{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T12:39:22Z","timestamp":1790167162576,"version":"4.1.0"},"reference-count":33,"publisher":"BMJ","issue":"2","license":[{"start":{"date-parts":[[2025,8,14]],"date-time":"2025-08-14T00:00:00Z","timestamp":1755129600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Science & Technology Project of Beijing Municipal Science & Technology","award":["Z181100001818003"],"award-info":[{"award-number":["Z181100001818003"]}]},{"name":"Science & Technology Project of Beijing Municipal Science & Technology","award":["Z201100005520045"],"award-info":[{"award-number":["Z201100005520045"]}]},{"name":"Scientific Research Common Program of Beijing Municipal Commission of Education","award":["KM202410025011"],"award-info":[{"award-number":["KM202410025011"]}]},{"DOI":"10.13039\/501100010270","name":"The Capital Health Research and Development of Special","doi-asserted-by":"crossref","award":["2020-1-2052"],"award-info":[{"award-number":["2020-1-2052"]}],"id":[{"id":"10.13039\/501100010270","id-type":"DOI","asserted-by":"crossref"}]},{"name":"The priming scientific research foundation for the junior researcher in Beijing Tongren Hospital, Capital Medical University","award":["2023-YJJ-ZZL-003"],"award-info":[{"award-number":["2023-YJJ-ZZL-003"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82141128"],"award-info":[{"award-number":["82141128"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82220108017"],"award-info":[{"award-number":["82220108017"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82401283"],"award-info":[{"award-number":["82401283"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["bmj.com"],"crossmark-restriction":true},"short-container-title":["Br J Ophthalmol"],"accepted":{"date-parts":[[2025,7,29]]},"published-print":{"date-parts":[[2026,2]]},"abstract":"<jats:sec>\n                    <jats:title>Background\/aims<\/jats:title>\n                    <jats:p>This study evaluated the performance of artificial intelligence (AI) algorithms in predicting best-corrected visual acuity (BCVA) for patients with multiple retinal diseases, using multimodal medical imaging including macular optical coherence tomography (OCT), optic disc OCT and fundus images. The goal was to enhance clinical BCVA evaluation efficiency and precision.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A retrospective study used data from 2545 patients (4028 eyes) for training, 896 (1006 eyes) for testing and 196 (200 eyes) for internal validation, with an external prospective dataset of 741 patients (1381 eyes). Single-modality analyses employed different backbone networks and feature fusion methods, while multimodal fusion combined modalities using average aggregation, concatenation\/reduction and maximum feature selection. Predictive accuracy was measured by mean absolute error (MAE), root mean squared error (RMSE) and R\u00b2 score.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Macular OCT achieved better single-modality prediction than optic disc OCT, with MAE of 3.851 vs 4.977 and RMSE of 7.844 vs 10.026. Fundus images showed an MAE of 3.795 and RMSE of 7.954. Multimodal fusion significantly improved accuracy, with the best results using average aggregation, achieving an MAE of 2.865, RMSE of 6.229 and R\u00b2 of 0.935. External validation yielded an MAE of 8.38 and RMSE of 10.62.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Multimodal fusion provided the most accurate BCVA predictions, demonstrating AI\u2019s potential to improve clinical evaluation. However, challenges remain regarding disease diversity and applicability in resource-limited settings.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1136\/bjo-2025-327189","type":"journal-article","created":{"date-parts":[[2025,8,14]],"date-time":"2025-08-14T09:20:12Z","timestamp":1755163212000},"page":"158-165","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":2,"title":["AI-based prediction of best-corrected visual acuity in patients with multiple retinal diseases using multimodal medical imaging"],"prefix":"10.1136","volume":"110","author":[{"given":"Li","family":"Dong","sequence":"first","affiliation":[{"name":"Beijing Tongren Eye Center, Beijing key Laboratory of Intraocular Tumor Diagnosis and Treatment, Beijing Ophthalmology\uff06Visual Sciences Key Lab, Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology, Beijing Key Laboratory of 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