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A multi\u2010label DL model was developed to grade the presence of 7 clinical signs: large soft drusen (&gt;125\u2009\u03bcm), intermediate soft (63\u2013125\u2009\u03bcm), large area of soft drusen (total area &gt;500\u2009\u03bcm), presence of central soft drusen (large or intermediate), hyperpigmentation, hypopigmentation, and advanced AMD (defined as neovascular or atrophic AMD). Prediction performances were evaluated using cross\u2010validation and the expert human interpretation of the clinical signs as the ground truth.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>A total of 1178 images were included in the study. Averaging the 7 clinical signs' detection performances, DeepAlienorNet achieved an overall sensitivity, specificity, and AUROC of 0.77, 0.83, and 0.87, respectively. The model demonstrated particularly strong performance in predicting advanced AMD and large areas of soft drusen. It can also generate heatmaps, highlighting the relevant image areas for interpretation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>DeepAlienorNet demonstrates promising performance in automatically identifying clinical signs of AMD from CFP, offering several notable advantages. Its high interpretability reduces the black box effect, addressing ethical concerns. Additionally, the model can be easily integrated to automate well\u2010established and validated AMD progression scores, and the user\u2010friendly interface further enhances its usability. The main value of DeepAlienorNet lies in its ability to assist in precise severity scoring for further adapted AMD management, all while preserving interpretability.<\/jats:p><\/jats:sec>","DOI":"10.1111\/aos.16660","type":"journal-article","created":{"date-parts":[[2024,2,12]],"date-time":"2024-02-12T12:15:49Z","timestamp":1707740149000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["DeepAlienorNet: A deep learning model to extract clinical features from colour fundus photography in age\u2010related macular degeneration"],"prefix":"10.1111","volume":"102","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0638-4354","authenticated-orcid":false,"given":"Alexis","family":"Mathieu","sequence":"first","affiliation":[{"name":"Inserm, Bordeaux Population Health Research Center, UMR 1219 University of Bordeaux  Bordeaux France"},{"name":"Service d'Ophtalmologie Centre Hospitalier Universitaire de Bordeaux  Bordeaux France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Soufiane","family":"Ajana","sequence":"additional","affiliation":[{"name":"Inserm, Bordeaux Population Health Research Center, UMR 1219 University of Bordeaux  Bordeaux France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4438-9535","authenticated-orcid":false,"given":"Jean\u2010Fran\u00e7ois","family":"Korobelnik","sequence":"additional","affiliation":[{"name":"Inserm, Bordeaux Population Health Research Center, UMR 1219 University of Bordeaux  Bordeaux France"},{"name":"Service d'Ophtalmologie Centre Hospitalier Universitaire de Bordeaux  Bordeaux France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M\u00e9lanie","family":"Le\u00a0Goff","sequence":"additional","affiliation":[{"name":"Inserm, Bordeaux Population Health Research Center, UMR 1219 University of Bordeaux  Bordeaux France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brigitte","family":"Gontier","sequence":"additional","affiliation":[{"name":"Service d'Ophtalmologie Centre Hospitalier Universitaire de Bordeaux  Bordeaux France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marie\u2010B\u00e9n\u00e9dicte","family":"Rougier","sequence":"additional","affiliation":[{"name":"Service d'Ophtalmologie Centre Hospitalier Universitaire de Bordeaux  Bordeaux France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2099-0481","authenticated-orcid":false,"given":"C\u00e9cile","family":"Delcourt","sequence":"additional","affiliation":[{"name":"Inserm, Bordeaux Population Health Research Center, UMR 1219 University of Bordeaux  Bordeaux France"},{"name":"Service d'Ophtalmologie Centre Hospitalier Universitaire de Bordeaux  Bordeaux France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3127-8617","authenticated-orcid":false,"given":"Marie\u2010No\u00eblle","family":"Delyfer","sequence":"additional","affiliation":[{"name":"Inserm, Bordeaux Population Health Research Center, UMR 1219 University of Bordeaux  Bordeaux France"},{"name":"Service d'Ophtalmologie Centre Hospitalier Universitaire de Bordeaux  Bordeaux France"},{"name":"FRCRnet\/FCRIN Network  Bordeaux France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,2,12]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1159\/000072920"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ophtha.2020.08.031"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.3390\/cancers13071590"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.3390\/app10134523"},{"key":"e_1_2_8_6_1","volume-title":"The global economic cost of visual impairment","author":"AMD Alliance International","year":"2010"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamaophthalmol.2018.1934"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.3390\/make3040048"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamaophthalmol.2017.3782"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1097-0258(20000515)19:9<1141::AID-SIM479>3.0.CO;2-F"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1167\/iovs.16-19487"},{"key":"e_1_2_8_12_1","unstructured":"Chollet F.(n.d.)Keras documentation: Grad\u2010CAM class activation visualization.https:\/\/keras.io\/examples\/vision\/grad_cam\/"},{"key":"e_1_2_8_13_1","unstructured":"Cross\u2010Validation: Evaluating Estimator Performance. 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