TY - JOUR
T1 - Deep Learning for Accurate Diagnosis of Glaucomatous Optic Neuropathy Using Digital Fundus Image
T2 - A Meta-Analysis
AU - Islam, Mohaimenul
AU - Poly, Tahmina Nasrin
AU - Yang, Hsuan Chia
AU - Atique, Suleman
AU - Li, Yu Chuan Jack
PY - 2020/6/16
Y1 - 2020/6/16
N2 - We conducted a study to evaluate the algorithms based on deep learning to automatically diagnosis of GON from digital fundus images. A systematic articles search was conducted in PubMed, EMBASE, Google Scholar for the study that investigated the performance of deep learning algorithms for the detection of GON. A total of eight studies were included in this study, of which 5 studies were used to conduct our meta-analysis. The pooled AUROC for detecting GON was 0.98. However, the sensitivity and specificity of deep learning to detect GON were 0.90 (95% CI: 0.90-0.91), and 0.94 (95%CI: 0.93-0.94), respectively.
AB - We conducted a study to evaluate the algorithms based on deep learning to automatically diagnosis of GON from digital fundus images. A systematic articles search was conducted in PubMed, EMBASE, Google Scholar for the study that investigated the performance of deep learning algorithms for the detection of GON. A total of eight studies were included in this study, of which 5 studies were used to conduct our meta-analysis. The pooled AUROC for detecting GON was 0.98. However, the sensitivity and specificity of deep learning to detect GON were 0.90 (95% CI: 0.90-0.91), and 0.94 (95%CI: 0.93-0.94), respectively.
KW - artificial intelligence
KW - deep learning
KW - fundus image
KW - Glaucoma
KW - glaucomatous optic neuropathy
UR - http://www.scopus.com/inward/record.url?scp=85086886309&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85086886309&partnerID=8YFLogxK
U2 - 10.3233/SHTI200141
DO - 10.3233/SHTI200141
M3 - Article
C2 - 32570365
AN - SCOPUS:85086886309
SN - 0926-9630
VL - 270
SP - 153
EP - 157
JO - Studies in Health Technology and Informatics
JF - Studies in Health Technology and Informatics
ER -