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The development of effective therapies faces the challenge of identifying appropriate outcome measures that accurately reflect treatment benefits. Optical Coherence Tomography (OCT) provides high-resolution retinal images, serving as a valuable tool for deriving potential outcome measures, such as retinal thickness. However, automated segmentation of OCT images, particularly in regions disrupted by degeneration, remains complex. In this study, we propose a deep learning-based approach that incorporates a pathology-aware loss function to segment retinal sublayers in OCT images from patients with STGD1. This method targets relatively unaffected regions for sublayer segmentation, ensuring accurate boundary delineation in areas with minimal disruption. In severely affected regions, identified by a box detection model, the total retina is segmented as a single layer to avoid errors. Our model significantly outperforms standard models, achieving an average Dice coefficient of <jats:inline-formula>\n              <jats:tex-math>$$99\\%$$<\/jats:tex-math>\n            <\/jats:inline-formula> for total retina and <jats:inline-formula>\n              <jats:tex-math>$$93\\%$$<\/jats:tex-math>\n            <\/jats:inline-formula> for retinal sublayers. The most substantial improvement was in the segmentation of the photoreceptor inner segment, with Dice coefficient increasing by <jats:inline-formula>\n              <jats:tex-math>$$25\\%$$<\/jats:tex-math>\n            <\/jats:inline-formula>. This approach provides a balance between granularity and reliability, making it suitable for clinical application in tracking disease progression and evaluating therapeutic efficacy.<\/jats:p>","DOI":"10.1038\/s41598-025-85213-w","type":"journal-article","created":{"date-parts":[[2025,2,8]],"date-time":"2025-02-08T10:52:47Z","timestamp":1739011967000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Looking outside the box with a pathology aware AI approach for analyzing OCT retinal images in Stargardt disease"],"prefix":"10.1038","volume":"15","author":[{"given":"Parisa","family":"Khateri","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tiana","family":"Koottungal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Damon","family":"Wong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rupert W.","family":"Strauss","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lucas","family":"Janeschitz-Kriegl","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maximilian","family":"Pfau","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leopold","family":"Schmetterer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hendrik P. 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He is member of the Scientific Advisory Board of: Boehringer Ingelheim Pharma GmbH & Co, Janssen Research & Development, LLC (J&J Innovative Medicine), Kerna Ventures, Okuvision GmbH, and Tenpoint Therapeutics. Dr. Scholl is member of the Investment Advisory Board of Droia NV. Dr. Scholl is a member of the Data Monitoring and Safety Board\/Committee of ViGeneron (NCT06291935) and adviser of the Steering Committee of Novo Nordisk (FOCUS trial; NCT03811561). The other authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"4739"}}