Multinational External Validation of Autonomous Retinopathy of Prematurity Screening

作者：Aaron S. Coyner, Tom Murickan, Minn A. Oh, Benjamin K. Young, Susan Ostmo, Praveer Singh, R.V. Paul Chan, Darius M. Moshfeghi, Parag K. Shah, Venkatapathy Narendran, Michael F. Chiang, Jayashree Kalpathy-Cramer, J. Peter Campbell · 发表于：JAMA Ophthalmology · 年份：2024 · DOI：10.1001/jamaophthalmol.2024.0045 · 被引用次数：34 · 研究领域：Retinopathy of Prematurity Studies、Neonatal and Maternal Infections、Retinal Imaging and Analysis

Importance: Retinopathy of prematurity (ROP) is a leading cause of blindness in children, with significant disparities in outcomes between high-income and low-income countries, due in part to insufficient access to ROP screening. Objective: To evaluate how well autonomous artificial intelligence (AI)-based ROP screening can detect more-than-mild ROP (mtmROP) and type 1 ROP. Design, Setting, and Participants: This diagnostic study evaluated the performance of an AI algorithm, trained and calibrated using 2530 examinations from 843 infants in the Imaging and Informatics in Retinopathy of Prematurity (i-ROP) study, on 2 external datasets (6245 examinations from 1545 infants in the Stanford University Network for Diagnosis of ROP [SUNDROP] and 5635 examinations from 2699 infants in the Aravind Eye Care Systems [AECS] telemedicine programs). Data were taken from 11 and 48 neonatal care units in the US and India, respectively. Data were collected from January 2012 to July 2021, and data were analyzed from July to December 2023. Exposures: An imaging processing pipeline was created using deep learning to autonomously identify mtmROP and type 1 ROP in eye examinations performed via telemedicine. Main Outcomes and Measures: The area under the receiver operating characteristics curve (AUROC) as well as sensitivity and specificity for detection of mtmROP and type 1 ROP at the eye examination and patient levels. Results: The prevalence of mtmROP and type 1 ROP were 5.9% (91 of 1545) and ...