An Evaluation System of Fundus Photograph-Based Intelligent Diagnostic Technology for Diabetic Retinopathy and Applicability for Research

• Published in: Diabetes therapy Vol. 10; no. 5; pp. 1811 - 1822
• Main Authors: Yang, Wei-Hua, Zheng, Bo, Wu, Mao-Nian, Zhu, Shao-Jun, Fei, Fang-Qin, Weng, Ming, Zhang, Xian, Lu, Pei-Rong
• Format: Journal Article
• Language: English
• Published: Cheshire Springer Healthcare 01.10.2019; Springer; Springer Nature B.V
• Subjects: Artificial intelligence; Cardiology; Deep learning; Diabetes; Diabetic retinopathy; Diagnosis; Drug therapy; Endocrinology; Health aspects; Internal Medicine; Medical diagnosis; Medicine; Medicine & Public Health; Original Research; China; Deep learning; Diabetic retinopathy; Evaluation studies; Ophthalmological diagnostic techniques
• ISSN: 1869-6953; 1869-6961
• DOI: 10.1007/s13300-019-0652-0

Introduction In April 2018, the US Food and Drug Administration (FDA) approved the world’s first artificial intelligence (AI) medical device for detecting diabetic retinopathy (DR), the IDx-DR. However, there is a lack of evaluation systems for DR intelligent diagnostic technology. Methods Five hundred color fundus photographs of diabetic patients were selected. DR severity varied from grade 0 to 4, with 100 photographs for each grade. Following that, these were diagnosed by both ophthalmologists and the intelligent technology, the results of which were compared by applying the evaluation system. The system includes primary, intermediate, and advanced evaluations, of which the intermediate evaluation incorporated two methods. Main evaluation indicators were sensitivity, specificity, and kappa value. Results The AI technology diagnosed 93 photographs with no DR, 107 with mild non-proliferative DR (NPDR), 107 with moderate NPDR, 108 with severe NPDR, and 85 with proliferative DR (PDR). The sensitivity, specificity, and kappa value of the AI diagnoses in the primary evaluation were 98.8%, 88.0%, and 0.89, respectively. According to method 1 of the intermediate evaluation, the sensitivity of AI diagnosis was 98.0%, specificity 97.0%, and the kappa value 0.95. In method 2 of the intermediate evaluation, the sensitivity of AI diagnosis was 95.5%, the specificity 99.3%, and kappa value 0.95. In the advanced evaluation, the kappa value of the intelligent diagnosis was 0.86. Conclusions This article proposes an evaluation system for color fundus photograph-based intelligent diagnostic technology of DR and demonstrates an application of this system in a clinical setting. The results from this evaluation system serve as the basis for the selection of scenarios in which DR intelligent diagnostic technology can be applied.