Artificial intelligence in diabetic retinopathy: Insights from a meta-analysis of deep learning

Tahmina Nasrin Poly, Md Mohaimenul Islam, Hsuan Chia Yang, Phung Anh Nguyen, Chieh Chen Wu, Yu Chuan Lia

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationMEDINFO 2019
Subtitle of host publicationHealth and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics
EditorsBrigitte Seroussi, Lucila Ohno-Machado, Lucila Ohno-Machado, Brigitte Seroussi
PublisherIOS Press
Pages1556-1557
Number of pages2
ISBN (Electronic)9781643680026
DOIs
Publication statusPublished - Aug 21 2019
Externally publishedYes
Event17th World Congress on Medical and Health Informatics, MEDINFO 2019 - Lyon, France
Duration: Aug 25 2019Aug 30 2019

Publication series

NameStudies in Health Technology and Informatics
Volume264
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365

Conference

Conference17th World Congress on Medical and Health Informatics, MEDINFO 2019
CountryFrance
CityLyon
Period8/25/198/30/19

Fingerprint

Artificial Intelligence
Diabetic Retinopathy
Artificial intelligence
Meta-Analysis
Learning
Sensitivity and Specificity
Deep learning

Keywords

  • Artificial intelligence
  • Deep learning
  • Diabetic retinopathy

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

Cite this

Poly, T. N., Mohaimenul Islam, M., Yang, H. C., Nguyen, P. A., Wu, C. C., & Lia, Y. C. (2019). Artificial intelligence in diabetic retinopathy: Insights from a meta-analysis of deep learning. In B. Seroussi, L. Ohno-Machado, L. Ohno-Machado, & B. Seroussi (Eds.), MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics (pp. 1556-1557). (Studies in Health Technology and Informatics; Vol. 264). IOS Press. https://doi.org/10.3233/SHTI190532

Artificial intelligence in diabetic retinopathy : Insights from a meta-analysis of deep learning. / Poly, Tahmina Nasrin; Mohaimenul Islam, Md; Yang, Hsuan Chia; Nguyen, Phung Anh; Wu, Chieh Chen; Lia, Yu Chuan.

MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics. ed. / Brigitte Seroussi; Lucila Ohno-Machado; Lucila Ohno-Machado; Brigitte Seroussi. IOS Press, 2019. p. 1556-1557 (Studies in Health Technology and Informatics; Vol. 264).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Poly, TN, Mohaimenul Islam, M, Yang, HC, Nguyen, PA, Wu, CC & Lia, YC 2019, Artificial intelligence in diabetic retinopathy: Insights from a meta-analysis of deep learning. in B Seroussi, L Ohno-Machado, L Ohno-Machado & B Seroussi (eds), MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics. Studies in Health Technology and Informatics, vol. 264, IOS Press, pp. 1556-1557, 17th World Congress on Medical and Health Informatics, MEDINFO 2019, Lyon, France, 8/25/19. https://doi.org/10.3233/SHTI190532
Poly TN, Mohaimenul Islam M, Yang HC, Nguyen PA, Wu CC, Lia YC. Artificial intelligence in diabetic retinopathy: Insights from a meta-analysis of deep learning. In Seroussi B, Ohno-Machado L, Ohno-Machado L, Seroussi B, editors, MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics. IOS Press. 2019. p. 1556-1557. (Studies in Health Technology and Informatics). https://doi.org/10.3233/SHTI190532
Poly, Tahmina Nasrin ; Mohaimenul Islam, Md ; Yang, Hsuan Chia ; Nguyen, Phung Anh ; Wu, Chieh Chen ; Lia, Yu Chuan. / Artificial intelligence in diabetic retinopathy : Insights from a meta-analysis of deep learning. MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics. editor / Brigitte Seroussi ; Lucila Ohno-Machado ; Lucila Ohno-Machado ; Brigitte Seroussi. IOS Press, 2019. pp. 1556-1557 (Studies in Health Technology and Informatics).
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