基于深度学习的帕金森震颤严重程度客观估计
Deep learning for objective estimation of Parkinsonian tremor severity
AI总结：
本文提出一种基于像素的深度学习模型，利用2,742次多中心视频评估数据客观估计帕金森震颤严重程度，可预测药物和DBS疗效并识别异常视频，为PD症状高频纵向监测提供可扩展方案。
摘要：
准确评估帕金森震颤对于监测疾病进展和评价治疗效果至关重要。我们提出了一种基于像素的深度学习模型，旨在从视频数据中分析帕金森病（PD）的姿势性震颤，克服了传统姿态估计技术的局限性。该模型在两个大洲五个专业运动障碍中心收集的2,742次评估数据上进行训练，表现出与临床评估高度一致的结果。它有效预测了左旋多巴和脑深部电刺激（DBS）的治疗效果，检测到症状的侧向不对称性，并区分了不同的震颤严重程度。特征空间分析揭示了震颤严重程度呈非线性、结构化分布，其中低严重程度评分在特征空间中占据更大比例。该模型还能有效识别异常视频，表明其在临床环境中用于自适应学习和质量控制的潜力。我们的方法为震颤评分提供了一种可扩展且客观的手段，并有望整合到其他MDS-UPDRS运动评估项目中，包括运动迟缓和步态。该系统的适应性和性能凸显了其在高频、纵向监测PD症状方面的前景，能够补充临床专业知识并增强患者管理中的决策能力。未来工作将把这种基于像素的方法扩展到PD的其他核心症状，旨在开发一个全面的多症状模型，用于帕金森病严重程度的自动化评估。
英文摘要：
Accurate assessment of Parkinsonian tremor is vital for monitoring disease progression and evaluating treatment efficacy. We introduce a pixel-based deep learning model designed to analyse postural tremor in Parkinson's disease (PD) from video data, overcoming the limitations of traditional pose estimation techniques. Trained on 2,742 assessments from five specialised movement disorder centres across two continents, the model demonstrated robust concordance with clinical evaluations. It effectively predicted treatment effects for levodopa and deep brain stimulation (DBS), detected lateral asymmetry of symptoms, and differentiated between different tremor severities. Feature space analysis revealed a non-linear, structured distribution of tremor severity, with low-severity scores occupying a larger portion of the feature space. The model also effectively identified outlier videos, suggesting its potential for adaptive learning and quality control in clinical settings. Our approach offers a scalable and objective method for tremor scoring, with potential integration into other MDS-UPDRS motor assessments, including bradykinesia and gait. The system's adaptability and performance underscore its promise for high-frequency, longitudinal monitoring of PD symptoms, complementing clinical expertise and enhancing decision-making in patient management. Future work will extend this pixel-based methodology to other cardinal symptoms of PD, aiming to develop a comprehensive, multi-symptom model for automated Parkinson's disease severity assessment.
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