Structural neuroimaging as clinical predictor: a review of machine learning applications

TL;DR

This review discusses how machine learning techniques applied to structural MRI data are used to develop clinical classifiers for various neurological and psychiatric disorders, highlighting current challenges and applications.

Contribution

It provides a comprehensive overview of machine learning applications to structural MRI for clinical diagnosis, addressing practical issues and summarizing applications across multiple diseases.

Findings

Machine learning improves disease classification accuracy.

Various algorithms are applied to different neurological disorders.

The review identifies common challenges and future directions.

Abstract

In this paper, we provide an extensive overview of machine learning techniques applied to structural magnetic resonance imaging (MRI) data to obtain clinical classifiers. We specifically address practical problems commonly encountered in the literature, with the aim of helping researchers improve the application of these techniques in future works. Additionally, we survey how these algorithms are applied to a wide range of diseases and disorders (e.g. Alzheimer's disease (AD), Parkinson's disease (PD), autism, multiple sclerosis, traumatic brain injury, etc.) in order to provide a comprehensive view of the state of the art in different fields.