Department of Radiology, University of California San Diego School of Medicine, La Jolla, CA 92093, USA.
Department of Neuroimaging and Neurointervention, Stanford University Medical Center, Stanford, CA 94305, USA.
Tomography. 2021 Mar 26;7(2):95-106. doi: 10.3390/tomography7020009.
[I]FP-CIT SPECT has been valuable for distinguishing Parkinson disease (PD) from essential tremor. However, its performance for quantitative assessment of motor dysfunction has not been established. A virtual reality (VR) application was developed and compared with [I]FP-CIT SPECT/CT for detection of severity of motor dysfunction. Forty-four patients (21 males, 23 females, age 64.5 ± 12.4) with abnormal [I]FP-CIT SPECT/CT underwent assessment of bradykinesia, activities of daily living, and tremor with VR. Support vector machines (SVM) machine learning models were applied to VR and SPECT data. Receiver operating characteristic (ROC) analysis demonstrated greater area under the curve (AUC) for VR (0.8418, 95% CI 0.6071-0.9617) compared with brain SPECT (0.5357, 95% CI 0.3373-0.7357, = 0.029) for detection of motor dysfunction. Logistic regression identified VR as an independent predictor of motor dysfunction (Odds Ratio 326.4, SE 2.17, = 0.008). SVM for prediction of the Unified Parkinson's Disease Rating Scale Part III (UPDRS-III) demonstrated greater R-squared of 0.713 ( = 0.008) for VR, compared with 0.0764 ( = 0.361) for brain SPECT. This study demonstrates that VR can be safely used in patients prior to [I]FP-CIT SPECT imaging and may improve prediction of motor dysfunction. This test has the potential to provide a simple, objective, quantitative analysis of motor symptoms in PD patients.
[I]FP-CIT SPECT 对于区分帕金森病 (PD) 和特发性震颤非常有价值。然而，其在运动功能障碍定量评估方面的性能尚未确定。开发了一种虚拟现实 (VR) 应用程序，并将其与 [I]FP-CIT SPECT/CT 进行比较，以检测运动功能障碍的严重程度。44 名患者（21 名男性，23 名女性，年龄 64.5 ± 12.4 岁）进行了异常 [I]FP-CIT SPECT/CT 检查，接受了 VR 下的运动迟缓、日常生活活动和震颤评估。支持向量机 (SVM) 机器学习模型应用于 VR 和 SPECT 数据。受试者工作特征 (ROC) 分析表明，VR 的曲线下面积 (AUC) 大于脑 SPECT (0.5357，95%CI 0.3373-0.7357， = 0.029)，用于检测运动功能障碍（0.8418，95%CI 0.6071-0.9617， = 0.029）。Logistic 回归确定 VR 是运动功能障碍的独立预测因子（优势比 326.4，SE 2.17， = 0.008）。用于预测统一帕金森病评定量表第 3 部分 (UPDRS-III) 的 SVM 表明，VR 的 R-squared 为 0.713（ = 0.008），而脑 SPECT 为 0.0764（ = 0.361）。这项研究表明，在进行 [I]FP-CIT SPECT 成像之前，VR 可安全用于患者，并且可能改善对运动功能障碍的预测。该测试有可能为 PD 患者的运动症状提供简单、客观、定量的分析。
