ArticlePloS one2017
High-accuracy detection of early Parkinson's Disease using multiple characteristics of finger movement while typing.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 3 of them syntheses that pooled it.
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.
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Who cites it
34 citing papers in PubMed, 3 syntheses or guidelines pooled it, 119 citations in OpenAlex.
- Diagnostic accuracy of keystroke dynamics as digital biomarkers for fine motor decline in neuropsychiatric disorders: a systematic review and meta-analysis.Scientific reports · 2022Pooled it
- Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature.Frontiers in aging neuroscience · 2021Pooled it
- Systematic Review Looking at the Use of Technology to Measure Free-Living Symptom and Activity Outcomes in Parkinson's Disease in the Home or a Home-like Environment.Journal of Parkinson's disease · 2020Pooled it
- The Use of Smartphone Keystroke Dynamics to Passively Monitor Upper Limb and Cognitive Function in Multiple Sclerosis: Longitudinal Analysis.Journal of medical Internet research · 2022Trial
- Modelling and Correcting the Effects of Tremor on Touchscreen Interaction.Sensors (Basel, Switzerland) · 2026Article
- Design, framework and benchmark of safety monitors for black-box classifiers.Scientific reports · 2026Article
- Using voice and speech data in healthcare: a scoping review of the ethical, legal and social implications.Frontiers in digital health · 2026Review
- Quantifying device type and handedness biases in a remote Parkinson's disease AI-powered assessment.NPJ digital medicine · 2025Article
- Diagnosing Parkinson's disease via behavioral biometrics of keystroke dynamics.Science advances · 2025Article
- Neurological Inflammation in Parkinsonism: Current Prognosticative Diagnostics and Pitfalls.Current topics in medicinal chemistry · 2025Review
- Inorganic Nanoparticles-based Drug Delivery Systems for Neurodegenerative Diseases Therapy.Current pharmaceutical design · 2025Review
- A robust Parkinson's disease detection model based on time-varying synaptic efficacy function in spiking neural network.BMC neurology · 2024Article
- Digital biomarkers for precision diagnosis and monitoring in Parkinson's disease.NPJ digital medicine · 2024Review
- Implication of regional selectivity of dopamine deficits in impaired suppressing of involuntary movements in Parkinson's disease.Neuroscience and biobehavioral reviews · 2024Review
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- Article
- Parkinson Disease Recognition Using a Gamified Website: Machine Learning Development and Usability Study.JMIR formative research · 2023Article
- Keystroke Biometrics as a Tool for the Early Diagnosis and Clinical Assessment of Parkinson's Disease.Diagnostics (Basel, Switzerland) · 2023Article
- Article
- Aerobic exercise interventions to address impaired quality of life in patients with pituitary tumors.PloS one · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Parkinson's Disease (PD) is a progressive neurodegenerative movement disease affecting over 6 million people worldwide. Loss of dopamine-producing neurons results in a range of both motor and non-motor symptoms, however there is currently no definitive test for PD by non-specialist clinicians, especially in the early disease stages where the symptoms may be subtle and poorly characterised. This results in a high misdiagnosis rate (up to 25% by non-specialists) and people can have the disease for many years before diagnosis. There is a need for a more accurate, objective means of early detection, ideally one which can be used by individuals in their home setting. In this investigation, keystroke timing information from 103 subjects (comprising 32 with mild PD severity and the remainder non-PD controls) was captured as they typed on a computer keyboard over an extended period and showed that PD affects various characteristics of hand and finger movement and that these can be detected. A novel methodology was used to classify the subjects' disease status, by utilising a combination of many keystroke features which were analysed by an ensemble of machine learning classification models. When applied to two separate participant groups, this approach was able to successfully discriminate between early-PD subjects and controls with 96% sensitivity, 97% specificity and an AUC of 0.98. The technique does not require any specialised equipment or medical supervision, and does not rely on the experience and skill of the practitioner. Regarding more general application, it currently does not incorporate a second cardinal disease symptom, so may not differentiate PD from similar movement-related disorders.
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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.