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

doi:10.3233/SHTI190532

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 proceeding › Conference contribution

9 Citations (Scopus)

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 language	English
Title of host publication	MEDINFO 2019
Subtitle of host publication	Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics
Editors	Brigitte Seroussi, Lucila Ohno-Machado, Lucila Ohno-Machado, Brigitte Seroussi
Publisher	IOS Press
Pages	1556-1557
Number of pages	2
Volume	264
ISBN (Electronic)	9781643680026
DOIs	https://doi.org/10.3233/SHTI190532
Publication status	Published - Aug 21 2019
Event	17th World Congress on Medical and Health Informatics, MEDINFO 2019 - Lyon, France Duration: Aug 25 2019 → Aug 30 2019

Publication series

Name	Studies in Health Technology and Informatics
Volume	264
ISSN (Print)	0926-9630
ISSN (Electronic)	1879-8365

Conference

Conference	17th World Congress on Medical and Health Informatics, MEDINFO 2019
Country/Territory	France
City	Lyon
Period	8/25/19 → 8/30/19

Keywords

Artificial intelligence
Deep learning
Diabetic retinopathy

ASJC Scopus subject areas

Biomedical Engineering
Health Informatics
Health Information Management

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.3233/SHTI190532

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 (Vol. 264, 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 et al.
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. Vol. 264 IOS Press, 2019. p. 1556-1557 (Studies in Health Technology and Informatics; Vol. 264).

Research output: Chapter in Book/Report/Conference proceeding › Conference 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. vol. 264, 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. Vol. 264. IOS Press. 2019. p. 1556-1557. (Studies in Health Technology and Informatics). doi: 10.3233/SHTI190532

Poly, Tahmina Nasrin ; Mohaimenul Islam, Md ; Yang, Hsuan Chia et al. / 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. Vol. 264 IOS Press, 2019. pp. 1556-1557 (Studies in Health Technology and Informatics).

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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.",

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

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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