Scientific article
OA Policy
English

Artificial intelligence to detect papilledema from ocular fundus photographs

ContributorsMilea, Dan; Biousse, Valérie; Bonzai, group; BONSAI Group
Published inNew England Journal of Medicine, vol. 382, no. 18, p. 1687-1695
Publication date2020
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.)

Keywords
  • DIABETIC-RETINOPATHY
  • EMERGENCY
  • OPHTHALMOSCOPY
  • FEASIBILITY
  • VALIDATION
  • HEADACHE
  • CAMERA
Citation (ISO format)
MILEA, Dan, BIOUSSE, Valérie, BONZAI, group. Artificial intelligence to detect papilledema from ocular fundus photographs. In: New England Journal of Medicine, 2020, vol. 382, n° 18, p. 1687–1695. doi: 10.1056/NEJMoa1917130
Main files (1)
Article (Published version)
accessLevelPublic
Secondary files (1)
Identifiers
Journal ISSN0028-4793
218views
186downloads

Technical informations

Creation09/23/2021 4:12:00 PM
First validation09/23/2021 4:12:00 PM
Update time03/16/2023 1:29:31 AM
Status update03/16/2023 1:29:30 AM
Last indexation10/31/2024 11:21:14 PM
All rights reserved by Archive ouverte UNIGE and the University of GenevaunigeBlack