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Status: Bibliographieeintrag

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Verfasst von:Dong, Li [VerfasserIn]   i
 He, Wanji [VerfasserIn]   i
 Zhang, Ruiheng [VerfasserIn]   i
 Ge, Zongyuan [VerfasserIn]   i
 Wang, Ya Xing [VerfasserIn]   i
 Zhou, Jinqiong [VerfasserIn]   i
 Xu, Jie [VerfasserIn]   i
 Shao, Lei [VerfasserIn]   i
 Wang, Qian [VerfasserIn]   i
 Yan, Yanni [VerfasserIn]   i
 Xie, Ying [VerfasserIn]   i
 Fang, Lijian [VerfasserIn]   i
 Wang, Haiwei [VerfasserIn]   i
 Wang, Yenan [VerfasserIn]   i
 Zhu, Xiaobo [VerfasserIn]   i
 Wang, Jinyuan [VerfasserIn]   i
 Zhang, Chuan [VerfasserIn]   i
 Wang, Heng [VerfasserIn]   i
 Wang, Yining [VerfasserIn]   i
 Chen, Rongtian [VerfasserIn]   i
 Wan, Qianqian [VerfasserIn]   i
 Yang, Jingyan [VerfasserIn]   i
 Zhou, Wenda [VerfasserIn]   i
 Li, Heyan [VerfasserIn]   i
 Yao, Xuan [VerfasserIn]   i
 Yang, Zhiwen [VerfasserIn]   i
 Xiong, Jianhao [VerfasserIn]   i
 Wang, Xin [VerfasserIn]   i
 Huang, Yelin [VerfasserIn]   i
 Chen, Yuzhong [VerfasserIn]   i
 Wang, Zhaohui [VerfasserIn]   i
 Rong, Ce [VerfasserIn]   i
 Gao, Jianxiong [VerfasserIn]   i
 Zhang, Huiliang [VerfasserIn]   i
 Wu, Shouling [VerfasserIn]   i
 Jonas, Jost B. [VerfasserIn]   i
 Wei, Wen Bin [VerfasserIn]   i
Titel:Artificial intelligence for screening of multiple retinal and optic nerve diseases
Titelzusatz: original investigation : ophthalmology
Verf.angabe:Li Dong, Wanji He, Ruiheng Zhang, Zongyuan Ge, Ya Xing Wang, Jinqiong Zhou, Jie Xu, Lei Shao, Qian Wang, Yanni Yan, Ying Xie, Lijian Fang, Haiwei Wang, Yenan Wang, Xiaobo Zhu, Jinyuan Wang, Chuan Zhang, Heng Wang, Yining Wang, Rongtian Chen, Qianqian Wan, Jingyan Yang, Wenda Zhou, Heyan Li, Xuan Yao, Zhiwen Yang, Jianhao Xiong, Xin Wang, Yelin Huang, Yuzhong Chen, Zhaohui Wang, Ce Rong, Jianxiong Gao, Huiliang Zhang, Shouling Wu, Jost B. Jonas, Wen Bin Wei
E-Jahr:2022
Jahr:May 3, 2022
Umfang:12 S.
Fussnoten:Gesehen am 19.02.2024
Titel Quelle:Enthalten in: JAMA network open
Ort Quelle:Chicago, Ill. : American Medical Association, 2018
Band/Heft Quelle:5(2022), 5, Artikel-ID e229960, Seite 1-12
ISSN Quelle:2574-3805
Abstract:The lack of experienced ophthalmologists limits the early diagnosis of retinal diseases. Artificial intelligence can be an efficient real-time way for screening retinal diseases.To develop and prospectively validate a deep learning (DL) algorithm that, based on ocular fundus images, recognizes numerous retinal diseases simultaneously in clinical practice.This multicenter, diagnostic study at 65 public medical screening centers and hospitals in 19 Chinese provinces included individuals attending annual routine medical examinations and participants of population-based and community-based studies.Based on 120002 ocular fundus photographs, the Retinal Artificial Intelligence Diagnosis System (RAIDS) was developed to identify 10 retinal diseases. RAIDS was validated in a prospective collected data set, and the performance between RAIDS and ophthalmologists was compared in the data sets of the population-based Beijing Eye Study and the community-based Kailuan Eye Study.The performance of each classifier included sensitivity, specificity, accuracy, F1 score, and Cohen κ score.In the prospective validation data set of 208758 images collected from 110784 individuals (median [range] age, 42 [8-87] years; 115443 [55.3%] female), RAIDS achieved a sensitivity of 89.8% (95% CI, 89.5%-90.1%) to detect any of 10 retinal diseases. RAIDS differentiated 10 retinal diseases with accuracies ranging from 95.3% to 99.9%, without marked differences between medical screening centers and geographical regions in China. Compared with retinal specialists, RAIDS achieved a higher sensitivity for detection of any retinal abnormality (RAIDS, 91.7% [95% CI, 90.6%-92.8%]; certified ophthalmologists, 83.7% [95% CI, 82.1%-85.1%]; junior retinal specialists, 86.4% [95% CI, 84.9%-87.7%]; and senior retinal specialists, 88.5% [95% CI, 87.1%-89.8%]). RAIDS reached a superior or similar diagnostic sensitivity compared with senior retinal specialists in the detection of 7 of 10 retinal diseases (ie, referral diabetic retinopathy, referral possible glaucoma, macular hole, epiretinal macular membrane, hypertensive retinopathy, myelinated fibers, and retinitis pigmentosa). It achieved a performance comparable with the performance by certified ophthalmologists in 2 diseases (ie, age-related macular degeneration and retinal vein occlusion). Compared with ophthalmologists, RAIDS needed 96% to 97% less time for the image assessment.In this diagnostic study, the DL system was associated with accurately distinguishing 10 retinal diseases in real time. This technology may help overcome the lack of experienced ophthalmologists in underdeveloped areas.
DOI:doi:10.1001/jamanetworkopen.2022.9960
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

kostenfrei: Volltext: https://doi.org/10.1001/jamanetworkopen.2022.9960
 kostenfrei: Volltext: https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2791807
 DOI: https://doi.org/10.1001/jamanetworkopen.2022.9960
Datenträger:Online-Ressource
Sprache:eng
K10plus-PPN:1881103099
Verknüpfungen:→ Zeitschrift

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