Published on in Vol 23, No 7 (2021): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/23863, first published .
Performance and Limitation of Machine Learning Algorithms for Diabetic Retinopathy Screening: Meta-analysis

Performance and Limitation of Machine Learning Algorithms for Diabetic Retinopathy Screening: Meta-analysis

Performance and Limitation of Machine Learning Algorithms for Diabetic Retinopathy Screening: Meta-analysis

Journals

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  5. Jimenez-Carmona S, Alemany-Marquez P, Alvarez-Ramos P, Mayoral E, Aguilar-Diosdado M. Validation of an Automated Screening System for Diabetic Retinopathy Operating under Real Clinical Conditions. Journal of Clinical Medicine 2021;11(1):14 View
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  10. Wu J, Liu T. Application of Deep Learning to Retinal-Image-Based Oculomics for Evaluation of Systemic Health: A Review. Journal of Clinical Medicine 2022;12(1):152 View
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  23. Shahsuvaryan M. Is it time to consider teleophthalmology as a game-changer in the management of diabetic retinopathy?. Revista Brasileira de Oftalmologia 2023;82 View
  24. Alabdulwahhab K. Diabetic Retinopathy Screening Using Non-Mydriatic Fundus Camera in Primary Health Care Settings – A Multicenter Study from Saudi Arabia. International Journal of General Medicine 2023;Volume 16:2255 View
  25. Kolasa K, Admassu B, Hołownia-Voloskova M, Kędzior K, Poirrier J, Perni S. Systematic reviews of machine learning in healthcare: a literature review. Expert Review of Pharmacoeconomics & Outcomes Research 2024;24(1):63 View
  26. Nur-A-Alam M, Nasir M, Ahsan M, Based M, Haider J, Palani S. A Faster RCNN-Based Diabetic Retinopathy Detection Method Using Fused Features From Retina Images. IEEE Access 2023;11:124331 View
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  28. Zhang Y, Chen B, Chen Z, Wan Q. Correlation study of renal function indices with diabetic peripheral neuropathy and diabetic retinopathy in T2DM patients with normal renal function. Frontiers in Public Health 2023;11 View
  29. Ren Z, Chen B, Hong C, Yuan J, Deng J, Chen Y, Ye J, Li Y. The value of machine learning in preoperative identification of lymph node metastasis status in endometrial cancer: a systematic review and meta-analysis. Frontiers in Oncology 2023;13 View
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  31. He S, Joseph S, Bulloch G, Jiang F, Kasturibai H, Kim R, Ravilla T, Wang Y, Shi D, He M. Bridging the Camera Domain Gap With Image-to-Image Translation Improves Glaucoma Diagnosis. Translational Vision Science & Technology 2023;12(12):20 View
  32. Lee C. Meta-Analysis and Machine Learning: Advancement of Analytic Methodology. Korean Journal of Neurotrauma 2023;19(4):407 View
  33. Wolf R, Channa R, Lehmann H, Abramoff M, Liu T. Clinical Implementation of Autonomous Artificial Intelligence Systems for Diabetic Eye Exams: Considerations for Success. Clinical Diabetes 2024;42(1):142 View
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  35. Asare J, Brown-Acquaye W, Ujakpa M, Freeman E, Appiahene P. Application of machine learning approach for iron deficiency anaemia detection in children using conjunctiva images. Informatics in Medicine Unlocked 2024;45:101451 View
  36. Malerbi F, Nakayama L, Melo G, Stuchi J, Lencione D, Prado P, Ribeiro L, Dib S, Regatieri C. Automated Identification of Different Severity Levels of Diabetic Retinopathy Using a Handheld Fundus Camera and Single-Image Protocol. Ophthalmology Science 2024;4(4):100481 View
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  39. Jiang A, Li J, Wang L, Zha W, Lin Y, Zhao J, Fang Z, Shen G. Multi‐feature, Chinese–Western medicine‐integrated prediction model for diabetic peripheral neuropathy based on machine learning and SHAP. Diabetes/Metabolism Research and Reviews 2024;40(4) View
  40. Guan H, Wang Y, Niu P, Zhang Y, Zhang Y, Miao R, Fang X, Yin R, Zhao S, Liu J, Tian J. The role of machine learning in advancing diabetic foot: a review. Frontiers in Endocrinology 2024;15 View
  41. Li X, Wen X, Shang X, Liu J, Zhang L, Cui Y, Luo X, Zhang G, Xie J, Huang T, Chen Z, Lyu Z, Wu X, Lan Y, Meng Q. Identification of diabetic retinopathy classification using machine learning algorithms on clinical data and optical coherence tomography angiography. Eye 2024;38(14):2813 View
  42. Ying B, Chandra R, Wang J, Cui H, Oatts J. Machine Learning Models for Predicting Cycloplegic Refractive Error and Myopia Status Based on Non-Cycloplegic Data in Chinese Students. Translational Vision Science & Technology 2024;13(8):16 View
  43. Riotto E, Gasser S, Potic J, Sherif M, Stappler T, Schlingemann R, Wolfensberger T, Konstantinidis L. Accuracy of Autonomous Artificial Intelligence-Based Diabetic Retinopathy Screening in Real-Life Clinical Practice. Journal of Clinical Medicine 2024;13(16):4776 View
  44. Richardson A, Kundu A, Henao R, Lee T, Scott B, Grewal D, Fekrat S. Multimodal Retinal Imaging Classification for Parkinson's Disease Using a Convolutional Neural Network. Translational Vision Science & Technology 2024;13(8):23 View
  45. dos Reis M, Künas C, da Silva Araújo T, Schneiders J, de Azevedo P, Nakayama L, Rados D, Umpierre R, Berwanger O, Lavinsky D, Malerbi F, Navaux P, Schaan B. Advancing healthcare with artificial intelligence: diagnostic accuracy of machine learning algorithm in diagnosis of diabetic retinopathy in the Brazilian population. Diabetology & Metabolic Syndrome 2024;16(1) View
  46. Aruleba I, Sun Y. Effective Credit Risk Prediction Using Ensemble Classifiers With Model Explanation. IEEE Access 2024;12:115015 View
  47. Li Y, Liang Z, Li Y, Cao Y, Zhang H, Dong B. Machine learning value in the diagnosis of vertebral fractures: A systematic review and meta-analysis. European Journal of Radiology 2024;181:111714 View
  48. Wu J, Lin S, Moghimi S. Big data to guide glaucoma treatment. Taiwan Journal of Ophthalmology 2024;14(3):333 View
  49. Wu J, Lin S, Moghimi S. Application of artificial intelligence in glaucoma care: An updated review. Taiwan Journal of Ophthalmology 2024;14(3):340 View
  50. Tao Y, Xiong M, Peng Y, Yao L, Zhu H, Zhou Q, Ouyang J. Machine learning-based identification and validation of immune-related biomarkers for early diagnosis and targeted therapy in diabetic retinopathy. Gene 2025;934:149015 View
  51. Basavaraju A, Davidson E, Diracca G, Chen C, Santra S. Pesticide Residue Coverage Estimation on Citrus Leaf Using Image Analysis Assisted by Machine Learning. Applied Sciences 2024;14(22):10087 View

Books/Policy Documents

  1. Swathishri B, Swetha R. Computational Intelligence in Data Science. View