Real-time artificial intelligence-based texture analysis of muscle ultrasound data for neuromuscular disorder assessment.

ABSTRACT:

Objective

Many artificial intelligence approaches to muscle ultrasound image analysis have not been implemented on usable devices in clinical neuromuscular medicine practice, owing to high computational demands and lack of standardised testing protocols. This study evaluated the feasibility of using real-time texture analysis to differentiate between various pathological conditions.

Methods

We analysed 17,021 cross-sectional ultrasound images of the biceps brachii of 75 participants, including 25 each with neurogenic disorders, myogenic disorders, and healthy controls. The size and location of the regions of interest were randomly selected to minimise bias. A random forest classifier utilising texture features such as Dissimilarity and Homogeneity was developed and deployed

SUBMITTER: Noda Y 

PROVIDER: S-EPMC11402302 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature