{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T18:44:58Z","timestamp":1784659498576,"version":"3.55.0"},"reference-count":115,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,3,28]],"date-time":"2023-03-28T00:00:00Z","timestamp":1679961600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cell Dev. Biol."],"abstract":"<jats:p>As the only blood vessels that can directly be seen in the whole body, pathological changes in retinal vessels are related to the metabolic state of the whole body and many systems, which seriously affect the vision and quality of life of patients. Timely diagnosis and treatment are key to improving vision prognosis. In recent years, with the rapid development of artificial intelligence, the application of artificial intelligence in ophthalmology has become increasingly extensive and in-depth, especially in the field of retinal vascular diseases. Research study results based on artificial intelligence and fundus images are remarkable and provides a great possibility for early diagnosis and treatment. This paper reviews the recent research progress on artificial intelligence in retinal vascular diseases (including diabetic retinopathy, hypertensive retinopathy, retinal vein occlusion, retinopathy of prematurity, and age-related macular degeneration). The limitations and challenges of the research process are also discussed.<\/jats:p>","DOI":"10.3389\/fcell.2023.1168327","type":"journal-article","created":{"date-parts":[[2023,3,28]],"date-time":"2023-03-28T05:01:18Z","timestamp":1679979678000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":22,"title":["Research progress on diagnosing retinal vascular diseases based on artificial intelligence and fundus images"],"prefix":"10.3389","volume":"11","author":[{"given":"Yuke","family":"Ji","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yun","family":"Ji","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunfang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liya","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2023,3,28]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"6936","DOI":"10.3390\/s21206936","article-title":"An automatic detection and classification system of five stages for hypertensive retinopathy using semantic and instance segmentation in DenseNet architecture","volume":"21","author":"Abbas","year":"2021","journal-title":"Sensors (Basel)"},{"key":"B2","doi-asserted-by":"publisher","first-page":"e000924","DOI":"10.1136\/bmjophth-2021-000924","article-title":"Deep learning-based classification of retinal vascular diseases using ultra-widefield colour fundus photographs","volume":"7","author":"Abitbol","year":"2022","journal-title":"BMJ Open Ophthalmol."},{"key":"B3","doi-asserted-by":"publisher","first-page":"932","DOI":"10.1007\/s10278-021-00477-8","article-title":"Assistive framework for automatic detection of all the zones in retinopathy of prematurity using deep learning","volume":"34","author":"Agrawal","year":"2021","journal-title":"J. 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