{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T12:27:21Z","timestamp":1753878441348,"version":"3.41.2"},"reference-count":0,"publisher":"Wiley","issue":"S284","license":[{"start":{"date-parts":[[2025,1,19]],"date-time":"2025-01-19T00:00:00Z","timestamp":1737244800000},"content-version":"vor","delay-in-days":18,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Acta Ophthalmologica"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:sec><jats:label\/><jats:p><jats:bold>Aims\/Purpose:<\/jats:bold> Artificial intelligence (AI) models have received considerable attention in recent years for their ability to identify optical coherence tomography (OCT) biomarkers with clinical diagnostic potential and predict disease progression. This study aims to externally validate a deep learning (DL) algorithm by comparing its segmentation of retinal layers and fluid with a gold\u2010standard method for manually adjusting the automatic segmentation of the Heidelberg Spectralis HRA + OCT software Version 6.16.8.0.<\/jats:p><jats:p><jats:bold>Methods:<\/jats:bold> A total of sixty OCT images of healthy subjects and patients with intermediate and exudative age\u2010related macular degeneration (AMD) were included. A quantitative analysis of the retinal thickness and fluid area was performed, and the discrepancy between these methods was investigated.<\/jats:p><jats:p><jats:bold>Results:<\/jats:bold> The results showed a moderate\u2010to\u2010strong correlation between the metrics extracted by both software types, in all the groups, and an overall near\u2010perfect area overlap was observed, except for in the inner segment ellipsoid (ISE) layer. The DL system detected a significant difference in the outer retinal thickness across disease stages and accurately identified fluid in exudative cases. In more diseased eyes, there was significantly more disagreement between these methods.<\/jats:p><jats:p><jats:bold>Conclusions:<\/jats:bold> This DL system appears to be a reliable method for accessing important OCT biomarkers in AMD. However, further accuracy testing should be conducted to confirm its validity in real\u2010world settings to ultimately aid ophthalmologists in OCT imaging management and guide timely treatment approaches.<\/jats:p><jats:p><jats:bold>References:<\/jats:bold> Pawloff, M.; Gerendas, B.S.; Deak, G.; Bogunovic, H.; Gruber, A.; Schmidt\u2010Erfurth, U. Performance of retinal fluid monitoring in OCT imaging by automated deep learning versus human expert grading in neovascular AMD. Eye 2023, 37, 3793\u20133800.<\/jats:p><\/jats:sec>","DOI":"10.1111\/aos.17385","type":"journal-article","created":{"date-parts":[[2025,1,20]],"date-time":"2025-01-20T00:37:32Z","timestamp":1737333452000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Human versus artificial intelligence: Validation of a deep learning model for retinal layer and fluid segmentation in optical coherence tomography images from patients with age\u2010related macular degeneration"],"prefix":"10.1111","volume":"103","author":[{"given":"Joana","family":"Santos\u2010Oliveira","sequence":"first","affiliation":[{"name":"Department of Ophthalmology, Centro Hospitalar Universit\u00e1rio de S\u00e3o Jo\u00e3o  Porto Portugal"}]},{"given":"Mariana","family":"Miranda","sequence":"additional","affiliation":[{"name":"Department of Surgery and Physiology, Faculty of Medicine of the University of Porto  Porto Portugal"}]},{"given":"Ana Maria","family":"Mendon\u00e7a","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering Department, Faculty of Engineering of the University of Porto  Porto Portugal"}]},{"given":"V\u00e2nia","family":"Sousa","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Centro Hospitalar Universit\u00e1rio de S\u00e3o Jo\u00e3o  Porto Portugal"}]},{"given":"T\u00e2nia","family":"Melo","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering Department, Faculty of Engineering of the University of Porto  Porto Portugal"}]},{"given":"\u00c2ngela","family":"Carneiro","sequence":"additional","affiliation":[{"name":"Department of Ophthalmology, Centro Hospitalar Universit\u00e1rio de S\u00e3o Jo\u00e3o  Porto Portugal"}]}],"member":"311","published-online":{"date-parts":[[2025,1,19]]},"container-title":["Acta Ophthalmologica"],"original-title":[],"language":"en","deposited":{"date-parts":[[2025,3,12]],"date-time":"2025-03-12T03:07:49Z","timestamp":1741748869000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/aos.17385"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1]]},"references-count":0,"journal-issue":{"issue":"S284","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["10.1111\/aos.17385"],"URL":"https:\/\/doi.org\/10.1111\/aos.17385","archive":["Portico"],"relation":{},"ISSN":["1755-375X","1755-3768"],"issn-type":[{"type":"print","value":"1755-375X"},{"type":"electronic","value":"1755-3768"}],"subject":[],"published":{"date-parts":[[2025,1]]},"assertion":[{"value":"2025-01-19","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"aos.17385"}}