Artificial Intelligence to Detect Papilledema from Ocular Fundus Photographs

Miléa, Dan; P. Najjar, Raymond; Jiang, Zhubo; Ting, Daniel; Vasseneix, Caroline; Xu, Xinxing; Aghsaei Fard, Masoud; Fonseca, Pedro; Vanikieti, Kavin; A. Lagrèze, Wolf; La Morgia, Chiara; Y. Cheung, Carol; Hamann, Steffen; Chiquet, Christophe; Sanda, Nicolae; Yang, Hui; J. Mejico, Luis; Rougier, Marie‐Bénédicte; C. Kho, Richard; Hà Châu Tran, Thi; Singhal, Shweta; Gohier, Philippe; Clermont-Vignal, C.; Cheng, Ching‐Yu; B. Jonas, J.; Yu‐Wai‐Man, Patrick; L. Fraser, Clare; J. Chen, John; Selvakumar, Ambika; R. Miller, Neil; Liu, Yong; J. Newman, Nancy; Yin Wong, Tien; Biousse, Valérie

doi:10.1056/nejmoa1917130

Public

Artificial Intelligence to Detect Papilledema from Ocular Fundus Photographs

Published in New England Journal of Medicine • Apr 14, 2020

Authors:

Dan Miléa

Raymond P. Najjar

Zhubo Jiang

Abstract

BACKGROUND: 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. METHODS: We trained, validated, and externally tested a deep-lear...

Subject

Papilledema

Fundus (uterus)

Optometry

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