Segmentation-model-based framework to detect aortic dissection on non-contrast CT images: a retrospective study.

ABSTRACT:

Objectives

To develop an automated deep learning framework for detecting aortic dissection (AD) and visualizing its morphology and extent on non-contrast CT (NCCT) images.

Materials and methods

This retrospective study included patients who underwent aortic CTA from January 2021 to January 2023 at two tertiary hospitals. Demographic data, medical history, and CT scans were collected. A segmentation-based deep learning model was trained to identify true and false lumens on NCCT images, with performance evaluated on internal and external test sets. Segmentation accuracy was measured using the Dice coefficient, while the intraclass correlation coefficient (ICC) assessed consistency between predicted and ground-truth false lumen volumes. Receiver operating characteristic (ROC) a

SUBMITTER: Wang Q 

PROVIDER: S-EPMC12463791 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature