Applications of Machine Learning in the Diagnosis and Prognosis of Patients with Chiari Malformation Type I: A Scoping Review.

ABSTRACT: Background: The implementation of machine learning (ML) models has significantly impacted neuroimaging. Recent data suggest that these models may improve the accuracy of diagnosing and predicting outcomes in patients with Chiari malformation type I (CMI). Methods: A scoping review was conducted according to the guidelines put forth by PRISMA. The literature search was performed in PubMed/MEDLINE, SCOPUS, and ScienceDirect databases. We included observational or experimental studies focusing on the applications of ML in patients with CMI. Results: A total of 9 articles were included. All the included articles were retrospective. Five out of the nine studies investigated the applicability of machine learning models for diagnosing CMI, whereas the remaining studies focuse

SUBMITTER: Symeou S 

PROVIDER: S-EPMC11853870 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature