External validation of an artificial intelligence–based model for retinopathy of prematurity screening using Phoenix ICON retinal images

作者：Lizanne A. Derks, Yaşar Tekin, Sjoukje E. Loudon, Johannes R. Vingerling, Aaron S. Coyner, J. Peter Campbell, Angela M. Tjiam · 发表于：Journal of American Association for Pediatric Ophthalmology and Strabismus · 年份：2025 · DOI：10.1016/j.jaapos.2025.104696 · 被引用次数：4 · 研究领域：Retinopathy of Prematurity Studies、Child Abuse and Related Trauma、Ophthalmology and Visual Impairment Studies

PURPOSE: To assess the performance of a RetCam-trained artificial intelligence (AI) algorithm for the autonomous detection of severe retinopathy of prematurity (ROP) using retinal images acquired with the smaller field-of-view Phoenix ICON retinal camera. METHODS: Retrospective external validation was performed using Phoenix ICON retinal images captured during ROP screening examinations in a Dutch cohort of infants born in 2021. Images of insufficient quality were excluded via automated quality assessment. Model performances for more-than-mild ROP (MTM-ROP)-type 1 or 2 ROP, or any ROP with pre-plus disease-and for type 1 ROP alone, were expressed as area under the precision-recall curve (AUPRC), sensitivity and specificity. RESULTS: A total of 4,411 images from 66 infants were captured during 419 individual eye examinations, averaging 67 ± 65 images per infant and 10 ± 6 images per eye examination. Sixty examinations (14.3%) had all images excluded in automated quality assessment. When using the best performance between both eyes to assess infant-level performance, AUPRC was 0.911 (95% CI, 0.638-1.000), sensitivity was 82.0% (95% CI, 73.0-89.0) and specificity was 77.0% (95% CI, 68.1-84.4) for MTM-ROP. For type 1 ROP alone, AUPRC was 0.983 (95% CI, 0.964-1.000), sensitivity was 100.0% (95% CI, 94.7-100.0), and specificity was 72.4% (95% CI, 64.4-79.5). CONCLUSIONS: The algorithm's performance with Phoenix ICON is similar to its performance with RetCam. All infants with treatm...