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Artificial Intelligence to Detect Papilledema from Ocular Fundus Photographs
Dan Milea1, Raymond P Najjar1, Jiang Zhubo1
1From the Singapore National Eye Center (D.M., D.T., S.S., C.-Y.C., T.Y.W.), Singapore Eye Research Institute (D.M., R.P.N., D.T., C.V., S.S., C.-Y.C., T.Y.W.), Duke-NUS Medical School (D.M., R.P.N., D.T., S.S., C.-Y.C., T.Y.W.), Institute of High Performance Computing, Agency for Science, Technology, and Research (J.Z., X.X., Y.L.), and Yong Loo Lin School of Medicine, National University of Singapore (S.S., T.Y.W.) - all in Singapore; Farabi Eye Hospital, Tehran University of Medical Science, Tehran, Iran (M.A.F.); the Department of Ophthalmology, Centro Hospitalar e Universitário de Coimbra, and the Coimbra Institute for Biomedical Imaging and Translational Research, University of Coimbra, Coimbra, Portugal (P.F.); the Department of Ophthalmology, Ramathibodi Hospital, Mahidol University, Bangkok, Thailand (K.V.); the Eye Center, Medical Center, University of Freiburg, Freiburg (W.A.L.), and the Department of Ophthalmology, Ruprecht Karl University of Heidelberg, Mannheim (J.B.J.) - both in Germany; IRCCS Istituto delle Scienze Neurologiche di Bologna, Unità Operativa Complessa Clinica Neurologica, and Dipartimento di Scienze Biomediche e Neuromotorie, Università di Bologna, Bologna, Italy (C.L.M.); the Department of Ophthalmology and Visual Sciences, Chinese University of Hong Kong, Hong Kong (C.Y.C.), and Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou (H.Y.) - both in China; the Department of Ophthalmology, Rigshospitalet, University of Copenhagen, Glostrup, Denmark (S.H.); the Department of Ophthalmology, University Hospital of Grenoble-Alpes, and Grenoble-Alpes University, HP2 Laboratory, INSERM Unité 1042, Grenoble (C.C.), Service d'Ophtalmologie, Unité Rétine-Uvéites-Neuro-Ophtalmologie, Hôpital Pellegrin, Centre Hospitalier Universitaire de Bordeaux, Bordeaux (M.-B.R.), the Department of Ophthalmology, Lille Catholic Hospital, Lille Catholic University, and INSERM Unité 1171, Lille (T.T.H.C.), the Department of Ophthalmology, University Hospital Angers, Angers (P.G.), and Rothschild Foundation Hospital, Paris (C.C.-V.) - all in France; the Department of Clinical Neurosciences, Geneva University Hospital, Geneva (N.S.); the Department of Neurology, SUNY Upstate Medical University, Syracuse, NY (L.J.M.); the American Eye Center, Mandaluyong City, Philippines (R.K.); Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, University College London, London (P.Y.-W.-M.), and Cambridge Eye Unit, Addenbrooke's Hospital, Cambridge University Hospitals, and Cambridge Centre for Brain Repair and Medical Research Council Mitochondrial Biology Unit, Department of Clinical Neurosciences, University of Cambridge, Cambridge (P.Y.-W.-M.) - all in the United Kingdom; the Save Sight Institute, Faculty of Health and Medicine, University of Sydney, Sydney (C.L.F.); the Department of Ophthalmology and Neurology, Mayo Clinic, Rochester, MN (J.J.C.); the Department of Neuro-ophthalmology, Sankara Nethralaya, Medical Research Foundation, Chennai, India (S.A.); the Departments of Ophthalmology, Neurology, and Neurosurgery, Johns Hopkins University School of Medicine, Baltimore (N.R.M.); and the Departments of Ophthalmology and Neurology, Emory University School of Medicine, Atlanta (N.J.N., V.B.).
A new artificial intelligence system can accurately detect papilledema and other optic disk abnormalities from fundus photographs, improving diagnostic capabilities for nonophthalmologist physicians.
Area of Science:
- Ophthalmology
- Medical Imaging
- Artificial Intelligence
Background:
- Nonophthalmologist physicians often lack confidence in performing direct ophthalmoscopy.
- The utility of artificial intelligence (AI) for detecting optic disk abnormalities from fundus photographs requires further investigation.
Purpose of the Study:
- To develop and validate a deep-learning system for classifying optic disks.
- To differentiate between normal optic disks, papilledema, and other abnormalities using fundus photography.
Main Methods:
- A deep-learning system was trained and validated on 14,341 fundus photographs from 19 sites in 11 countries.
- The system was externally tested on 1505 photographs from 5 additional sites.
- Performance was evaluated using area under the receiver-operating-characteristic curve (AUC), sensitivity, and specificity against neuro-ophthalmologist diagnoses.
Main Results:
- The system achieved an AUC of 0.99 in the validation set for differentiating papilledema from normal and abnormal disks.
- In external testing, the system demonstrated an AUC of 0.96 for papilledema detection, with 96.4% sensitivity and 84.7% specificity.
- The AI system successfully classified optic disks into normal, papilledema, or other abnormality categories.
Conclusions:
- A deep-learning system utilizing fundus photographs with pharmacologically dilated pupils can effectively distinguish between optic disks with papilledema, normal disks, and those with other abnormalities.
- This AI tool shows promise in assisting physicians with the diagnosis of optic disk conditions.

