Performance of the AIDRScreening system in detecting diabetic retinopathy in the fundus photographs of Chinese patients: a prospective, multicenter, clinical study

作者：Yaowen Yang, Jianying Pan, Miner Yuan, Kunbei Lai, Huirui Xie, Li Ma, Suzhong Xu, Ruzhi Deng, Mingwei Zhao, Yan Luo, Xiaofeng Lin · 发表于：Annals of Translational Medicine · 年份：2022 · DOI：10.21037/atm-22-350 · 被引用次数：26 · 研究领域：Retinal Imaging and Analysis、Retinal Diseases and Treatments、Retinal and Optic Conditions

Background: Diabetic retinopathy (DR) is the leading cause of blindness in the working-age population worldwide, and there is a large unmet need for DR screening in China. This observational, prospective, multicenter, gold standard-controlled study sought to evaluate the effectiveness and safety of the AIDRScreening system (v. 1.0), which is an artificial intelligence (AI)-enabled system that detects DR in the Chinese population based on fundus photographs. Methods: Participants with diabetes mellitus (DM) were recruited. Fundus photographs (field 1 and field 2) of 1 eye in each participant were graded by the AIDRScreening system (v. 1.0) to detect referable DR (RDR). The results were compared to those of the masked manual grading (gold standard) system by the Zhongshan Image Reading Center. The primary outcomes were the sensitivity and specificity of the AIDRScreening system in detecting RDR. The other outcomes evaluated included the system's diagnostic accuracy, positive predictive value, negative predictive value, diagnostic accuracy gain rate, and average diagnostic time gain rate. Results: Among the 1,001 enrolled participants with DM, 962 (96.1%) were included in the final analyses. The participants had a median age of 60.61 years (range: 20.18-85.78 years), and 48.2% were men. The manual grading system detected RDR in 399 (41.48%) participants. The AIDRScreening system had a sensitivity of 86.72% (95% CI: 83.39-90.05%) and a specificity of 96.09% (95% CI: 94.14-97.54%)...