Deep learning algorithm for predicting rapid progression of abdominal aortic aneurysm by integrating CT images and clinical features.

ABSTRACT: Abdominal aortic aneurysm (AAA) progression carries a significant rupture risk, demanding accurate prediction models beyond traditional methods that rely on limited clinical parameters and often overlook complex factor interplay. We aimed to enhance prediction by developing and validating an end-to-end multi-modal deep learning (DL) model that integrates features extracted using ResNet from computed tomography (CT) images, geometric features derived from radiomics based on CT annotations, and clinical features obtained from clinical records. This retrospective study utilized data from 561 AAA patients sourced from Boramae Medical Center and Seoul National University Hospital, including 14,252 annotated CT axial images alongside detailed clinical information. Patients were categorized into

SUBMITTER: Oh SJ 

PROVIDER: S-EPMC12583574 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature