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Despite their prevalence\u2014up to 30% of PD cases exhibit subclinical tremors\u2014these tremors remain inadequately studied. Therefore, this study explores the potential of artificial intelligence (AI) to address this differentiation uncertainty. Our objective is to develop a deep learning model that can differentiate among subclinical tremors due to PD, ET, and normal physiological tremors. Subclinical tremor data were obtained from inertial sensors placed on the hands and arms of 51 PD, 15 ET, and 58 normal subjects. The AI architecture used was designed using a long short-term memory network (LSTM) and was trained on the short-time Fourier transformed subclinical tremor data as the input features. The network was trained separately to differentiate firstly between PD and ET tremors and then between PD, ET, and physiological tremors and yielded accuracies of 95% and 93%, respectively. Comparative analysis with existing convolutional LSTM demonstrated the superior performance of our work. The proposed method has 30\u201350% better accuracies when classifying low amplitude tremors as compared to the reference method. Future enhancements aim to enhance model interpretability and validate on larger, more diverse datasets, including action tremors. The proposed work can potentially serve as a valuable tool for clinicians, aiding in the differentiation of subclinical tremors common in Parkinson\u2019s disease, which in turn enhances diagnostic accuracy and informs treatment decisions.<\/jats:p>","DOI":"10.1007\/s13246-025-01526-0","type":"journal-article","created":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T13:13:22Z","timestamp":1740402802000},"page":"591-602","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Subclinical tremor differentiation using long short-term memory networks"],"prefix":"10.1007","volume":"48","author":[{"given":"Gerard Ruchin Randil","family":"Nanayakkara","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4213-0263","authenticated-orcid":false,"given":"Ping Yi","family":"Chan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,2,24]]},"reference":[{"key":"1526_CR1","doi-asserted-by":"publisher","first-page":"901","DOI":"10.1007\/s00702-017-1686-y","volume":"124","author":"O-B Tysnes","year":"2017","unstructured":"Tysnes O-B, Storstein A (2017) Epidemiology of Parkinson\u2019s disease. 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