ROI-Guided Echocardiographic Image Analysis and Case Aggregation for Differentiating Atrial and Ventricular Septal Defects
Echocardiography is the primary imaging modality for congenital heart disease (CHD) assessment. However, the real-world clinical application of artificial intelligence in this domain is often hindered by hidden data leakage from homologous frames and the high diagnostic variance of single-frame static inference. To bridge the gap between idealized model evaluation and clinical reality, this study proposes a standardized, region-of-interest (ROI)-guided deep learning workflow for differentiating atrial septal defect (ASD), ventricular septal defect (VSD), and normal cases. Specifically, utilizing a retrospective cohort of 1,987 patients (11,638 images) from Tengzhou Central People’s Hospital, we implemented strict subject-disjoint partitioning to eliminate data leakage, and simultaneously introduced cross-frame case aggregation to emulate the multi-frame visual synthesis process of expert echocardiographers. Among the evaluated model configurations, fully fine-tuned EfficientNet-B0 achieved the highest internal held-out test performance, with an accuracy of 0.9950 (95% CI, 0.9875–1.0000), a macro-F1 of 0.9941, and a macro-AUROC of 0.9997; 2 of 401 test patients were misclassified. Five-fold patient-level cross-validation yielded an accuracy of $0.9945 \pm 0.0040$ , further supporting the stability of the model across patient partitions. These findings suggest that the proposed workflow has the potential to serve as an adjunctive tool for septal defect screening.