Implementation of Artificial Intelligence in Retinopathy of Prematurity Care: Challenges and Opportunities
- PMID: 39480203
- DOI: 10.1097/IIO.0000000000000532
Implementation of Artificial Intelligence in Retinopathy of Prematurity Care: Challenges and Opportunities
Abstract
The diagnosis of retinopathy of prematurity (ROP) is primarily image-based and suitable for implementation of artificial intelligence (AI) systems. Increasing incidence of ROP, especially in low and middle-income countries, has also put tremendous stress on health care systems. Barriers to the implementation of AI include infrastructure, regulatory, legal, cost, sustainability, and scalability. This review describes currently available AI and imaging systems, how a stable telemedicine infrastructure is crucial to AI implementation, and how successful ROP programs have been run in both low and middle-income countries and high-income countries. More work is needed in terms of validating AI systems with different populations with various low-cost imaging devices that have recently been developed. A sustainable and cost-effective ROP screening program is crucial in the prevention of childhood blindness.
Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.
Conflict of interest statement
National Institute of Health (R01 HD107494, R01 EY019474, P30 EY010572); Research to Prevent Blindness (Career Advancement Award, Career Development Award, Unrestricted departmental funding grant); J.P.C. and R.V.P.C. have a financial interest in Siloam Vision, a company that may have a commercial interest in the results of this research and technology. OHSU and UIC have reviewed and managed this potential conflict of interest. A.C. is a consultant to Siloam Vision. The sponsor or funding organization had no role in the design or conduct of this research. The remaining authors have no conflicts of interest to disclose.
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