Artificial intelligence for telemedicine diabetic retinopathy screening: a review
Name
Artificial intelligence for telemedicine diabetic retinopathy screening a review.pdf
Description
Published version
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1.85 MB
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5f2d0b501e0e3c236937f2b5e63d8528
Author(s)
Nakayama, Luis Filipe
Zago Ribeiro, Lucas
Novaes, Frederico
Miyawaki, Isabele Ayumi
Miyawaki, Andresa Emy
de Oliveira, Juliana Angélica Estevão
Oliveira, Talita
Malerbi, Fernando Korn
Regatieri, Caio Vinicius Saito
Celi, Leo Anthony
Date Issued
December 12, 2023
Journal
Annals of Medicine
Publisher
Taylor & Francis
Citation
Nakayama, L. F., Zago Ribeiro, L., Novaes, F., Miyawaki, I. A., Miyawaki, A. E., de Oliveira, J. A. E., … Silva, P. S. (2023). Artificial intelligence for telemedicine diabetic retinopathy screening: a review. Annals of Medicine, 55(2).
Version
Final published version
Abstract
PURPOSE: This study aims to compare artificial intelligence (AI) systems applied in diabetic retinopathy (DR) teleophthalmology screening, currently deployed systems, fairness initiatives and the challenges for implementation.
METHODS: The review included articles retrieved from PubMed/Medline/EMBASE literature search strategy regarding telemedicine, DR and AI. The screening criteria included human articles in English, Portuguese or Spanish and related to telemedicine and AI for DR screening. The author's affiliations and the study's population income group were classified according to the World Bank Country and Lending Groups.
RESULTS: The literature search yielded a total of 132 articles, and nine were included after full-text assessment. The selected articles were published between 2004 and 2020 and were grouped as telemedicine systems, algorithms, economic analysis and image quality assessment. Four telemedicine systems that perform a quality assessment, image preprocessing and pathological screening were reviewed. A data and post-deployment bias assessment are not performed in any of the algorithms, and none of the studies evaluate the social impact implementations. There is a lack of representativeness in the reviewed articles, with most authors and target populations from high-income countries and no low-income country representation.
CONCLUSIONS: Telemedicine and AI hold great promise for augmenting decision-making in medical care, expanding patient access and enhancing cost-effectiveness. Economic studies and social science analysis are crucial to support the implementation of AI in teleophthalmology screening programs. Promoting fairness and generalizability in automated systems combined with telemedicine screening programs is not straightforward. Improving data representativeness, reducing biases and promoting equity in deployment and post-deployment studies are all critical steps in model development.
MIT Department
Institute for Medical Engineering and Science
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DOI of Published Version
https://doi.org/10.1080/07853890.2023.2258149
