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

Yao Yang; Jianying Pan; Miner Yuan; Kunbei Lai; Huirui Xie; Li Ma; Suzhong Xu; Ruzhi Deng; Mingwei Zhao; Yan Luo; Xiaofeng Lin

doi:10.21037/atm-22-350

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

Ann Transl Med. 2022 Oct;10(20):1088. doi: 10.21037/atm-22-350.

Authors

Yao Yang¹, Jianying Pan¹, Miner Yuan¹, Kunbei Lai¹, Huirui Xie¹, Li Ma¹, Suzhong Xu², Ruzhi Deng², Mingwei Zhao³, Yan Luo¹, Xiaofeng Lin¹

Affiliations

¹ State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China.
² Eye Hospital, Wenzhou Medical University, Wenzhou, China.
³ Department of Ophthalmology, Peking University People's Hospital, Beijing, China.

Abstract

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%) in the detection of RDR, and a false-positive rate of 3.91%. The diagnostic accuracy gain rate of the AIDRScreening system was 16.57% higher than that of the investigator, while the average diagnostic time gain rate was -37.32% lower.

Conclusions: The automated AIDRScreening system can detect RDR with high accuracy, but cannot detect maculopathy. The implementation of the AIDRScreening system may increase the efficiency of DR screening.

Keywords: Fundus photographs; artificial intelligence; diabetic retinopathy (DR); multicenter; screening.