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REVIEWSMachine Learning for Differentiating Essential Tremor: A Scoping ReviewDAVID M. FLETCHER** KAITLYN E. HEINTZELMAN** SUMESH B. RAMASAMY ALLISON MARKSJOSEPH C. MELOTTAMY W. AMARA ADEEL A. MEMON *Author aff i liations can be found in the back matter of this article** All authors contributed equally to this work.ABSTRACTBackground: Essential tremor (ET) is the most common movement disorder, affecting ~6% of adults over 65 [1]. Differentiating ET from other tremors remains clinically challenging due to overlapping features and variable presentation. Artif i cial intelligence (AI), particularly machine learning (ML), has emerged a potential tool to support neurologists by enhancing pattern recognition and complementing traditional assessments in complex cases. This is the fi rst scoping review examining ML’s potential role in distinguishing essential tremor from other tremor types.Methods: A systematic, scoping search was conducted using PubMed, Cochrane, and Scopus through April 2025, in accordance with PRISMA guidelines-ScR [2]. Studies applying AI to distinguish ET from other tremors were included. Of 548 studies screened, 97 underwent full-text review, with data extracted from 46.Results: 46 included studies encompassed 6,051 patients, including 2,358 with ET. ML models utilized diverse inputs: accelerometers, gyroscopes, voice recordings, Archimedes spirals, EMG, and video. Common algorithms were included vector machines (18 articles), k-nearest neighbors (9 articles), and convolutional neural networks (8 articles). There was a high amount of heterogeneity in reporting data, severely limiting between study comparisons. Reported classif i cation accuracies ranged from 60% to 100% (mean: 89%). However, heterogeneity in data types, methodologies, and reporting limited cross-study comparability.Conclusions: ML shows promise as a decision-support tool by recognizing tremor features that may complement, but not replace, expert clinical assessment, particularly in diagnostically ambiguous cases. To enable clinical adoption, future studies must address current heterogeneity, develop standardized datasets, implement automated preprocessing, and focus on clinically feasible data sources.CORRESPONDING AUTHOR:David M. FletcherMD/PhD program, Department of Neuroscience, West Virginia University, 33 Medical Drive Center, Morgantown, WV, 26505, USAdmf00013@mix.wvu.eduKEYWORDS:Scoping Review; Essential Tremor; Machine Learning; Artif i cial Intelligence; Movement DisordersTO CITE THIS ARTICLE:Fletcher DM, Heintzelman KE, Ramasamy SB, Marks A, Melott JC, Amara AW, Memon AA. Machine Learning for Differentiating Essential Tremor: A Scoping Review. Tremor and Other Hyperkinetic Movements. 2026; 16(1): 28, pp. 1–15. DOI: https://doi.org/10.5334/tohm.1182