Deep learning for objective estimation of Parkinsonian tremor severity

TL;DR

This paper presents a deep learning model that analyzes video data to objectively assess Parkinsonian tremor severity, offering a scalable, accurate alternative to traditional clinical evaluations.

Contribution

The study introduces a novel pixel-based deep learning approach for tremor assessment that overcomes pose estimation limitations and demonstrates high concordance with clinical evaluations.

Findings

Robust prediction of tremor severity from video data

Effective detection of treatment effects and symptom asymmetry

Potential for integration into comprehensive PD symptom monitoring

Abstract

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.