Single-Examination Risk Prediction of Severe Retinopathy of Prematurity.

ABSTRACT:

Background and objectives

Retinopathy of prematurity (ROP) is a leading cause of childhood blindness. Screening and treatment reduces this risk, but requires multiple examinations of infants, most of whom will not develop severe disease. Previous work has suggested that artificial intelligence may be able to detect incident severe disease (treatment-requiring retinopathy of prematurity [TR-ROP]) before clinical diagnosis. We aimed to build a risk model that combined artificial intelligence with clinical demographics to reduce the number of examinations without missing cases of TR-ROP.

Methods

Infants undergoing routine ROP screening examinations (1579 total eyes, 190 with TR-ROP) were recruited from 8 North American study centers. A vascular severity score (VSS) was derived

SUBMITTER: Coyner AS 

PROVIDER: S-EPMC8919718 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature