This paper is in the following e-collection/theme issue:

Artificial Intelligence (1371) Computer-Aided Diagnosis (128) mHealth in a Clinical Setting (615) mHealth in the Developing World/LMICs, Underserved Communities, and for Global Health (565) Gestational Diabetes (28) Mobile Health and Apps for Maternal and Child Health (150)

Published on in Vol 22, No 9 (2020): September

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/21573, first published .
An Innovative Artificial Intelligence–Based App for the Diagnosis of Gestational Diabetes Mellitus (GDM-AI): Development Study

An Innovative Artificial Intelligence–Based App for the Diagnosis of Gestational Diabetes Mellitus (GDM-AI): Development Study

An Innovative Artificial Intelligence–Based App for the Diagnosis of Gestational Diabetes Mellitus (GDM-AI): Development Study

Authors of this article:

Jiayi Shen1, 2 Author Orcid Image ;   Jiebin Chen3 Author Orcid Image ;   Zequan Zheng4 Author Orcid Image ;   Jiabin Zheng4 Author Orcid Image ;   Zherui Liu3 Author Orcid Image ;   Jian Song5 Author Orcid Image ;   Sum Yi Wong4 Author Orcid Image ;   Xiaoling Wang6 Author Orcid Image ;   Mengqi Huang6 Author Orcid Image ;   Po-Han Fang4 Author Orcid Image ;   Bangsheng Jiang4 Author Orcid Image ;   Winghei Tsang1 Author Orcid Image ;   Zonglin He4 Author Orcid Image ;   Taoran Liu7 Author Orcid Image ;   Babatunde Akinwunmi8, 9 Author Orcid Image ;   Chi Chiu Wang10 Author Orcid Image ;   Casper J P Zhang11 Author Orcid Image ;   Jian Huang12 Author Orcid Image ;   Wai-Kit Ming1 Author Orcid Image
Article Authors Cited by (35) Tweetations (5) Metrics

Journals

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  16. Gao W, Xie J, Ke Y, Tian M, Zeng Z, Ma X, Zhi M. A two-stage prediction filling method with support vector technologies optimized competitively in stages by grey wolf optimizer and particle swarm optimization for missing fasting blood glucose. Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 2023;237(12):1427 View
  17. Chen T, Wu H, Chiu M. A deep neural network with modified random forest incremental interpretation approach for diagnosing diabetes in smart healthcare. Applied Soft Computing 2024;152:111183 View
  18. Lu H, Ding X, Hirst J, Yang Y, Yang J, Mackillop L, Clifton D. Digital Health and Machine Learning Technologies for Blood Glucose Monitoring and Management of Gestational Diabetes. IEEE Reviews in Biomedical Engineering 2024;17:98 View
  19. Zhou T, Gu S, Shao F, Li P, Wu Y, Xiong J, Wang B, Zhou C, Gao P, Hua X. Prediction of preeclampsia from retinal fundus images via deep learning in singleton pregnancies: a prospective cohort study. Journal of Hypertension 2024;42(4):701 View
  20. Gebremariam B, Aboye G, Dessalegn A, Simegn G. Rule-based expert system for the diagnosis of maternal complications during pregnancy: For low resource settings. DIGITAL HEALTH 2024;10 View
  21. Kannaiyan A, Bagchi S, Vijayan V, Georgiy P, Manickavasagam S, Kumar D. Revolutionizing Women\'s Health: Artificial Intelligence\'s Impact on Obstetrics and Gynecology. Journal of South Asian Federation of Obstetrics and Gynaecology 2024;16(2):161 View
  22. Kolozali Ş, White S, Norris S, Fasli M, van Heerden A. Explainable Early Prediction of Gestational Diabetes Biomarkers by Combining Medical Background and Wearable Devices: A Pilot Study With a Cohort Group in South Africa. IEEE Journal of Biomedical and Health Informatics 2024;28(4):1860 View
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