Retinal AI Predicts Neonatal Lung Disease - Summary - MDSpire

Retinal AI Predicts Neonatal Lung Disease

  • March 16, 2026

  • 4 min

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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:

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