Differentiation of Diabetic Status Using Statistical and Machine Learning Techniques on Optical Coherence Tomography Angiography Images
Author:
Affiliation:
1. School of Pharmacy and Optometry, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
2. Manchester Royal Eye Hospital, Manchester University NHS Foundation Trust, Manchester, UK
Publisher
Association for Research in Vision and Ophthalmology (ARVO)
Subject
Ophthalmology,Biomedical Engineering
Cited by 13 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Comparison of Widefield OCT Angiography Features Between Severe Non-Proliferative and Proliferative Diabetic Retinopathy;Ophthalmology and Therapy;2024-01-25
2. Retinal Microvasculature Image Analysis Using Optical Coherence Tomography Angiography in Patients with Post-COVID-19 Syndrome;Journal of Imaging;2023-10-24
3. Detection of systemic cardiovascular illnesses and cardiometabolic risk factors with machine learning and optical coherence tomography angiography: a pilot study;Eye;2023-05-23
4. Use of artificial intelligence algorithms to predict systemic diseases from retinal images;WIREs Data Mining and Knowledge Discovery;2023-05-16
5. Sensitivity and specificity of optical coherence tomography angiography for diagnosis and classification of diabetic retinopathy; a systematic review and meta-analysis;European Journal of Ophthalmology;2023-04-03
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