磁共振支持向量机区分帕金森病和进行性核上性麻痹。
Magnetic resonance support vector machine discriminates between Parkinson disease and progressive supranuclear palsy.
机构信息
Neuroimaging Research Unit, Institute of Neurological Sciences-National Research Council, Catanzaro, Italy.
出版信息
Mov Disord. 2014 Feb;29(2):266-9. doi: 10.1002/mds.25737. Epub 2013 Dec 3.
BACKGROUND
The aim of the current study was to distinguish patients with Parkinson disease (PD) from those with progressive supranuclear palsy (PSP) at the individual level using pattern recognition of magnetic resonance imaging data.
METHODS
We combined diffusion tensor imaging and voxel-based morphometry in a support vector machine algorithm to evaluate 21 patients with PSP and 57 patients with PD.
RESULTS
The automated algorithm correctly distinguished patients who had PD from those who had PSP with 100% accuracy. This accuracy value was obtained when white matter atrophy was considered. Diffusion parameters combined with gray matter atrophy exhibited 90% sensitivity and 96% specificity.
CONCLUSIONS
Our findings demonstrate that automated pattern recognition can help distinguish patients with PSP from those with PD on an individual basis.
背景
本研究的目的是使用磁共振成像数据的模式识别，在个体水平上区分帕金森病（PD）患者和进行性核上性麻痹（PSP）患者。
方法
我们将弥散张量成像和基于体素的形态计量学结合在支持向量机算法中，对 21 例 PSP 患者和 57 例 PD 患者进行评估。
结果
自动化算法能够准确地将 PD 患者与 PSP 患者区分开来，准确率为 100%。这一准确率是在考虑到白质萎缩的情况下得出的。将弥散参数与灰质萎缩相结合，可获得 90%的灵敏度和 96%的特异性。
结论
我们的研究结果表明，自动化模式识别可以帮助在个体水平上区分 PSP 患者和 PD 患者。
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