Neuroimaging-based single subject prediction of brain disorders has gained increasing
attention in recent years. Using a variety of neuroimaging modalities such as structural …

In this paper, we give a short introduction to machine learning and survey its applications in
radiology. We focused on six categories of applications in radiology: medical image …

Neuroimaging‐based approaches have been extensively applied to study mental illness in
recent years and have deepened our understanding of both cognitively healthy and …

There is a growing interest in automatic classification of mental disorders based on
neuroimaging data. Small training data sets (subjects) and very large amount of high …

Artificial Intelligence in healthcare employs machine learning algorithms to emulate human
cognition in the analysis of complicated or large sets of data. Specifically, artificial …

Objectives Since the development of diffusion tensor imaging (DTI) nearly a decade ago, it
has been extensively applied to a number of different psychiatric disorders. Its rapid …

This paper presents a paradigm for generating a quantifiable marker of pathology that
supports diagnosis and provides a potential biomarker of neuropsychiatric disorders, such …

Diffusion tensor imaging (DTI) is a magnetic resonance imaging technique that is
increasingly being used for the non-invasive evaluation of brain white matter abnormalities …

The objective of this research was to determine whether fractional anisotropy (FA) and mean
diffusivity (MD) maps derived from diffusion tensor imaging (DTI) of the brain are able to …

Machine-learning approaches are becoming commonplace in the neuroimaging literature
as potential diagnostic and prognostic tools for the study of clinical populations. However …