Pose-based tremor type and level analysis for Parkinson’s disease from video

Zhang, H., Ho, E. S.L. , Zhang, X., Del Din, S. and Shum, H. P.H. (2024) Pose-based tremor type and level analysis for Parkinson’s disease from video. International Journal of Computer Assisted Radiology and Surgery, (doi: 10.1007/s11548-023-03052-4) (Early Online Publication)

Abstract

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 and interpretable supporting system for PD symptom identification would support clinicians in making more robust PD diagnostic decisions. Methods: We propose to analyze Parkinson’s tremor (PT) to support the analysis of PD, since PT is one of the most typical symptoms of PD with broad generalizability. To realize the idea, we present SPA-PTA, a deep learning-based PT classification and severity estimation system that takes consumer-grade videos of front-facing humans as input. The core of the system is a novel attention module with a lightweight pyramidal channel-squeezing–fusion architecture that effectively extracts relevant PT information and filters noise. It enhances modeling performance while improving system interpretability. Results: We validate our system via individual-based leave-one-out cross-validation on two tasks: the PT classification task and the tremor severity rating estimation task. Our system presents a 91.3% accuracy and 80.0% F1-score in classifying PT with non-PT class, while providing a 76.4% accuracy and 76.7% F1-score in more complex multiclass tremor rating classification task. Conclusion: Our system offers a cost-effective PT classification and tremor severity estimation results as warning signs of PD for undiagnosed patients with PT symptoms. In addition, it provides a potential solution for supporting PD diagnosis in regions with limited clinical resources.

• Item Type: Articles
• Additional Information: Shum received support from the EPSRC NortHFutures project (Ref: EP/X031012/1). S. Del Din has received support from Innovative Medicines Initiative 2 Joint Undertaking (Ref: 820820 Mobilise-D, 853981 IDEA-FAST), NIHR Newcastle, Newcastle upon Tyne Hospitals NHS Foundation Trust, Cumbria Northumberland and Tyne and Wear NHS Foundation Trust.
• Status: Early Online Publication
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Ho, Dr Edmond S. L
• Authors: Zhang, H., Ho, E. S.L., Zhang, X., Del Din, S., and Shum, H. P.H.
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: International Journal of Computer Assisted Radiology and Surgery
• Publisher: Springer
• ISSN: 1861-6410
• ISSN (Online): 1861-6429
• Published Online: 18 January 2024
• Copyright Holders: Copyright © 2024 The Authors
• First Published: First published in International Journal of Computer Assisted Radiology and Surgery 2024
• Publisher Policy: Reproduced under a Creative Commons licence