Machine learning-enhanced electrical impedance myography to diagnose and track spinal muscular atrophy progression.

ABSTRACT: Objective.To evaluate electrical impedance myography (EIM) in conjunction with machine learning (ML) to detect infantile spinal muscular atrophy (SMA) and disease progression.Approach. Twenty-six infants with SMA and twenty-seven healthy infants had been enrolled and assessed with EIM as part of the NeuroNEXT SMA biomarker study. We applied a variety of modern, supervised ML approaches to this data, first seeking to differentiate healthy from SMA muscle, and then, using the best method, to track SMA progression.Main Results.Several of the ML algorithms worked well, but linear discriminant analysis (LDA) achieved 88.6% accuracy on subject muscles studied. This contrasts with a maximum of 60% accuracy that could be achieved using the single or multifrequency assessment a

SUBMITTER: Sonbas Cobb B 

PROVIDER: S-EPMC11446569 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature