Explainable Multimodal Deep Learning Model for Early Prediction of Treatment-Requiring Retinopathy of Prematurity

Abstract Background: Retinopathy of prematurity (ROP) is a leading preventable cause of childhood blindness. Current screening guidelines, based primarily on gestational age and birth weight, result in numerous unnecessary examinations. We aimed to develop an explainable multimodal deep learning model for early prediction of treatment-requiring ROP. ‏ Methods: In a retrospective cohort of 384 preterm infants (203 treated, 181 untreated) from a tertiary center in Iran (2021-2024), we integrated four directional fundus images, semi-supervised vessel segmentation maps (using a U-Net-based adversarial domain-adaptation approach), and comprehensive clinical/demographic data. A multi-view fusion model with an attention mechanism extracted vessel-aware features, reduced via PCA, and combined with clinical variables. Six multimodal feature sets were evaluated using 14 machine learning classifiers with 5-fold stratified cross-validation. ‏ Results: The best-performing models (Extra Trees and Random Forest) on the full multimodal feature set achieved a test accuracy of 0.987, an AUC-ROC of 0.999, an F1-score of 0.988, and near-perfect specificity (up to 1.000). Interpretability analyses (SHAP and Grad-CAM) confirmed that predictions were primarily driven by vascular morphology features (PCA1) and posterior pole abnormalities consistent with plus disease. ‏ Conclusions: The proposed explainable multimodal model significantly outperforms clinical-only approaches and represents a promising tool for risk stratification in ROP screening. It has the potential to reduce unnecessary examinations, infant stress, and healthcare burden while facilitating timely intervention. External multicenter validation is warranted.