TY - GEN
T1 - Artificial intelligence in diabetic retinopathy
T2 - 17th World Congress on Medical and Health Informatics, MEDINFO 2019
AU - Poly, Tahmina Nasrin
AU - Mohaimenul Islam, Md
AU - Yang, Hsuan Chia
AU - Nguyen, Phung Anh
AU - Wu, Chieh Chen
AU - Lia, Yu Chuan
N1 - Publisher Copyright:
© 2019 International Medical Informatics Association (IMIA) and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).
PY - 2019/8/21
Y1 - 2019/8/21
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Deep learning
KW - Diabetic retinopathy
UR - http://www.scopus.com/inward/record.url?scp=85071494094&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85071494094&partnerID=8YFLogxK
U2 - 10.3233/SHTI190532
DO - 10.3233/SHTI190532
M3 - Conference contribution
C2 - 31438229
AN - SCOPUS:85071494094
VL - 264
T3 - Studies in Health Technology and Informatics
SP - 1556
EP - 1557
BT - MEDINFO 2019
A2 - Seroussi, Brigitte
A2 - Ohno-Machado, Lucila
A2 - Ohno-Machado, Lucila
A2 - Seroussi, Brigitte
PB - IOS Press
Y2 - 25 August 2019 through 30 August 2019
ER -
