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Scientific article
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Accuracy of a Deep Learning System for Classification of Papilledema Severity on Ocular Fundus Photographs

CollaboratorsThumann, Gabriele
Published inNeurology, vol. 97, no. 4, p. e369-e377
Publication date2021
Abstract

Objective: To evaluate the performance of a deep learning system (DLS) in classifying the severity of papilledema associated with increased intracranial pressure, on standard retinal fundus photographs. Methods: A DLS was trained to automatically classify papilledema severity in 965 patients (2103 mydriatic fundus photographs), representing a multiethnic cohort of patients with confirmed elevated intracranial pressure. Training was performed on 1052 photographs with mild/moderate papilledema (MP) and 1051 photographs with severe papilledema (SP) classified by a panel of experts. The performance of the DLS and that of three independent neuro-ophthalmologists were tested in 111 patients (214 photographs, 92 with MP and 122 with SP), by calculating the area under the receiver operating characteristics curve (AUC), accuracy, sensitivity and specificity. Kappa agreement scores between the DLS and each of the three graders and among the three graders were calculated. Results: The DLS successfully discriminated between photographs of MP and SP, with an AUC of 0.93 (95%CI: 0.89-0.96) and an accuracy, sensitivity and specificity of 87.9%, 91.8% and 86.2%, respectively. This performance was comparable with that of the three neuro-ophthalmologists (84.1%, 91.8% and 73.9%, P=0.19, P=1, P=0.09, respectively). Misclassification by the DLS was mainly observed for moderate papilledema (Frisén grade 3). Agreement scores between the DLS and the neuro-ophthalmologists' evaluation was 0.62 (CI 95% 0.57-0.68), whereas the inter-grader agreement among the three neuro-ophthalmologists was 0.54 (CI 95% 0.47-0.62). Conclusions: Our DLS accurately classified the severity of papilledema on an independent set of mydriatic fundus photographs, achieving a comparable performance with that of independent neuro-ophthalmologists. Classification of Evidence: This study provides Class II evidence that a deep learning system using mydriatic retinal fundus photographs accurately classified the severity of papilledema associated in patients with a diagnosis of increased intracranial pressure.

Citation (ISO format)
VASSENEIX, Caroline et al. Accuracy of a Deep Learning System for Classification of Papilledema Severity on Ocular Fundus Photographs. In: Neurology, 2021, vol. 97, n° 4, p. e369–e377. doi: 10.1212/WNL.0000000000012226
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ISSN of the journal0028-3878
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Creation06/17/2021 6:03:00 PM
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