Artificial Intelligence in Diabetic Retinopathy: Insights from a Meta-Analysis of Deep Learning

被引：8

作者：

Poly, Tahmina Nasrin ^{[1
,2
]}

Islam, Md Mohaimenul ^{[1
,2
]}

Yang, Hsuan Chia ^{[2
]}

Nguyen, Phung-Anh ^{[2
]}

Wu, Chieh Chen ^{[1
,2
]}

Li, Yu-Chuan ^{[1
,2
]}

机构：

[1] Taipei Med Univ, Grad Inst Biomed Informat, Taipei, Taiwan

[2] Int Ctr Hlth Informat & Technol ICHIT, Taipei, Taiwan

来源：

MEDINFO 2019: HEALTH AND WELLBEING E-NETWORKS FOR ALL | 2019年 / 264卷

关键词：

artificial intelligence; deep learning; diabetic retinopathy; VALIDATION; SYSTEM;

D O I：

10.3233/SHTI190532

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

The demand for AI to improve patients outcome has been increased,- we, therefore, aim to establish the diagnostic values of AI in diabetic retinopathy by pooling the published studies of deep learning on this subject. A total of eight studies included which evaluated deep learning in a total of 706,922 retinal images. The overall pooled area under receiver operating curve (AUROC) was 98.93% (95%CI:98.37%-99.49%). However, the overall pooled sensitivity and specificity for detecting referable diabetic retinopathy (RDR) was 74% (95% CI: 73%-74%), and 95% (95% CI: 95%-95%). The findings of this study show that deep learning had high sensitivity and specificity for identifying diabetic retinopathy.

引用

页码：1556 / 1557

页数：2

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