Multimodal Artificial Intelligence Model Integrating Chest CT and Cardiac Ultrasound for the Auxiliary Diagnosis of Pulmonary Arterial Hypertension.

Published in Journal of visualized experiments : JoVE. Abstract: Pulmonary arterial hypertension (PAH) is a progressive vascular disorder characterized by elevated pulmonary vascular resistance and right ventricular dysfunction. Conventional imaging modalities, chest computed tomography (CT) and cardiac ultrasound (echocardiography), provide complementary structural and functional information but exhibit incomplete sensitivity and specificity when used alone. This study aimed to evaluate a multimodal artificial intelligence (AI) model integrating chest CT and cardiac ultrasound for auxiliary PAH diagnosis, benchmarked against right heart catheterization (RHC) as the reference standard. A retrospective observational study was conducted on 100 patients (mean age 52.6 ± 15.2 years; 56% female) with suspected PAH between January 2020 and December 2024. Each underwent high-resolution chest CT, transthoracic echocardiography, and RHC within a 2-week interval. Imaging-derived structural and functional features were integrated within an attention-based multimodal AI framework. Diagnostic metrics were calculated for chest CT, cardiac ultrasound, and multimodal AI against the archived RHC clinical classification. The multimodal AI system achieved sensitivity of 92.2%, specificity of 88.9%, accuracy of 91.0%, and AUC of 0.930, compared with CT (sensitivity 84.4%, specificity 80.6%, accuracy 83.0%, AUC 0.860) and echocardiography (sensitivity 79.7%, specificity 77.8%, accuracy 79.0%, AUC 0.840). McNemar tests of paired binary classifications favored A...