Using Functional or Structural Magnetic Resonance Images and Personal Characteristic Data to Identify ADHD and Autism.

ABSTRACT: A clinical tool that can diagnose psychiatric illness using functional or structural magnetic resonance (MR) brain images has the potential to greatly assist physicians and improve treatment efficacy. Working toward the goal of automated diagnosis, we propose an approach for automated classification of ADHD and autism based on histogram of oriented gradients (HOG) features extracted from MR brain images, as well as personal characteristic data features. We describe a learning algorithm that can produce effective classifiers for ADHD and autism when run on two large public datasets. The algorithm is able to distinguish ADHD from control with hold-out accuracy of 69.6% (over baseline 55.0%) using personal characteristics and structural brain scan features when trained on the ADHD-200 dataset

SUBMITTER: Ghiassian S 

PROVIDER: S-EPMC5193362 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature