Deep learning for objective estimation of Parkinsonian tremor severity
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Deep LearningPose EstimationSimilar Papers 제목 키워드 기반
Clinical Facts Along With a Feedback Control Perspective Suggest That Increased Response Time Might be the Cause of Parkinsonian Rest Tremor
Parkinson's disease (PD) is a neurodegenerative disorder characterized by increased response times leading to a variety of biomechanical symptoms such as tremors, stooping and gait instability. Although the deterioration…
GAITGen: Disentangled Motion-Pathology Impaired Gait Generative Model -- Bringing Motion Generation to the Clinical Domain
Gait analysis is crucial for the diagnosis and monitoring of movement disorders like Parkinson's Disease. While computer vision models have shown potential for objectively evaluating parkinsonian gait, their effectivenes…
Motion GenerationPose-based Tremor Type and Level Analysis for Parkinson's Disease from Video
Purpose:Current methods for diagnosis of PD rely on clinical examination. The accuracy of diagnosis ranges between 73% and 84%, and is influenced by the experience of the clinical assessor. Hence, an automatic, effective…
DiagnosticSingle Particle AnalysisReal-time estimation of phase and amplitude with application to neural data
Computation of the instantaneous phase and amplitude via the Hilbert Transform is a powerful tool of data analysis. This approach finds many applications in various science and engineering branches but is not proper for …
Machine Learning Strategies for Parkinson Tremor Classification Using Wearable Sensor Data
Parkinson's disease (PD) is a neurological disorder requiring early and accurate diagnosis for effective management. Machine learning (ML) has emerged as a powerful tool to enhance PD classification and diagnostic accura…
Diagnosticfeature selectionSurvey