Chien Chung-Yao, Hsu Szu-Wei, Lee Tsung-Lin, Sung Pi-Shan, Lin Chou-Ching
Department of Biomedical Engineering, National Cheng Kung University, Tainan 704, Taiwan.
Department of Neurology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan.
Biomedicines. 2020 Dec 24;9(1):12. doi: 10.3390/biomedicines9010012.
The challenge of differentiating, at an early stage, Parkinson's disease from parkinsonism caused by other disorders remains unsolved. We proposed using an artificial neural network (ANN) to process images of dopamine transporter single-photon emission computed tomography (DAT-SPECT).
Abnormal DAT-SPECT images of subjects with Parkinson's disease and parkinsonism caused by other disorders were divided into training and test sets. Striatal regions of the images were segmented by using an active contour model and were used as the data to perform transfer learning on a pre-trained ANN to discriminate Parkinson's disease from parkinsonism caused by other disorders. A support vector machine trained using parameters of semi-quantitative measurements including specific binding ratio and asymmetry index was used for comparison.
The predictive accuracy of the ANN classifier (86%) was higher than that of the support vector machine classifier (68%). The sensitivity and specificity of the ANN classifier in predicting Parkinson's disease were 81.8% and 88.6%, respectively.
The ANN classifier outperformed classical biomarkers in differentiating Parkinson's disease from parkinsonism caused by other disorders. This classifier can be readily included into standalone computer software for clinical application.
在早期阶段区分帕金森病与其他疾病引起的帕金森综合征这一挑战仍未解决。我们提议使用人工神经网络（ANN）来处理多巴胺转运体单光子发射计算机断层扫描（DAT-SPECT）图像。
将帕金森病患者和其他疾病引起的帕金森综合征患者的异常DAT-SPECT图像分为训练集和测试集。使用主动轮廓模型对图像的纹状体区域进行分割，并将其用作数据，在预训练的人工神经网络上进行迁移学习，以区分帕金森病与其他疾病引起的帕金森综合征。使用基于包括特异性结合率和不对称指数在内的半定量测量参数训练的支持向量机进行比较。
人工神经网络分类器的预测准确率（86%）高于支持向量机分类器（68%）。人工神经网络分类器预测帕金森病的敏感性和特异性分别为81.8%和88.6%。
在区分帕金森病与其他疾病引起的帕金森综合征方面，人工神经网络分类器优于传统生物标志物。该分类器可轻松集成到独立的计算机软件中用于临床应用。
