Abstract
Child and adolescent mental disorders are common disorders with various symptoms, and attracting more attention due to the increasing prevalence. Mental disorders, especially the attention-deficit hyperactivity disorder (ADHD) and the autism spectrum disorder (ASD), have great influence on the development of children and adolescents. Nowadays, the biomarkers from neuroimaging such as magnetic resonance imaging (MRI) have a great importance on the diagnosis of mental disorders, and machine learning has been proved to be very powerful in the processing for neuroimages. Nowadays, many researchers are focusing on the studies of computer-aided diagnosis (CAD) based on machine learning and neuroimaging. In this review, the technical details of machine learning based CAD of child and adolescent mental disorders are briefly introduced, and the research progress in CAD of ADHD and ASD based on machine learning and structural MRI are summarized. These studies showed that many machine learning methods have been used in the diagnosis of child and adolescent mental disorders, but the relevant methods cannot be applied to clinical diagnosis. Further studies should be conducted to improve the diagnostic ability of machine learning methods from multiple perspectives, and provide an objective and reliable tool for the clinical diagnosis of child and adolescent mental disorders. Key words: ADHD; ASD; Structural magnetic resonance imaging; Machine learning
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: Chinese Journal of Behavioral Medicine and Brain Science
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.