Artificial Intelligence to Detect Papilledema from Ocular Fundus Photographs
Author(s) -
Dan Miléa,
Raymond P. Najjar,
Zhubo Jiang,
Daniel Ting,
Caroline Vasseneix,
Xinxing Xu,
Masoud Aghsaei Fard,
Pedro Fonseca,
Kavin Vanikieti,
Wolf A. Lagrèze,
Chiara La Morgia,
Carol Y. Cheung,
Steffen Hamann,
Christophe Chiquet,
Nicolae Sanda,
Hui Yang,
Luis J. Mejico,
MarieBénédicte Rougier,
Richard C. Kho,
Thi Hà Châu Tran,
Shweta Singhal,
Philippe Gohier,
C. Clermont-Vignal,
ChingYu Cheng,
J. B. Jonas,
Patrick YuWaiMan,
Clare L. Fraser,
John J. Chen,
Ambika Selvakumar,
Neil R. Miller,
Yong Liu,
Nancy J. Newman,
Tien Yin Wong,
Valérie Biousse
Publication year - 2020
Publication title -
new england journal of medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 19.889
H-Index - 1030
eISSN - 1533-4406
pISSN - 0028-4793
DOI - 10.1056/nejmoa1917130
Subject(s) - papilledema , medicine , fundus (uterus) , confidence interval , receiver operating characteristic , ophthalmology , optic disk , ophthalmoscopy , optometry , artificial intelligence , glaucoma , computer science , retinal
Nonophthalmologist physicians do not confidently perform direct ophthalmoscopy. The use of artificial intelligence to detect papilledema and other optic-disk abnormalities from fundus photographs has not been well studied.
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