乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T09:09:36Z","timestamp":1745917776270,"version":"3.37.3"},"reference-count":37,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2022,7,10]],"date-time":"2022-07-10T00:00:00Z","timestamp":1657411200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82141128"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012401","name":"Beijing Science and Technology Planning Project","doi-asserted-by":"publisher","award":["Z201100005520045","Z181100001818003"],"id":[{"id":"10.13039\/501100012401","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Advanced Intelligent Systems"],"published-print":{"date-parts":[[2022,9]]},"abstract":"It is important to detect recurrent retinal detachment (RD) among patients after retinal reattachment surgery. The application of deep learning (DL) methods to detect recurrent RD with ultra\u2010widefield (UWF) fundus images is promising, but the feasibility and efficiency have not been studied. A DL system with ResNet\u201050 and Inception\u2010ResNet\u2010V2 is developed and internally validated to identify recurrent RD and retina reattachment after surgery. The performance is further validated and compared with human ophthalmologists in a prospective dataset assessed by area under curve (AUC), accuracy, sensitivity, and specificity. Five hundred fifty\u2010four UWF fundus images from 173 RD patients (mean [standard deviation] age: 39.2\u2009\u00b1\u200916.2\u2009years; male: 115 [66.5%]) are used to develop the DL system. DL shows AUCs of 0.912 (95% confidence interval [CI]: 0.855\u20130.968) and 0.906 (95% CI: 0.818\u20130.995) for the two models. Eighty\u2010nine UWF fundus images from 23 RD patients (mean [standard deviation] age: 31.4\u2009\u00b1\u200912.3\u2009years; male: 15 [65.2%]) are collected as prospective dataset. DL also shows the ability to detect recurrent RD with the AUCs of 0.929 and 0.930 for the two models, respectively. DL reaches a similar and even better diagnostic performance than junior ophthalmologists and performs much better than medical students.<\/jats:p><\/jats:sec>","DOI":"10.1002\/aisy.202200067","type":"journal-article","created":{"date-parts":[[2022,7,10]],"date-time":"2022-07-10T16:20:06Z","timestamp":1657470006000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Deep Learning for Automatic Detection of Recurrent Retinal Detachment after Surgery Using Ultra\u2010Widefield Fundus Images: A Single\u2010Center Study"],"prefix":"10.1002","volume":"4","author":[{"given":"Wen-Da","family":"Zhou","sequence":"first","affiliation":[{"name":"Beijing Tongren Eye Centre, Beijing Key Laboratory of Intraocular Tumour Diagnosis and Treatment Beijing Ophthalmology & Visual Sciences Key Lab Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology Beijing Tongren Hospital Capital Medical University Beijing China"}]},{"given":"Li","family":"Dong","sequence":"additional","affiliation":[{"name":"Beijing Tongren Eye Centre, Beijing Key Laboratory of Intraocular Tumour Diagnosis and Treatment Beijing Ophthalmology & Visual Sciences Key Lab Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology Beijing Tongren Hospital Capital Medical University Beijing China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9054-288X","authenticated-orcid":false,"given":"Kai","family":"Zhang","sequence":"additional","affiliation":[{"name":"InferVision Healthcare Science and Technology Limited Company Shanghai 200032 China"}]},{"given":"Qian","family":"Wang","sequence":"additional","affiliation":[{"name":"Beijing Tongren Eye Centre, Beijing Key Laboratory of Intraocular Tumour Diagnosis and Treatment Beijing Ophthalmology & Visual Sciences Key Lab Medical Artificial Intelligence Research and 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