24 March 2016 Using support vector machines with tract-based spatial statistics for automated classification of Tourette syndrome children
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Abstract
Tourette syndrome (TS) is a developmental neuropsychiatric disorder with the cardinal symptoms of motor and vocal tics which emerges in early childhood and fluctuates in severity in later years. To date, the neural basis of TS is not fully understood yet and TS has a long-term prognosis that is difficult to accurately estimate. Few studies have looked at the potential of using diffusion tensor imaging (DTI) in conjunction with machine learning algorithms in order to automate the classification of healthy children and TS children. Here we apply Tract-Based Spatial Statistics (TBSS) method to 44 TS children and 48 age and gender matched healthy children in order to extract the diffusion values from each voxel in the white matter (WM) skeleton, and a feature selection algorithm (ReliefF) was used to select the most salient voxels for subsequent classification with support vector machine (SVM). We use a nested cross validation to yield an unbiased assessment of the classification method and prevent overestimation. The accuracy (88.04%), sensitivity (88.64%) and specificity (87.50%) were achieved in our method as peak performance of the SVM classifier was achieved using the axial diffusion (AD) metric, demonstrating the potential of a joint TBSS and SVM pipeline for fast, objective classification of healthy and TS children. These results support that our methods may be useful for the early identification of subjects with TS, and hold promise for predicting prognosis and treatment outcome for individuals with TS.
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Hongwei Wen, Hongwei Wen, Yue Liu, Yue Liu, Jieqiong Wang, Jieqiong Wang, Jishui Zhang, Jishui Zhang, Yun Peng, Yun Peng, Huiguang He, Huiguang He, } "Using support vector machines with tract-based spatial statistics for automated classification of Tourette syndrome children", Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 97852Q (24 March 2016); doi: 10.1117/12.2216647; https://doi.org/10.1117/12.2216647
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