Prediction of adolescent idiopathic scoliosis with machine learning algorithms using brain volumetric measurements.

ABSTRACT:

Background

It is known that neuroanatomical and neurofunctional changes observed in the brain, brainstem and cerebellum play a role in the etiology of adolescent idiopathic scoliosis (AIS). This study aimed to investigate whether volumetric measurements of brain regions can be used as predictive indicators for AIS through machine learning techniques.

Methods

Patients with a severe degree of curvature in AIS (n = 32) and healthy individuals (n = 31) were enrolled in the study. Volumetric data from 169 brain regions, acquired from magnetic resonance imaging (MRI) of these individuals, were utilized as predictive factors. A comprehensive analysis was conducted using the twelve most prevalent machine learning algorithms, encompassing thorough parameter adjustments

SUBMITTER: Payas A 

PROVIDER: S-EPMC11247394 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature