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Updated: Jan 10, 2026
Author Spotlight: An Automated Method for Assessing Visual Acuity in Infants and Toddlers Using an Eye-Tracking System
Published on: March 17, 2023
External validation of an artificial intelligence-based model for retinopathy of prematurity screening using Phoenix
Lizanne A Derks1, Y Selim Tekin1, Sjoukje E Loudon1
1Department of Ophthalmology, Erasmus University Medical Center, Rotterdam, The Netherlands.
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 treatment-requiring type 1 ROP were detected by the algorithm. The presence of eye examinations without images of sufficient quality underlines the need for imaging protocols, especially when using this algorithm, with a smaller field-of-view camera.
