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Artificial Intelligence to Detect Papilledema from Ocular Fundus Photographs

Authors :
Valérie Biousse
Nancy J. Newman
Nicolae Sanda
Clare L. Fraser
Chiara La Morgia
John J. Chen
Catherine Clermont-Vignal
Caroline Vasseneix
Pedro Fonseca
Steffen Hamann
Kavin Vanikieti
Raymond P. Najjar
Daniel S W Ting
Dan Milea
Shweta Singhal
Selvakumar Ambika
Masoud Aghsaei Fard
Xinxing Xu
Tien Yin Wong
Carol Y. Cheung
Jiang Zhubo
Philippe Gohier
Marie Bénédicte Rougier
Yong Liu
Ching-Yu Cheng
Wolf A. Lagrèze
Patrick Yu-Wai-Man
Richard Kho
Neil R. Miller
Jost B. Jonas
Hui Yang
Tran Thi Ha Chau
Christophe Chiquet
Luis J. Mejico
Milea, Dan
Najjar, Raymond P
Zhubo, Jiang
Ting, Daniel
Vasseneix, Caroline
Xu, Xinxing
Aghsaei Fard, Masoud
Fonseca, Pedro
Vanikieti, Kavin
Lagrèze, Wolf A
La Morgia, Chiara
Cheung, Carol Y
Hamann, Steffen
Chiquet, Christophe
Sanda, Nicolae
Yang, Hui
Mejico, Luis J
Rougier, Marie-Bénédicte
Kho, Richard
Thi Ha Chau, Tran
Singhal, Shweta
Gohier, Philippe
Clermont-Vignal, Catherine
Cheng, Ching-Yu
Jonas, Jost B
Yu-Wai-Man, Patrick
Fraser, Clare L
Chen, John J
Ambika, Selvakumar
Miller, Neil R
Liu, Yong
Newman, Nancy J
Wong, Tien Y
Biousse, Valérie
BONSAI Group
Amore, Giulia
Carelli, Valerio
Yu Wai Man, Patrick [0000-0001-7847-9320]
Apollo - University of Cambridge Repository
Thumann, Gabriele
Source :
Milea, D, Najjar, R P, Zhubo, J, Ting, D, Vasseneix, C, Xu, X, Fard, M A, Fonseca, P, Vanikieti, K, Lagrèze, W A, La Morgia, C, Cheung, C Y, Hamann, S, Chiquet, C, Sanda, N, Yang, H, Mejico, L J, Rougier, M B, Kho, R, Chau, T T H, Singhal, S, Gohier, P, Clermont-Vignal, C, Cheng, C Y, Jonas, J B, Yu-Wai-Man, P, Fraser, C L, Chen, J J, Ambika, S, Miller, N R, Liu, Y, Newman, N J, Wong, T Y & Biousse, V 2020, ' Artificial intelligence to detect papilledema from ocular fundus photographs ', New England Journal of Medicine, vol. 382, no. 18, pp. 1687-1695 . https://doi.org/10.1056/NEJMoa1917130, New England Journal of Medicine, Vol. 382, No 18 (2020) pp. 1687-1695
Publication Year :
2020

Abstract

BACKGROUND: Nonophthalmologist physicians do not confidently perform direct ophthalmos-copy. 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 coun-tries were used for training and validation, and 1505 photographs from 5 other sites were used for external testing. Performance at classifying the optic-disk ap-pearance 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 photo-graphs: 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 abnor-malities 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).CONCLUSIONSA deep-learning system using fundus photographs with pharmacologically dilated pupils differentiated among optic disks with papilledema, normal disks, and disks with nonpapilledema abnormalities. (Funded by the Singapore National Medical Research Council and the SingHealth Duke–NUS Ophthalmology and Visual Sci-ences Academic Clinical Program.)

Details

Language :
English
ISSN :
00284793
Database :
OpenAIRE
Journal :
Milea, D, Najjar, R P, Zhubo, J, Ting, D, Vasseneix, C, Xu, X, Fard, M A, Fonseca, P, Vanikieti, K, Lagrèze, W A, La Morgia, C, Cheung, C Y, Hamann, S, Chiquet, C, Sanda, N, Yang, H, Mejico, L J, Rougier, M B, Kho, R, Chau, T T H, Singhal, S, Gohier, P, Clermont-Vignal, C, Cheng, C Y, Jonas, J B, Yu-Wai-Man, P, Fraser, C L, Chen, J J, Ambika, S, Miller, N R, Liu, Y, Newman, N J, Wong, T Y & Biousse, V 2020, ' Artificial intelligence to detect papilledema from ocular fundus photographs ', New England Journal of Medicine, vol. 382, no. 18, pp. 1687-1695 . https://doi.org/10.1056/NEJMoa1917130, New England Journal of Medicine, Vol. 382, No 18 (2020) pp. 1687-1695
Accession number :
edsair.doi.dedup.....9490101b7bc7304cfbb6565904461edd
Full Text :
https://doi.org/10.1056/NEJMoa1917130