Abstract Details

Title Decoding Tremor: A Scoping Review of Machine Learning Approaches to Essential Tremor Differentiation

Topic Movement Disorders

Presentation(s) P2 - Poster Session 2 (11:45 AM-12:45 PM)

Poster/Presentation
Number 17-005

Objective To conduct the first comprehensive scoping review of Artificial Intelligence (AI) and Machine Learning (ML) applications aimed at distinguishing Essential Tremor (ET) from other tremor types.

Background ET is the most common movement disorder, affecting approximately 6% of adults 65 years and older. Differentiating ET from other tremor types remains clinically challenging due to overlapping features and variable presentation. AI, particularly ML, has emerged as a potential tool to support neurologists by enhancing pattern recognition and complementing traditional clinical assessments in complex cases.

Design/Methods A systematic scoping search was conducted using PubMed, Cochrane, and Scopus through April 2025, in accordance with PRISMA-ScR guidelines. Studies were included if they used AI methodologies to distinguish ET from other tremor types. Of 548 studies screened, 97 underwent full-text review, and data were extracted from the 46 studies that met the predefined inclusion criteria.

Results The 46 included studies encompassed 6,051 patients, including 2,358 with ET. The ML models used employed a wide array of inputs, including accelerometers (26 articles), electromyography (EMG) (9 studies), Archimedes spirals (6 studies), gyroscopes (5 studies), voice recordings (5 studies), and video recordings (5 studies). Commonly applied algorithms included support vector machines (18 articles), k-nearest neighbors (9 articles), convolutional neural networks (8 articles), decision trees (7 studies), random forests (7 studies), and gradient boosting (6 studies). Reported classification accuracies ranged from 60% to 100%, though high heterogeneity in data types, reporting standards, and methodologies severely limited comparability across studies.

Conclusions ML demonstrates significant promise in supporting neurologists with ET diagnosis through the automated identification of subtle, distinguishing tremor features. To facilitate clinical translation, future studies should prioritize the development of standardized datasets, improved reporting consistency, automated preprocessing pipelines, and the use of clinically feasible data sources.

Authors/Disclosures
Kaitlyn E. Heintzelman, MD/PhD Student PRESENTER	Ms. Heintzelman has nothing to disclose.
David Fletcher	Mr. Fletcher has nothing to disclose.
Sumesh Ramasamy	Mr. Ramasamy has nothing to disclose.
Allison G. Marks	Miss Marks has nothing to disclose.
Joseph C. Melott	Mr. Melott has nothing to disclose.
Amy W. Amara, MD PhD (University of Colorado Anschutz Medical Center)	The institution of Dr. Amara has received personal compensation in the range of $10,000-$49,999 for serving as a Consultant for Photopharmics, Inc. The institution of Dr. Amara has received research support from Michael J Fox Foundation for Parkinson's Research . The institution of Dr. Amara has received research support from Biogen Idec. The institution of Dr. Amara has received research support from NIH.
Adeel A. Memon, MD (West Virginia University)	The institution of Dr. Memon has received research support from NIH/NINDS.

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