ReviewHealthcare (Basel, Switzerland)2024
Computer Vision for Parkinson's Disease Evaluation: A Survey on Finger Tapping.
Review in Healthcare (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed, 10 citations in OpenAlex.
- Distinct finger-tapping feature in progressive supranuclear palsy correlates with motor function and brain atrophy.NPJ Parkinson's disease · 2026Article
- Interpretable and granular video-based quantification of motor characteristics from the finger-tapping test in Parkinson's disease.NPJ Parkinson's disease · 2026Article
- Automated video analysis for early detection of bradykinesia in Parkinson's disease.Journal of neuroengineering and rehabilitation · 2026Article
- Video-Based Data-Driven Models for Diagnosing Movement Disorders: Review and Future Directions.Movement disorders : official journal of the Movement Disorder Society · 2025Review
- Development of Neurodegenerative Disease Diagnosis and Monitoring from Traditional to Digital Biomarkers.Biosensors · 2025Review
- VisionMD: an open-source tool for video-based analysis of motor function in movement disorders.NPJ Parkinson's disease · 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
6 authors at 2 institutions in 1 country.
Funding
Abstract
Parkinson's disease (PD) is a progressive neurodegenerative disorder whose prevalence has steadily been rising over the years. Specialist neurologists across the world assess and diagnose patients with PD, although the diagnostic process is time-consuming and various symptoms take years to appear, which means that the diagnosis is prone to human error. The partial automatization of PD assessment and diagnosis through computational processes has therefore been considered for some time. One well-known tool for PD assessment is finger tapping (FT), which can now be assessed through computer vision (CV). Artificial intelligence and related advances over recent decades, more specifically in the area of CV, have made it possible to develop computer systems that can help specialists assess and diagnose PD. The aim of this study is to review some advances related to CV techniques and FT so as to offer insight into future research lines that technological advances are now opening up.
Indexed as
Identifiers
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.