Retinal AI Predicts Neonatal Lung Disease
Retinal scans already performed in the NICU could flag infants at risk for serious lung disease before standard diagnosis
By
Conexiant News Staff
February 17, 2026
Clinical Scorecard: Retinal AI Predicts Neonatal Lung Disease
At a Glance
Category Detail
Condition Bronchopulmonary Dysplasia and Pulmonary Hypertension in Premature Infants Key Mechanisms Deep learning models analyzing retinal images to predict cardiopulmonary disease. Target Population Premature infants, particularly those screened for retinopathy of prematurity. Care Setting Neonatal Intensive Care Units (NICUs)
Key Highlights
Deep learning models can predict bronchopulmonary dysplasia and pulmonary hypertension from retinal images. Multimodal models combining imaging and demographic data outperformed single-modality models. The study utilized data from the i-ROP study, focusing on infants at 34 weeks' postmenstrual age or less. Guideline-Based Recommendations
Diagnosis
Use retinal imaging as a predictive tool for bronchopulmonary dysplasia and pulmonary hypertension. Management
Consider integrating retinal imaging into routine screening protocols for at-risk infants. Monitoring & Follow-up
Monitor infants with abnormal retinal findings for potential cardiopulmonary complications. Risks
Be aware of limitations in model performance across different imaging devices and settings. Patient & Prescribing Data
Infants enrolled in the i-ROP study, particularly those with gestational age and birth weight data.
Retinal imaging may provide a non-invasive method to identify infants at risk for severe lung disease.
Clinical Best Practices
Incorporate retinal imaging into standard care for premature infants. Utilize multimodal predictive models for better diagnostic accuracy. Related Resources & Content
