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Automatic two-dimensional & three-dimensional video analysis with deep learning for movement disorders: A systematic
Wei Tang1, Peter M A van Ooijen2, Deborah A Sival3
1Department of Neurology, University Medical Center Groningen, University of Groningen, P.O. Box 30001, 9700 RB Groningen, The Netherlands; Data Science Center in Health, University Medical Center Groningen, University of Groningen, P.O. Box 30001, 9700 RB Groningen, The Netherlands.
Deep learning and video analysis offer objective, low-cost methods for diagnosing movement disorders like Parkinson's disease. This review highlights advancements in automatic video analysis for accurate and early detection of various motor conditions.
Area of Science:
- Neurology
- Computer Science
- Biomedical Engineering
Background:
- Traditional movement disorder diagnosis relies on subjective assessments, often missing early or overlapping symptoms.
- Computer vision and deep learning offer objective, quantitative analysis of motor symptoms.
- Video analysis provides a practical, low-cost solution for movement disorder assessment.
Purpose of the Study:
- To systematically review advancements in deep learning-based video analysis for movement disorders.
- To consolidate knowledge on objective video analysis for conditions like Parkinson's disease, ataxia, and Tourette syndrome.
- To identify key methodologies, datasets, and challenges in the field.
Main Methods:
- Systematic literature review of studies published until September 2023.
- Searched major scientific databases (Web of Science, PubMed, Scopus, Embase).
- Analyzed 68 relevant studies focusing on objectives, datasets, modalities, and deep learning techniques.
Main Results:
- Identified diverse applications including Parkinson's disease symptom quantification, ataxia assessment, and tic detection.
- Examined various video modalities (2D/3D) and deep learning architectures used.
- Cataloged datasets and methodologies for objective movement disorder analysis.
Conclusions:
- Deep learning-powered video analysis presents significant advancements for objective movement disorder diagnosis.
- Opportunities exist in improving datasets, interpretability, and enabling remote monitoring.
- Further research is needed to address current challenges and enhance clinical utility.
