{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T21:55:44Z","timestamp":1781646944632,"version":"3.54.5"},"reference-count":22,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T00:00:00Z","timestamp":1779148800000},"content-version":"vor","delay-in-days":48,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"UCSD Vision Research Center Core Grant","award":["P30EY022589"],"award-info":[{"award-number":["P30EY022589"]}]},{"name":"NIH grant","award":["R01EY033847,"],"award-info":[{"award-number":["R01EY033847,"]}]},{"DOI":"10.13039\/100001818","name":"Research to Prevent Blindness","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100001818","id-type":"DOI","asserted-by":"publisher"}]},{"name":"UCSD Jacobs Retina Center"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Artificial intelligence (AI) has shown promise in retinal imaging, yet its application to longitudinal optical coherence tomography angiography (OCTA) remains limited. This study developed and evaluated an AI model for classifying treatment response in neovascular age-related macular degeneration (nAMD) using paired OCTA images acquired before and after anti-VEGF therapy. In this retrospective cohort study, paired OCTA en-face images and corresponding OCT B-scans were collected for each treatment course. OCTA image pairs were manually segmented and aligned for AI input, while ground-truth labels (Improved, Unchanged, Worsened) were determined based on structural OCT findings and clinical visual acuity outcomes. After quality exclusion, 1033 OCTA pairs were included and divided into training, validation, and independent testing subsets. Two experienced retina specialists graded all OCTA pairs for comparison. On the test set, the AI model achieved an overall accuracy of 82.08%, with class-specific accuracies of 74.29% (worsened), 81.48% (unchanged), and 88.64% (improved). In contrast, overall human grading accuracy was 61.40%. Human graders were significantly more likely to misclassify treatment response than the AI model for all groups (odds ratio\u2009=\u20092.88; 95% CI 1.68\u20134.92;\n                    <jats:italic>p<\/jats:italic>\n                    \u2009&lt;\u20090.0001). These findings demonstrate that AI-based paired OCTA analysis can provide a more accurate and objective assessment of treatment response in nAMD.\n                  <\/jats:p>","DOI":"10.1038\/s41598-026-42999-7","type":"journal-article","created":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T07:26:15Z","timestamp":1775028375000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence based assessment of treatment response in wet age related macular degeneration using paired OCT angiography"],"prefix":"10.1038","volume":"16","author":[{"given":"Mohamed Sherif","family":"Morsy","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nandini Avijit","family":"Dutta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elsaid Ibrahim","family":"Eldessouky","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mamdouh Mahmoud","family":"Kabil","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hamdy Abd El Azim","family":"El-Koumy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nehal Nailesh","family":"Mehta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amr Lotfy","family":"Ali","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Soumya","family":"Jena","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haochen","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dirk-Uwe","family":"Bartsch","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lingyun","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cheolhong","family":"An","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Truong","family":"Nguyen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"William R.","family":"Freeman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,1]]},"reference":[{"key":"42999_CR1","doi-asserted-by":"crossref","unstructured":"Bourne, R. 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Due to the retrospective nature of the study, the requirement for informed consent was waived by the IRB. The study was conducted in accordance with the tenets of the Declaration of Helsinki and complied with the Health Insurance Portability and Accountability Act (HIPAA) regulations. All data were de-identified to ensure patient confidentiality.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Statement"}}],"article-number":"15405"}}