Accuracy of a Deep Learning System for Classification of Papilledema Severity on Ocular Fundus Photographs
Caroline Vasseneix, Raymond P. Najjar, Xinxing Xu, Zhiqun Tang, Jing Liang Loo, Shweta Singhal, Sharon Tow, Leonard Milea, Daniel Shu Wei Ting, Yong Liu, Tien Yin Wong, Nancy J. Newman, Valérie Biousse, Dan Miléa, on behalf of the BONSAI Group, Philippe Gohier, Neil R. Miller, Tanyatuth Padungkiatsagul, Anuchit Poonyathalang, Yanin Suwan, Kavin Vanikieti, Giulia Amore, Piero Barboni, Michele Carbonelli, Valério Carelli, Chiara La Morgia, Martina Romagnoli, Marie‐Bénédicte Rougier, Ambika Selvakumar, Komma Swetha, Pedro Fonseca, Miguel Raimundo, Steffen Hamann, Isabelle Karlesand, Lars Fuhrmann, Sebastian Küchlin, Wolf A. Lagrèze, Nicolae Sanda, Gabriele Thumann, Florent Aptel, Christophe Chiquet, Kaiqun Liu, Hui Yang, Carmen K. M. Chan, Noel C. Y. Chan, Carol Y. Cheung, Tran Thi Ha Chau, James Acheson, Maged Habib, Neringa Jurkutė, Patrick Yu‐Wai‐Man, Richard C. Kho, Jost B Jonas, John J. Chen, Nouran Sabbagh, Catherine Vignal‐Clermont, Rabih Hage, Raoul Kanav Khanna, Jeong‐Min Hwang, Dong Hyun Kim, Hee Kyung Yang, Tin Aung, Ching‐Yu Cheng, Ecosse L. Lamoureux, Leopold Schmetterer, Zhubo Jiang, Clare L Fraser, Luis J. Mejico, Masoud Aghsaei Fard
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 (2,103 mydriatic fundus photographs), representing a multiethnic cohort of patients with confirmed elevated intracranial pressure. Training was performed on 1,052 photographs with mild/moderate papilledema (MP) and 1,051 photographs with severe papilledema (SP) classified by a panel of experts. The performance of the DLS and that of 3 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 3 graders and among the 3 graders were calculated. Results The DLS successfully discriminated between photographs of MP and SP, with an AUC of 0.93 (95% confidence interval [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 3 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 (95% CI 0.57–0.68), whereas the intergrader agreement among the 3 neuro-ophthalmologists was 0.54 (95% CI 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 DLS using mydriatic retinal fundus photographs accurately classified the severity of papilledema associated in patients with a diagnosis of increased intracranial pressure. AUC= : area under the receiver operating characteristic curve; BONSAI= : Brain and Optic Nerve Study with Artificial Intelligence; CI= : confidence interval; DLS= : deep learning system; GCC= : ganglion cell complex; ICP= : intracranial pressure; IIH= : idiopathic intracranial hypertension; OCT= : optical coherence tomography