Implementation of Artificial Intelligence in Retinopathy of Prematurity Care: Challenges and Opportunities.

Michelle Yip, Gavin S W Tan, J Peter Campbell, Daniel S W Ting, Andrew S H Tsai, Amy Song, Aaron Coyner, Robison Vernon Paul Chan

Journal: International ophthalmology clinics 2024;64(4):9-14

PMID: 39480203

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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.

Address: Singapore National Eye Centre, Singapore.; Duke-NUS Medical School, Singapore.; Singapore National Eye Centre, Singapore.; Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Illinois Eye and Ear Infirmary, Chicago, IL.; Casey Eye Institute, Oregon Health & Science University, Portland, OR.

DOI: 10.1097/IIO.0000000000000532

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MeSH Terms: Humans, Retinopathy of Prematurity, Artificial Intelligence, Infant, Newborn, Telemedicine, Neonatal Screening