Development of a Machine Learning Model for Diagnosing Schizophrenia and Bipolar Disorder Based on Diffusion Tensor Imaging: A Preliminary Study
10.22802/jksbtp.2023.29.2.35
- Author:
Dong-Kyun LEE
1
;
Hyeongrae LEE
;
Hyung Jun CHOI
;
Chul-Eung KIM
;
Sung-Wan KIM
;
Seunghyong RYU
Author Information
1. Department of Mental Health Research, National Center for Mental Health, Seoul, Korea
- Publication Type:Original Articles
- From:
Journal of the Korean Society of Biological Therapies in Psychiatry
2023;29(2):35-42
- CountryRepublic of Korea
- Language:English
-
Abstract:
Objectives:This study aimed to develop a machine learning model for diagnosing schizophrenia (SZ) and bipolar disorder (BD) based on diffusion tensor imaging (DTI) data.
Methods:We used 3T-magnetic resonance imaging to examine SZ, BD, healthy control (HC) subjects (aged 20-50 years, n=65 in each group). Applying Support Vector Machine (SVM) to fractional anisotropy (FA) values, we built classification models of SZ and HC, BD and HC, and SZ and BD. Features of white matter (WM) tracts were selected through recursive feature elimination, and 5-fold cross validation was performed.
Results:The SVM models classified SZ and BD from HC with a mean accuracy of 83.5% and 75.4%, respectively. The SZ-BD classification model archived 75.0% accuracy. These classification models used FA values in 15-18 WM tracts as features, including the retrolenticular part of the internal capsule, superior corona radiata, cingulum, and superior fronto-occipital fasciculus.
Conclusions:This study presented a preliminary machine learning model to diagnose SZ and BD based on DTI data. Our findings also suggest that there might be a specific pattern of abnormalities in WM integrity that can differentiate the two psychotic disorders.
