| Literature DB >> 31438229 |
Tahmina Nasrin Poly1,2, Md Mohaimenul Islam1,2, Hsuan Chia Yang2, Phung-Anh Nguyen2, Chieh Chen Wu1,2, Yu-Chuan Jack Li1,2.
Abstract
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.Entities:
Keywords: artificial intelligence; deep learning; diabetic retinopathy
Mesh：
Year: 2019 PMID： 31438229 DOI： 10.3233/SHTI190532
Source DB: PubMed Journal: Stud Health Technol Inform ISSN： 0926-9630