Retinal Photograph-based Deep Learning System for Detection of Thyroid-Associated Ophthalmopathy

Abstract

Background: The diagnosis of thyroid-associated ophthalmopathy (TAO) usually requires a comprehensive examination, including clinical symptoms, radiological examinations, and blood tests. Therefore, cost-effective and noninvasive methods for the detection of TAO are needed. This study aimed to establish a deep learning-based system to detect TAO based on retinal photographs. Materials and methods: The multicenter observational study included retinal photographs taken from TAO patients and normal participants in 2 hospitals in China. Forty-five-degree retinal photographs, centered on the midpoint between the optic disc and the macula, were captured by trained ophthalmologists. The authors first trained a convolutional neural network model to identify TAO using data collected from one hospital. After internal validation, the model was further evaluated in another hospital as an external validation data set. Results: The study included 1182 retinal photographs of 708 participants for model development, and 365 retinal photographs (189 participants) were obtained as the external validation data set. In the internal validation, the area under the receiver operator curve was 0.900 (95% CI: 0.889–0.910) and the accuracy was 0.860 (95% CI: 0.849–0.869). In the external data set, the model reached an area under the curve of 0.747 (95% CI: 0.728–0.763) and achieved an accuracy of 0.709 (95% CI: 0.690–0.724). Conclusions: Deep learning-based systems may be promising for identifying TAO in normal subjects using retinal fundus photographs. It may serve as a cost-effective and noninvasive method to detect TAO in the future.