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Title: Artificial intelligence to detect papilledema from ocular fundus photographs
Authors: Dan Milea
Raymond P. Najjar
Jiang Zhubo
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
Marie Bénédicte Rougier
Richard Kho
Tran Thi Ha Chau
Shweta Singhal
Philippe Gohier
Catherine Clermont-Vignal
Ching Yu Cheng
Jost B. Jonas
Patrick Yu-Wai-Man
Clare L. Fraser
John J. Chen
Selvakumar Ambika
Neil R. Miller
Yong Liu
Nancy J. Newman
Tien Y. Wong
Valérie Biousse
Farabi Eye Hospital
John van Geest Centre for Brain Repair
Istituto delle Scienze Neurologiche di Bologna
Universite Grenoble Alpes
Duke-NUS Medical School Singapore
Fondation Adolphe de Rothschild
University of Cambridge
Københavns Universitet
Alma Mater Studiorum Università di Bologna
Yong Loo Lin School of Medicine
Medical Research Foundation, Chennai
Universität Freiburg im Breisgau
Singapore Eye Research Institute
Universite Catholique de Lille
Sun Yat-Sen University
Universidade de Coimbra
Centro Hospitalar e Universitário de Coimbra
State University of New York Upstate Medical University
Universität Heidelberg
Moorfields Eye Hospital NHS Foundation Trust
Faculty of Medicine, Ramathibodi Hospital, Mahidol University
Save Sight Institute
Hôpitaux universitaires de Genève
CHU Angers
Singapore National Eye Centre
Centre Hospitalier Universitaire de Grenoble
Mayo Clinic
A-Star, Institute of High Performance Computing
Groupe Hospitalier Pellegrin
Chinese University of Hong Kong
Emory University School of Medicine
Johns Hopkins School of Medicine
American Eye Center
Keywords: Medicine
Issue Date: 30-Apr-2020
Citation: New England Journal of Medicine. Vol.382, No.18 (2020), 1687-1695
Abstract: Copyright © 2020 Massachusetts Medical Society. 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-learning system to classify optic disks as being normal or having papilledema or other abnormalities from 15,846 retrospectively collected ocular fundus photographs that had been obtained with pharmacologic pupillary dilation and various digital cameras in persons from multiple ethnic populations. Of these photographs, 14,341 from 19 sites in 11 countries were used for training and validation, and 1505 photographs from 5 other sites were used for external testing. Performance at classifying the optic-disk appearance was evaluated by calculating the area under the receiver-operating-characteristic curve (AUC), sensitivity, and specificity, as compared with a reference standard of clinical diagnoses by neuro-ophthalmologists. RESULTS The training and validation data sets from 6779 patients included 14,341 photographs: 9156 of normal disks, 2148 of disks with papilledema, and 3037 of disks with other abnormalities. The percentage classified as being normal ranged across sites from 9.8 to 100%; the percentage classified as having papilledema ranged across sites from zero to 59.5%. In the validation set, the system discriminated disks with papilledema from normal disks and disks with nonpapilledema abnormalities with an AUC of 0.99 (95% confidence interval [CI], 0.98 to 0.99) and normal from abnormal disks with an AUC of 0.99 (95% CI, 0.99 to 0.99). In the external-testing data set of 1505 photographs, the system had an AUC for the detection of papilledema of 0.96 (95% CI, 0.95 to 0.97), a sensitivity of 96.4% (95% CI, 93.9 to 98.3), and a specificity of 84.7% (95% CI, 82.3 to 87.1). CONCLUSIONS A deep-learning system using fundus photographs with pharmacologically dilated pupils differentiated among optic disks with papilledema, normal disks, and disks with nonpapilledema abnormalities.
ISSN: 15334406
Appears in Collections:Scopus 2020

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