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Artificial Intelligence to Detect Papilledema from Ocular Fundus Photographs

dc.rights.licenseopenen_US
dc.contributor.authorMILEA, D.
dc.contributor.authorNAJJAR, R. P.
dc.contributor.authorZHUBO, J.
dc.contributor.authorTING, D.
dc.contributor.authorVASSENEIX, C.
dc.contributor.authorXU, X.
dc.contributor.authorAGHSAEI FARD, M.
dc.contributor.authorFONSECA, P.
dc.contributor.authorVANIKIETI, K.
dc.contributor.authorLAGREZE, W. A.
dc.contributor.authorLA MORGIA, C.
dc.contributor.authorCHEUNG, C. Y.
dc.contributor.authorHAMANN, S.
dc.contributor.authorCHIQUET, C.
dc.contributor.authorSANDA, N.
dc.contributor.authorYANG, H.
dc.contributor.authorMEJICO, L. J.
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorROUGIER, Marie-Benedicte
dc.contributor.authorKHO, R.
dc.contributor.authorTHI HA CHAU, T.
dc.contributor.authorSINGHAL, S.
dc.contributor.authorGOHIER, P.
dc.contributor.authorCLERMONT-VIGNAL, C.
dc.contributor.authorCHENG, C. Y.
dc.contributor.authorJONAS, J. B.
dc.contributor.authorYU-WAI-MAN, P.
dc.contributor.authorFRASER, C. L.
dc.contributor.authorCHEN, J. J.
dc.contributor.authorAMBIKA, S.
dc.contributor.authorMILLER, N. R.
dc.contributor.authorLIU, Y.
dc.contributor.authorNEWMAN, N. J.
dc.contributor.authorWONG, T. Y.
dc.contributor.authorBIOUSSE, V.
dc.date.accessioned2021-02-24T11:05:23Z
dc.date.available2021-02-24T11:05:23Z
dc.date.issued2020
dc.identifier.issn1533-4406 (Electronic) 0028-4793 (Linking)en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/26341
dc.description.abstractEnBackground 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. (Funded by the Singapore National Medical Research Council and the SingHealth Duke–NUS Ophthalmology and Visual Sciences Academic Clinical Program.)
dc.language.isoENen_US
dc.subjectLEHA
dc.title.enArtificial Intelligence to Detect Papilledema from Ocular Fundus Photographs
dc.title.alternativeN Engl J Meden_US
dc.typeArticle de revueen_US
dc.identifier.doi10.1056/NEJMoa1917130en_US
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologieen_US
dc.identifier.pubmed32286748en_US
bordeaux.journalNew England Journal of Medicineen_US
bordeaux.page1687-1695en_US
bordeaux.volume382en_US
bordeaux.hal.laboratoriesBordeaux Population Health Research Center (BPH) - UMR 1219en_US
bordeaux.issue18en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.teamLEHA_BPH
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
hal.identifierhal-03150966
hal.version1
hal.date.transferred2021-02-24T11:05:28Z
hal.exporttrue
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