Fairness and generalisability in deep learning of retinopathy of prematurity screening algorithms: a literature review
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e001216.full.pdf
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Author(s)
Nakayama, Luis Filipe
Mitchell, William Greig
Ribeiro, Lucas Zago
Dychiao, Robyn Gayle
Phanphruk, Warachaya
Celi, Leo Anthony
Kalua, Khumbo
Santiago, Alvina Pauline Dy
Regatieri, Caio Vinicius Saito
Moraes, Nilva Simeren Bueno
Date Issued
August 2023
Publisher
BMJ
Citation
Nakayama LF, Mitchell WG, Ribeiro LZ, et alFairness and generalisability in deep learning of retinopathy of prematurity screening algorithms: a literature reviewBMJ Open Ophthalmology 2023;8:e001216.
Version
Final published version
Abstract
Background
Retinopathy of prematurity (ROP) is a vasoproliferative disease responsible for more than 30 000 blind children worldwide. Its diagnosis and treatment are challenging due to the lack of specialists, divergent diagnostic concordance and variation in classification standards. While artificial intelligence (AI) can address the shortage of professionals and provide more cost-effective management, its development needs fairness, generalisability and bias controls prior to deployment to avoid producing harmful unpredictable results. This review aims to compare AI and ROP study’s characteristics, fairness and generalisability efforts. Methods Our review yielded 220 articles, of which 18 were included after full-text assessment. The articles were classified into ROP severity grading, plus detection, detecting treatment requiring, ROP prediction and detection of retinal zones.ResultsAll the article’s authors and included patients are from middle-income and high-income countries, with no low-income countries, South America, Australia and Africa Continents representation.Code is available in two articles and in one on request, while data are not available in any article. 88.9% of the studies use the same retinal camera. In two articles, patients’ sex was described, but none applied a bias control in their models. Conclusion The reviewed articles included 180 228 images and reported good metrics, but fairness, generalisability and bias control remained limited. Reproducibility is also a critical limitation, with few articles sharing codes and none sharing data. Fair and generalisable ROP and AI studies are needed that include diverse datasets, data and code sharing, collaborative research, and bias control to avoid unpredictable and harmful deployments.
Retinopathy of prematurity (ROP) is a vasoproliferative disease responsible for more than 30 000 blind children worldwide. Its diagnosis and treatment are challenging due to the lack of specialists, divergent diagnostic concordance and variation in classification standards. While artificial intelligence (AI) can address the shortage of professionals and provide more cost-effective management, its development needs fairness, generalisability and bias controls prior to deployment to avoid producing harmful unpredictable results. This review aims to compare AI and ROP study’s characteristics, fairness and generalisability efforts. Methods Our review yielded 220 articles, of which 18 were included after full-text assessment. The articles were classified into ROP severity grading, plus detection, detecting treatment requiring, ROP prediction and detection of retinal zones.ResultsAll the article’s authors and included patients are from middle-income and high-income countries, with no low-income countries, South America, Australia and Africa Continents representation.Code is available in two articles and in one on request, while data are not available in any article. 88.9% of the studies use the same retinal camera. In two articles, patients’ sex was described, but none applied a bias control in their models. Conclusion The reviewed articles included 180 228 images and reported good metrics, but fairness, generalisability and bias control remained limited. Reproducibility is also a critical limitation, with few articles sharing codes and none sharing data. Fair and generalisable ROP and AI studies are needed that include diverse datasets, data and code sharing, collaborative research, and bias control to avoid unpredictable and harmful deployments.
Subjects
Ophthalmology
MIT Department
Harvard--MIT Program in Health Sciences and Technology. Laboratory for Computational Physiology
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Creative Commons Attribution Noncommercial
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DOI of Published Version
https://doi.org/10.1136/bmjophth-2022-001216
