Validation and application of an artificial intelligence robot assisted diagnosis system for diabetic retinopathy
10.3760/cma.j.issn.2095-0160.2019.08.016
- VernacularTitle:糖尿病视网膜病变人工智能机器人辅助诊断系统的建立及应用
- Author:
Shaohui GAO
1
;
Xuemin JIN
;
Zhaoxia ZHAO
;
Weihong YU
;
Youxin CHEN
;
Yuhui SUN
;
Dayong DING
Author Information
1. 河南省人民医院眼科 河南省立眼科医院 河南省眼科研究所
- Keywords:
Diabetic retinopathy;
Artificial intelligence;
Robot;
Assisted diagnosis;
Deep learning
- From:
Chinese Journal of Experimental Ophthalmology
2019;37(8):669-673
- CountryChina
- Language:Chinese
-
Abstract:
Objective To evaluate the performance of an artificial intelligence ( AI ) assisted diagnosis system for diabetic retinopathy ( DR) based on deep learning theory. Methods Diagnostic performance of a robot assisted diagnosis system called SongYue for DR was trained by using 25297 retinal images tagged by fundus doctors from multiple hospitals in China. Four types of DR detection model consisting of abnormal DR,referable DR,severe non-proliferative and proliferative DR as well as proliferative DR according to fundus leisions identification were established. The ability of the system to distinguish DR was determined by using receiver operator characteristic (ROC) analysis,sensitivity and specificity of the system. Results SongYue system achieved an area under the ROC curve ( AUC) of 0. 920 for successfully distinguishing normal images from those DR with a sensitivity of 96. 0％at a specificity of 87. 9％. The AUC of SongYue for referable DR was 0. 925,sensitivity was 90. 4％,and specificity was 95. 2％. For severe non-proliferative and proliferative DR,AUC was 0. 845,sensitivity was 72. 7％,and specificity was 96. 2％. For proliferative DR, AUC was 0. 855, sensitivity was 73. 5％, and specificity was 97. 3％. Conclusions SongYue robot assisted diagnosis system has high AUC,sensitivity and specificity for identifying DR, showing good clinical applicable benefits.
