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

Journals

1. Lu R, Bagdasarova Y, Lee A. Machine Learning–Based Anomaly Detection Techniques in Ophthalmology. JAMA Ophthalmology 2022;140(2):189 View
2. Pandey P, Ballios B, Christakis P, Kaplan A, Mathew D, Ong Tone S, Wan M, Micieli J, Wong J. Ensemble of deep convolutional neural networks is more accurate and reliable than board-certified ophthalmologists at detecting multiple diseases in retinal fundus photographs. British Journal of Ophthalmology 2024;108(3):417 View
3. Li C, Chen K, Yang K, Li J, Zhong Y, Yu H, Yang Y, Yang X, Liu L. Progress on application of spatial epidemiology in ophthalmology. Frontiers in Public Health 2022;10 View
4. Guan J, Zhu Y, Hu Q, Ma S, Mu J, Li Z, Fang D, Zhuo X, Guan H, Sun Q, An L, Zhang S, Qin P, Zhuo Y. Prevalence Patterns and Onset Prediction of High Myopia for Children and Adolescents in Southern China via Real-World Screening Data: Retrospective School-Based Study. Journal of Medical Internet Research 2023;25:e39507 View
5. Burlina P, Paul W, Liu T, Bressler N. Detecting Anomalies in Retinal Diseases Using Generative, Discriminative, and Self-supervised Deep Learning. JAMA Ophthalmology 2022;140(2):185 View
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 2024 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