School of Electrical and Data Engineering, University of Technology Sydney, Sydney, Australia.
School of Electrical and Data Engineering, University of Technology Sydney, Sydney, Australia.
Comput Med Imaging Graph. 2021 Jan;87:101810. doi: 10.1016/j.compmedimag.2020.101810. Epub 2020 Nov 24.
Accurate diagnosis of Parkinson's Disease (PD) at its early stages remains a challenge for modern clinicians. In this study, we utilize a convolutional neural network (CNN) approach to address this problem. In particular, we develop a CNN-based network model highly capable of discriminating PD patients based on Single Photon Emission Computed Tomography (SPECT) images from healthy controls. A total of 2723 SPECT images are analyzed in this study, of which 1364 images from the healthy control group, and the other 1359 images are in the PD group. Image normalization process is carried out to enhance the regions of interests (ROIs) necessary for our network to learn distinguishing features from them. A 10-fold cross-validation is implemented to evaluate the performance of the network model. Our approach demonstrates outstanding performance with an accuracy of 99.34 %, sensitivity of 99.04 % and specificity of 99.63 %, outperforming all previously published results. Given the high performance and easy-to-use features of our network, it can be deduced that our approach has the potential to revolutionize the diagnosis of PD and its management.
准确诊断帕金森病（PD）的早期阶段仍然是现代临床医生面临的挑战。在这项研究中，我们利用卷积神经网络（CNN）方法来解决这个问题。具体来说，我们开发了一个基于 CNN 的网络模型，该模型能够非常准确地根据健康对照组的单光子发射计算机断层扫描（SPECT）图像来区分 PD 患者。本研究共分析了 2723 个 SPECT 图像，其中 1364 个图像来自健康对照组，另外 1359 个图像来自 PD 组。进行图像归一化处理，以增强我们的网络从中学习区分特征所需的感兴趣区域（ROI）。采用 10 倍交叉验证来评估网络模型的性能。我们的方法表现出色，准确率为 99.34%，灵敏度为 99.04%，特异性为 99.63%，优于所有以前发表的结果。鉴于我们的网络具有高性能和易于使用的特点，可以推断我们的方法有可能彻底改变 PD 的诊断和管理。
