Tracking False Lumen Remodeling with AI: A Variational Autoencoder Approach After Frozen Elephant Trunk Surgery.

ABSTRACT: Objective: False lumen (FL) thrombosis plays a key role in aortic remodeling after Frozen Elephant Trunk (FET) surgery, yet current imaging assessments are limited to categorical classifications. This study aimed to evaluate an unsupervised artificial intelligence (AI) algorithm based on a variational autoencoder (VAE) for automated, continuous quantification of FL thrombosis using serial computed tomography angiography (CTA). Methods: In this retrospective study, a VAE model was applied to axial CTA slices from 30 patients with aortic dissection who underwent FET surgery. The model encoded each image into a structured latent space, from which a continuous "thrombus score" was developed and derived to quantify the extent of FL thrombosis. Thrombus scores were compared between

SUBMITTER: Osswald A 

PROVIDER: S-EPMC12565649 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature