ArticleScientific reports2025
A deep learning approach to remotely assessing essential tremor with handwritten images.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Evolution and trends in tremor diagnosis and differential diagnosis-a bibliometric study.Frontiers in aging neuroscience · 2026Review
- Machine Learning for Differentiating Essential Tremor: A Scoping Review.Tremor and other hyperkinetic movements (New York, N.Y.) · 2026Article
- Essential Tremor Severity Assessment Using Handwriting Analysis and Machine Learning.Sensors (Basel, Switzerland) · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
Essential tremor (ET) is the most prevalent movement disorder, with its incidence increasing with age, significantly impacting motor functions and quality of life. Traditional methods for assessing ET severity are often time-consuming, subjective, and require in-person visits to medical facilities. This study introduces a novel deep learning-based approach for remotely assessing ET severity using handwriting images, which improves both efficiency and accessibility. We collected approximately 1000 high-quality Archimedean spiral handwriting images from patients in both medical institutions and home settings, creating a robust and diverse dataset. A transfer learning-based model, ETSD-Net, was developed and trained to evaluate ET severity. The model achieved an accuracy of 88.44%, demonstrating superior performance over baseline models. Our approach offers a cost-effective, scalable, and reliable solution for ET assessment, particularly in remote or resource-limited settings, and provides a valuable contribution to the development of more accessible diagnostic tools for movement disorders.
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What OpenQuestion holds
Registered trials
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.