Using machine learning to predict carotid artery symptoms from CT angiography: A radiomics and deep learning approach.

ABSTRACT:

Purpose

To assess radiomics and deep learning (DL) methods in identifying symptomatic Carotid Artery Disease (CAD) from carotid CT angiography (CTA) images. We further compare the performance of these novel methods to the conventional calcium score.

Methods

Carotid CT angiography (CTA) images from symptomatic patients (ischaemic stroke/transient ischaemic attack within the last 3 months) and asymptomatic patients were analysed. Carotid arteries were classified into culprit, non-culprit and asymptomatic. The calcium score was assessed using the Agatston method. 93 radiomic features were extracted from regions-of-interest drawn on 14 consecutive CTA slices. For DL, convolutional neural networks (CNNs) with and without transfer learning were trained directly on CTA slices. Pred

SUBMITTER: Le EPV 

PROVIDER: S-EPMC11402422 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature