论文标题
通过分析磁共振成像脑扫描和患者特征来早期诊断帕金森病
Early Diagnosis of Parkinsons Disease by Analyzing Magnetic Resonance Imaging Brain Scans and Patient Characteristics
论文作者
论文摘要
PD帕金森病是一种慢性病，会影响运动技能，并包括震颤和僵化等症状。当前的诊断程序使用患者评估来评估症状，有时进行磁共振成像或MRI扫描。但是，症状变化会导致评估不正确，并且对MRI扫描的分析需要经验丰富的专家。这项研究建议通过将症状数据和来自帕金森氏症进步标志物计划数据库的症状数据和MRI数据结合到深度学习中准确诊断PD严重程度。实施了一种新的混合模型结构，以充分利用两种形式的临床数据，并且仅基于症状，并且还开发了MRI扫描。基于症状的模型集成了完全连接的深度学习神经网络，MRI扫描和混合模型集成了基于转移学习的卷积神经网络。所有模型都不只进行二元分类，而是将患者诊断为五个严重性类别，零阶段代表健康的患者，四个和五个代表PD患者。仅症状，仅MRI扫描，而混合模型的精度分别为0.77、0.68和0.94。混合模型的精度也很高，召回得分为0.94和0.95。实际临床病例证实了混合动力的强烈表现，其中患者与其他两个模型进行了错误分类，但通过混合动力进行了正确的分类。它在五个严重性阶段也是一致的，表明准确的早期检测。这是将症状数据和MRI扫描结合使用的第一个报告，并在如此大的规模上使用机器学习方法。
Parkinsons disease, PD, is a chronic condition that affects motor skills and includes symptoms like tremors and rigidity. The current diagnostic procedure uses patient assessments to evaluate symptoms and sometimes a magnetic resonance imaging or MRI scan. However, symptom variations cause inaccurate assessments, and the analysis of MRI scans requires experienced specialists. This research proposes to accurately diagnose PD severity with deep learning by combining symptoms data and MRI data from the Parkinsons Progression Markers Initiative database. A new hybrid model architecture was implemented to fully utilize both forms of clinical data, and models based on only symptoms and only MRI scans were also developed. The symptoms based model integrates a fully connected deep learning neural network, and the MRI scans and hybrid models integrate transfer learning based convolutional neural networks. Instead of performing only binary classification, all models diagnose patients into five severity categories, with stage zero representing healthy patients and stages four and five representing patients with PD. The symptoms only, MRI scans only, and hybrid models achieved accuracies of 0.77, 0.68, and 0.94, respectively. The hybrid model also had high precision and recall scores of 0.94 and 0.95. Real clinical cases confirm the strong performance of the hybrid, where patients were classified incorrectly with both other models but correctly by the hybrid. It is also consistent across the five severity stages, indicating accurate early detection. This is the first report to combine symptoms data and MRI scans with a machine learning approach on such a large scale.
