]27,Nanayakkara ND, Munasingha SC, Ruwanpathirana GP. Non-invasive blood glucose monitoring using a hybrid technique. 2018. Presented at: Moratuwa Engineering Research Conference (MERCon)/4th International Multidisciplinary Engineering Research Conference; June 01, 2018; Katubedda, Sri Lanka. [CrossRef]29-Habbu S, Dale M, Ghongade R. Estimation of blood glucose by non-invasive method using photoplethysmography. Sādhanā. May 6, 2019;44(6):135. [CrossRef]31,Liu WJ, Huang A, Wang P, Chu C. PbFG: physique-based fuzzy granular modeling for non-invasive blood glucose monitoring. Inf Sci. 2019;497:56-76. [CrossRef]33-Song K, Ha U, Park S, Bae J, Yoo H. An impedance and multi-wavelength near-infrared spectroscopy IC for non-invasive blood glucose estimation. IEEE J Solid-State Circuits. 2015;50(4):1025-1037. [CrossRef]38,Zhang G, Mei Z, Zhang Y, Ma X, Lo B, Chen D, et al. A noninvasive blood glucose monitoring system based on smartphone PPG signal processing and machine learning. IEEE Trans Ind Inf. 2020;16(11):7209-7218. [CrossRef]40-Rajeshwaran K, Thirunavukkarasu T, Pooja S, Rajeshkumar S. Machine learning based non-invasive glucose observation for diabetes. 2022. Presented at: 2022 Smart Technologies, Communication and Robotics (STCR); December 11, 2022; Sathyamangalam, India. [CrossRef]43,Krishnan SH, Vinupritha P, Kathirvelu D. Non-invasive glucose monitoring using machine learning. 2020. Presented at: 2020 International Conference on Communication and Signal Processing (ICCSP); July 28, 2020; Chennai, India. [CrossRef]45-Arbi KF, Soulimane S, Saffih F, Bechar MA, Azzoug O. Blood glucose estimation based on ECG signal. Phys Eng Sci Med. Mar 2023;46(1):255-264. [CrossRef] [Medline]48,Bogue-Jimenez B, Huang X, Powell D, Doblas A. Selection of noninvasive features in wrist-based wearable sensors to predict blood glucose concentrations using machine learning algorithms. Sensors (Basel). May 06, 2022;22(9):3534. [FREE Full text] [CrossRef] [Medline]50-Jain P, Joshi AM, Mohanty SP. iGLU: an intelligent device for accurate noninvasive blood glucose-level monitoring in smart healthcare. IEEE Consumer Electron Mag. 2020;9(1):35-42. [CrossRef]53,Malinin L. Development of a non-invasive blood glucose monitor based on impedance measurements. IJBET. 2012;8(1):60. [CrossRef]55-Francisco-García V, Guzmán-Guzmán IP, Salgado-Rivera R, Alarcón-Paredes A. Non-invasive glucose level estimation: a comparison of regression models using the MFCC as feature extractor. In: Pattern Recognition. Cham. Springer International Publishing; 2019:206-215.58] (Table 1). Most of the studies did not report the population characteristics [Agrawal H, Jain P, Joshi AM. Machine learning models for non-invasive glucose measurement: towards diabetes management in smart healthcare. Health Technol (Berl). 2022;12(5):955-970. [FREE Full text] [CrossRef] [Medline]10,Sumaiya J, Hasan MR, Hossain E. Noninvasive blood glucose measurement using live video by smartphone. 2020. Presented at: 2020 IEEE 8th R10 Humanitarian Technology Conference (R10-HTC); December 01, 2020; Kuching, Malaysia. [CrossRef]27,Nanayakkara ND, Munasingha SC, Ruwanpathirana GP. Non-invasive blood glucose monitoring using a hybrid technique. 2018. Presented at: Moratuwa Engineering Research Conference (MERCon)/4th International Multidisciplinary Engineering Research Conference; June 01, 2018; Katubedda, Sri Lanka. [CrossRef]29,Liu WJ, Huang A, Wang P, Chu C. PbFG: physique-based fuzzy granular modeling for non-invasive blood glucose monitoring. Inf Sci. 2019;497:56-76. [CrossRef]33,Nie Z, Rong M, Li K. Blood glucose prediction based on imagingphotoplethysmography in combination with machine learning. Biomed Signal Process Control. 2023;79:104179. [CrossRef]35-Yu Y, Huang J, Zhu J, Liang S. An accurate noninvasive blood glucose measurement system using portable near-infrared spectrometer and transfer learning framework. IEEE Sens J. 2021;21(3):3506-3519. [CrossRef]39,Zhu J, Zhou Y, Huang J, Zhou A, Chen Z. Noninvasive blood glucose concentration measurement based on conservation of energy metabolism and machine learning. Sensors (Basel). Oct 21, 2021;21(21):6989. [FREE Full text] [CrossRef] [Medline]41,Manurung BE, Munggaran HR, Ramadhan GF, Koesoema AP. Non-invasive blood glucose monitoring using near-infrared spectroscopy based on internet of things using machine learning. 2019. Presented at: 7th IEEE Region10 Humanitarian Technology Conference (IEEE R10 HTC); November 12, 2019; Depok, West Java, Indonesia. [CrossRef]42,Abubeker KM, Baskar S. A machine learning strategy for internet-of-things-enabled diabetic prediction to mitigate pneumonia risk. 2022. Presented at: 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO); October 12, 2022; Noida, India. [CrossRef]44-Ali MS, Shoumy NJ, Khatun S, Kamarudin LM. Non-invasive blood glucose measurement performance analysis through UWB imaging. 2016. Presented at: 2016 3rd International Conference on Electronic Design (ICED); August 11, 2016; Phuket, Thailand. [CrossRef]46,Arbi KF, Soulimane S, Saffih F, Bechar MA, Azzoug O. Blood glucose estimation based on ECG signal. Phys Eng Sci Med. Mar 2023;46(1):255-264. [CrossRef] [Medline]48,Khanam JJ, Foo SY. A comparison of machine learning algorithms for diabetes prediction. ICT Express. 2021;7(4):432-439. [CrossRef]54,Monte-Moreno E. Non-invasive estimate of blood glucose and blood pressure from a photoplethysmograph by means of machine learning techniques. Artif Intell Med. Oct 2011;53(2):127-138. [CrossRef] [Medline]56-Francisco-García V, Guzmán-Guzmán IP, Salgado-Rivera R, Alarcón-Paredes A. Non-invasive glucose level estimation: a comparison of regression models using the MFCC as feature extractor. In: Pattern Recognition. Cham. Springer International Publishing; 2019:206-215.58], mean age [Agrawal H, Jain P, Joshi AM. Machine learning models for non-invasive glucose measurement: towards diabetes management in smart healthcare. Health Technol (Berl). 2022;12(5):955-970. [FREE Full text] [CrossRef] [Medline]10,Sumaiya J, Hasan MR, Hossain E. Noninvasive blood glucose measurement using live video by smartphone. 2020. Presented at: 2020 IEEE 8th R10 Humanitarian Technology Conference (R10-HTC); December 01, 2020; Kuching, Malaysia. [CrossRef]27-Rachim VP, Chung W. Wearable-band type visible-near infrared optical biosensor for non-invasive blood glucose monitoring. Sens Actuators B. 2019;286:173-180. [CrossRef]36,Yu Y, Huang J, Zhu J, Liang S. An accurate noninvasive blood glucose measurement system using portable near-infrared spectrometer and transfer learning framework. IEEE Sens J. 2021;21(3):3506-3519. [CrossRef]39,Zhu J, Zhou Y, Huang J, Zhou A, Chen Z. Noninvasive blood glucose concentration measurement based on conservation of energy metabolism and machine learning. Sensors (Basel). Oct 21, 2021;21(21):6989. [FREE Full text] [CrossRef] [Medline]41-Rajeshwaran K, Thirunavukkarasu T, Pooja S, Rajeshkumar S. Machine learning based non-invasive glucose observation for diabetes. 2022. Presented at: 2022 Smart Technologies, Communication and Robotics (STCR); December 11, 2022; Sathyamangalam, India. [CrossRef]43,Krishnan SH, Vinupritha P, Kathirvelu D. Non-invasive glucose monitoring using machine learning. 2020. Presented at: 2020 International Conference on Communication and Signal Processing (ICCSP); July 28, 2020; Chennai, India. [CrossRef]45,Ali MS, Shoumy NJ, Khatun S, Kamarudin LM. Non-invasive blood glucose measurement performance analysis through UWB imaging. 2016. Presented at: 2016 3rd International Conference on Electronic Design (ICED); August 11, 2016; Phuket, Thailand. [CrossRef]46,Arbi KF, Soulimane S, Saffih F, Bechar MA, Azzoug O. Blood glucose estimation based on ECG signal. Phys Eng Sci Med. Mar 2023;46(1):255-264. [CrossRef] [Medline]48,Bogue-Jimenez B, Huang X, Powell D, Doblas A. Selection of noninvasive features in wrist-based wearable sensors to predict blood glucose concentrations using machine learning algorithms. Sensors (Basel). May 06, 2022;22(9):3534. [FREE Full text] [CrossRef] [Medline]50-Jain P, Joshi AM, Mohanty SP. iGLU: an intelligent device for accurate noninvasive blood glucose-level monitoring in smart healthcare. IEEE Consumer Electron Mag. 2020;9(1):35-42. [CrossRef]53,Malinin L. Development of a non-invasive blood glucose monitor based on impedance measurements. IJBET. 2012;8(1):60. [CrossRef]55,Valero M, Pola P, Falaiye O, Ingram KH, Zhao L, Shahriar H, et al. Development of a noninvasive blood glucose monitoring system prototype: pilot study. JMIR Form Res. Aug 26, 2022;6(8):e38664. [FREE Full text] [CrossRef] [Medline]57], and sex [Sumaiya J, Hasan MR, Hossain E. Noninvasive blood glucose measurement using live video by smartphone. 2020. Presented at: 2020 IEEE 8th R10 Humanitarian Technology Conference (R10-HTC); December 01, 2020; Kuching, Malaysia. [CrossRef]27-Nanayakkara ND, Munasingha SC, Ruwanpathirana GP. Non-invasive blood glucose monitoring using a hybrid technique. 2018. Presented at: Moratuwa Engineering Research Conference (MERCon)/4th International Multidisciplinary Engineering Research Conference; June 01, 2018; Katubedda, Sri Lanka. [CrossRef]29,Liu WJ, Huang A, Wang P, Chu C. PbFG: physique-based fuzzy granular modeling for non-invasive blood glucose monitoring. Inf Sci. 2019;497:56-76. [CrossRef]33,Nie Z, Rong M, Li K. Blood glucose prediction based on imagingphotoplethysmography in combination with machine learning. Biomed Signal Process Control. 2023;79:104179. [CrossRef]35,Rachim VP, Chung W. Wearable-band type visible-near infrared optical biosensor for non-invasive blood glucose monitoring. Sens Actuators B. 2019;286:173-180. [CrossRef]36,Song K, Ha U, Park S, Bae J, Yoo H. An impedance and multi-wavelength near-infrared spectroscopy IC for non-invasive blood glucose estimation. IEEE J Solid-State Circuits. 2015;50(4):1025-1037. [CrossRef]38-Zhu J, Zhou Y, Huang J, Zhou A, Chen Z. Noninvasive blood glucose concentration measurement based on conservation of energy metabolism and machine learning. Sensors (Basel). Oct 21, 2021;21(21):6989. [FREE Full text] [CrossRef] [Medline]41,Rajeshwaran K, Thirunavukkarasu T, Pooja S, Rajeshkumar S. Machine learning based non-invasive glucose observation for diabetes. 2022. Presented at: 2022 Smart Technologies, Communication and Robotics (STCR); December 11, 2022; Sathyamangalam, India. [CrossRef]43-Arbi KF, Soulimane S, Saffih F, Bechar MA, Azzoug O. Blood glucose estimation based on ECG signal. Phys Eng Sci Med. Mar 2023;46(1):255-264. [CrossRef] [Medline]48,Bogue-Jimenez B, Huang X, Powell D, Doblas A. Selection of noninvasive features in wrist-based wearable sensors to predict blood glucose concentrations using machine learning algorithms. Sensors (Basel). May 06, 2022;22(9):3534. [FREE Full text] [CrossRef] [Medline]50-Geelhoed-Duijvestijn P, Vegelyte D, Kownacka A, Anton N, Joosse M, Wilson C. Performance of the prototype NovioSense noninvasive biosensor for tear glucose in type 1 diabetes. J Diabetes Sci Technol. Nov 2021;15(6):1320-1325. [FREE Full text] [CrossRef] [Medline]52,Khanam JJ, Foo SY. A comparison of machine learning algorithms for diabetes prediction. ICT Express. 2021;7(4):432-439. [CrossRef]54,Malinin L. Development of a non-invasive blood glucose monitor based on impedance measurements. IJBET. 2012;8(1):60. [CrossRef]55,Valero M, Pola P, Falaiye O, Ingram KH, Zhao L, Shahriar H, et al. Development of a noninvasive blood glucose monitoring system prototype: pilot study. JMIR Form Res. Aug 26, 2022;6(8):e38664. [FREE Full text] [CrossRef] [Medline]57,Francisco-García V, Guzmán-Guzmán IP, Salgado-Rivera R, Alarcón-Paredes A. Non-invasive glucose level estimation: a comparison of regression models using the MFCC as feature extractor. In: Pattern Recognition. Cham. Springer International Publishing; 2019:206-215.58].

Table 1. Summary of study characteristics.
Study characteristicsValues (N=33), n (%)
Country

Algeria [Arbi KF, Soulimane S, Saffih F, Bechar MA, Azzoug O. Blood glucose estimation based on ECG signal. Phys Eng Sci Med. Mar 2023;46(1):255-264. [CrossRef] [Medline]48]1 (3)

Bangladesh [Sumaiya J, Hasan MR, Hossain E. Noninvasive blood glucose measurement using live video by smartphone. 2020. Presented at: 2020 IEEE 8th R10 Humanitarian Technology Conference (R10-HTC); December 01, 2020; Kuching, Malaysia. [CrossRef]27]1 (3)

China [Liu WJ, Huang A, Wang P, Chu C. PbFG: physique-based fuzzy granular modeling for non-invasive blood glucose monitoring. Inf Sci. 2019;497:56-76. [CrossRef]33,Nie Z, Rong M, Li K. Blood glucose prediction based on imagingphotoplethysmography in combination with machine learning. Biomed Signal Process Control. 2023;79:104179. [CrossRef]35,Yu Y, Huang J, Zhu J, Liang S. An accurate noninvasive blood glucose measurement system using portable near-infrared spectrometer and transfer learning framework. IEEE Sens J. 2021;21(3):3506-3519. [CrossRef]39-Zhu J, Zhou Y, Huang J, Zhou A, Chen Z. Noninvasive blood glucose concentration measurement based on conservation of energy metabolism and machine learning. Sensors (Basel). Oct 21, 2021;21(21):6989. [FREE Full text] [CrossRef] [Medline]41]5 (15)

China (Hong Kong) [Guo D, Zhang D, Zhang L, Lu G. Non-invasive blood glucose monitoring for diabetics by means of breath signal analysis. Sens Actuators B. 2012;173:106-113. [CrossRef]30]1 (3)

India [Agrawal H, Jain P, Joshi AM. Machine learning models for non-invasive glucose measurement: towards diabetes management in smart healthcare. Health Technol (Berl). 2022;12(5):955-970. [FREE Full text] [CrossRef] [Medline]10,Habbu S, Dale M, Ghongade R. Estimation of blood glucose by non-invasive method using photoplethysmography. Sādhanā. May 6, 2019;44(6):135. [CrossRef]31,Lekha S, Suchetha M. Real-time non-invasive detection and classification of diabetes using modified convolution neural network. IEEE J Biomed Health Inform. 2018;22(5):1630-1636. [CrossRef]32,Malik S, Khadgawat R, Anand S, Gupta S. Non-invasive detection of fasting blood glucose level via electrochemical measurement of saliva. Springerplus. 2016;5(1):701. [FREE Full text] [CrossRef] [Medline]34,Rajeshwaran K, Thirunavukkarasu T, Pooja S, Rajeshkumar S. Machine learning based non-invasive glucose observation for diabetes. 2022. Presented at: 2022 Smart Technologies, Communication and Robotics (STCR); December 11, 2022; Sathyamangalam, India. [CrossRef]43-Krishnan SH, Vinupritha P, Kathirvelu D. Non-invasive glucose monitoring using machine learning. 2020. Presented at: 2020 International Conference on Communication and Signal Processing (ICCSP); July 28, 2020; Chennai, India. [CrossRef]45]7 (21)

Indonesia [Manurung BE, Munggaran HR, Ramadhan GF, Koesoema AP. Non-invasive blood glucose monitoring using near-infrared spectroscopy based on internet of things using machine learning. 2019. Presented at: 7th IEEE Region10 Humanitarian Technology Conference (IEEE R10 HTC); November 12, 2019; Depok, West Java, Indonesia. [CrossRef]42]1 (3)

Israel [Segman YJ. Device and method for noninvasive glucose assessment. J Diabetes Sci Technol. Nov 2018;12(6):1159-1168. [FREE Full text] [CrossRef] [Medline]37]1 (3)

Malaysia [Ali MS, Shoumy NJ, Khatun S, Kamarudin LM. Non-invasive blood glucose measurement performance analysis through UWB imaging. 2016. Presented at: 2016 3rd International Conference on Electronic Design (ICED); August 11, 2016; Phuket, Thailand. [CrossRef]46]1 (3)

Mexico [Alarcón-Paredes A, Francisco-García V, Guzmán-Guzmán I, Cantillo-Negrete J, Cuevas-Valencia R, Alonso-Silverio G. An IoT-based non-invasive glucose level monitoring system using raspberry pi. Appl Sci. Jul 28, 2019;9(15):3046. [CrossRef]47,Francisco-García V, Guzmán-Guzmán IP, Salgado-Rivera R, Alarcón-Paredes A. Non-invasive glucose level estimation: a comparison of regression models using the MFCC as feature extractor. In: Pattern Recognition. Cham. Springer International Publishing; 2019:206-215.58]2 (6)

Netherlands [Geelhoed-Duijvestijn P, Vegelyte D, Kownacka A, Anton N, Joosse M, Wilson C. Performance of the prototype NovioSense noninvasive biosensor for tear glucose in type 1 diabetes. J Diabetes Sci Technol. Nov 2021;15(6):1320-1325. [FREE Full text] [CrossRef] [Medline]52]1 (3)

South Korea [Rachim VP, Chung W. Wearable-band type visible-near infrared optical biosensor for non-invasive blood glucose monitoring. Sens Actuators B. 2019;286:173-180. [CrossRef]36,Song K, Ha U, Park S, Bae J, Yoo H. An impedance and multi-wavelength near-infrared spectroscopy IC for non-invasive blood glucose estimation. IEEE J Solid-State Circuits. 2015;50(4):1025-1037. [CrossRef]38]2 (6)

Spain [Monte-Moreno E. Non-invasive estimate of blood glucose and blood pressure from a photoplethysmograph by means of machine learning techniques. Artif Intell Med. Oct 2011;53(2):127-138. [CrossRef] [Medline]56]1 (3)

Sri Lanka [Balasooriya K, Nanayakkara ND. Predicting short-term changing blood glucose level of diabetes patients using noninvasive data. 2020. Presented at: 2020 IEEE Region 10 Conference (TENCON); November 16, 2020; Osaka, Japan. [CrossRef]28,Nanayakkara ND, Munasingha SC, Ruwanpathirana GP. Non-invasive blood glucose monitoring using a hybrid technique. 2018. Presented at: Moratuwa Engineering Research Conference (MERCon)/4th International Multidisciplinary Engineering Research Conference; June 01, 2018; Katubedda, Sri Lanka. [CrossRef]29]2 (6)

United States [Bent B, Cho PJ, Wittmann A, Thacker C, Muppidi S, Snyder M, et al. Non-invasive wearables for remote monitoring of HbA1c and glucose variability: proof of concept. BMJ Open Diabetes Res Care. Jun 2021;9(1):e002027. [FREE Full text] [CrossRef] [Medline]49-Enejder AMK, Scecina TG, Oh J, Hunter M, Shih W, Sasic S, et al. Raman spectroscopy for noninvasive glucose measurements. J Biomed Opt. 2005;10(3):031114. [FREE Full text] [CrossRef] [Medline]51,Jain P, Joshi AM, Mohanty SP. iGLU: an intelligent device for accurate noninvasive blood glucose-level monitoring in smart healthcare. IEEE Consumer Electron Mag. 2020;9(1):35-42. [CrossRef]53-Malinin L. Development of a non-invasive blood glucose monitor based on impedance measurements. IJBET. 2012;8(1):60. [CrossRef]55,Valero M, Pola P, Falaiye O, Ingram KH, Zhao L, Shahriar H, et al. Development of a noninvasive blood glucose monitoring system prototype: pilot study. JMIR Form Res. Aug 26, 2022;6(8):e38664. [FREE Full text] [CrossRef] [Medline]57]7 (21)
Type of publication

Journal paper [Agrawal H, Jain P, Joshi AM. Machine learning models for non-invasive glucose measurement: towards diabetes management in smart healthcare. Health Technol (Berl). 2022;12(5):955-970. [FREE Full text] [CrossRef] [Medline]10,Guo D, Zhang D, Zhang L, Lu G. Non-invasive blood glucose monitoring for diabetics by means of breath signal analysis. Sens Actuators B. 2012;173:106-113. [CrossRef]30-Zhu J, Zhou Y, Huang J, Zhou A, Chen Z. Noninvasive blood glucose concentration measurement based on conservation of energy metabolism and machine learning. Sensors (Basel). Oct 21, 2021;21(21):6989. [FREE Full text] [CrossRef] [Medline]41,Alarcón-Paredes A, Francisco-García V, Guzmán-Guzmán I, Cantillo-Negrete J, Cuevas-Valencia R, Alonso-Silverio G. An IoT-based non-invasive glucose level monitoring system using raspberry pi. Appl Sci. Jul 28, 2019;9(15):3046. [CrossRef]47-Valero M, Pola P, Falaiye O, Ingram KH, Zhao L, Shahriar H, et al. Development of a noninvasive blood glucose monitoring system prototype: pilot study. JMIR Form Res. Aug 26, 2022;6(8):e38664. [FREE Full text] [CrossRef] [Medline]57]24 (73)

Conference papers [Sumaiya J, Hasan MR, Hossain E. Noninvasive blood glucose measurement using live video by smartphone. 2020. Presented at: 2020 IEEE 8th R10 Humanitarian Technology Conference (R10-HTC); December 01, 2020; Kuching, Malaysia. [CrossRef]27-Nanayakkara ND, Munasingha SC, Ruwanpathirana GP. Non-invasive blood glucose monitoring using a hybrid technique. 2018. Presented at: Moratuwa Engineering Research Conference (MERCon)/4th International Multidisciplinary Engineering Research Conference; June 01, 2018; Katubedda, Sri Lanka. [CrossRef]29,Manurung BE, Munggaran HR, Ramadhan GF, Koesoema AP. Non-invasive blood glucose monitoring using near-infrared spectroscopy based on internet of things using machine learning. 2019. Presented at: 7th IEEE Region10 Humanitarian Technology Conference (IEEE R10 HTC); November 12, 2019; Depok, West Java, Indonesia. [CrossRef]42-Ali MS, Shoumy NJ, Khatun S, Kamarudin LM. Non-invasive blood glucose measurement performance analysis through UWB imaging. 2016. Presented at: 2016 3rd International Conference on Electronic Design (ICED); August 11, 2016; Phuket, Thailand. [CrossRef]46,Francisco-García V, Guzmán-Guzmán IP, Salgado-Rivera R, Alarcón-Paredes A. Non-invasive glucose level estimation: a comparison of regression models using the MFCC as feature extractor. In: Pattern Recognition. Cham. Springer International Publishing; 2019:206-215.58]9 (27)
Study design

Prospective cohort [Guo D, Zhang D, Zhang L, Lu G. Non-invasive blood glucose monitoring for diabetics by means of breath signal analysis. Sens Actuators B. 2012;173:106-113. [CrossRef]30-Liu WJ, Huang A, Wang P, Chu C. PbFG: physique-based fuzzy granular modeling for non-invasive blood glucose monitoring. Inf Sci. 2019;497:56-76. [CrossRef]33,Nie Z, Rong M, Li K. Blood glucose prediction based on imagingphotoplethysmography in combination with machine learning. Biomed Signal Process Control. 2023;79:104179. [CrossRef]35-Rajeshwaran K, Thirunavukkarasu T, Pooja S, Rajeshkumar S. Machine learning based non-invasive glucose observation for diabetes. 2022. Presented at: 2022 Smart Technologies, Communication and Robotics (STCR); December 11, 2022; Sathyamangalam, India. [CrossRef]43,Alarcón-Paredes A, Francisco-García V, Guzmán-Guzmán I, Cantillo-Negrete J, Cuevas-Valencia R, Alonso-Silverio G. An IoT-based non-invasive glucose level monitoring system using raspberry pi. Appl Sci. Jul 28, 2019;9(15):3046. [CrossRef]47,Bent B, Cho PJ, Wittmann A, Thacker C, Muppidi S, Snyder M, et al. Non-invasive wearables for remote monitoring of HbA1c and glucose variability: proof of concept. BMJ Open Diabetes Res Care. Jun 2021;9(1):e002027. [FREE Full text] [CrossRef] [Medline]49,Enejder AMK, Scecina TG, Oh J, Hunter M, Shih W, Sasic S, et al. Raman spectroscopy for noninvasive glucose measurements. J Biomed Opt. 2005;10(3):031114. [FREE Full text] [CrossRef] [Medline]51-Jain P, Joshi AM, Mohanty SP. iGLU: an intelligent device for accurate noninvasive blood glucose-level monitoring in smart healthcare. IEEE Consumer Electron Mag. 2020;9(1):35-42. [CrossRef]53,Valero M, Pola P, Falaiye O, Ingram KH, Zhao L, Shahriar H, et al. Development of a noninvasive blood glucose monitoring system prototype: pilot study. JMIR Form Res. Aug 26, 2022;6(8):e38664. [FREE Full text] [CrossRef] [Medline]57,Francisco-García V, Guzmán-Guzmán IP, Salgado-Rivera R, Alarcón-Paredes A. Non-invasive glucose level estimation: a comparison of regression models using the MFCC as feature extractor. In: Pattern Recognition. Cham. Springer International Publishing; 2019:206-215.58]20 (61)

Retrospective cohort [Agrawal H, Jain P, Joshi AM. Machine learning models for non-invasive glucose measurement: towards diabetes management in smart healthcare. Health Technol (Berl). 2022;12(5):955-970. [FREE Full text] [CrossRef] [Medline]10,Balasooriya K, Nanayakkara ND. Predicting short-term changing blood glucose level of diabetes patients using noninvasive data. 2020. Presented at: 2020 IEEE Region 10 Conference (TENCON); November 16, 2020; Osaka, Japan. [CrossRef]28,Abubeker KM, Baskar S. A machine learning strategy for internet-of-things-enabled diabetic prediction to mitigate pneumonia risk. 2022. Presented at: 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO); October 12, 2022; Noida, India. [CrossRef]44,Arbi KF, Soulimane S, Saffih F, Bechar MA, Azzoug O. Blood glucose estimation based on ECG signal. Phys Eng Sci Med. Mar 2023;46(1):255-264. [CrossRef] [Medline]48,Bogue-Jimenez B, Huang X, Powell D, Doblas A. Selection of noninvasive features in wrist-based wearable sensors to predict blood glucose concentrations using machine learning algorithms. Sensors (Basel). May 06, 2022;22(9):3534. [FREE Full text] [CrossRef] [Medline]50,Khanam JJ, Foo SY. A comparison of machine learning algorithms for diabetes prediction. ICT Express. 2021;7(4):432-439. [CrossRef]54,Monte-Moreno E. Non-invasive estimate of blood glucose and blood pressure from a photoplethysmograph by means of machine learning techniques. Artif Intell Med. Oct 2011;53(2):127-138. [CrossRef] [Medline]56]7 (21)

Others [Sumaiya J, Hasan MR, Hossain E. Noninvasive blood glucose measurement using live video by smartphone. 2020. Presented at: 2020 IEEE 8th R10 Humanitarian Technology Conference (R10-HTC); December 01, 2020; Kuching, Malaysia. [CrossRef]27,Nanayakkara ND, Munasingha SC, Ruwanpathirana GP. Non-invasive blood glucose monitoring using a hybrid technique. 2018. Presented at: Moratuwa Engineering Research Conference (MERCon)/4th International Multidisciplinary Engineering Research Conference; June 01, 2018; Katubedda, Sri Lanka. [CrossRef]29,Malik S, Khadgawat R, Anand S, Gupta S. Non-invasive detection of fasting blood glucose level via electrochemical measurement of saliva. Springerplus. 2016;5(1):701. [FREE Full text] [CrossRef] [Medline]34,Krishnan SH, Vinupritha P, Kathirvelu D. Non-invasive glucose monitoring using machine learning. 2020. Presented at: 2020 International Conference on Communication and Signal Processing (ICCSP); July 28, 2020; Chennai, India. [CrossRef]45,Ali MS, Shoumy NJ, Khatun S, Kamarudin LM. Non-invasive blood glucose measurement performance analysis through UWB imaging. 2016. Presented at: 2016 3rd International Conference on Electronic Design (ICED); August 11, 2016; Phuket, Thailand. [CrossRef]46,Malinin L. Development of a non-invasive blood glucose monitor based on impedance measurements. IJBET. 2012;8(1):60. [CrossRef]55]6 (18)
Population characteristics

Type 1 DMa [Bogue-Jimenez B, Huang X, Powell D, Doblas A. Selection of noninvasive features in wrist-based wearable sensors to predict blood glucose concentrations using machine learning algorithms. Sensors (Basel). May 06, 2022;22(9):3534. [FREE Full text] [CrossRef] [Medline]50,Geelhoed-Duijvestijn P, Vegelyte D, Kownacka A, Anton N, Joosse M, Wilson C. Performance of the prototype NovioSense noninvasive biosensor for tear glucose in type 1 diabetes. J Diabetes Sci Technol. Nov 2021;15(6):1320-1325. [FREE Full text] [CrossRef] [Medline]52]2 (6)

Type 2 DM [Balasooriya K, Nanayakkara ND. Predicting short-term changing blood glucose level of diabetes patients using noninvasive data. 2020. Presented at: 2020 IEEE Region 10 Conference (TENCON); November 16, 2020; Osaka, Japan. [CrossRef]28]1 (3)

Healthy [Alarcón-Paredes A, Francisco-García V, Guzmán-Guzmán I, Cantillo-Negrete J, Cuevas-Valencia R, Alonso-Silverio G. An IoT-based non-invasive glucose level monitoring system using raspberry pi. Appl Sci. Jul 28, 2019;9(15):3046. [CrossRef]47,Enejder AMK, Scecina TG, Oh J, Hunter M, Shih W, Sasic S, et al. Raman spectroscopy for noninvasive glucose measurements. J Biomed Opt. 2005;10(3):031114. [FREE Full text] [CrossRef] [Medline]51]2 (6)

Mixture [Guo D, Zhang D, Zhang L, Lu G. Non-invasive blood glucose monitoring for diabetics by means of breath signal analysis. Sens Actuators B. 2012;173:106-113. [CrossRef]30-Lekha S, Suchetha M. Real-time non-invasive detection and classification of diabetes using modified convolution neural network. IEEE J Biomed Health Inform. 2018;22(5):1630-1636. [CrossRef]32,Malik S, Khadgawat R, Anand S, Gupta S. Non-invasive detection of fasting blood glucose level via electrochemical measurement of saliva. Springerplus. 2016;5(1):701. [FREE Full text] [CrossRef] [Medline]34,Zhang G, Mei Z, Zhang Y, Ma X, Lo B, Chen D, et al. A noninvasive blood glucose monitoring system based on smartphone PPG signal processing and machine learning. IEEE Trans Ind Inf. 2020;16(11):7209-7218. [CrossRef]40,Rajeshwaran K, Thirunavukkarasu T, Pooja S, Rajeshkumar S. Machine learning based non-invasive glucose observation for diabetes. 2022. Presented at: 2022 Smart Technologies, Communication and Robotics (STCR); December 11, 2022; Sathyamangalam, India. [CrossRef]43,Bent B, Cho PJ, Wittmann A, Thacker C, Muppidi S, Snyder M, et al. Non-invasive wearables for remote monitoring of HbA1c and glucose variability: proof of concept. BMJ Open Diabetes Res Care. Jun 2021;9(1):e002027. [FREE Full text] [CrossRef] [Medline]49,Jain P, Joshi AM, Mohanty SP. iGLU: an intelligent device for accurate noninvasive blood glucose-level monitoring in smart healthcare. IEEE Consumer Electron Mag. 2020;9(1):35-42. [CrossRef]53,Malinin L. Development of a non-invasive blood glucose monitor based on impedance measurements. IJBET. 2012;8(1):60. [CrossRef]55]9 (27)

NRb [Agrawal H, Jain P, Joshi AM. Machine learning models for non-invasive glucose measurement: towards diabetes management in smart healthcare. Health Technol (Berl). 2022;12(5):955-970. [FREE Full text] [CrossRef] [Medline]10,Sumaiya J, Hasan MR, Hossain E. Noninvasive blood glucose measurement using live video by smartphone. 2020. Presented at: 2020 IEEE 8th R10 Humanitarian Technology Conference (R10-HTC); December 01, 2020; Kuching, Malaysia. [CrossRef]27,Nanayakkara ND, Munasingha SC, Ruwanpathirana GP. Non-invasive blood glucose monitoring using a hybrid technique. 2018. Presented at: Moratuwa Engineering Research Conference (MERCon)/4th International Multidisciplinary Engineering Research Conference; June 01, 2018; Katubedda, Sri Lanka. [CrossRef]29,Liu WJ, Huang A, Wang P, Chu C. PbFG: physique-based fuzzy granular modeling for non-invasive blood glucose monitoring. Inf Sci. 2019;497:56-76. [CrossRef]33,Nie Z, Rong M, Li K. Blood glucose prediction based on imagingphotoplethysmography in combination with machine learning. Biomed Signal Process Control. 2023;79:104179. [CrossRef]35-Yu Y, Huang J, Zhu J, Liang S. An accurate noninvasive blood glucose measurement system using portable near-infrared spectrometer and transfer learning framework. IEEE Sens J. 2021;21(3):3506-3519. [CrossRef]39,Zhu J, Zhou Y, Huang J, Zhou A, Chen Z. Noninvasive blood glucose concentration measurement based on conservation of energy metabolism and machine learning. Sensors (Basel). Oct 21, 2021;21(21):6989. [FREE Full text] [CrossRef] [Medline]41,Manurung BE, Munggaran HR, Ramadhan GF, Koesoema AP. Non-invasive blood glucose monitoring using near-infrared spectroscopy based on internet of things using machine learning. 2019. Presented at: 7th IEEE Region10 Humanitarian Technology Conference (IEEE R10 HTC); November 12, 2019; Depok, West Java, Indonesia. [CrossRef]42,Abubeker KM, Baskar S. A machine learning strategy for internet-of-things-enabled diabetic prediction to mitigate pneumonia risk. 2022. Presented at: 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO); October 12, 2022; Noida, India. [CrossRef]44-Ali MS, Shoumy NJ, Khatun S, Kamarudin LM. Non-invasive blood glucose measurement performance analysis through UWB imaging. 2016. Presented at: 2016 3rd International Conference on Electronic Design (ICED); August 11, 2016; Phuket, Thailand. [CrossRef]46,Arbi KF, Soulimane S, Saffih F, Bechar MA, Azzoug O. Blood glucose estimation based on ECG signal. Phys Eng Sci Med. Mar 2023;46(1):255-264. [CrossRef] [Medline]48,Khanam JJ, Foo SY. A comparison of machine learning algorithms for diabetes prediction. ICT Express. 2021;7(4):432-439. [CrossRef]54,Monte-Moreno E. Non-invasive estimate of blood glucose and blood pressure from a photoplethysmograph by means of machine learning techniques. Artif Intell Med. Oct 2011;53(2):127-138. [CrossRef] [Medline]56-Francisco-García V, Guzmán-Guzmán IP, Salgado-Rivera R, Alarcón-Paredes A. Non-invasive glucose level estimation: a comparison of regression models using the MFCC as feature extractor. In: Pattern Recognition. Cham. Springer International Publishing; 2019:206-215.58]19 (58)
Age (years)

21-40 [Song K, Ha U, Park S, Bae J, Yoo H. An impedance and multi-wavelength near-infrared spectroscopy IC for non-invasive blood glucose estimation. IEEE J Solid-State Circuits. 2015;50(4):1025-1037. [CrossRef]38,Abubeker KM, Baskar S. A machine learning strategy for internet-of-things-enabled diabetic prediction to mitigate pneumonia risk. 2022. Presented at: 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO); October 12, 2022; Noida, India. [CrossRef]44,Alarcón-Paredes A, Francisco-García V, Guzmán-Guzmán I, Cantillo-Negrete J, Cuevas-Valencia R, Alonso-Silverio G. An IoT-based non-invasive glucose level monitoring system using raspberry pi. Appl Sci. Jul 28, 2019;9(15):3046. [CrossRef]47,Khanam JJ, Foo SY. A comparison of machine learning algorithms for diabetes prediction. ICT Express. 2021;7(4):432-439. [CrossRef]54,Monte-Moreno E. Non-invasive estimate of blood glucose and blood pressure from a photoplethysmograph by means of machine learning techniques. Artif Intell Med. Oct 2011;53(2):127-138. [CrossRef] [Medline]56,Francisco-García V, Guzmán-Guzmán IP, Salgado-Rivera R, Alarcón-Paredes A. Non-invasive glucose level estimation: a comparison of regression models using the MFCC as feature extractor. In: Pattern Recognition. Cham. Springer International Publishing; 2019:206-215.58]6 (18)

40-65 [Segman YJ. Device and method for noninvasive glucose assessment. J Diabetes Sci Technol. Nov 2018;12(6):1159-1168. [FREE Full text] [CrossRef] [Medline]37,Zhang G, Mei Z, Zhang Y, Ma X, Lo B, Chen D, et al. A noninvasive blood glucose monitoring system based on smartphone PPG signal processing and machine learning. IEEE Trans Ind Inf. 2020;16(11):7209-7218. [CrossRef]40,Bent B, Cho PJ, Wittmann A, Thacker C, Muppidi S, Snyder M, et al. Non-invasive wearables for remote monitoring of HbA1c and glucose variability: proof of concept. BMJ Open Diabetes Res Care. Jun 2021;9(1):e002027. [FREE Full text] [CrossRef] [Medline]49]3 (9)

NR [Agrawal H, Jain P, Joshi AM. Machine learning models for non-invasive glucose measurement: towards diabetes management in smart healthcare. Health Technol (Berl). 2022;12(5):955-970. [FREE Full text] [CrossRef] [Medline]10,Sumaiya J, Hasan MR, Hossain E. Noninvasive blood glucose measurement using live video by smartphone. 2020. Presented at: 2020 IEEE 8th R10 Humanitarian Technology Conference (R10-HTC); December 01, 2020; Kuching, Malaysia. [CrossRef]27-Rachim VP, Chung W. Wearable-band type visible-near infrared optical biosensor for non-invasive blood glucose monitoring. Sens Actuators B. 2019;286:173-180. [CrossRef]36,Yu Y, Huang J, Zhu J, Liang S. An accurate noninvasive blood glucose measurement system using portable near-infrared spectrometer and transfer learning framework. IEEE Sens J. 2021;21(3):3506-3519. [CrossRef]39,Zhu J, Zhou Y, Huang J, Zhou A, Chen Z. Noninvasive blood glucose concentration measurement based on conservation of energy metabolism and machine learning. Sensors (Basel). Oct 21, 2021;21(21):6989. [FREE Full text] [CrossRef] [Medline]41-Rajeshwaran K, Thirunavukkarasu T, Pooja S, Rajeshkumar S. Machine learning based non-invasive glucose observation for diabetes. 2022. Presented at: 2022 Smart Technologies, Communication and Robotics (STCR); December 11, 2022; Sathyamangalam, India. [CrossRef]43,Krishnan SH, Vinupritha P, Kathirvelu D. Non-invasive glucose monitoring using machine learning. 2020. Presented at: 2020 International Conference on Communication and Signal Processing (ICCSP); July 28, 2020; Chennai, India. [CrossRef]45,Ali MS, Shoumy NJ, Khatun S, Kamarudin LM. Non-invasive blood glucose measurement performance analysis through UWB imaging. 2016. Presented at: 2016 3rd International Conference on Electronic Design (ICED); August 11, 2016; Phuket, Thailand. [CrossRef]46,Arbi KF, Soulimane S, Saffih F, Bechar MA, Azzoug O. Blood glucose estimation based on ECG signal. Phys Eng Sci Med. Mar 2023;46(1):255-264. [CrossRef] [Medline]48,Bogue-Jimenez B, Huang X, Powell D, Doblas A. Selection of noninvasive features in wrist-based wearable sensors to predict blood glucose concentrations using machine learning algorithms. Sensors (Basel). May 06, 2022;22(9):3534. [FREE Full text] [CrossRef] [Medline]50-Jain P, Joshi AM, Mohanty SP. iGLU: an intelligent device for accurate noninvasive blood glucose-level monitoring in smart healthcare. IEEE Consumer Electron Mag. 2020;9(1):35-42. [CrossRef]53,Malinin L. Development of a non-invasive blood glucose monitor based on impedance measurements. IJBET. 2012;8(1):60. [CrossRef]55,Valero M, Pola P, Falaiye O, Ingram KH, Zhao L, Shahriar H, et al. Development of a noninvasive blood glucose monitoring system prototype: pilot study. JMIR Form Res. Aug 26, 2022;6(8):e38664. [FREE Full text] [CrossRef] [Medline]57]24 (73)
Sex (male)

0 [Agrawal H, Jain P, Joshi AM. Machine learning models for non-invasive glucose measurement: towards diabetes management in smart healthcare. Health Technol (Berl). 2022;12(5):955-970. [FREE Full text] [CrossRef] [Medline]10]1 (3)

<50 [Malik S, Khadgawat R, Anand S, Gupta S. Non-invasive detection of fasting blood glucose level via electrochemical measurement of saliva. Springerplus. 2016;5(1):701. [FREE Full text] [CrossRef] [Medline]34,Bent B, Cho PJ, Wittmann A, Thacker C, Muppidi S, Snyder M, et al. Non-invasive wearables for remote monitoring of HbA1c and glucose variability: proof of concept. BMJ Open Diabetes Res Care. Jun 2021;9(1):e002027. [FREE Full text] [CrossRef] [Medline]49]2 (6)

>50 [Guo D, Zhang D, Zhang L, Lu G. Non-invasive blood glucose monitoring for diabetics by means of breath signal analysis. Sens Actuators B. 2012;173:106-113. [CrossRef]30-Lekha S, Suchetha M. Real-time non-invasive detection and classification of diabetes using modified convolution neural network. IEEE J Biomed Health Inform. 2018;22(5):1630-1636. [CrossRef]32,Segman YJ. Device and method for noninvasive glucose assessment. J Diabetes Sci Technol. Nov 2018;12(6):1159-1168. [FREE Full text] [CrossRef] [Medline]37,Manurung BE, Munggaran HR, Ramadhan GF, Koesoema AP. Non-invasive blood glucose monitoring using near-infrared spectroscopy based on internet of things using machine learning. 2019. Presented at: 7th IEEE Region10 Humanitarian Technology Conference (IEEE R10 HTC); November 12, 2019; Depok, West Java, Indonesia. [CrossRef]42,Jain P, Joshi AM, Mohanty SP. iGLU: an intelligent device for accurate noninvasive blood glucose-level monitoring in smart healthcare. IEEE Consumer Electron Mag. 2020;9(1):35-42. [CrossRef]53,Monte-Moreno E. Non-invasive estimate of blood glucose and blood pressure from a photoplethysmograph by means of machine learning techniques. Artif Intell Med. Oct 2011;53(2):127-138. [CrossRef] [Medline]56]7 (21)

NR [Sumaiya J, Hasan MR, Hossain E. Noninvasive blood glucose measurement using live video by smartphone. 2020. Presented at: 2020 IEEE 8th R10 Humanitarian Technology Conference (R10-HTC); December 01, 2020; Kuching, Malaysia. [CrossRef]27-Nanayakkara ND, Munasingha SC, Ruwanpathirana GP. Non-invasive blood glucose monitoring using a hybrid technique. 2018. Presented at: Moratuwa Engineering Research Conference (MERCon)/4th International Multidisciplinary Engineering Research Conference; June 01, 2018; Katubedda, Sri Lanka. [CrossRef]29,Liu WJ, Huang A, Wang P, Chu C. PbFG: physique-based fuzzy granular modeling for non-invasive blood glucose monitoring. Inf Sci. 2019;497:56-76. [CrossRef]33,Nie Z, Rong M, Li K. Blood glucose prediction based on imagingphotoplethysmography in combination with machine learning. Biomed Signal Process Control. 2023;79:104179. [CrossRef]35,Rachim VP, Chung W. Wearable-band type visible-near infrared optical biosensor for non-invasive blood glucose monitoring. Sens Actuators B. 2019;286:173-180. [CrossRef]36,Song K, Ha U, Park S, Bae J, Yoo H. An impedance and multi-wavelength near-infrared spectroscopy IC for non-invasive blood glucose estimation. IEEE J Solid-State Circuits. 2015;50(4):1025-1037. [CrossRef]38-Zhu J, Zhou Y, Huang J, Zhou A, Chen Z. Noninvasive blood glucose concentration measurement based on conservation of energy metabolism and machine learning. Sensors (Basel). Oct 21, 2021;21(21):6989. [FREE Full text] [CrossRef] [Medline]41,Rajeshwaran K, Thirunavukkarasu T, Pooja S, Rajeshkumar S. Machine learning based non-invasive glucose observation for diabetes. 2022. Presented at: 2022 Smart Technologies, Communication and Robotics (STCR); December 11, 2022; Sathyamangalam, India. [CrossRef]43-Arbi KF, Soulimane S, Saffih F, Bechar MA, Azzoug O. Blood glucose estimation based on ECG signal. Phys Eng Sci Med. Mar 2023;46(1):255-264. [CrossRef] [Medline]48,Bogue-Jimenez B, Huang X, Powell D, Doblas A. Selection of noninvasive features in wrist-based wearable sensors to predict blood glucose concentrations using machine learning algorithms. Sensors (Basel). May 06, 2022;22(9):3534. [FREE Full text] [CrossRef] [Medline]50-Geelhoed-Duijvestijn P, Vegelyte D, Kownacka A, Anton N, Joosse M, Wilson C. Performance of the prototype NovioSense noninvasive biosensor for tear glucose in type 1 diabetes. J Diabetes Sci Technol. Nov 2021;15(6):1320-1325. [FREE Full text] [CrossRef] [Medline]52,Khanam JJ, Foo SY. A comparison of machine learning algorithms for diabetes prediction. ICT Express. 2021;7(4):432-439. [CrossRef]54,Malinin L. Development of a non-invasive blood glucose monitor based on impedance measurements. IJBET. 2012;8(1):60. [CrossRef]55,Valero M, Pola P, Falaiye O, Ingram KH, Zhao L, Shahriar H, et al. Development of a noninvasive blood glucose monitoring system prototype: pilot study. JMIR Form Res. Aug 26, 2022;6(8):e38664. [FREE Full text] [CrossRef] [Medline]57,Francisco-García V, Guzmán-Guzmán IP, Salgado-Rivera R, Alarcón-Paredes A. Non-invasive glucose level estimation: a comparison of regression models using the MFCC as feature extractor. In: Pattern Recognition. Cham. Springer International Publishing; 2019:206-215.58]23 (69.70)
Noninvasive techniques

Optical (NIRc, PPGd, Raman) [Agrawal H, Jain P, Joshi AM. Machine learning models for non-invasive glucose measurement: towards diabetes management in smart healthcare. Health Technol (Berl). 2022;12(5):955-970. [FREE Full text] [CrossRef] [Medline]10,Sumaiya J, Hasan MR, Hossain E. Noninvasive blood glucose measurement using live video by smartphone. 2020. Presented at: 2020 IEEE 8th R10 Humanitarian Technology Conference (R10-HTC); December 01, 2020; Kuching, Malaysia. [CrossRef]27,Nanayakkara ND, Munasingha SC, Ruwanpathirana GP. Non-invasive blood glucose monitoring using a hybrid technique. 2018. Presented at: Moratuwa Engineering Research Conference (MERCon)/4th International Multidisciplinary Engineering Research Conference; June 01, 2018; Katubedda, Sri Lanka. [CrossRef]29,Habbu S, Dale M, Ghongade R. Estimation of blood glucose by non-invasive method using photoplethysmography. Sādhanā. May 6, 2019;44(6):135. [CrossRef]31,Liu WJ, Huang A, Wang P, Chu C. PbFG: physique-based fuzzy granular modeling for non-invasive blood glucose monitoring. Inf Sci. 2019;497:56-76. [CrossRef]33,Nie Z, Rong M, Li K. Blood glucose prediction based on imagingphotoplethysmography in combination with machine learning. Biomed Signal Process Control. 2023;79:104179. [CrossRef]35-Segman YJ. Device and method for noninvasive glucose assessment. J Diabetes Sci Technol. Nov 2018;12(6):1159-1168. [FREE Full text] [CrossRef] [Medline]37,Yu Y, Huang J, Zhu J, Liang S. An accurate noninvasive blood glucose measurement system using portable near-infrared spectrometer and transfer learning framework. IEEE Sens J. 2021;21(3):3506-3519. [CrossRef]39,Zhang G, Mei Z, Zhang Y, Ma X, Lo B, Chen D, et al. A noninvasive blood glucose monitoring system based on smartphone PPG signal processing and machine learning. IEEE Trans Ind Inf. 2020;16(11):7209-7218. [CrossRef]40,Manurung BE, Munggaran HR, Ramadhan GF, Koesoema AP. Non-invasive blood glucose monitoring using near-infrared spectroscopy based on internet of things using machine learning. 2019. Presented at: 7th IEEE Region10 Humanitarian Technology Conference (IEEE R10 HTC); November 12, 2019; Depok, West Java, Indonesia. [CrossRef]42,Abubeker KM, Baskar S. A machine learning strategy for internet-of-things-enabled diabetic prediction to mitigate pneumonia risk. 2022. Presented at: 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO); October 12, 2022; Noida, India. [CrossRef]44,Krishnan SH, Vinupritha P, Kathirvelu D. Non-invasive glucose monitoring using machine learning. 2020. Presented at: 2020 International Conference on Communication and Signal Processing (ICCSP); July 28, 2020; Chennai, India. [CrossRef]45,Alarcón-Paredes A, Francisco-García V, Guzmán-Guzmán I, Cantillo-Negrete J, Cuevas-Valencia R, Alonso-Silverio G. An IoT-based non-invasive glucose level monitoring system using raspberry pi. Appl Sci. Jul 28, 2019;9(15):3046. [CrossRef]47,Enejder AMK, Scecina TG, Oh J, Hunter M, Shih W, Sasic S, et al. Raman spectroscopy for noninvasive glucose measurements. J Biomed Opt. 2005;10(3):031114. [FREE Full text] [CrossRef] [Medline]51,Jain P, Joshi AM, Mohanty SP. iGLU: an intelligent device for accurate noninvasive blood glucose-level monitoring in smart healthcare. IEEE Consumer Electron Mag. 2020;9(1):35-42. [CrossRef]53,Monte-Moreno E. Non-invasive estimate of blood glucose and blood pressure from a photoplethysmograph by means of machine learning techniques. Artif Intell Med. Oct 2011;53(2):127-138. [CrossRef] [Medline]56-Francisco-García V, Guzmán-Guzmán IP, Salgado-Rivera R, Alarcón-Paredes A. Non-invasive glucose level estimation: a comparison of regression models using the MFCC as feature extractor. In: Pattern Recognition. Cham. Springer International Publishing; 2019:206-215.58]19 (58)

Impedance [Malinin L. Development of a non-invasive blood glucose monitor based on impedance measurements. IJBET. 2012;8(1):60. [CrossRef]55]1 (3)

Biosensor (breath, saliva, tears) [Guo D, Zhang D, Zhang L, Lu G. Non-invasive blood glucose monitoring for diabetics by means of breath signal analysis. Sens Actuators B. 2012;173:106-113. [CrossRef]30,Lekha S, Suchetha M. Real-time non-invasive detection and classification of diabetes using modified convolution neural network. IEEE J Biomed Health Inform. 2018;22(5):1630-1636. [CrossRef]32,Malik S, Khadgawat R, Anand S, Gupta S. Non-invasive detection of fasting blood glucose level via electrochemical measurement of saliva. Springerplus. 2016;5(1):701. [FREE Full text] [CrossRef] [Medline]34,Geelhoed-Duijvestijn P, Vegelyte D, Kownacka A, Anton N, Joosse M, Wilson C. Performance of the prototype NovioSense noninvasive biosensor for tear glucose in type 1 diabetes. J Diabetes Sci Technol. Nov 2021;15(6):1320-1325. [FREE Full text] [CrossRef] [Medline]52]4 (12)

Imaging (ECGe, UWBf) [Ali MS, Shoumy NJ, Khatun S, Kamarudin LM. Non-invasive blood glucose measurement performance analysis through UWB imaging. 2016. Presented at: 2016 3rd International Conference on Electronic Design (ICED); August 11, 2016; Phuket, Thailand. [CrossRef]46,Arbi KF, Soulimane S, Saffih F, Bechar MA, Azzoug O. Blood glucose estimation based on ECG signal. Phys Eng Sci Med. Mar 2023;46(1):255-264. [CrossRef] [Medline]48]2 (6)

Mixture [Song K, Ha U, Park S, Bae J, Yoo H. An impedance and multi-wavelength near-infrared spectroscopy IC for non-invasive blood glucose estimation. IEEE J Solid-State Circuits. 2015;50(4):1025-1037. [CrossRef]38,Bogue-Jimenez B, Huang X, Powell D, Doblas A. Selection of noninvasive features in wrist-based wearable sensors to predict blood glucose concentrations using machine learning algorithms. Sensors (Basel). May 06, 2022;22(9):3534. [FREE Full text] [CrossRef] [Medline]50]2 (6)

NR [Balasooriya K, Nanayakkara ND. Predicting short-term changing blood glucose level of diabetes patients using noninvasive data. 2020. Presented at: 2020 IEEE Region 10 Conference (TENCON); November 16, 2020; Osaka, Japan. [CrossRef]28,Zhu J, Zhou Y, Huang J, Zhou A, Chen Z. Noninvasive blood glucose concentration measurement based on conservation of energy metabolism and machine learning. Sensors (Basel). Oct 21, 2021;21(21):6989. [FREE Full text] [CrossRef] [Medline]41,Rajeshwaran K, Thirunavukkarasu T, Pooja S, Rajeshkumar S. Machine learning based non-invasive glucose observation for diabetes. 2022. Presented at: 2022 Smart Technologies, Communication and Robotics (STCR); December 11, 2022; Sathyamangalam, India. [CrossRef]43,Bent B, Cho PJ, Wittmann A, Thacker C, Muppidi S, Snyder M, et al. Non-invasive wearables for remote monitoring of HbA1c and glucose variability: proof of concept. BMJ Open Diabetes Res Care. Jun 2021;9(1):e002027. [FREE Full text] [CrossRef] [Medline]49,Khanam JJ, Foo SY. A comparison of machine learning algorithms for diabetes prediction. ICT Express. 2021;7(4):432-439. [CrossRef]54]5 (15)
Applications

Predict DM [Agrawal H, Jain P, Joshi AM. Machine learning models for non-invasive glucose measurement: towards diabetes management in smart healthcare. Health Technol (Berl). 2022;12(5):955-970. [FREE Full text] [CrossRef] [Medline]10,Abubeker KM, Baskar S. A machine learning strategy for internet-of-things-enabled diabetic prediction to mitigate pneumonia risk. 2022. Presented at: 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO); October 12, 2022; Noida, India. [CrossRef]44]2 (6)

Monitoring by physician [Rajeshwaran K, Thirunavukkarasu T, Pooja S, Rajeshkumar S. Machine learning based non-invasive glucose observation for diabetes. 2022. Presented at: 2022 Smart Technologies, Communication and Robotics (STCR); December 11, 2022; Sathyamangalam, India. [CrossRef]43,Jain P, Joshi AM, Mohanty SP. iGLU: an intelligent device for accurate noninvasive blood glucose-level monitoring in smart healthcare. IEEE Consumer Electron Mag. 2020;9(1):35-42. [CrossRef]53]2 (6)

Estimate BGg levels [Agrawal H, Jain P, Joshi AM. Machine learning models for non-invasive glucose measurement: towards diabetes management in smart healthcare. Health Technol (Berl). 2022;12(5):955-970. [FREE Full text] [CrossRef] [Medline]10,Sumaiya J, Hasan MR, Hossain E. Noninvasive blood glucose measurement using live video by smartphone. 2020. Presented at: 2020 IEEE 8th R10 Humanitarian Technology Conference (R10-HTC); December 01, 2020; Kuching, Malaysia. [CrossRef]27,Nanayakkara ND, Munasingha SC, Ruwanpathirana GP. Non-invasive blood glucose monitoring using a hybrid technique. 2018. Presented at: Moratuwa Engineering Research Conference (MERCon)/4th International Multidisciplinary Engineering Research Conference; June 01, 2018; Katubedda, Sri Lanka. [CrossRef]29-Manurung BE, Munggaran HR, Ramadhan GF, Koesoema AP. Non-invasive blood glucose monitoring using near-infrared spectroscopy based on internet of things using machine learning. 2019. Presented at: 7th IEEE Region10 Humanitarian Technology Conference (IEEE R10 HTC); November 12, 2019; Depok, West Java, Indonesia. [CrossRef]42,Krishnan SH, Vinupritha P, Kathirvelu D. Non-invasive glucose monitoring using machine learning. 2020. Presented at: 2020 International Conference on Communication and Signal Processing (ICCSP); July 28, 2020; Chennai, India. [CrossRef]45-Arbi KF, Soulimane S, Saffih F, Bechar MA, Azzoug O. Blood glucose estimation based on ECG signal. Phys Eng Sci Med. Mar 2023;46(1):255-264. [CrossRef] [Medline]48,Bogue-Jimenez B, Huang X, Powell D, Doblas A. Selection of noninvasive features in wrist-based wearable sensors to predict blood glucose concentrations using machine learning algorithms. Sensors (Basel). May 06, 2022;22(9):3534. [FREE Full text] [CrossRef] [Medline]50-Geelhoed-Duijvestijn P, Vegelyte D, Kownacka A, Anton N, Joosse M, Wilson C. Performance of the prototype NovioSense noninvasive biosensor for tear glucose in type 1 diabetes. J Diabetes Sci Technol. Nov 2021;15(6):1320-1325. [FREE Full text] [CrossRef] [Medline]52,Khanam JJ, Foo SY. A comparison of machine learning algorithms for diabetes prediction. ICT Express. 2021;7(4):432-439. [CrossRef]54-Francisco-García V, Guzmán-Guzmán IP, Salgado-Rivera R, Alarcón-Paredes A. Non-invasive glucose level estimation: a comparison of regression models using the MFCC as feature extractor. In: Pattern Recognition. Cham. Springer International Publishing; 2019:206-215.58]27 (82)

Estimate HbA1ch levels [Bent B, Cho PJ, Wittmann A, Thacker C, Muppidi S, Snyder M, et al. Non-invasive wearables for remote monitoring of HbA1c and glucose variability: proof of concept. BMJ Open Diabetes Res Care. Jun 2021;9(1):e002027. [FREE Full text] [CrossRef] [Medline]49]1 (3)

Predict future BG levels [Balasooriya K, Nanayakkara ND. Predicting short-term changing blood glucose level of diabetes patients using noninvasive data. 2020. Presented at: 2020 IEEE Region 10 Conference (TENCON); November 16, 2020; Osaka, Japan. [CrossRef]28]1 (3)

aDM: diabetes mellitus.

bNR: not reported.

cNIR: near-infrared.

dPPG: photoplethysmography.

eECG: electrocardiography.

fUWB: ultrawideband.

gBG: blood glucose.

hHbA1c: hemoglobin A1c.

Use Cases of AI-Assisted NIBGM

The majority of the use cases were to estimate blood glucose levels (n=29, 88%), [Agrawal H, Jain P, Joshi AM. Machine learning models for non-invasive glucose measurement: towards diabetes management in smart healthcare. Health Technol (Berl). 2022;12(5):955-970. [FREE Full text] [CrossRef] [Medline]10,Sumaiya J, Hasan MR, Hossain E. Noninvasive blood glucose measurement using live video by smartphone. 2020. Presented at: 2020 IEEE 8th R10 Humanitarian Technology Conference (R10-HTC); December 01, 2020; Kuching, Malaysia. [CrossRef]27,Nanayakkara ND, Munasingha SC, Ruwanpathirana GP. Non-invasive blood glucose monitoring using a hybrid technique. 2018. Presented at: Moratuwa Engineering Research Conference (MERCon)/4th International Multidisciplinary Engineering Research Conference; June 01, 2018; Katubedda, Sri Lanka. [CrossRef]29-Habbu S, Dale M, Ghongade R. Estimation of blood glucose by non-invasive method using photoplethysmography. Sādhanā. May 6, 2019;44(6):135. [CrossRef]31,Liu WJ, Huang A, Wang P, Chu C. PbFG: physique-based fuzzy granular modeling for non-invasive blood glucose monitoring. Inf Sci. 2019;497:56-76. [CrossRef]33-Song K, Ha U, Park S, Bae J, Yoo H. An impedance and multi-wavelength near-infrared spectroscopy IC for non-invasive blood glucose estimation. IEEE J Solid-State Circuits. 2015;50(4):1025-1037. [CrossRef]38,Zhang G, Mei Z, Zhang Y, Ma X, Lo B, Chen D, et al. A noninvasive blood glucose monitoring system based on smartphone PPG signal processing and machine learning. IEEE Trans Ind Inf. 2020;16(11):7209-7218. [CrossRef]40-Arbi KF, Soulimane S, Saffih F, Bechar MA, Azzoug O. Blood glucose estimation based on ECG signal. Phys Eng Sci Med. Mar 2023;46(1):255-264. [CrossRef] [Medline]48,Bogue-Jimenez B, Huang X, Powell D, Doblas A. Selection of noninvasive features in wrist-based wearable sensors to predict blood glucose concentrations using machine learning algorithms. Sensors (Basel). May 06, 2022;22(9):3534. [FREE Full text] [CrossRef] [Medline]50-Jain P, Joshi AM, Mohanty SP. iGLU: an intelligent device for accurate noninvasive blood glucose-level monitoring in smart healthcare. IEEE Consumer Electron Mag. 2020;9(1):35-42. [CrossRef]53,Malinin L. Development of a non-invasive blood glucose monitor based on impedance measurements. IJBET. 2012;8(1):60. [CrossRef]55-Francisco-García V, Guzmán-Guzmán IP, Salgado-Rivera R, Alarcón-Paredes A. Non-invasive glucose level estimation: a comparison of regression models using the MFCC as feature extractor. In: Pattern Recognition. Cham. Springer International Publishing; 2019:206-215.58], 3 (9%) were to detect DM [Agrawal H, Jain P, Joshi AM. Machine learning models for non-invasive glucose measurement: towards diabetes management in smart healthcare. Health Technol (Berl). 2022;12(5):955-970. [FREE Full text] [CrossRef] [Medline]10,Lekha S, Suchetha M. Real-time non-invasive detection and classification of diabetes using modified convolution neural network. IEEE J Biomed Health Inform. 2018;22(5):1630-1636. [CrossRef]32,Khanam JJ, Foo SY. A comparison of machine learning algorithms for diabetes prediction. ICT Express. 2021;7(4):432-439. [CrossRef]54], 1 (3%) was to estimate suitable insulin doses [Abubeker KM, Baskar S. A machine learning strategy for internet-of-things-enabled diabetic prediction to mitigate pneumonia risk. 2022. Presented at: 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO); October 12, 2022; Noida, India. [CrossRef]44], and another was to predict future blood glucose level (Table 1 and

Multimedia Appendix 3

Characteristics of blood glucose monitoring systems.

DOCX File , 25 KBMultimedia Appendix 3 [Agrawal H, Jain P, Joshi AM. Machine learning models for non-invasive glucose measurement: towards diabetes management in smart healthcare. Health Technol (Berl). 2022;12(5):955-970. [FREE Full text] [CrossRef] [Medline]10,Sumaiya J, Hasan MR, Hossain E. Noninvasive blood glucose measurement using live video by smartphone. 2020. Presented at: 2020 IEEE 8th R10 Humanitarian Technology Conference (R10-HTC); December 01, 2020; Kuching, Malaysia. [CrossRef]27-Francisco-García V, Guzmán-Guzmán IP, Salgado-Rivera R, Alarcón-Paredes A. Non-invasive glucose level estimation: a comparison of regression models using the MFCC as feature extractor. In: Pattern Recognition. Cham. Springer International Publishing; 2019:206-215.58]). Only one study used AI-assisted NIBGM to estimate hemoglobin A1c (HbA1c) levels and blood glucose variability among adults with prediabetes [Bent B, Cho PJ, Wittmann A, Thacker C, Muppidi S, Snyder M, et al. Non-invasive wearables for remote monitoring of HbA1c and glucose variability: proof of concept. BMJ Open Diabetes Res Care. Jun 2021;9(1):e002027. [FREE Full text] [CrossRef] [Medline]49], which is noteworthy as glucose variability refers to oscillations in blood glucose levels throughout the day and could suggest the severity of diabetic complications.

BGM Technology

A summary of the technology used for NIBGM is shown in Multimedia Appendices 4 and Rodbard D. Continuous glucose monitoring: a review of recent studies demonstrating improved glycemic outcomes. Diabetes Technol Ther. Jun 2017;19(S3):S25-S37. [FREE Full text] [CrossRef] [Medline]5 [Agrawal H, Jain P, Joshi AM. Machine learning models for non-invasive glucose measurement: towards diabetes management in smart healthcare. Health Technol (Berl). 2022;12(5):955-970. [FREE Full text] [CrossRef] [Medline]10,Sumaiya J, Hasan MR, Hossain E. Noninvasive blood glucose measurement using live video by smartphone. 2020. Presented at: 2020 IEEE 8th R10 Humanitarian Technology Conference (R10-HTC); December 01, 2020; Kuching, Malaysia. [CrossRef]27-Francisco-García V, Guzmán-Guzmán IP, Salgado-Rivera R, Alarcón-Paredes A. Non-invasive glucose level estimation: a comparison of regression models using the MFCC as feature extractor. In: Pattern Recognition. Cham. Springer International Publishing; 2019:206-215.58] and Figure 2. Of the 33 studies, 19 studies experimented with devices to estimate blood glucose levels using optical methods including PPG (n=7, 21%) [Sumaiya J, Hasan MR, Hossain E. Noninvasive blood glucose measurement using live video by smartphone. 2020. Presented at: 2020 IEEE 8th R10 Humanitarian Technology Conference (R10-HTC); December 01, 2020; Kuching, Malaysia. [CrossRef]27,Habbu S, Dale M, Ghongade R. Estimation of blood glucose by non-invasive method using photoplethysmography. Sādhanā. May 6, 2019;44(6):135. [CrossRef]31,Nie Z, Rong M, Li K. Blood glucose prediction based on imagingphotoplethysmography in combination with machine learning. Biomed Signal Process Control. 2023;79:104179. [CrossRef]35,Zhang G, Mei Z, Zhang Y, Ma X, Lo B, Chen D, et al. A noninvasive blood glucose monitoring system based on smartphone PPG signal processing and machine learning. IEEE Trans Ind Inf. 2020;16(11):7209-7218. [CrossRef]40,Abubeker KM, Baskar S. A machine learning strategy for internet-of-things-enabled diabetic prediction to mitigate pneumonia risk. 2022. Presented at: 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO); October 12, 2022; Noida, India. [CrossRef]44,Krishnan SH, Vinupritha P, Kathirvelu D. Non-invasive glucose monitoring using machine learning. 2020. Presented at: 2020 International Conference on Communication and Signal Processing (ICCSP); July 28, 2020; Chennai, India. [CrossRef]45,Monte-Moreno E. Non-invasive estimate of blood glucose and blood pressure from a photoplethysmograph by means of machine learning techniques. Artif Intell Med. Oct 2011;53(2):127-138. [CrossRef] [Medline]56], NIRS (n=7, 21%) [Agrawal H, Jain P, Joshi AM. Machine learning models for non-invasive glucose measurement: towards diabetes management in smart healthcare. Health Technol (Berl). 2022;12(5):955-970. [FREE Full text] [CrossRef] [Medline]10,Nanayakkara ND, Munasingha SC, Ruwanpathirana GP. Non-invasive blood glucose monitoring using a hybrid technique. 2018. Presented at: Moratuwa Engineering Research Conference (MERCon)/4th International Multidisciplinary Engineering Research Conference; June 01, 2018; Katubedda, Sri Lanka. [CrossRef]29,Rachim VP, Chung W. Wearable-band type visible-near infrared optical biosensor for non-invasive blood glucose monitoring. Sens Actuators B. 2019;286:173-180. [CrossRef]36,Yu Y, Huang J, Zhu J, Liang S. An accurate noninvasive blood glucose measurement system using portable near-infrared spectrometer and transfer learning framework. IEEE Sens J. 2021;21(3):3506-3519. [CrossRef]39,Manurung BE, Munggaran HR, Ramadhan GF, Koesoema AP. Non-invasive blood glucose monitoring using near-infrared spectroscopy based on internet of things using machine learning. 2019. Presented at: 7th IEEE Region10 Humanitarian Technology Conference (IEEE R10 HTC); November 12, 2019; Depok, West Java, Indonesia. [CrossRef]42,Jain P, Joshi AM, Mohanty SP. iGLU: an intelligent device for accurate noninvasive blood glucose-level monitoring in smart healthcare. IEEE Consumer Electron Mag. 2020;9(1):35-42. [CrossRef]53,Valero M, Pola P, Falaiye O, Ingram KH, Zhao L, Shahriar H, et al. Development of a noninvasive blood glucose monitoring system prototype: pilot study. JMIR Form Res. Aug 26, 2022;6(8):e38664. [FREE Full text] [CrossRef] [Medline]57], RS (n=1, 3%) [Enejder AMK, Scecina TG, Oh J, Hunter M, Shih W, Sasic S, et al. Raman spectroscopy for noninvasive glucose measurements. J Biomed Opt. 2005;10(3):031114. [FREE Full text] [CrossRef] [Medline]51], absorption spectroscopy (n=1, 3%) [Liu WJ, Huang A, Wang P, Chu C. PbFG: physique-based fuzzy granular modeling for non-invasive blood glucose monitoring. Inf Sci. 2019;497:56-76. [CrossRef]33], noninvasive optical analysis of visible light capture by specialized cameras (n=1, 3%) [Alarcón-Paredes A, Francisco-García V, Guzmán-Guzmán I, Cantillo-Negrete J, Cuevas-Valencia R, Alonso-Silverio G. An IoT-based non-invasive glucose level monitoring system using raspberry pi. Appl Sci. Jul 28, 2019;9(15):3046. [CrossRef]47], color image sensor (n=1, 3%) [Segman YJ. Device and method for noninvasive glucose assessment. J Diabetes Sci Technol. Nov 2018;12(6):1159-1168. [FREE Full text] [CrossRef] [Medline]37], and laser beam and light diode resistor (n=1, 3%) [Francisco-García V, Guzmán-Guzmán IP, Salgado-Rivera R, Alarcón-Paredes A. Non-invasive glucose level estimation: a comparison of regression models using the MFCC as feature extractor. In: Pattern Recognition. Cham. Springer International Publishing; 2019:206-215.58], 4 studies used devices that detected biological substances, including biosensor for tear glucose [Segman YJ. Device and method for noninvasive glucose assessment. J Diabetes Sci Technol. Nov 2018;12(6):1159-1168. [FREE Full text] [CrossRef] [Medline]37] and breath analysis [Arbi KF, Soulimane S, Saffih F, Bechar MA, Azzoug O. Blood glucose estimation based on ECG signal. Phys Eng Sci Med. Mar 2023;46(1):255-264. [CrossRef] [Medline]48] (two were not reported [Guo D, Zhang D, Zhang L, Lu G. Non-invasive blood glucose monitoring for diabetics by means of breath signal analysis. Sens Actuators B. 2012;173:106-113. [CrossRef]30,Geelhoed-Duijvestijn P, Vegelyte D, Kownacka A, Anton N, Joosse M, Wilson C. Performance of the prototype NovioSense noninvasive biosensor for tear glucose in type 1 diabetes. J Diabetes Sci Technol. Nov 2021;15(6):1320-1325. [FREE Full text] [CrossRef] [Medline]52]), 3 (6%) studies used imaging techniques, including ECG [Arbi KF, Soulimane S, Saffih F, Bechar MA, Azzoug O. Blood glucose estimation based on ECG signal. Phys Eng Sci Med. Mar 2023;46(1):255-264. [CrossRef] [Medline]48] and ultrawideband (UWB) [Ali MS, Shoumy NJ, Khatun S, Kamarudin LM. Non-invasive blood glucose measurement performance analysis through UWB imaging. 2016. Presented at: 2016 3rd International Conference on Electronic Design (ICED); August 11, 2016; Phuket, Thailand. [CrossRef]46], 2 studies used mixed methods, including Optical+Electromagnetic+Thermal techniques [Rachim VP, Chung W. Wearable-band type visible-near infrared optical biosensor for non-invasive blood glucose monitoring. Sens Actuators B. 2019;286:173-180. [CrossRef]36] and Impedance+Multi-Wavelength NIR Spectroscopy [Song K, Ha U, Park S, Bae J, Yoo H. An impedance and multi-wavelength near-infrared spectroscopy IC for non-invasive blood glucose estimation. IEEE J Solid-State Circuits. 2015;50(4):1025-1037. [CrossRef]38], and 1 study used a device that measured tissue impedance (

Multimedia Appendix 4

Characteristics of noninvasive blood glucose monitoring systems.

DOCX File , 26 KBMultimedia Appendix 4 [Agrawal H, Jain P, Joshi AM. Machine learning models for non-invasive glucose measurement: towards diabetes management in smart healthcare. Health Technol (Berl). 2022;12(5):955-970. [FREE Full text] [CrossRef] [Medline]10,Sumaiya J, Hasan MR, Hossain E. Noninvasive blood glucose measurement using live video by smartphone. 2020. Presented at: 2020 IEEE 8th R10 Humanitarian Technology Conference (R10-HTC); December 01, 2020; Kuching, Malaysia. [CrossRef]27-Francisco-García V, Guzmán-Guzmán IP, Salgado-Rivera R, Alarcón-Paredes A. Non-invasive glucose level estimation: a comparison of regression models using the MFCC as feature extractor. In: Pattern Recognition. Cham. Springer International Publishing; 2019:206-215.58]). NIR spectroscopy detects the intensity of the reflected near-infrared light by glucose molecules in the blood to estimate blood glucose levels [Nie Z, Rong M, Li K. Blood glucose prediction based on imagingphotoplethysmography in combination with machine learning. Biomed Signal Process Control. 2023;79:104179. [CrossRef]35]. PPG operates on the same principles as that of the pulse oximeter, by calculating blood glucose levels based on the light intensity detected on a receiver and sent out by a transmitter [Kavakiotis I, Tsave O, Salifoglou A, Maglaveras N, Vlahavas I, Chouvarda I. Machine learning and data mining methods in diabetes research. Comput Struct Biotechnol J. 2017;15:104-116. [FREE Full text] [CrossRef] [Medline]12]. RS works by comparing the Raman light emitted from a scattering medium (tissue) for transcutaneous determination of compositions of molecules, such as glucose, in the tissue-blood matrix [Bolla AS, Priefer R. Blood glucose monitoring—an overview of current and future non-invasive devices. Diabetes Metab Syndr. 2020;14(5):739-751. [CrossRef] [Medline]15,Enejder AMK, Scecina TG, Oh J, Hunter M, Shih W, Sasic S, et al. Raman spectroscopy for noninvasive glucose measurements. J Biomed Opt. 2005;10(3):031114. [FREE Full text] [CrossRef] [Medline]51]. RS can noninvasively monitor variations in glucose present at low concentrations in the blood-tissue matrix of the skin due to its distinct characteristic spectral features.

Figure 2. Blood glucose monitoring technology.

NIBGMs based on biosensors use breath, saliva, or tear samples to derive blood glucose concentrations based on their components, such as sodium, potassium, and calcium ions. ECGs measure the electrical activities of the heart and present them as a PQRST wave, with various abnormalities of the waves seemingly correlated with hyper- and hypoglycemia [Bent B, Cho PJ, Wittmann A, Thacker C, Muppidi S, Snyder M, et al. Non-invasive wearables for remote monitoring of HbA1c and glucose variability: proof of concept. BMJ Open Diabetes Res Care. Jun 2021;9(1):e002027. [FREE Full text] [CrossRef] [Medline]49]. UWB imaging estimates blood glucose change via changes in the blood dielectric properties [Ali MS, Shoumy NJ, Khatun S, Kamarudin LM. Non-invasive blood glucose measurement performance analysis through UWB imaging. 2016. Presented at: 2016 3rd International Conference on Electronic Design (ICED); August 11, 2016; Phuket, Thailand. [CrossRef]46]. Two devices had mixed methods of detection. The device by Song et al [Song K, Ha U, Park S, Bae J, Yoo H. An impedance and multi-wavelength near-infrared spectroscopy IC for non-invasive blood glucose estimation. IEEE J Solid-State Circuits. 2015;50(4):1025-1037. [CrossRef]38] used both impedance and NIR to estimate blood glucose levels, while the device by Abubeker and Baskar [Abubeker KM, Baskar S. A machine learning strategy for internet-of-things-enabled diabetic prediction to mitigate pneumonia risk. 2022. Presented at: 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO); October 12, 2022; Noida, India. [CrossRef]44] integrated ultrasonic, electromagnetic, and thermal data from the patient. Finally, the device by Malinin et al [Malinin L. Development of a non-invasive blood glucose monitor based on impedance measurements. IJBET. 2012;8(1):60. [CrossRef]55] measured the impedance of tissues via bracelet-type electrodes to detect blood glucose levels by monitoring the transfer functions of a tissue segment in the electromagnetic field.

A total of 19 (58%) devices used light-related signals as the input data (PPG signals [Sumaiya J, Hasan MR, Hossain E. Noninvasive blood glucose measurement using live video by smartphone. 2020. Presented at: 2020 IEEE 8th R10 Humanitarian Technology Conference (R10-HTC); December 01, 2020; Kuching, Malaysia. [CrossRef]27,Habbu S, Dale M, Ghongade R. Estimation of blood glucose by non-invasive method using photoplethysmography. Sādhanā. May 6, 2019;44(6):135. [CrossRef]31,Nie Z, Rong M, Li K. Blood glucose prediction based on imagingphotoplethysmography in combination with machine learning. Biomed Signal Process Control. 2023;79:104179. [CrossRef]35,Rachim VP, Chung W. Wearable-band type visible-near infrared optical biosensor for non-invasive blood glucose monitoring. Sens Actuators B. 2019;286:173-180. [CrossRef]36,Zhang G, Mei Z, Zhang Y, Ma X, Lo B, Chen D, et al. A noninvasive blood glucose monitoring system based on smartphone PPG signal processing and machine learning. IEEE Trans Ind Inf. 2020;16(11):7209-7218. [CrossRef]40,Krishnan SH, Vinupritha P, Kathirvelu D. Non-invasive glucose monitoring using machine learning. 2020. Presented at: 2020 International Conference on Communication and Signal Processing (ICCSP); July 28, 2020; Chennai, India. [CrossRef]45,Monte-Moreno E. Non-invasive estimate of blood glucose and blood pressure from a photoplethysmograph by means of machine learning techniques. Artif Intell Med. Oct 2011;53(2):127-138. [CrossRef] [Medline]56], UWB imaging [Ali MS, Shoumy NJ, Khatun S, Kamarudin LM. Non-invasive blood glucose measurement performance analysis through UWB imaging. 2016. Presented at: 2016 3rd International Conference on Electronic Design (ICED); August 11, 2016; Phuket, Thailand. [CrossRef]46], NIR signals [Agrawal H, Jain P, Joshi AM. Machine learning models for non-invasive glucose measurement: towards diabetes management in smart healthcare. Health Technol (Berl). 2022;12(5):955-970. [FREE Full text] [CrossRef] [Medline]10,Nanayakkara ND, Munasingha SC, Ruwanpathirana GP. Non-invasive blood glucose monitoring using a hybrid technique. 2018. Presented at: Moratuwa Engineering Research Conference (MERCon)/4th International Multidisciplinary Engineering Research Conference; June 01, 2018; Katubedda, Sri Lanka. [CrossRef]29,Yu Y, Huang J, Zhu J, Liang S. An accurate noninvasive blood glucose measurement system using portable near-infrared spectrometer and transfer learning framework. IEEE Sens J. 2021;21(3):3506-3519. [CrossRef]39,Jain P, Joshi AM, Mohanty SP. iGLU: an intelligent device for accurate noninvasive blood glucose-level monitoring in smart healthcare. IEEE Consumer Electron Mag. 2020;9(1):35-42. [CrossRef]53,Valero M, Pola P, Falaiye O, Ingram KH, Zhao L, Shahriar H, et al. Development of a noninvasive blood glucose monitoring system prototype: pilot study. JMIR Form Res. Aug 26, 2022;6(8):e38664. [FREE Full text] [CrossRef] [Medline]57], Raman Spectra [Enejder AMK, Scecina TG, Oh J, Hunter M, Shih W, Sasic S, et al. Raman spectroscopy for noninvasive glucose measurements. J Biomed Opt. 2005;10(3):031114. [FREE Full text] [CrossRef] [Medline]51], nonvisible light signals [Liu WJ, Huang A, Wang P, Chu C. PbFG: physique-based fuzzy granular modeling for non-invasive blood glucose monitoring. Inf Sci. 2019;497:56-76. [CrossRef]33,Segman YJ. Device and method for noninvasive glucose assessment. J Diabetes Sci Technol. Nov 2018;12(6):1159-1168. [FREE Full text] [CrossRef] [Medline]37,Francisco-García V, Guzmán-Guzmán IP, Salgado-Rivera R, Alarcón-Paredes A. Non-invasive glucose level estimation: a comparison of regression models using the MFCC as feature extractor. In: Pattern Recognition. Cham. Springer International Publishing; 2019:206-215.58], visible light signals [Alarcón-Paredes A, Francisco-García V, Guzmán-Guzmán I, Cantillo-Negrete J, Cuevas-Valencia R, Alonso-Silverio G. An IoT-based non-invasive glucose level monitoring system using raspberry pi. Appl Sci. Jul 28, 2019;9(15):3046. [CrossRef]47], and LED light [Manurung BE, Munggaran HR, Ramadhan GF, Koesoema AP. Non-invasive blood glucose monitoring using near-infrared spectroscopy based on internet of things using machine learning. 2019. Presented at: 7th IEEE Region10 Humanitarian Technology Conference (IEEE R10 HTC); November 12, 2019; Depok, West Java, Indonesia. [CrossRef]42]), 5 (15%) devices collected biological samples (tears [Geelhoed-Duijvestijn P, Vegelyte D, Kownacka A, Anton N, Joosse M, Wilson C. Performance of the prototype NovioSense noninvasive biosensor for tear glucose in type 1 diabetes. J Diabetes Sci Technol. Nov 2021;15(6):1320-1325. [FREE Full text] [CrossRef] [Medline]52,Ramasahayam S, Haindavi KS, Chowdhury SR. Noninvasive estimation of blood glucose concentration using near infrared optodes. In: Sensing Technology: Current Status and Future Trends IV. Hyderabad, India. Springer International Publishing; 2015:67-82.59], breath [Guo D, Zhang D, Zhang L, Lu G. Non-invasive blood glucose monitoring for diabetics by means of breath signal analysis. Sens Actuators B. 2012;173:106-113. [CrossRef]30,Lekha S, Suchetha M. Real-time non-invasive detection and classification of diabetes using modified convolution neural network. IEEE J Biomed Health Inform. 2018;22(5):1630-1636. [CrossRef]32], and saliva [Malik S, Khadgawat R, Anand S, Gupta S. Non-invasive detection of fasting blood glucose level via electrochemical measurement of saliva. Springerplus. 2016;5(1):701. [FREE Full text] [CrossRef] [Medline]34]), 4 (12%) devices used images or videos (video of finger [Sumaiya J, Hasan MR, Hossain E. Noninvasive blood glucose measurement using live video by smartphone. 2020. Presented at: 2020 IEEE 8th R10 Humanitarian Technology Conference (R10-HTC); December 01, 2020; Kuching, Malaysia. [CrossRef]27,Zhang G, Mei Z, Zhang Y, Ma X, Lo B, Chen D, et al. A noninvasive blood glucose monitoring system based on smartphone PPG signal processing and machine learning. IEEE Trans Ind Inf. 2020;16(11):7209-7218. [CrossRef]40], facial video [Nie Z, Rong M, Li K. Blood glucose prediction based on imagingphotoplethysmography in combination with machine learning. Biomed Signal Process Control. 2023;79:104179. [CrossRef]35], and image of finger or ear [Valero M, Pola P, Falaiye O, Ingram KH, Zhao L, Shahriar H, et al. Development of a noninvasive blood glucose monitoring system prototype: pilot study. JMIR Form Res. Aug 26, 2022;6(8):e38664. [FREE Full text] [CrossRef] [Medline]57]), 4 (12%) devices collected vitals (eg, oxygen saturation, heart rate, and skin temperature [Zhu J, Zhou Y, Huang J, Zhou A, Chen Z. Noninvasive blood glucose concentration measurement based on conservation of energy metabolism and machine learning. Sensors (Basel). Oct 21, 2021;21(21):6989. [FREE Full text] [CrossRef] [Medline]41,Abubeker KM, Baskar S. A machine learning strategy for internet-of-things-enabled diabetic prediction to mitigate pneumonia risk. 2022. Presented at: 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO); October 12, 2022; Noida, India. [CrossRef]44,Bent B, Cho PJ, Wittmann A, Thacker C, Muppidi S, Snyder M, et al. Non-invasive wearables for remote monitoring of HbA1c and glucose variability: proof of concept. BMJ Open Diabetes Res Care. Jun 2021;9(1):e002027. [FREE Full text] [CrossRef] [Medline]49,Bogue-Jimenez B, Huang X, Powell D, Doblas A. Selection of noninvasive features in wrist-based wearable sensors to predict blood glucose concentrations using machine learning algorithms. Sensors (Basel). May 06, 2022;22(9):3534. [FREE Full text] [CrossRef] [Medline]50]), 1 (3%) device collected impedance data [Malinin L. Development of a non-invasive blood glucose monitor based on impedance measurements. IJBET. 2012;8(1):60. [CrossRef]55], 1 (3%) device used ECG data [Arbi KF, Soulimane S, Saffih F, Bechar MA, Azzoug O. Blood glucose estimation based on ECG signal. Phys Eng Sci Med. Mar 2023;46(1):255-264. [CrossRef] [Medline]48], 1 (3%) device used a combination of types of inputs (intensity modulated photocurrent spectroscopy and multiwavelength NIRS) [Song K, Ha U, Park S, Bae J, Yoo H. An impedance and multi-wavelength near-infrared spectroscopy IC for non-invasive blood glucose estimation. IEEE J Solid-State Circuits. 2015;50(4):1025-1037. [CrossRef]38], 2 (6%) devices extracted various attributes from the patient’s lifestyle and background, with one using medication intake, food intake, daily activities, and measured blood glucose levels as the input [Balasooriya K, Nanayakkara ND. Predicting short-term changing blood glucose level of diabetes patients using noninvasive data. 2020. Presented at: 2020 IEEE Region 10 Conference (TENCON); November 16, 2020; Osaka, Japan. [CrossRef]28], and the other using pregnancy, BMI, insulin level, age, blood pressure, skin thickness, glucose, and diabetes pedigree function [Khanam JJ, Foo SY. A comparison of machine learning algorithms for diabetes prediction. ICT Express. 2021;7(4):432-439. [CrossRef]54].

AI Models Developed for NIBGM

A summary of the characteristics of ML is shown in

Multimedia Appendix 5

Characteristics of Machine Learning.

DOCX File , 25 KBMultimedia Appendix 5 [Agrawal H, Jain P, Joshi AM. Machine learning models for non-invasive glucose measurement: towards diabetes management in smart healthcare. Health Technol (Berl). 2022;12(5):955-970. [FREE Full text] [CrossRef] [Medline]10,Sumaiya J, Hasan MR, Hossain E. Noninvasive blood glucose measurement using live video by smartphone. 2020. Presented at: 2020 IEEE 8th R10 Humanitarian Technology Conference (R10-HTC); December 01, 2020; Kuching, Malaysia. [CrossRef]27-Francisco-García V, Guzmán-Guzmán IP, Salgado-Rivera R, Alarcón-Paredes A. Non-invasive glucose level estimation: a comparison of regression models using the MFCC as feature extractor. In: Pattern Recognition. Cham. Springer International Publishing; 2019:206-215.58]. The accuracy of NIBGM estimating blood glucose ranged from 35.56% [Song K, Ha U, Park S, Bae J, Yoo H. An impedance and multi-wavelength near-infrared spectroscopy IC for non-invasive blood glucose estimation. IEEE J Solid-State Circuits. 2015;50(4):1025-1037. [CrossRef]38] to 94.23% [Song K, Ha U, Park S, Bae J, Yoo H. An impedance and multi-wavelength near-infrared spectroscopy IC for non-invasive blood glucose estimation. IEEE J Solid-State Circuits. 2015;50(4):1025-1037. [CrossRef]38], mean absolute error (MAE) ranged from 0.248 [Khanam JJ, Foo SY. A comparison of machine learning algorithms for diabetes prediction. ICT Express. 2021;7(4):432-439. [CrossRef]54] to 11.8 [Bolla AS, Priefer R. Blood glucose monitoring—an overview of current and future non-invasive devices. Diabetes Metab Syndr. 2020;14(5):739-751. [CrossRef] [Medline]15], R2 ranged from 0.11 [Abubeker KM, Baskar S. A machine learning strategy for internet-of-things-enabled diabetic prediction to mitigate pneumonia risk. 2022. Presented at: 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO); October 12, 2022; Noida, India. [CrossRef]44] to 0.91 [Segman YJ. Device and method for noninvasive glucose assessment. J Diabetes Sci Technol. Nov 2018;12(6):1159-1168. [FREE Full text] [CrossRef] [Medline]37], and Clarke error grid (CEG; A+B) ranged from 86.91% [Bogue-Jimenez B, Huang X, Powell D, Doblas A. Selection of noninvasive features in wrist-based wearable sensors to predict blood glucose concentrations using machine learning algorithms. Sensors (Basel). May 06, 2022;22(9):3534. [FREE Full text] [CrossRef] [Medline]50] to 100% [Kavakiotis I, Tsave O, Salifoglou A, Maglaveras N, Vlahavas I, Chouvarda I. Machine learning and data mining methods in diabetes research. Comput Struct Biotechnol J. 2017;15:104-116. [FREE Full text] [CrossRef] [Medline]12,Bolla AS, Priefer R. Blood glucose monitoring—an overview of current and future non-invasive devices. Diabetes Metab Syndr. 2020;14(5):739-751. [CrossRef] [Medline]15,Levac D, Colquhoun H, O'Brien KK. Scoping studies: advancing the methodology. Implement Sci. 2010;5(1):69. [FREE Full text] [CrossRef] [Medline]23,Cabitza F, Campagner A. The need to separate the wheat from the chaff in medical informatics: introducing a comprehensive checklist for the (self)-assessment of medical AI studies. Int J Med Inform. Sep 2021;153:104510. [FREE Full text] [CrossRef] [Medline]25,Rachim VP, Chung W. Wearable-band type visible-near infrared optical biosensor for non-invasive blood glucose monitoring. Sens Actuators B. 2019;286:173-180. [CrossRef]36,Segman YJ. Device and method for noninvasive glucose assessment. J Diabetes Sci Technol. Nov 2018;12(6):1159-1168. [FREE Full text] [CrossRef] [Medline]37,Yu Y, Huang J, Zhu J, Liang S. An accurate noninvasive blood glucose measurement system using portable near-infrared spectrometer and transfer learning framework. IEEE Sens J. 2021;21(3):3506-3519. [CrossRef]39,Zhu J, Zhou Y, Huang J, Zhou A, Chen Z. Noninvasive blood glucose concentration measurement based on conservation of energy metabolism and machine learning. Sensors (Basel). Oct 21, 2021;21(21):6989. [FREE Full text] [CrossRef] [Medline]41]. Both MAE and R2 were used to evaluate regression models, with lower MAE scores meaning a more accurate model and higher R2 scores meaning a model that can cover a greater variety of data points. CEG was developed to measure the efficacy of BGM systems, and it consists of a grid divided into five zones. Zone A represents values that are clinically accurate and safe, while zones B, C, D, and E represent progressively more significant clinical errors. Typically, only data points within zones A and B are accepted by clinicians. A total of 8 (55%) devices achieved a CEG (A+B) of 100%, all of which included supervised learning models.

Various ML and deep learning (DL) algorithms were used. Nine [Cabitza F, Campagner A. The need to separate the wheat from the chaff in medical informatics: introducing a comprehensive checklist for the (self)-assessment of medical AI studies. Int J Med Inform. Sep 2021;153:104510. [FREE Full text] [CrossRef] [Medline]25,Balasooriya K, Nanayakkara ND. Predicting short-term changing blood glucose level of diabetes patients using noninvasive data. 2020. Presented at: 2020 IEEE Region 10 Conference (TENCON); November 16, 2020; Osaka, Japan. [CrossRef]28,Habbu S, Dale M, Ghongade R. Estimation of blood glucose by non-invasive method using photoplethysmography. Sādhanā. May 6, 2019;44(6):135. [CrossRef]31,Rachim VP, Chung W. Wearable-band type visible-near infrared optical biosensor for non-invasive blood glucose monitoring. Sens Actuators B. 2019;286:173-180. [CrossRef]36,Segman YJ. Device and method for noninvasive glucose assessment. J Diabetes Sci Technol. Nov 2018;12(6):1159-1168. [FREE Full text] [CrossRef] [Medline]37,Zhang G, Mei Z, Zhang Y, Ma X, Lo B, Chen D, et al. A noninvasive blood glucose monitoring system based on smartphone PPG signal processing and machine learning. IEEE Trans Ind Inf. 2020;16(11):7209-7218. [CrossRef]40,Manurung BE, Munggaran HR, Ramadhan GF, Koesoema AP. Non-invasive blood glucose monitoring using near-infrared spectroscopy based on internet of things using machine learning. 2019. Presented at: 7th IEEE Region10 Humanitarian Technology Conference (IEEE R10 HTC); November 12, 2019; Depok, West Java, Indonesia. [CrossRef]42,Ali MS, Shoumy NJ, Khatun S, Kamarudin LM. Non-invasive blood glucose measurement performance analysis through UWB imaging. 2016. Presented at: 2016 3rd International Conference on Electronic Design (ICED); August 11, 2016; Phuket, Thailand. [CrossRef]46,Enejder AMK, Scecina TG, Oh J, Hunter M, Shih W, Sasic S, et al. Raman spectroscopy for noninvasive glucose measurements. J Biomed Opt. 2005;10(3):031114. [FREE Full text] [CrossRef] [Medline]51] devices used only DL models (often some kind of neural network [NN]), while 8 [Bolla AS, Priefer R. Blood glucose monitoring—an overview of current and future non-invasive devices. Diabetes Metab Syndr. 2020;14(5):739-751. [CrossRef] [Medline]15,Levac D, Colquhoun H, O'Brien KK. Scoping studies: advancing the methodology. Implement Sci. 2010;5(1):69. [FREE Full text] [CrossRef] [Medline]23,Arksey H, O'Malley L. Scoping studies: towards a methodological framework. Int J Soc Res Meth. 2005;8(1):19-32. [CrossRef]24,Sumaiya J, Hasan MR, Hossain E. Noninvasive blood glucose measurement using live video by smartphone. 2020. Presented at: 2020 IEEE 8th R10 Humanitarian Technology Conference (R10-HTC); December 01, 2020; Kuching, Malaysia. [CrossRef]27,Song K, Ha U, Park S, Bae J, Yoo H. An impedance and multi-wavelength near-infrared spectroscopy IC for non-invasive blood glucose estimation. IEEE J Solid-State Circuits. 2015;50(4):1025-1037. [CrossRef]38,Yu Y, Huang J, Zhu J, Liang S. An accurate noninvasive blood glucose measurement system using portable near-infrared spectrometer and transfer learning framework. IEEE Sens J. 2021;21(3):3506-3519. [CrossRef]39,Bogue-Jimenez B, Huang X, Powell D, Doblas A. Selection of noninvasive features in wrist-based wearable sensors to predict blood glucose concentrations using machine learning algorithms. Sensors (Basel). May 06, 2022;22(9):3534. [FREE Full text] [CrossRef] [Medline]50,Francisco-García V, Guzmán-Guzmán IP, Salgado-Rivera R, Alarcón-Paredes A. Non-invasive glucose level estimation: a comparison of regression models using the MFCC as feature extractor. In: Pattern Recognition. Cham. Springer International Publishing; 2019:206-215.58] devices included only ML models. The rest used a mix of models. Among the DL models or NNs, 6 devices [Duckworth W, Abraira C, Moritz T. Glucose control and vascular complications in veterans with type 2 diabetes. J Vasc Surg. Apr 2009;49(4):1084. [CrossRef]2,Habbu S, Dale M, Ghongade R. Estimation of blood glucose by non-invasive method using photoplethysmography. Sādhanā. May 6, 2019;44(6):135. [CrossRef]31,Zhang G, Mei Z, Zhang Y, Ma X, Lo B, Chen D, et al. A noninvasive blood glucose monitoring system based on smartphone PPG signal processing and machine learning. IEEE Trans Ind Inf. 2020;16(11):7209-7218. [CrossRef]40,Manurung BE, Munggaran HR, Ramadhan GF, Koesoema AP. Non-invasive blood glucose monitoring using near-infrared spectroscopy based on internet of things using machine learning. 2019. Presented at: 7th IEEE Region10 Humanitarian Technology Conference (IEEE R10 HTC); November 12, 2019; Depok, West Java, Indonesia. [CrossRef]42,Bent B, Cho PJ, Wittmann A, Thacker C, Muppidi S, Snyder M, et al. Non-invasive wearables for remote monitoring of HbA1c and glucose variability: proof of concept. BMJ Open Diabetes Res Care. Jun 2021;9(1):e002027. [FREE Full text] [CrossRef] [Medline]49,Monte-Moreno E. Non-invasive estimate of blood glucose and blood pressure from a photoplethysmograph by means of machine learning techniques. Artif Intell Med. Oct 2011;53(2):127-138. [CrossRef] [Medline]56] used artificial neural networks (ANN), and three devices [Kavakiotis I, Tsave O, Salifoglou A, Maglaveras N, Vlahavas I, Chouvarda I. Machine learning and data mining methods in diabetes research. Comput Struct Biotechnol J. 2017;15:104-116. [FREE Full text] [CrossRef] [Medline]12,Cabitza F, Campagner A. The need to separate the wheat from the chaff in medical informatics: introducing a comprehensive checklist for the (self)-assessment of medical AI studies. Int J Med Inform. Sep 2021;153:104510. [FREE Full text] [CrossRef] [Medline]25,Khanam JJ, Foo SY. A comparison of machine learning algorithms for diabetes prediction. ICT Express. 2021;7(4):432-439. [CrossRef]54] used deep NNs. Among the ML models, 10 devices [Kavakiotis I, Tsave O, Salifoglou A, Maglaveras N, Vlahavas I, Chouvarda I. Machine learning and data mining methods in diabetes research. Comput Struct Biotechnol J. 2017;15:104-116. [FREE Full text] [CrossRef] [Medline]12,Contreras I, Vehi J. Artificial intelligence for diabetes management and decision support: literature review. J Med Internet Res. May 30, 2018;20(5):e10775. [FREE Full text] [CrossRef] [Medline]14,Chaki J, Ganesh ST, Cidham S, Theertan SA. Machine learning and artificial intelligence based diabetes mellitus detection and self-management: a systematic review. J King Saud Univ Comp Inf Sci. 2022;34(6):3204-3225. [CrossRef]22,Sumaiya J, Hasan MR, Hossain E. Noninvasive blood glucose measurement using live video by smartphone. 2020. Presented at: 2020 IEEE 8th R10 Humanitarian Technology Conference (R10-HTC); December 01, 2020; Kuching, Malaysia. [CrossRef]27,Malik S, Khadgawat R, Anand S, Gupta S. Non-invasive detection of fasting blood glucose level via electrochemical measurement of saliva. Springerplus. 2016;5(1):701. [FREE Full text] [CrossRef] [Medline]34,Song K, Ha U, Park S, Bae J, Yoo H. An impedance and multi-wavelength near-infrared spectroscopy IC for non-invasive blood glucose estimation. IEEE J Solid-State Circuits. 2015;50(4):1025-1037. [CrossRef]38,Zhu J, Zhou Y, Huang J, Zhou A, Chen Z. Noninvasive blood glucose concentration measurement based on conservation of energy metabolism and machine learning. Sensors (Basel). Oct 21, 2021;21(21):6989. [FREE Full text] [CrossRef] [Medline]41,Abubeker KM, Baskar S. A machine learning strategy for internet-of-things-enabled diabetic prediction to mitigate pneumonia risk. 2022. Presented at: 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO); October 12, 2022; Noida, India. [CrossRef]44,Jain P, Joshi AM, Mohanty SP. iGLU: an intelligent device for accurate noninvasive blood glucose-level monitoring in smart healthcare. IEEE Consumer Electron Mag. 2020;9(1):35-42. [CrossRef]53,Khanam JJ, Foo SY. A comparison of machine learning algorithms for diabetes prediction. ICT Express. 2021;7(4):432-439. [CrossRef]54] used random forest (RF), 8 devices [Kavakiotis I, Tsave O, Salifoglou A, Maglaveras N, Vlahavas I, Chouvarda I. Machine learning and data mining methods in diabetes research. Comput Struct Biotechnol J. 2017;15:104-116. [FREE Full text] [CrossRef] [Medline]12,Malik S, Khadgawat R, Anand S, Gupta S. Non-invasive detection of fasting blood glucose level via electrochemical measurement of saliva. Springerplus. 2016;5(1):701. [FREE Full text] [CrossRef] [Medline]34,Zhu J, Zhou Y, Huang J, Zhou A, Chen Z. Noninvasive blood glucose concentration measurement based on conservation of energy metabolism and machine learning. Sensors (Basel). Oct 21, 2021;21(21):6989. [FREE Full text] [CrossRef] [Medline]41,Rajeshwaran K, Thirunavukkarasu T, Pooja S, Rajeshkumar S. Machine learning based non-invasive glucose observation for diabetes. 2022. Presented at: 2022 Smart Technologies, Communication and Robotics (STCR); December 11, 2022; Sathyamangalam, India. [CrossRef]43,Abubeker KM, Baskar S. A machine learning strategy for internet-of-things-enabled diabetic prediction to mitigate pneumonia risk. 2022. Presented at: 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO); October 12, 2022; Noida, India. [CrossRef]44,Bent B, Cho PJ, Wittmann A, Thacker C, Muppidi S, Snyder M, et al. Non-invasive wearables for remote monitoring of HbA1c and glucose variability: proof of concept. BMJ Open Diabetes Res Care. Jun 2021;9(1):e002027. [FREE Full text] [CrossRef] [Medline]49,Khanam JJ, Foo SY. A comparison of machine learning algorithms for diabetes prediction. ICT Express. 2021;7(4):432-439. [CrossRef]54,Monte-Moreno E. Non-invasive estimate of blood glucose and blood pressure from a photoplethysmograph by means of machine learning techniques. Artif Intell Med. Oct 2011;53(2):127-138. [CrossRef] [Medline]56] used linear regression, 5 devices [Contreras I, Vehi J. Artificial intelligence for diabetes management and decision support: literature review. J Med Internet Res. May 30, 2018;20(5):e10775. [FREE Full text] [CrossRef] [Medline]14,Zhou Y, Ge Y, Shi X, Wu K, Chen W, Ding Y, et al. Machine learning predictive models for acute pancreatitis: a systematic review. Int J Med Inform. Jan 2022;157:104641. [FREE Full text] [CrossRef] [Medline]26,Nanayakkara ND, Munasingha SC, Ruwanpathirana GP. Non-invasive blood glucose monitoring using a hybrid technique. 2018. Presented at: Moratuwa Engineering Research Conference (MERCon)/4th International Multidisciplinary Engineering Research Conference; June 01, 2018; Katubedda, Sri Lanka. [CrossRef]29,Malik S, Khadgawat R, Anand S, Gupta S. Non-invasive detection of fasting blood glucose level via electrochemical measurement of saliva. Springerplus. 2016;5(1):701. [FREE Full text] [CrossRef] [Medline]34,Bent B, Cho PJ, Wittmann A, Thacker C, Muppidi S, Snyder M, et al. Non-invasive wearables for remote monitoring of HbA1c and glucose variability: proof of concept. BMJ Open Diabetes Res Care. Jun 2021;9(1):e002027. [FREE Full text] [CrossRef] [Medline]49] used support vector machines (SVM) and 5 devices [Chaki J, Ganesh ST, Cidham S, Theertan SA. Machine learning and artificial intelligence based diabetes mellitus detection and self-management: a systematic review. J King Saud Univ Comp Inf Sci. 2022;34(6):3204-3225. [CrossRef]22,Zhu J, Zhou Y, Huang J, Zhou A, Chen Z. Noninvasive blood glucose concentration measurement based on conservation of energy metabolism and machine learning. Sensors (Basel). Oct 21, 2021;21(21):6989. [FREE Full text] [CrossRef] [Medline]41,Abubeker KM, Baskar S. A machine learning strategy for internet-of-things-enabled diabetic prediction to mitigate pneumonia risk. 2022. Presented at: 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO); October 12, 2022; Noida, India. [CrossRef]44,Jain P, Joshi AM, Mohanty SP. iGLU: an intelligent device for accurate noninvasive blood glucose-level monitoring in smart healthcare. IEEE Consumer Electron Mag. 2020;9(1):35-42. [CrossRef]53,Khanam JJ, Foo SY. A comparison of machine learning algorithms for diabetes prediction. ICT Express. 2021;7(4):432-439. [CrossRef]54] used support vector regression. Datasets were split according to ratios ranging from 70:30 to the traditional 80:20 for training and testing according to different studies.

The most popular ML algorithm used was RF. Incidentally, RF is widely recognized as one of the most effective machine learning algorithms for classification tasks [Chaki J, Ganesh ST, Cidham S, Theertan SA. Machine learning and artificial intelligence based diabetes mellitus detection and self-management: a systematic review. J King Saud Univ Comp Inf Sci. 2022;34(6):3204-3225. [CrossRef]22]. Increasing the number of trees in the forest improves prediction accuracy, allowing for tailored models based on specific characteristics. One study which used the use of RF had an accuracy of 94.2% [Krishnan SH, Vinupritha P, Kathirvelu D. Non-invasive glucose monitoring using machine learning. 2020. Presented at: 2020 International Conference on Communication and Signal Processing (ICCSP); July 28, 2020; Chennai, India. [CrossRef]45], while another study that examined the use of RF to predict HbA1c achieved a low mean average percent error of 4.87% [Bent B, Cho PJ, Wittmann A, Thacker C, Muppidi S, Snyder M, et al. Non-invasive wearables for remote monitoring of HbA1c and glucose variability: proof of concept. BMJ Open Diabetes Res Care. Jun 2021;9(1):e002027. [FREE Full text] [CrossRef] [Medline]49].

Another popular algorithm used for data classification is SVM. SVM uses nonlinear mapping to transform DM training data into a higher dimension and seeks the optimal linear separating hyperplane [Chaki J, Ganesh ST, Cidham S, Theertan SA. Machine learning and artificial intelligence based diabetes mellitus detection and self-management: a systematic review. J King Saud Univ Comp Inf Sci. 2022;34(6):3204-3225. [CrossRef]22]. It aims to create distinct margins between different classes, improving the training and testing speed. In a study based on salivary electrochemical signals, SVM outperformed other models in estimating blood glucose levels with 85% accuracy, 84% precision, and 85% sensitivity [Malik S, Khadgawat R, Anand S, Gupta S. Non-invasive detection of fasting blood glucose level via electrochemical measurement of saliva. Springerplus. 2016;5(1):701. [FREE Full text] [CrossRef] [Medline]34]. SVM had the best performance in another study which used PPG signals with an accuracy of 81.7% [Abubeker KM, Baskar S. A machine learning strategy for internet-of-things-enabled diabetic prediction to mitigate pneumonia risk. 2022. Presented at: 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO); October 12, 2022; Noida, India. [CrossRef]44].

NNs are a popular DL model extensively used for the detection and diagnosis of DM. This was evident in a study that used CNN to estimate blood glucose levels using breath signals [Lekha S, Suchetha M. Real-time non-invasive detection and classification of diabetes using modified convolution neural network. IEEE J Biomed Health Inform. 2018;22(5):1630-1636. [CrossRef]32]. Performance was promising with a low mean square error of 0.14 and area under the curves as 0.97, 0.96, and 0.96 for T1DM, T2DM, and healthy, respectively [Lekha S, Suchetha M. Real-time non-invasive detection and classification of diabetes using modified convolution neural network. IEEE J Biomed Health Inform. 2018;22(5):1630-1636. [CrossRef]32]. ANN performed best when using input from the Pima Indian diabetes dataset, achieving an overall accuracy of 88.6% [Khanam JJ, Foo SY. A comparison of machine learning algorithms for diabetes prediction. ICT Express. 2021;7(4):432-439. [CrossRef]54].

Study Quality

The mean overall ChAMAI score on the included papers was 33.5 (SD 3.09), suggesting an average of medium quality (Table 2). Most of the studies were of medium quality ranging between 30 and 41, while 10 studies were of high quality with a score equal to or more than 35. The proportion of “OK,” “mR,” and “MR” in high-priority items range from 20% to 80%, 0% to 6.7%, and 6.7% to 80%, respectively (Figure 3). The proportion of “OK,” “mR,” and “MR” in low-priority items range from 10% to 50%, 0% to 20%, and 50% to 90%, respectively (Figure 4). The interrater agreement in using ChAMAI indicated moderate agreement (Cohen κ=0.49).

Table 2. Study quality rated based on ChAMAIa.
Author (Year)Problem understanding (10)Data understanding (6)Data preparation (8)Modeling (6)Validation (12)Deployment (8)Overall (50)
Abubeker and Baskar (2022) [Abubeker KM, Baskar S. A machine learning strategy for internet-of-things-enabled diabetic prediction to mitigate pneumonia risk. 2022. Presented at: 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO); October 12, 2022; Noida, India. [CrossRef]44]74567433
Agrawal et al (2022) [Agrawal H, Jain P, Joshi AM. Machine learning models for non-invasive glucose measurement: towards diabetes management in smart healthcare. Health Technol (Berl). 2022;12(5):955-970. [FREE Full text] [CrossRef] [Medline]10]84468434
Alarcón-Paredes et al (2019) [Alarcón-Paredes A, Francisco-García V, Guzmán-Guzmán I, Cantillo-Negrete J, Cuevas-Valencia R, Alonso-Silverio G. An IoT-based non-invasive glucose level monitoring system using raspberry pi. Appl Sci. Jul 28, 2019;9(15):3046. [CrossRef]47]734610333
Ali et al (2016) [Ali MS, Shoumy NJ, Khatun S, Kamarudin LM. Non-invasive blood glucose measurement performance analysis through UWB imaging. 2016. Presented at: 2016 3rd International Conference on Electronic Design (ICED); August 11, 2016; Phuket, Thailand. [CrossRef]46]74569334
Arbi et al (2023) [Arbi KF, Soulimane S, Saffih F, Bechar MA, Azzoug O. Blood glucose estimation based on ECG signal. Phys Eng Sci Med. Mar 2023;46(1):255-264. [CrossRef] [Medline]48]73567432
Balasooriya and Nanayakkara (2020) [Balasooriya K, Nanayakkara ND. Predicting short-term changing blood glucose level of diabetes patients using noninvasive data. 2020. Presented at: 2020 IEEE Region 10 Conference (TENCON); November 16, 2020; Osaka, Japan. [CrossRef]28]73457329
Bent et al (2021) [Bent B, Cho PJ, Wittmann A, Thacker C, Muppidi S, Snyder M, et al. Non-invasive wearables for remote monitoring of HbA1c and glucose variability: proof of concept. BMJ Open Diabetes Res Care. Jun 2021;9(1):e002027. [FREE Full text] [CrossRef] [Medline]49]1044510437
Bogue-Jimenez et al (2022) [Bogue-Jimenez B, Huang X, Powell D, Doblas A. Selection of noninvasive features in wrist-based wearable sensors to predict blood glucose concentrations using machine learning algorithms. Sensors (Basel). May 06, 2022;22(9):3534. [FREE Full text] [CrossRef] [Medline]50]735610334
Enejder et al (2005) [Enejder AMK, Scecina TG, Oh J, Hunter M, Shih W, Sasic S, et al. Raman spectroscopy for noninvasive glucose measurements. J Biomed Opt. 2005;10(3):031114. [FREE Full text] [CrossRef] [Medline]51]94558233
Francisco-García et al (2019) [Francisco-García V, Guzmán-Guzmán IP, Salgado-Rivera R, Alarcón-Paredes A. Non-invasive glucose level estimation: a comparison of regression models using the MFCC as feature extractor. In: Pattern Recognition. Cham. Springer International Publishing; 2019:206-215.58]74568333
Geelhoed-Duijvestijn et al (2021) [Geelhoed-Duijvestijn P, Vegelyte D, Kownacka A, Anton N, Joosse M, Wilson C. Performance of the prototype NovioSense noninvasive biosensor for tear glucose in type 1 diabetes. J Diabetes Sci Technol. Nov 2021;15(6):1320-1325. [FREE Full text] [CrossRef] [Medline]52]105456333
Guo et al (2012) [Guo D, Zhang D, Zhang L, Lu G. Non-invasive blood glucose monitoring for diabetics by means of breath signal analysis. Sens Actuators B. 2012;173:106-113. [CrossRef]30]73566330
Habbu et al (2019) [Habbu S, Dale M, Ghongade R. Estimation of blood glucose by non-invasive method using photoplethysmography. Sādhanā. May 6, 2019;44(6):135. [CrossRef]31]84559334
Jain et al (2020) [Jain P, Joshi AM, Mohanty SP. iGLU: an intelligent device for accurate noninvasive blood glucose-level monitoring in smart healthcare. IEEE Consumer Electron Mag. 2020;9(1):35-42. [CrossRef]53]845510335
Khanam and Foo (2021) [Khanam JJ, Foo SY. A comparison of machine learning algorithms for diabetes prediction. ICT Express. 2021;7(4):432-439. [CrossRef]54]73758333
Krishnan et al (2020) [Krishnan SH, Vinupritha P, Kathirvelu D. Non-invasive glucose monitoring using machine learning. 2020. Presented at: 2020 International Conference on Communication and Signal Processing (ICCSP); July 28, 2020; Chennai, India. [CrossRef]45]53456225
Lekha and Suchetha (2018) [Lekha S, Suchetha M. Real-time non-invasive detection and classification of diabetes using modified convolution neural network. IEEE J Biomed Health Inform. 2018;22(5):1630-1636. [CrossRef]32]74569334
Liu et al (2019) [Liu WJ, Huang A, Wang P, Chu C. PbFG: physique-based fuzzy granular modeling for non-invasive blood glucose monitoring. Inf Sci. 2019;497:56-76. [CrossRef]33]95469437
Malik et al (2016) [Malik S, Khadgawat R, Anand S, Gupta S. Non-invasive detection of fasting blood glucose level via electrochemical measurement of saliva. Springerplus. 2016;5(1):701. [FREE Full text] [CrossRef] [Medline]34]104569337
Malinin (2012) [Malinin L. Development of a non-invasive blood glucose monitor based on impedance measurements. IJBET. 2012;8(1):60. [CrossRef]55]63569231
Manurung et al (2019) [Manurung BE, Munggaran HR, Ramadhan GF, Koesoema AP. Non-invasive blood glucose monitoring using near-infrared spectroscopy based on internet of things using machine learning. 2019. Presented at: 7th IEEE Region10 Humanitarian Technology Conference (IEEE R10 HTC); November 12, 2019; Depok, West Java, Indonesia. [CrossRef]42]755610336
Monte-Moreno (2011) [Monte-Moreno E. Non-invasive estimate of blood glucose and blood pressure from a photoplethysmograph by means of machine learning techniques. Artif Intell Med. Oct 2011;53(2):127-138. [CrossRef] [Medline]56]967610341
Nanayakkara et al (2018) [Nanayakkara ND, Munasingha SC, Ruwanpathirana GP. Non-invasive blood glucose monitoring using a hybrid technique. 2018. Presented at: Moratuwa Engineering Research Conference (MERCon)/4th International Multidisciplinary Engineering Research Conference; June 01, 2018; Katubedda, Sri Lanka. [CrossRef]29]94459334
Nie et al (2023) [Nie Z, Rong M, Li K. Blood glucose prediction based on imagingphotoplethysmography in combination with machine learning. Biomed Signal Process Control. 2023;79:104179. [CrossRef]35]955610338
Rachim and Chung (2019) [Rachim VP, Chung W. Wearable-band type visible-near infrared optical biosensor for non-invasive blood glucose monitoring. Sens Actuators B. 2019;286:173-180. [CrossRef]36]63469331
Rajeshwaran et al (2022) [Rajeshwaran K, Thirunavukkarasu T, Pooja S, Rajeshkumar S. Machine learning based non-invasive glucose observation for diabetes. 2022. Presented at: 2022 Smart Technologies, Communication and Robotics (STCR); December 11, 2022; Sathyamangalam, India. [CrossRef]43]63568331
Segman (2018) [Segman YJ. Device and method for noninvasive glucose assessment. J Diabetes Sci Technol. Nov 2018;12(6):1159-1168. [FREE Full text] [CrossRef] [Medline]37]95466333
Song et al (2015) [Song K, Ha U, Park S, Bae J, Yoo H. An impedance and multi-wavelength near-infrared spectroscopy IC for non-invasive blood glucose estimation. IEEE J Solid-State Circuits. 2015;50(4):1025-1037. [CrossRef]38]73458330
Sumaiya et al (2020) [Sumaiya J, Hasan MR, Hossain E. Noninvasive blood glucose measurement using live video by smartphone. 2020. Presented at: 2020 IEEE 8th R10 Humanitarian Technology Conference (R10-HTC); December 01, 2020; Kuching, Malaysia. [CrossRef]27]53568330
Valero et al (2022) [Valero M, Pola P, Falaiye O, Ingram KH, Zhao L, Shahriar H, et al. Development of a noninvasive blood glucose monitoring system prototype: pilot study. JMIR Form Res. Aug 26, 2022;6(8):e38664. [FREE Full text] [CrossRef] [Medline]57]94468435
Yu et al (2021) [Yu Y, Huang J, Zhu J, Liang S. An accurate noninvasive blood glucose measurement system using portable near-infrared spectrometer and transfer learning framework. IEEE Sens J. 2021;21(3):3506-3519. [CrossRef]39]94568335
Zhang et al (2020) [Zhang G, Mei Z, Zhang Y, Ma X, Lo B, Chen D, et al. A noninvasive blood glucose monitoring system based on smartphone PPG signal processing and machine learning. IEEE Trans Ind Inf. 2020;16(11):7209-7218. [CrossRef]40]96569338
Zhu et al (2021) [Zhu J, Zhou Y, Huang J, Zhou A, Chen Z. Noninvasive blood glucose concentration measurement based on conservation of energy metabolism and machine learning. Sensors (Basel). Oct 21, 2021;21(21):6989. [FREE Full text] [CrossRef] [Medline]41]64468331

aChAMAI: Checklist for assessment of medical artificial intelligence.

Figure 3. Proportion of OK=adequately addressed, mR=sufficient but improvable, MR= inadequately addressed ratings on each high priority items.
Figure 4. Proportion of OK=adequately addressed, mR=sufficient but improvable MR= inadequately addressed ratings on each low priority items.

Principal Findings

Findings from this scoping review revealed the applications of AI-assisted NIBGM systems, available technology developed, and types of AI algorithms from the 33 included studies published between 2005 and 2023. Most studies (n=20, 60%) originated from just 3 countries mainly China, India, and the United States.

The bulk of the evidence comes from Asian studies, potentially due to the alarming increase in the prevalence of DM in Asia compared to their European counterparts [Rhee EJ. Diabetes in Asians. Endocrinol Metab. 2015;30(3):263-269. [CrossRef]60]. There was an even mix of studies from low-, middle- and high-income countries but it is unclear whether AI technologies can be made affordable and accessible to individuals in low- and middle-income countries.

More research can be done to determine the cost and accessibility of AI-assisted glucose monitoring systems and their barriers to widespread adoption. A significant number of studies were reported in conference proceedings, which reflect the emerging evidence regarding AI in NIBGM. Perhaps more research relating to diagnostic accuracy can be conducted to increase the strength of evidence for the adoption of such technology over current traditional glucose monitoring systems.

The majority of studies that develop ML algorithms to predict DM used the Pima Indian diabetes dataset which comprises 8 parameters. These criteria include the number of pregnancies, BMI, plasma glucose concentration, blood pressure, skinfold thickness, diabetes pedigree function, and an outcome variable of class 0 or 1 (where 0 denotes patient without diabetes and 1 denotes patient with diabetes) [Sisodia D, Sisodia DS. Prediction of diabetes using classification algorithms. Procedia Comp Sci. 2018;132:1578-1585. [CrossRef]61,Alam TM, Iqbal MA, Ali Y, Wahab A, Ijaz S, Baig TI, et al. A model for early prediction of diabetes. Inf Med Unlocked. 2019;16:100204. [CrossRef]62]. Other features include waveform characteristics from optical signals, such as shape and amplitude, to estimate blood glucose levels [Liu WJ, Huang A, Wang P, Chu C. PbFG: physique-based fuzzy granular modeling for non-invasive blood glucose monitoring. Inf Sci. 2019;497:56-76. [CrossRef]33,Rachim VP, Chung W. Wearable-band type visible-near infrared optical biosensor for non-invasive blood glucose monitoring. Sens Actuators B. 2019;286:173-180. [CrossRef]36,Zhang G, Mei Z, Zhang Y, Ma X, Lo B, Chen D, et al. A noninvasive blood glucose monitoring system based on smartphone PPG signal processing and machine learning. IEEE Trans Ind Inf. 2020;16(11):7209-7218. [CrossRef]40]. AI advances in the field of blood glucose estimation research in the context of NIBGM have the potential to improve the quality of life for patients with DM and minimize invasiveness.

Application

AI was mainly used in NIBGM to estimate real-time blood glucose levels using optical, biosensing, imaging, and tissue impedance measurement technology instead of current widely used methods such as blood tests or finger pricks [Chew HSJ, Lim SL, Kim G, Kayambu G, So BYJ, Shabbir A, et al. Essential elements of weight loss apps for a multi-ethnic population with high BMI: a qualitative study with practical recommendations. Transl Behav Med. Apr 03, 2023;13(3):140-148. [CrossRef] [Medline]63]. AI was also used to predict future blood glucose levels (up to 30 minutes later) [Chaki J, Ganesh ST, Cidham S, Theertan SA. Machine learning and artificial intelligence based diabetes mellitus detection and self-management: a systematic review. J King Saud Univ Comp Inf Sci. 2022;34(6):3204-3225. [CrossRef]22] and detect DM [Agrawal H, Jain P, Joshi AM. Machine learning models for non-invasive glucose measurement: towards diabetes management in smart healthcare. Health Technol (Berl). 2022;12(5):955-970. [FREE Full text] [CrossRef] [Medline]10,Abubeker KM, Baskar S. A machine learning strategy for internet-of-things-enabled diabetic prediction to mitigate pneumonia risk. 2022. Presented at: 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO); October 12, 2022; Noida, India. [CrossRef]44], suggesting the potential of AI-assisted NIBGM for continuous BGM and diagnostic purposes.

Technology Used

Two broad classifications for NIBGM emerged namely sample- and non–sample-based methods of detection. Sample-based include studies like Malik et al [Malik S, Khadgawat R, Anand S, Gupta S. Non-invasive detection of fasting blood glucose level via electrochemical measurement of saliva. Springerplus. 2016;5(1):701. [FREE Full text] [CrossRef] [Medline]34] which use salivary electrochemical signals to train ML models. Concentrations of sodium, potassium, and calcium ions were measured and correlated with blood glucose levels [Malik S, Khadgawat R, Anand S, Gupta S. Non-invasive detection of fasting blood glucose level via electrochemical measurement of saliva. Springerplus. 2016;5(1):701. [FREE Full text] [CrossRef] [Medline]34]. Other sample-based techniques include the use of breath signals to detect acetone to estimate blood glucose levels [Guo D, Zhang D, Zhang L, Lu G. Non-invasive blood glucose monitoring for diabetics by means of breath signal analysis. Sens Actuators B. 2012;173:106-113. [CrossRef]30,Lekha S, Suchetha M. Real-time non-invasive detection and classification of diabetes using modified convolution neural network. IEEE J Biomed Health Inform. 2018;22(5):1630-1636. [CrossRef]32]. A major challenge for the development of NIBGM systems which rely on bodily fluid is that the concentration glucose level is miniscule [Alhaddad AY, Aly H, Gad H, Al-Ali A, Sadasivuni KK, Cabibihan J, et al. Sense and learn: recent advances in wearable sensing and machine learning for blood glucose monitoring and trend-detection. Front Bioeng Biotechnol. 2022;10:876672. [FREE Full text] [CrossRef] [Medline]64]. Hence, there is a need to enhance sensitivity and remove other interference in such sensors [Yao H, Shum AJ, Cowan M, Lähdesmäki I, Parviz BA. A contact lens with embedded sensor for monitoring tear glucose level. Biosens Bioelectron. Mar 15, 2011;26(7):3290-3296. [FREE Full text] [CrossRef] [Medline]65].

Out of the non–sample-based noninvasive techniques developed to predict blood glucose levels, PPG, akin to the technology of pulse oximetry, appears the most among the studies followed by other optical techniques such as NIRS and RS. The results were not surprising as the use of optical methods for measuring glucose levels is presently one of the best approaches in noninvasive glucose estimation research [Alarcón-Paredes A, Francisco-García V, Guzmán-Guzmán I, Cantillo-Negrete J, Cuevas-Valencia R, Alonso-Silverio G. An IoT-based non-invasive glucose level monitoring system using raspberry pi. Appl Sci. Jul 28, 2019;9(15):3046. [CrossRef]47]. For example, Monte-Moreno [Monte-Moreno E. Non-invasive estimate of blood glucose and blood pressure from a photoplethysmograph by means of machine learning techniques. Artif Intell Med. Oct 2011;53(2):127-138. [CrossRef] [Medline]56] used a PPG-based sensor to measure changes in blood volume changes and developed an ML algorithm to estimate blood glucose levels [Contreras I, Vehi J. Artificial intelligence for diabetes management and decision support: literature review. J Med Internet Res. May 30, 2018;20(5):e10775. [FREE Full text] [CrossRef] [Medline]14]. While traditional PPG requires skin contact, typically using a finger over a smartphone camera, to detect blood volume changes, advanced remote PPG allows the detection of subtle skin color changes to estimate blood volume changes [Sumaiya J, Hasan MR, Hossain E. Noninvasive blood glucose measurement using live video by smartphone. 2020. Presented at: 2020 IEEE 8th R10 Humanitarian Technology Conference (R10-HTC); December 01, 2020; Kuching, Malaysia. [CrossRef]27,Nie Z, Rong M, Li K. Blood glucose prediction based on imagingphotoplethysmography in combination with machine learning. Biomed Signal Process Control. 2023;79:104179. [CrossRef]35,Zhang G, Mei Z, Zhang Y, Ma X, Lo B, Chen D, et al. A noninvasive blood glucose monitoring system based on smartphone PPG signal processing and machine learning. IEEE Trans Ind Inf. 2020;16(11):7209-7218. [CrossRef]40]. These technologies have to be validated against the conventional BGM methods in a larger clinical population to establish their usefulness and efficiency [Jansson MM, Kögler M, Hörkkö S, Ala-Kokko T, Rieppo L. Vibrational spectroscopy and its future applications in microbiology. Appl Spectrosc Rev. 2021;58(2):132-158. [CrossRef]66].

Such technology may be useful for self-monitoring since it has a low barrier of entry and only requires a smartphone. Such setup may also be useful in clinical settings for monitoring or diagnostic purposes and reduces the need for retraining since staff are familiar with similar setups in the hospitals. On the other hand, others have commented that the use of PPG is often corrupted by measurement artifacts from movements, restricting one’s movement during continuous glucose monitoring [Rachim VP, Chung W. Wearable-band type visible-near infrared optical biosensor for non-invasive blood glucose monitoring. Sens Actuators B. 2019;286:173-180. [CrossRef]36,Tamura T, Maeda Y, Sekine M, Yoshida M. Wearable photoplethysmographic sensors—past and present. Electronics. 2014;3(2):282-302. [CrossRef]67].

ML Models

This review maps out the various DL and traditional ML algorithms used by the studies. Previous studies have adopted ML for risk stratification and identification of patients with DM [Maniruzzaman M, Rahman MJ, Al-MehediHasan M, Suri HS, Abedin MM, El-Baz A, et al. Accurate diabetes risk stratification using machine learning: role of missing value and outliers. J Med Syst. Apr 10, 2018;42(5):92. [FREE Full text] [CrossRef] [Medline]68]. Several ML processes, such as SVM, regression trees, k-nearest neighbor, ANN, naïve Bayes, and RF, have been used in transforming diabetes care [Dankwa-Mullan I, Rivo M, Sepulveda M, Park Y, Snowdon J, Rhee K. Transforming diabetes care through artificial intelligence: the future is here. Popul Health Manag. Jun 2019;22(3):229-242. [FREE Full text] [CrossRef] [Medline]69].

The main uses of ML processes include feature selection and classification. ML methods require the extraction of features from signals. However, extracting fiducial points from real-life signals can be highly challenging [Zanelli S, Ammi M, Hallab M, El Yacoubi MA. Diabetes detection and management through photoplethysmographic and electrocardiographic signals analysis: a systematic review. Sensors (Basel). Jun 29, 2022;22(13):4890. [FREE Full text] [CrossRef] [Medline]21]. Not only is it difficult to develop a feature extraction algorithm that can handle diverse waveform types but there is also a need to assess the quality of the computed features as the feature extraction algorithm is unable to effectively operate if the input signal is corrupted [Zanelli S, Ammi M, Hallab M, El Yacoubi MA. Diabetes detection and management through photoplethysmographic and electrocardiographic signals analysis: a systematic review. Sensors (Basel). Jun 29, 2022;22(13):4890. [FREE Full text] [CrossRef] [Medline]21].

The emergence of DL has facilitated the analysis of large volumes of data without the need for explicit feature extraction. However, DL approaches experience limited interpretability, which can be problematic in a clinical setting where understanding why and how a pathology was detected is crucial for validating the diagnosis [Zanelli S, Ammi M, Hallab M, El Yacoubi MA. Diabetes detection and management through photoplethysmographic and electrocardiographic signals analysis: a systematic review. Sensors (Basel). Jun 29, 2022;22(13):4890. [FREE Full text] [CrossRef] [Medline]21].

Future Research

In different studies, researchers have used various ML algorithms to construct classification models using derived feature vectors to evaluate the performance of different algorithms on the datasets used. Conversely, some researchers have opted to use a single ML method for their classification model. However, it is important to note that no single ML algorithm is universally optimal for all types of input data [Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. 2014. Presented at: International Conference on Learning Representations (ICLR 2015); May 07-09, 2015:1-14; San Diego, CA. URL: https:/​/www.​semanticscholar.org/​paper/​Very-Deep-Convolutional-Networks-for-Large-Scale-Simonyan-Zisserman/​eb42cf88027de515750f230b23b1a057dc78210870]. Therefore, it is beneficial to test multiple ML algorithms and determine which one produces the best outcomes for a given task. Comparisons among different AI models can help identify the strengths and limitations of each approach, guiding further improvements in accuracy and performance.

Given the heterogeneity of AI models and input data applied in each study, it is beyond the scope of this review to ascertain the best NIBGM system based on performance metrics alone. Furthermore, the lack of standardized reporting and analysis of results, leads to heterogeneity that hampers the comparison of findings across studies. Perhaps a diagnostic accuracy review may be more suited to address the question of which system is best suited to be adopted in various settings. As with all AI studies, efforts should be made to standardize and regulate the use of AI technologies in diabetes care. Consensus guidelines and protocols should be developed to ensure the quality and safety of AI-assisted monitoring systems [Varsi C, Nes LS, Kristjansdottir OB, Kelders SM, Stenberg U, Zangi HA, et al. Implementation strategies to enhance the implementation of eHealth programs for patients with chronic illnesses: realist systematic review. J Med Internet Res. Sep 27, 2019;21(9):e14255. [FREE Full text] [CrossRef] [Medline]71].

Another potential area of research in the field of NIBGM is the use of digital twin (DT) techniques. DT serves as a digital representation that mirrors the state of a physical entity or system by capturing real-time data through sensors and reflecting it in digital devices [Sun T, He X, Song X, Shu L, Li Z. The digital twin in medicine: a key to the future of healthcare? Front Med (Lausanne). 2022;9:907066. [FREE Full text] [CrossRef] [Medline]72]. DT offers a powerful solution for real-time monitoring, accurate diagnosis, and effective treatment [Sun T, He X, Song X, Shu L, Li Z. The digital twin in medicine: a key to the future of healthcare? Front Med (Lausanne). 2022;9:907066. [FREE Full text] [CrossRef] [Medline]72]. However, the main challenges include data acquisition, data privacy, and security concerns [Bruynseels K. When nature goes digital: routes for responsible innovation. J Responsible Innovation. 2020;7(3):342-360. [CrossRef]73]. Further advancement in Big Data is required to develop holistic and accurate DTs.

Strengths and Limitations

To the best of our knowledge, this is the first study to report the current state-of-the-art in AI-assisted NIBGM, which informs the direction of future systematic reviews and interventional research. To enhance the rigor of the study, we adhered to the PRISMA-ScR guidelines and had 2 independent reviewers in the paper selection process.

This study had several limitations. First, as this review limits in vivo methods of verification (human participants), certain relevant evidence could have been precluded, such as studies that use in vitro methods for verification of their AI models such as skin models or varied concentrations of glucose solutions. Second, a simple keyword search strategy and only papers written in English were retrieved, possibly limiting the scope of our findings. However, we conducted a hand search of previous systematic reviews to identify relevant papers.

Conclusions

The use of AI for NIBGM is a promising area of research that has the potential to revolutionize diabetes management. The studies reviewed demonstrate that some AI techniques can accurately predict glucose levels from noninvasive sources while enhancing comfort and ease of use for patients. However, the overall range of accuracy is wide due to the heterogeneity of models and input data. As such, we propose that there is a need for further efforts to standardize and regulate the use of AI technologies in diabetes care, as well as develop consensus guidelines and protocols to ensure the quality and safety of AI-assisted monitoring systems.

Acknowledgments

No funding was received for conducting this study.

Data Availability

All data generated or analyzed during this study are included in this published article.

Authors' Contributions

PZC wrote the manuscript with support from EJ. PZC and EJ conducted the methodological appraisal. PZC and HSJC extracted the data. All authors reviewed the final manuscript.

Conflicts of Interest

None declared.

Multimedia Appendix 1

PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta‐Analyses extension for Scoping Reviews) checklist.

DOCX File , 17 KB

Multimedia Appendix 2

Specific search strategy for each database.

DOCX File , 15 KB

Multimedia Appendix 3

Characteristics of blood glucose monitoring systems.

DOCX File , 25 KB

Multimedia Appendix 4

Characteristics of noninvasive blood glucose monitoring systems.

DOCX File , 26 KB

Multimedia Appendix 5

Characteristics of Machine Learning.

DOCX File , 25 KB

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AI: artificial intelligence
ANN: artificial neural network
BGM: blood glucose monitoring
CEG: Clarke error grid
ChAMAI: checklist for assessment of medical AI
DL: deep learning
DM: diabetes mellitus
DT: digital twin
ECG: electrocardiography
HbA1c: hemoglobin A1c
MAE: mean absolute error
ML: machine learning
NIBGM: noninvasive blood glucose monitoring
NIRS: near-infrared spectroscopy
NN: neural network
PPG: photoplethysmography
PRISMA-ScR: Preferred Reporting Items for Systematic Reviews and Meta‐Analyses extension for Scoping Reviews
RF: random forest
RS: Raman spectroscopy
SVM: support vector machines
UWB: ultrawideband


Edited by A Coristine; submitted 01.04.24; peer-reviewed by S Saeedi, AN Ali; comments to author 21.06.24; revised version received 24.06.24; accepted 08.10.24; published 19.11.24.

Copyright

©Pin Zhong Chan, Eric Jin, Miia Jansson, Han Shi Jocelyn Chew. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 19.11.2024.

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