Language
English
Publication Date
2-1-2024
Journal
Magnetic Resonance Imaging
DOI
10.1016/j.mri.2023.11.014
PMID
38065273
PMCID
PMC11728603
PubMedCentral® Posted Date
2-1-2025
PubMedCentral® Full Text Version
Author MSS
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 patients and matched controls. CE-MRIs from 36 PAD patients and 20 matched controls were used for preparing training and testing data at a 7:3 ratio with cross-validation (CV) techniques. We employed feature arrangement and selection methods to optimize the number of features. The proposed method achieved a peak accuracy of 94.11% and a mean testing accuracy of 84.85% in a 2-class classification approach (controls vs. PAD). A three-class classification approach was performed to identify a high-risk PAD sub-group which yielded an average test accuracy of 83.23% (matched controls vs. PAD without diabetes vs. PAD with diabetes). Similarly, we obtained 78.60% average accuracy among matched controls, PAD treadmill exercise completers, and PAD exercise treadmill non-completers. Machine learning and imaging-based texture features may be of interest in the study of lower extremity ischemia.
Keywords
Humans, Peripheral Arterial Disease, Magnetic Resonance Imaging, Intermittent Claudication, Muscle, Skeletal, Diabetes Mellitus, Atherosclerosis, Machine learning, Magnetic resonance imaging, Peripheral artery disease, Texture features
Published Open-Access
yes
Recommended Citation
Khagi, Bijen; Belousova, Tatiana; Short, Christina M; et al., "A Machine Learning-Based Approach To Identify Peripheral Artery Disease Using Texture Features From Contrast-Enhanced Magnetic Resonance Imaging" (2024). Faculty and Staff Publications. 4154.
https://digitalcommons.library.tmc.edu/baylor_docs/4154