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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  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
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  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
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  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
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  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
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  54. Wang T, Chen R, Fan N, Zang L, Yuan S, Du P, Wu Q, Wang A, Li J, Kong X, Zhu W. Machine Learning and Deep Learning for Diagnosis of Lumbar Spinal Stenosis: Systematic Review and Meta-Analysis. Journal of Medical Internet Research 2024;26:e54676 View
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Books/Policy Documents

  1. Swathishri B, Swetha R. Computational Intelligence in Data Science. View
  2. Hegde N, Lewis J, Malghan R. International Conference on Signal, Machines, Automation, and Algorithm. View
  3. González J, García T, Moro D, Criado F, Rodríguez J. Proceedings of TEEM 2024. View

Conference Proceedings

  1. Ramesh R, Sathiamoorthy S. 2023 2nd International Conference on Applied Artificial Intelligence and Computing (ICAAIC). Deep Learning with Heuristic Optimization Driven Diabetic Retinopathy Detection on Fundus Images View
  2. Vázquez Noguera J, Mello-Román J, Pinto-Roa D, Gomez S, Ayala J, Aquino-Brítez D, Gardel-Sotomayor P, García-Torres M, Facon J, Castillo Benítez V, Matto I, Pérez-Estigarribia P. 2023 XLIX Latin American Computer Conference (CLEI). Multiclass Diabetic Retinopathy Classification of Eye Fundus Images Small Datasets Performance Improvement – A Neuroevolution Approach View
  3. Siddiqui A, Kaur H, Naaz S, Tanveer S. 2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS). Comprehensive Review and Meta-Analysis of Machine Learning Applications in Screening for Diabetic Retinopathy Analysis View
  4. Zhang W, Shi D, He M. 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Improving Consistency in Cardiovascular Disease Risk Assessment: Cross-Camera Adaptation for Retinal Images View
  5. Ferreira-Caballero S, Pinto-Roa D, Noguera J, Ayala J, Gardel-Sotomayor P, Pérez-Estigarribia P. 2024 L Latin American Computer Conference (CLEI). Low-Rank Adaptation Applied to Multiclass Diabetic Retinopathy Classification View