A machine learning-based approach to identify peripheral artery disease using texture features from contrast-enhanced magnetic resonance imaging.

ABSTRACT: Diagnosing and assessing the risk of peripheral artery disease (PAD) has long been a focal point for medical practitioners. The impaired blood circulation in PAD patients results in altered microvascular perfusion patterns in the calf muscles which is the primary location of intermittent claudication pain. Consequently, we hypothesized that changes in perfusion and increase in connective tissue could lead to alterations in the appearance or texture patterns of the skeletal calf muscles, as visualized with non-invasive imaging techniques. We designed an automatic pipeline for textural feature extraction from contrast-enhanced magnetic resonance imaging (CE-MRI) scans and used the texture features to train machine learning models to detect the heterogeneity in the muscle pattern among PAD pa

SUBMITTER: Khagi B 

PROVIDER: S-EPMC11728603 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature