Shuai Ming1, Kunpeng Xie1, Xiang Lei1, Yingrui Yang1, Zhaoxia Zhao1, Shuyin Li1, Xuemin Jin1, Bo Lei2. 1. Department of Ophthalmology, Clinical Research Center, Henan Eye Hospital, Henan Provincial People's Hospital, Henan Eye Institute, People's Hospital of Zhengzhou University, No.7 Weiwu Road, Jinshui District, Zhengzhou, 450003, China. 2. Department of Ophthalmology, Clinical Research Center, Henan Eye Hospital, Henan Provincial People's Hospital, Henan Eye Institute, People's Hospital of Zhengzhou University, No.7 Weiwu Road, Jinshui District, Zhengzhou, 450003, China. bolei99@126.com.
Abstract
PURPOSE: To evaluate the performance of an AI-based diabetic retinopathy (DR) grading model in real-world community clinical setting. METHODS: Participants with diabetes on record in the chosen community were recruited by health care staffs in a primary clinic of Zhengzhou city, China. Retinal images were prospectively collected during December 2018 and April 2019 based on intent-to-screen principle. A pre-validated AI system based on deep learning algorithm was deployed to screen DR graded according to the International Clinical Diabetic Retinopathy scale. Kappa value of DR severity, the sensitivity, specificity of detecting referable DR (RDR) and any DR were generated based on the standard of the majority manual grading decision of a retina specialist panel. RESULTS: Of the 193 eligible participants, 173 (89.6%) were readable with at least one eye image. Mean [SD] age was 69.3 (9.0) years old. Total of 321 eyes (83.2%) were graded both by AI and the specialist panel. The κ value in eye image grading was 0.715. The sensitivity, specificity and area under curve for detection of RDR were 84.6% (95% CI: 54.6- 98.1%), 98.0% (95% CI: 94.3-99.6%) and 0.913 (95% CI: 0.797-1.000), respectively. For detection of any DR, the upper indicators were 90.0% (95% CI: 68.3-98.8), 96.6% (95% CI: 92.1-98.9) and 0.933 (95% CI: 0.933-1.000), respectively. CONCLUSION: The AI system showed relatively good consistency with ophthalmologist diagnosis in DR grading, high specificity and acceptable sensitivity for identifying RDR and any DR. TRANSLATIONAL RELEVANCE: It is feasible to apply AI-based DR screening in community. PRECIS: Deployed in community real-world clinic setting, AI-based DR screening system showed high specificity and acceptable sensitivity in identifying RDR and any DR. Good DR diagnostic consistency was found between AI and manual grading. These prospective evidences were essential for regulatory approval.
PURPOSE: To evaluate the performance of an AI-based diabetic retinopathy (DR) grading model in real-world community clinical setting. METHODS: Participants with diabetes on record in the chosen community were recruited by health care staffs in a primary clinic of Zhengzhou city, China. Retinal images were prospectively collected during December 2018 and April 2019 based on intent-to-screen principle. A pre-validated AI system based on deep learning algorithm was deployed to screen DR graded according to the International Clinical Diabetic Retinopathy scale. Kappa value of DR severity, the sensitivity, specificity of detecting referable DR (RDR) and any DR were generated based on the standard of the majority manual grading decision of a retina specialist panel. RESULTS: Of the 193 eligible participants, 173 (89.6%) were readable with at least one eye image. Mean [SD] age was 69.3 (9.0) years old. Total of 321 eyes (83.2%) were graded both by AI and the specialist panel. The κ value in eye image grading was 0.715. The sensitivity, specificity and area under curve for detection of RDR were 84.6% (95% CI: 54.6- 98.1%), 98.0% (95% CI: 94.3-99.6%) and 0.913 (95% CI: 0.797-1.000), respectively. For detection of any DR, the upper indicators were 90.0% (95% CI: 68.3-98.8), 96.6% (95% CI: 92.1-98.9) and 0.933 (95% CI: 0.933-1.000), respectively. CONCLUSION: The AI system showed relatively good consistency with ophthalmologist diagnosis in DR grading, high specificity and acceptable sensitivity for identifying RDR and any DR. TRANSLATIONAL RELEVANCE: It is feasible to apply AI-based DR screening in community. PRECIS: Deployed in community real-world clinic setting, AI-based DR screening system showed high specificity and acceptable sensitivity in identifying RDR and any DR. Good DR diagnostic consistency was found between AI and manual grading. These prospective evidences were essential for regulatory approval.
Entities:
Keywords:
Artificial intelligence; Deep neural network; Diabetic retinopathy; Fundus photography; Rreal-world study
Authors: Helena W Rodbard; Lawrence Blonde; Susan S Braithwaite; Elise M Brett; Rhoda H Cobin; Yehuda Handelsman; Richard Hellman; Paul S Jellinger; Lois G Jovanovic; Philip Levy; Jeffrey I Mechanick; Farhad Zangeneh Journal: Endocr Pract Date: 2007 May-Jun Impact factor: 3.443