ArticleJournal of neurology2022
Are smartphones and machine learning enough to diagnose tremor?
Article in Journal of neurology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled it, 35 citations in OpenAlex.
- The Use of Artificial Intelligence and Wearable Inertial Measurement Units in Medicine: Systematic Review.JMIR mHealth and uHealth · 2025Pooled it
- Machine learning and wearable sensors for automated Parkinson's disease diagnosis aid: a systematic review.Journal of neurology · 2024Pooled it
- Neurophysiological Basis of Tremor Disorders: Parkinson's Disease, Essential Tremor, Tremor in Dystonia, and Holmes Tremor.Seminars in neurology · 2026Review
- Expanding the Clinical Spectrum of Head Snap Sign.Movement disorders clinical practice · 2026Article
- Machine Learning for Differentiating Essential Tremor: A Scoping Review.Tremor and other hyperkinetic movements (New York, N.Y.) · 2026Article
- Phenotypical Differentiation of Tremor Using Time Series Feature Extraction and Machine Learning.Movement disorders : official journal of the Movement Disorder Society · 2025Article
- Modern Phenomenology of Tremor: How Novel Technology Can Assist in the Diagnosis of Tremor.Movement disorders clinical practice · 2025Article
- Tremor: Clinical Frameworks, Network Dysfunction and Therapeutics.Brain sciences · 2025Review
- Subclinical tremor differentiation using long short-term memory networks.Physical and engineering sciences in medicine · 2025Article
- RT-ring: a small wearable device for tremulous Parkinson's disease diagnosis in primary care.Frontiers in neurology · 2025Article
- Describing Clinical Features of Movement Disorders for Effective Research Reporting.Movement disorders clinical practice · 2024Article
- Artificial Intelligence in the Diagnosis and Quantitative Phenotyping of Hyperkinetic Movement Disorders: A Systematic Review.Journal of clinical medicine · 2024Review
- Validation and application of computer vision algorithms for video-based tremor analysis.NPJ digital medicine · 2024Article
- A Novel Method for Full-Section Assessment of High-Speed Railway Subgrade Compaction Quality Based on ML-Interval Prediction Theory.Sensors (Basel, Switzerland) · 2024Article
- Bedside clinical assessment of patients with common upper limb tremor and algorithmic approach.Asian biomedicine : research, reviews and news · 2024Review
- Development of a New Wearable Device for the Characterization of Hand Tremor.Bioengineering (Basel, Switzerland) · 2023Article
- Exercise therapy as a digital therapeutic for chronic disease management: consideration for clinical product development.Frontiers in digital health · 2023Review
- Wearable sensors and features for diagnosis of neurodegenerative diseases: A systematic review.Digital healthReview
Corrections and comments
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Authors and funding
11 authors at 6 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPatients with essential tremor (ET), Parkinson's disease (PD) and dystonic tremor (DT) can be difficult to classify and often share similar characteristics.
objectivesTo use ubiquitous smartphone accelerometers with and without clinical features to automate tremor classification using supervised machine learning, and to use unsupervised learning to evaluate if natural clusterings of patients correspond to assigned clinical diagnoses.
methodsA supervised machine learning classifier was trained to classify 78 tremor patients using leave-one-out cross-validation to estimate performance on unseen accelerometer data. An independent cohort of 27 patients were also studied. Next, we focused on a subset of 48 patients with both smartphone-based tremor measurements and detailed clinical assessment metrics and compared two separate machine learning classifiers trained on these data.
resultsThe classifier yielded a total accuracy of 74.4% and F1-score of 0.74 for a trinary classification with an area under the curve of 0.904, average F1-score of 0.94, specificity of 97% and sensitivity of 84% in classifying PD from ET or DT. The algorithm classified ET from non-ET with 88% accuracy, but only classified DT from non-DT with 29% accuracy. A poorer performance was found in the independent cohort. Classifiers trained on accelerometer and clinical data respectively obtained similar results.
conclusionsMachine learning classifiers achieved a high accuracy of PD, however moderate accuracy of ET, and poor accuracy of DT classification. This underscores the difficulty of using AI to classify some tremors due to lack of specificity in clinical and neuropathological features, reinforcing that they may represent overlapping syndromes.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.