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The Ophthalmologist / Issues / 2026 / March / Retinal AI Predicts Neonatal Lung Disease
Health Economics and Policy Retina News

Retinal AI Predicts Neonatal Lung Disease

AI models trained on routine ROP images identify bronchopulmonary dysplasia and pulmonary hypertension in premature infants

3/16/2026 2 min read

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Objective:

To evaluate whether retinal images from routine ROP screening can predict bronchopulmonary dysplasia and pulmonary hypertension in premature infants.

Approach:
    Key Findings:
    • The multimodal model for bronchopulmonary dysplasia achieved an AUC of 0.82, outperforming both demographics-only and imaging-only models (0.72 each).
    • The imaging-only model for pulmonary hypertension achieved an AUC of 0.91, significantly outperforming the demographics-only model (0.68).
    • Adding demographic data did not improve the performance of the multimodal model for pulmonary hypertension (AUC 0.91).
    Interpretation:

    The study suggests that retinal imaging can provide valuable predictive information for severe cardiopulmonary complications in premature infants, potentially enhancing clinical management.

    Limitations:
    • Small pulmonary hypertension cohort limits statistical power.
    • Lack of external validation across different imaging devices.
    • Absence of model explainability analyses.
    Conclusion:

    Retinal imaging embedded in neonatal care pathways could support earlier identification of infants at high risk for severe cardiopulmonary complications, prompting timely interventions.

    Sources:
    • JAMA Ophthalmology

    This content is an AI-generated, fully rewritten summary based on a published scholarly article. It does not reproduce the original text and is not a substitute for the original publication. Readers are encouraged to consult the source for full context, data, and methodology.

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