Evaluating the severity of aortic coarctation in infants using anatomic features measured on CTA

Objectives A machine learning model was developed to evaluate the severity of aortic coarctation (CoA) in infants based on anatomical features measured on CTA. Methods In total, 239 infant patients undergoing both thorax CTA and echocardiography were retrospectively reviewed. The patients were assigned to either mild or severe CoA group based on their pressure gradient on echocardiography. They were further divided into patent ductus arteriosus (PDA) and non-PDA groups. The anatomical features were measured on double-oblique multiplanar reconstructed CTA images. Then, the optimal features were identified by using the Boruta algorithm. Subsequently, the coarctation severity was classified using linear discriminant analysis (LDA). We further investigated the relationship between the anatomical features and re-coarctation using Cox regression. Results Four anatomical features showed significant differences between the mild and severe CoA groups, including the smallest aortic cross-sectional area indexed to body surface area ( p