ArticleSensors (Basel, Switzerland)2023
Clinically Informed Automated Assessment of Finger Tapping Videos in Parkinson's Disease.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
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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
18 citing papers in PubMed.
- Remote Video Assessment of Ataxia via SARAMovement disorders clinical practice · 2026Article
- Remote Assessment of Parkinson Disease Using Deep Learning on Structured Mouse-Trace Data From Suspected Cases: Machine-Learning Pilot Feasibility Study.JMIR formative research · 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
- PARQ: A Complexity-Consensus Aware Automatic Assessment of Motor Disease Severity in Parkinson's Disease.Research square · 2026Article
- Dynamic Assessment of Fine Motor Control and Vocalization in Parkinson Disease Through a Smartphone App: Cross-Sectional Study of Time-Severity Interaction Effects.JMIR mHealth and uHealth · 2025Article
- Video-Based Data-Driven Models for Diagnosing Movement Disorders: Review and Future Directions.Movement disorders : official journal of the Movement Disorder Society · 2025Review
- Early detection of Parkinson's disease: Machine learning-based prediction of UPDRS Part III scores inJournal of Parkinson's disease · 2025Article
- Validation of the Comprehensive Augmented Reality Testing Platform to Quantify Parkinson's Disease Fine Motor Performance.Journal of clinical medicine · 2025Article
- Computer vision uncovers three fundamental dimensions of levodopa-responsive motor improvement in Parkinson's disease.NPJ Parkinson's disease · 2025Article
- A quantum inspired machine learning approach for multimodal Parkinson's disease screening.Scientific reports · 2025Article
- VisionMD: an open-source tool for video-based analysis of motor function in movement disorders.NPJ Parkinson's disease · 2025Article
- Motor symptoms of Parkinson's disease: critical markers for early AI-assisted diagnosis.Frontiers in aging neuroscience · 2025Review
- An IoT-Based Framework for Automated Assessing and Reporting of Light Sensitivities in Children with Autism Spectrum Disorder.Sensors (Basel, Switzerland) · 2024Article
- Medical-informed machine learning: integrating prior knowledge into medical decision systems.BMC medical informatics and decision making · 2024Review
- Bradykinesia in dystonic hand tremor: kinematic analysis and clinical rating.Frontiers in human neuroscience · 2024Article
- Characterizing Disease Progression in Parkinson's Disease from Videos of the Finger Tapping Test.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2024Article
- Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
The utilization of Artificial Intelligence (AI) for assessing motor performance in Parkinson's Disease (PD) offers substantial potential, particularly if the results can be integrated into clinical decision-making processes. However, the precise quantification of PD symptoms remains a persistent challenge. The current standard Unified Parkinson's Disease Rating Scale (UPDRS) and its variations serve as the primary clinical tools for evaluating motor symptoms in PD, but are time-intensive and prone to inter-rater variability. Recent work has applied data-driven machine learning techniques to analyze videos of PD patients performing motor tasks, such as finger tapping, a UPDRS task to assess bradykinesia. However, these methods often use abstract features that are not closely related to clinical experience. In this paper, we introduce a customized machine learning approach for the automated scoring of UPDRS bradykinesia using single-view RGB videos of finger tapping, based on the extraction of detailed features that rigorously conform to the established UPDRS guidelines. We applied the method to 75 videos from 50 PD patients collected in both a laboratory and a realistic clinic environment. The classification performance agreed well with expert assessors, and the features selected by the Decision Tree aligned with clinical knowledge. Our proposed framework was designed to remain relevant amid ongoing patient recruitment and technological progress. The proposed approach incorporates features that closely resonate with clinical reasoning and shows promise for clinical implementation in the foreseeable future.
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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.