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

Digital Health Reviews (1318) Machine Learning (1491) Artificial Intelligence (1409) Clinical Information and Decision Making (1426) Decision Support for Health Professionals (1203) Imaging Informatics (198) Computer-Aided Diagnosis (134)

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

Authors of this article:

Jo-Hsuan Wu1 Author Orcid Image ;   T Y Alvin Liu2 Author Orcid Image ;   Wan-Ting Hsu3 Author Orcid Image ;   Jennifer Hui-Chun Ho4 Author Orcid Image ;   Chien-Chang Lee5, 6, 7 Author Orcid Image
Article Authors Cited by (59) Tweetations (8) Metrics

Journals

  1. Tucker A, Kannampallil T, Fodeh S, Peleg M. New JBI policy emphasizes clinically-meaningful novel machine learning methods. Journal of Biomedical Informatics 2022;127:104003 View
  2. Canayaz M. Classification of diabetic retinopathy with feature selection over deep features using nature-inspired wrapper methods. Applied Soft Computing 2022;128:109462 View
  3. Bathelt F, Reinecke I, Peng Y, Henke E, Weidner J, Bartos M, Gött R, Waltemath D, Engelmann K, Schwarz P, Sedlmayr M. Opportunities of Digital Infrastructures for Disease Management—Exemplified on COVID-19-Related Change in Diagnosis Counts for Diabetes-Related Eye Diseases. Nutrients 2022;14(10):2016 View
  4. Gupta I, Choubey A, Choubey S. Artifical intelligence with optimal deep learning enabled automated retinal fundus image classification model. Expert Systems 2022;39(10) View
  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
  6. Socia D, Brady C, West S, Cockrell R, Vinetz J. Detection of trachoma using machine learning approaches. PLOS Neglected Tropical Diseases 2022;16(12):e0010943 View
  7. Lohiniva A, Nurzhynska A, Hudi A, Anim B, Aboagye D. Infodemic Management Using Digital Information and Knowledge Cocreation to Address COVID-19 Vaccine Hesitancy: Case Study From Ghana. JMIR Infodemiology 2022;2(2):e37134 View
  8. Lalithadevi B, Krishnaveni S. Detection of diabetic retinopathy and related retinal disorders using fundus images based on deep learning and image processing techniques: A comprehensive review. Concurrency and Computation: Practice and Experience 2022;34(19) View
  9. Kushwaha S, Srivastava R, Jain R, Sagar V, Aggarwal A, Bhadada S, Khanna P. Harnessing machine learning models for non-invasive pre-diabetes screening in children and adolescents. Computer Methods and Programs in Biomedicine 2022;226:107180 View
  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
  11. WU J, NISHIDA T, WEINREB R, LIN J. Performances of Machine Learning in Detecting Glaucoma Using Fundus and Retinal Optical Coherence Tomography Images: A Meta-Analysis. American Journal of Ophthalmology 2022;237:1 View
  12. Wellnhofer E. Real-World and Regulatory Perspectives of Artificial Intelligence in Cardiovascular Imaging. Frontiers in Cardiovascular Medicine 2022;9 View
  13. Halfpenny W, Baxter S. Towards effective data sharing in ophthalmology: data standardization and data privacy. Current Opinion in Ophthalmology 2022 View
  14. Liu L, Wang M, Li G, Wang Q. Construction of Predictive Model for Type 2 Diabetic Retinopathy Based on Extreme Learning Machine. Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy 2022;Volume 15:2607 View
  15. Miao J, Yu J, Zou W, Su N, Peng Z, Wu X, Huang J, Fang Y, Yuan S, Xie P, Huang K, Chen Q, Hu Z, Liu Q. Deep Learning Models for Segmenting Non-perfusion Area of Color Fundus Photographs in Patients With Branch Retinal Vein Occlusion. Frontiers in Medicine 2022;9 View
  16. Aranha G, Fernandes R, Morales P. Deep Transfer Learning Strategy to Diagnose Eye-Related Conditions and Diseases: An Approach Based on Low-Quality Fundus Images. IEEE Access 2023;11:37403 View
  17. Wang Z, Li Z, Li K, Mu S, Zhou X, Di Y. Performance of artificial intelligence in diabetic retinopathy screening: a systematic review and meta-analysis of prospective studies. Frontiers in Endocrinology 2023;14 View
  18. Xie L, Dou X, Ge T, Han X, Zhang Q, Wang Q, Chen S, He D, Tian W. Deep learning–based identification of spine growth potential on EOS radiographs. European Radiology 2023;34(5):2849 View
  19. Prashar J, Tay N. Performance of artificial intelligence for the detection of pathological myopia from colour fundus images: a systematic review and meta-analysis. Eye 2024;38(2):303 View
  20. OLTU B, KARACA B, ERDEM H, ÖZGÜR A. A Systematic Review of Transfer Learning-Based Approaches for Diabetic Retinopathy Detection. Gazi University Journal of Science 2023;36(3):1140 View
  21. Vilela M, Arrigo A, Parodi M, da Silva Mengue C. Smartphone Eye Examination: Artificial Intelligence and Telemedicine. Telemedicine and e-Health 2024;30(2):341 View
  22. Li Y, Dong B, Yuan P. The diagnostic value of machine learning for the classification of malignant bone tumor: a systematic evaluation and meta-analysis. Frontiers in Oncology 2023;13 View
  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
  27. Wu J, Koseoglu N, Jones C, Liu T. Vision transformers: The next frontier for deep learning-based ophthalmic image analysis. Saudi Journal of Ophthalmology 2023;37(3):173 View
  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
  30. Kemp O, Bascaran C, Cartwright E, McQuillan L, Matthew N, Shillingford-Ricketts H, Zondervan M, Foster A, Burton M. Real-world evaluation of smartphone-based artificial intelligence to screen for diabetic retinopathy in Dominica: a clinical validation study. BMJ Open Ophthalmology 2023;8(1):e001491 View
  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
  34. Oliveira L, Silva M, Santiago R, Benevides C, Cunha C, Matos A. Diagnóstico da retinopatia diabética por inteligência artificial por meio de smartphone. Revista Brasileira de Oftalmologia 2024;83 View
  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
  37. C M, K V, B.M. K, Murthy A, Sinha S. Retinal image analysis for detection of diabetic retinopathy- a simplified approach. Multimedia Tools and Applications 2024 View
  38. Zhang D, Wu C, Yang Z, Yin H, Liu Y, Li W, Huang H, Jin Z. The application of artificial intelligence in EUS. Endoscopic Ultrasound 2024;13(2):65 View
  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
  52. Abdalla M, Mohanraj J. Revolutionizing diabetic retinopathy screening and management: The role of artificial intelligence and machine learning. World Journal of Clinical Cases 2025;13(5) View
  53. Islam S, Deo R, Datta Barua P, Soar J, Yu P, Rajendra Acharya U. Retinal Health Screening Using Artificial Intelligence With Digital Fundus Images: A Review of the Last Decade (2012–2023). IEEE Access 2024;12:176630 View

Books/Policy Documents

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