Back to Search
Start Over
Artificial Intelligence to Detect Papilledema from Ocular Fundus Photographs
- 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.)
- Subjects :
- Validation study
FEASIBILITY
genetic structures
Fundus Oculi
Datasets as Topic
CAMERA
030204 cardiovascular system & hematology
Sensitivity and Specificity
VALIDATION
LEHA
Retina
Direct Ophthalmoscopy
Diagnosis, Differential
03 medical and health sciences
0302 clinical medicine
Deep Learning
HEADACHE
Predictive Value of Tests
Area under curve
medicine
Photography
Humans
030212 general & internal medicine
Papilledema
papilledema, artificial intelligence, optic disk, optic nerve
OPHTHALMOSCOPY
Retrospective Studies
business.industry
General Medicine
EMERGENCY
eye diseases
ddc:616.8
3. Good health
Ophthalmoscopy
Multicenter study
ROC Curve
Area Under Curve
Artificial intelligence
Neural Networks, Computer
sense organs
medicine.symptom
business
Algorithms
DIABETIC-RETINOPATHY
Subjects
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