Magesh Pavan Rajkumar, Myloth Richard Delwin, Tom Rijo Jackson
Department of Computer Science and Engineering, CMR Institute of Technology, Bengaluru, India.
Comput Biol Med. 2020 Nov;126:104041. doi: 10.1016/j.compbiomed.2020.104041. Epub 2020 Oct 8.
Parkinson's Disease (PD) is a degenerative and progressive neurological condition. Early diagnosis can improve treatment for patients and is performed through dopaminergic imaging techniques like the SPECT DaTSCAN. In this study, we propose a machine learning model that accurately classifies any given DaTSCAN as having Parkinson's disease or not, in addition to providing a plausible reason for the prediction. This kind of reasoning is done through the use of visual indicators generated using Local Interpretable Model-Agnostic Explainer (LIME) methods. DaTSCANs were drawn from the Parkinson's Progression Markers Initiative database and trained on a CNN (VGG16) using transfer learning, yielding an accuracy of 95.2%, a sensitivity of 97.5%, and a specificity of 90.9%. Keeping model interpretability of paramount importance, especially in the healthcare field, this study utilises LIME explanations to distinguish PD from non-PD, using visual superpixels on the DaTSCANs. It could be concluded that the proposed system, in union with its measured interpretability and accuracy may effectively aid medical workers in the early diagnosis of Parkinson's Disease.
帕金森病（PD）是一种退行性和进行性神经疾病。早期诊断可以改善患者的治疗，并且通过多巴胺能成像技术如SPECT DaTSCAN来进行。在本研究中，我们提出了一种机器学习模型，该模型除了能为预测提供合理理由外，还能准确地将任何给定的DaTSCAN分类为患有帕金森病或未患帕金森病。这种推理是通过使用基于局部可解释模型无关解释器（LIME）方法生成的视觉指标来完成的。DaTSCAN数据取自帕金森病进展标志物倡议数据库，并使用迁移学习在卷积神经网络（VGG16）上进行训练，准确率达到95.2%，灵敏度为97.5%，特异性为90.9%。鉴于模型可解释性至关重要，尤其是在医疗保健领域，本研究利用LIME解释，通过DaTSCAN上的视觉超像素来区分帕金森病和非帕金森病。可以得出结论，所提出的系统及其可测量的可解释性和准确性相结合，可能有效地帮助医护人员进行帕金森病的早期诊断。
