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Published on: December 13, 2017
Machine Learning for Differentiating Essential Tremor: A Scoping Review
David M Fletcher1,2, Kaitlyn E Heintzelman1,3, Sumesh B Ramasamy4
1School of Medicine, West Virginia University, Morgantown, WV, 26505, USA.
Summary
Machine learning (ML) shows promise in distinguishing essential tremor (ET) from other tremors by analyzing diverse data. However, study heterogeneity currently limits clinical adoption, requiring standardization for future applications.
Area of Science:
- Neurology
- Biomedical Engineering
- Artificial Intelligence
Background:
- Essential tremor (ET) is a common movement disorder, often challenging to differentiate from other tremor types due to overlapping clinical features.
- Artificial intelligence (AI), specifically machine learning (ML), offers potential for enhanced pattern recognition to aid in diagnosing complex ET cases.
- This scoping review is the first to systematically examine the role of ML in differentiating ET from other tremor disorders.
Conclusions:
- ML demonstrates potential as a clinical decision-support tool for essential tremor diagnosis, particularly in challenging cases.
- ML algorithms can identify tremor features that complement, rather than replace, expert clinical evaluation.
- Addressing heterogeneity, standardizing datasets, and focusing on feasible data sources are crucial for the clinical adoption of ML in ET diagnosis.
