지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2023.6
- 수록면
- 35 - 42 (8page)
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초록· 키워드
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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.
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.
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