| Summary: | Abstract Epiretinal membrane (ERM) is a common ophthalmological disorder of high prevalence. Its symptoms include metamorphopsia, blurred vision, and decreased visual acuity. Early diagnosis and timely treatment of ERM is crucial to preventing vision loss. Although optical coherence tomography (OCT) is regarded as a de facto standard for ERM diagnosis due to its intuitiveness and high sensitivity, ophthalmoscopic examination or fundus photographs still have the advantages of price and accessibility. Artificial intelligence (AI) has been widely applied in the health care industry for its robust and significant performance in detecting various diseases. In this study, we validated the use of a previously trained deep neural network based-AI model in ERM detection based on color fundus photographs. An independent test set of fundus photographs was labeled by a group of ophthalmologists according to their corresponding OCT images as the gold standard. Then the test set was interpreted by other ophthalmologists and AI model without knowing their OCT results. Compared with manual diagnosis based on fundus photographs alone, the AI model had comparable accuracy (AI model 77.08% vs. integrated manual diagnosis 75.69%, χ2 = 0.038, P = 0.845, McNemar’s test), higher sensitivity (75.90% vs. 63.86%, χ2 = 4.500, P = 0.034, McNemar’s test), under the cost of lower but reasonable specificity (78.69% vs. 91.80%, χ2 = 6.125, P = 0.013, McNemar’s test). Thus our AI model can serve as a possible alternative for manual diagnosis in ERM screening.
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