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

Artificial Intelligence (1407) Machine Learning (1488) Imaging Informatics (198) Decision Support for Health Professionals (1201) Ophthalmology (47) Diabetic Retinopathy (11)

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

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/27822, first published .
Application of an Anomaly Detection Model to Screen for Ocular Diseases Using Color Retinal Fundus Images: Design and Evaluation Study

Application of an Anomaly Detection Model to Screen for Ocular Diseases Using Color Retinal Fundus Images: Design and Evaluation Study

Application of an Anomaly Detection Model to Screen for Ocular Diseases Using Color Retinal Fundus Images: Design and Evaluation Study

Authors of this article:

Yong Han1, 2 Author Orcid Image ;   Weiming Li1, 2 Author Orcid Image ;   Mengmeng Liu1, 2 Author Orcid Image ;   Zhiyuan Wu1, 2 Author Orcid Image ;   Feng Zhang1, 2 Author Orcid Image ;   Xiangtong Liu1, 2 Author Orcid Image ;   Lixin Tao1, 2 Author Orcid Image ;   Xia Li3 Author Orcid Image ;   Xiuhua Guo1, 2 Author Orcid Image
Article Authors Cited by (18) Tweetations (3) Metrics

Journals

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  6. Chung Y, Choi I. Detection of abnormal extraocular muscles in small datasets of computed tomography images using a three-dimensional variational autoencoder. Scientific Reports 2023;13(1) View
  7. Ji Y, Ji Y, Liu Y, Zhao Y, Zhang L. Research progress on diagnosing retinal vascular diseases based on artificial intelligence and fundus images. Frontiers in Cell and Developmental Biology 2023;11 View
  8. Chłopowiec A, Karanowski K, Skrzypczak T, Grzesiuk M, Chłopowiec A, Tabakov M. Counteracting Data Bias and Class Imbalance—Towards a Useful and Reliable Retinal Disease Recognition System. Diagnostics 2023;13(11):1904 View
  9. Wang M, Lin T, Wang L, Lin A, Zou K, Xu X, Zhou Y, Peng Y, Meng Q, Qian Y, Deng G, Wu Z, Chen J, Lin J, Zhang M, Zhu W, Zhang C, Zhang D, Goh R, Liu Y, Pang C, Chen X, Chen H, Fu H. Uncertainty-inspired open set learning for retinal anomaly identification. Nature Communications 2023;14(1) View
  10. Sankari V, Umapathy U, Alasmari S, Aslam S. Automated Detection of Retinopathy of Prematurity Using Quantum Machine Learning and Deep Learning Techniques. IEEE Access 2023;11:94306 View
  11. Bhimavarapu U, Chintalapudi N, Battineni G. Automatic Detection and Classification of Hypertensive Retinopathy with Improved Convolution Neural Network and Improved SVM. Bioengineering 2024;11(1):56 View
  12. Parmar U, Surico P, Singh R, Romano F, Salati C, Spadea L, Musa M, Gagliano C, Mori T, Zeppieri M. Artificial Intelligence (AI) for Early Diagnosis of Retinal Diseases. Medicina 2024;60(4):527 View
  13. Waisberg E, Ong J, Kamran S, Masalkhi M, Paladugu P, Zaman N, Lee A, Tavakkoli A. Generative artificial intelligence in ophthalmology. Survey of Ophthalmology 2025;70(1):1 View
  14. Bansal V, Jain A, Kaur Walia N. Diabetic retinopathy detection through generative AI techniques: A review. Results in Optics 2024;16:100700 View
  15. Tiosano L, Abutbul R, Lender R, Shwartz Y, Chowers I, Hoshen Y, Levy J. Anomaly Detection and Biomarkers Localization in Retinal Images. Journal of Clinical Medicine 2024;13(11):3093 View
  16. Avhad V, Bakal J. Iridology based human health conditions predictions with computer vision and deep learning. Biomedical Signal Processing and Control 2024;96:106656 View