磁共振支持向量机可区分伴有静止性震颤的特发性震颤与震颤为主型帕金森病。
Magnetic resonance support vector machine discriminates essential tremor with rest tremor from tremor-dominant Parkinson disease.
作者信息
Cherubini Andrea, Nisticó Rita, Novellino Fabiana, Salsone Maria, Nigro Salvatore, Donzuso Giulia, Quattrone Aldo
机构信息
Neuroimaging Research Unit, Institute of Molecular Bioimaging and Physiology-National Research Council, Catanzaro, Italy.
出版信息
Mov Disord. 2014 Aug;29(9):1216-9. doi: 10.1002/mds.25869. Epub 2014 Apr 13.
BACKGROUND
The aim of the current study was to distinguish patients who had tremor-dominant Parkinson's disease (tPD) from those who had essential tremor with rest tremor (rET).
METHODS
We combined voxel-based morphometry-derived gray matter and white matter volumes and diffusion tensor imaging-derived mean diffusivity and fractional anisotropy in a support vector machine (SVM) to evaluate 15 patients with rET and 15 patients with tPD. Dopamine transporter single-photon emission computed tomography imaging was used as ground truth.
RESULTS
SVM classification of individual patients showed that no single predictor was able to fully discriminate patients with tPD from those with rET. By contrast, when all predictors were combined in a multi-modal algorithm, SVM distinguished patients with rET from those with tPD with an accuracy of 100%.
CONCLUSIONS
SVM is an operator-independent and automatic technique that may help distinguish patients with tPD from those with rET at the individual level.
背景
本研究的目的是区分震颤为主型帕金森病（tPD）患者和伴有静止性震颤的特发性震颤（rET）患者。
方法
我们将基于体素的形态学测量得出的灰质和白质体积以及扩散张量成像得出的平均扩散率和分数各向异性结合到支持向量机（SVM）中，以评估15例rET患者和15例tPD患者。多巴胺转运体单光子发射计算机断层扫描成像用作金标准。
结果
对个体患者的SVM分类显示，没有单一预测指标能够完全区分tPD患者和rET患者。相比之下，当所有预测指标在多模态算法中结合时，SVM区分rET患者和tPD患者的准确率为100%。
结论
SVM是一种独立于操作者的自动技术，可能有助于在个体水平上区分tPD患者和rET患者。
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