Application of artificial intelligence remote screening system for diabetic retinopathy in Yinchuan community of Ningxia
10.3980/j.issn.1672-5123.2022.8.24
- VernacularTitle:人工智能在宁夏银川社区糖尿病视网膜病变远程筛查中的应用
- Author:
Zhen LI
1
,
2
;
Jun-Feng PIAO
1
,
2
;
Xiao-Ting LI
1
,
2
;
Ning WANG
1
,
2
;
Xue XIAO
1
,
2
;
Wei-Ning RONG
1
,
2
Author Information
1. Ningxia Eye Hospital, People's Hospital of Ningxia Hui Autonomous Region
2. the Third Clinical Medical College of Ningxia Medical University, Yinchuan 750001, Ningxia Hui Autonomous Region,China
- Publication Type:Journal Article
- Keywords:
artificial intelligence;
diabetic retinopathy;
screening;
consistency analysis
- From:
International Eye Science
2022;22(8):1365-1368
- CountryChina
- Language:Chinese
-
Abstract:
AIM:To evaluate the application effect of artificial intelligence(AI)assisted diagnosis system in screening diabetic retinopathy(DR)in Yinchuan Community, Ningxia Hui Autonomous Region.METHODS:From July 2020 to July 2021, fundus photograph of 2 707 eyes from 1 358 diabetic patients with type 2 diabetes in two communities of Ningxia and Yinchuan were included in this study. The Eye Wisdom AI assisted screening and diagnosis system was used to analyze automatically and detect the characteristic changes of DR, such as hemorrhage, microaneurysms and retinal microvascular abnormalities. The results of fundus photograph were automatically graded according to the standard of DR international stage standard. The manual analysis group gave feedback after image interpretation, analyzed the sensitivity, specificity, misdiagnosis rate and missed diagnosis rate of the AI-assisted screening system for DR diagnosis, and compared the consistency between AI and manual analysis. Kappa consistency test was performed for the results of AI screening system and manual analysis.RESULTS:Compared with manual analysis, the sensitivity, specificity, missed diagnosis rate and misdiagnosis rate of AI were 91.84%, 99.06%, 8.16% and 0.94% respectively. The Kappa value of consistency analysis of the two diagnosis results was 0.817(P<0.001). Compared with manual analysis, the sensitivity and specificity of AI group to diagnose non-DR were 99.06% and 91.84% respectively. The sensitivity and specificity of mild NPDR were 85.36% and 98.52% respectively. The sensitivity and specificity of moderate NPDR were 81.53% and 98.55% respectively. The sensitivity and specificity of severe NPDR were 70% and 99.51% respectively. The sensitivity and specificity of PDR were 86.67% and 99.63% respectively. The Kappa value of the consistency analysis of DR staging diagnosis was 0.878(P<0.01).CONCLUSION: The AI remote screening system adopted in this study showed good consistency with the results of manual analysis, which can meet the needs of DR screening and provide a new effective prevention and treatment mode for DR patients in the community.
