Praveer Singh, Sourav Kumar, Riya Tyagi, Benjamin K. Young, Brian K. Jordan, Brian Scottoline, Patrick D. Evers, Susan Ostmo, Aaron S. Coyner, Wei-Chun Lin, Aarushi Gupta, Deniz Erdogmus, Paul Chan, Emily A. McCourt, James S. Barry, Cindy T. McEvoy, Micheal F. Chiang, Peter Campbell, Jayashree Kalpathy-Cramer
University of Colorado School of Medicine. Massachusetts General Hospital and Harvard Medical School. Oregon Health and Science University. Northeastern University. University of Illinois Chicago. National Institutes of Health.
United States
Journal of the American Medical Association Ophthalmology
JAMA Ophthalmol 2026;
DOI: 10.1001/jamaophthalmol.2025.5814
Abstract
Importance: Bronchopulmonary dysplasia (BPD) and pulmonary hypertension (PH) are leading causes of morbidity and mortality in premature infants.
Objective: To determine whether images obtained as part of retinopathy of prematurity (ROP) screening might contain features associated with BPD and PH in infants and whether a multimodal model integrating imaging features with demographic risk factors might outperform a model based on demographic risk alone.
Design, setting, and participants: A deep learning model was used to study retinal images collected from patients enrolled in the multi-institutional Imaging and Informatics in Retinopathy of Prematurity (i-ROP) study. The analysis included infants at risk for ROP undergoing routine ROP screening examinations from 2012 to 2020. Infants were recruited from 7 neonatal intensive care units. Images were limited to 34 weeks’ or less postmenstrual age (PMA) so as to precede the clinical diagnosis of BPD or PH. The dataset included the period from June 2015 to April 2020. Data were analyzed from April to June 2025.
Exposures: BPD was diagnosed by the presence of an oxygen requirement at 36 weeks’ PMA, and PH was diagnosed by echocardiogram at 34 weeks. A support vector machine model was trained to predict BPD or PH diagnosis using (1) image features alone (extracted using ResNet18), (2) demographics alone, or (3) image features concatenated with demographics. To reduce the possibility of confounding with ROP, secondary models were trained using only images without clinical signs of ROP.
Main outcomes and measures: For both BPD and PH, performance was reported on a held-out test set and assessed by the area under receiver operating characteristic curve (AUROC).
Results: A total of 493 infants (mean [SD] gestational age, BPD, 25.7 [1.8] weeks; normal, 27.3 [1.8] weeks; 267 male [54.2%]) were included in this analysis. Performance was reported on a held-out test set (99 patients from the BPD cohort and 37 patients from the PH cohort). For BPD, the multimodal model showed higher accuracy (AUC, 0.82; 95% CI, 0.72-0.90) than demographics-only (0.72; ∆AUC, 0.1; 95% CI, -0.008 to 0.21; P = .07) or imaging-only (0.72; ∆AUC, 0.1; 95% CI, 0.04-0.16; P = .002) models. For PH, multimodal AUC was 0.91 vs the demographics-only 0.68 (∆AUC, 0.14; 95% CI, 0.006-0.27; P = .04) and imaging-only 0.91 (∆AUC, -0.09; 95% CI, -0.3 to 0.12; P = .40) models. Results persisted when trained on images lacking clinical ROP signs.
Conclusions and relevance: Results suggest that retinal images obtained during ROP screening may be used to predict the diagnosis of BPD and PH in preterm infants, which may lead to earlier diagnosis and avoid the need for invasive diagnostic testing in the future.
Category
Class III. Pulmonary Hypertension Associated with Lung Disease
Diagnostic Testing for Pulmonary Vascular Disease. Non-invasive Testing
Mechanical and Computer Models of Pulmonary Vascular Disease and Therapy
Age Focus: Pediatric Pulmonary Vascular Disease
Fresh or Filed Publication: Fresh (PHresh). Less than 1-2 years since publication
Article Access
Free PDF File or Full Text Article Available Through PubMed or DOI: No