Anatomy-Derived Blood Flow Proxy Features Improve Image-Based Classification of Congenital Heart Disease

Congenital heart disease (CHD) encompasses many types of cardiovascular abnormalities, making image-supported diagnosis particularly challenging.In this study, we evaluated whether anatomy-derived hemodynamic-proxy features could improve machine learning classification of CHD subtypes (healthy, conotruncal defects, septal defects, and complex) when physiological measurements were unavailable.Publicly available human cardiac segmentation datasets were harmonized into a single dataset containing 194 cases spread into four classes.Fifteen anatomy-derived hemodynamic-proxy features and fifteen conventional anatomical features were extracted from the segmentation masks.Four machine learning models were optimized and evaluated using repeated stratified five-fold cross-validation.Machine learning models were assessed on accuracy, class-balanced accuracy, and macro F1 score for the anatomy-derived hemodynamic-proxy feature set, conventional anatomical feature set, and combined feature set.Across all four models, the combined anatomical plus hemodynamic-proxy feature set outperformed the anatomical-only feature set, with the best combined models reaching approximately 0.64-0.66macro F1-score.These exploratory findings suggest that anatomy-derived hemodynamic-proxy features may provide complementary information for CHD classification, but validation in larger, source-balanced external cohorts and against direct hemodynamic measurements is required before generalizability or clinical utility can be established.