Varghese Julian, Brenner Alexander, Fujarski Michael, van Alen Catharina Marie, Plagwitz Lucas, Warnecke Tobias
Institute of Medical Informatics, University of Münster, Münster, Germany.
European Research Centre of Information Systems, University of Münster, Münster, Germany.
NPJ Parkinsons Dis. 2024 Jan 5;10(1):9. doi: 10.1038/s41531-023-00625-7.
The utilisation of smart devices, such as smartwatches and smartphones, in the field of movement disorders research has gained significant attention. However, the absence of a comprehensive dataset with movement data and clinical annotations, encompassing a wide range of movement disorders including Parkinson's disease (PD) and its differential diagnoses (DD), presents a significant gap. The availability of such a dataset is crucial for the development of reliable machine learning (ML) models on smart devices, enabling the detection of diseases and monitoring of treatment efficacy in a home-based setting. We conducted a three-year cross-sectional study at a large tertiary care hospital. A multi-modal smartphone app integrated electronic questionnaires and smartwatch measures during an interactive assessment designed by neurologists to provoke subtle changes in movement pathologies. We captured over 5000 clinical assessment steps from 504 participants, including PD, DD, and healthy controls (HC). After age-matching, an integrative ML approach combining classical signal processing and advanced deep learning techniques was implemented and cross-validated. The models achieved an average balanced accuracy of 91.16% in the classification PD vs. HC, while PD vs. DD scored 72.42%. The numbers suggest promising performance while distinguishing similar disorders remains challenging. The extensive annotations, including details on demographics, medical history, symptoms, and movement steps, provide a comprehensive database to ML techniques and encourage further investigations into phenotypical biomarkers related to movement disorders.
智能设备，如智能手表和智能手机，在运动障碍研究领域的应用已备受关注。然而，缺乏一个包含运动数据和临床注释的综合数据集，涵盖包括帕金森病（PD）及其鉴别诊断（DD）在内的广泛运动障碍，这存在一个重大缺口。这样一个数据集的可用性对于在智能设备上开发可靠的机器学习（ML）模型至关重要，能够在家庭环境中检测疾病并监测治疗效果。我们在一家大型三级护理医院进行了一项为期三年的横断面研究。一款多模式智能手机应用程序在神经科医生设计的交互式评估过程中整合了电子问卷和智能手表测量，以引发运动病理学的细微变化。我们从504名参与者（包括PD、DD和健康对照（HC））中获取了超过5000个临床评估步骤。在年龄匹配后，实施并交叉验证了一种结合经典信号处理和先进深度学习技术的综合ML方法。在区分PD与HC的分类中，模型的平均平衡准确率达到91.16%，而PD与DD的区分准确率为72.42%。这些数字表明性能有望，但区分相似疾病仍然具有挑战性。广泛的注释，包括人口统计学、病史、症状和运动步骤的详细信息，为ML技术提供了一个综合数据库，并鼓励对与运动障碍相关的表型生物标志物进行进一步研究。
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