ArticleScientific reports2020
Screening of Parkinsonian subtle fine-motor impairment from touchscreen typing via deep learning.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it, 41 citations in OpenAlex.
- Machine learning and wearable sensors for automated Parkinson's disease diagnosis aid: a systematic review.Journal of neurology · 2024Pooled it
- 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
- 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
- Development and clinical translation of smartphone-based digital biomarkers for Parkinson's disease: a structured review.Journal of neural transmission (Vienna, Austria : 1996) · 2026Review
- Distributed Precision Stroke Care: Artificial Intelligence-Driven Stroke Management Using Multimodal Sensor Data.Stroke · 2026Review
- Designing and conducting interventional trials with passive-sensing applications on patient-owned smartphones: challenges and recommendations from the BD4QoL study.Frontiers in digital health · 2026Article
- Characteristics and Validity of Commercially Available Technologies Analyzing Voice Features to Assess Parkinson's Disease.Movement disorders clinical practice · 2026Review
- Parkinson's disease functional movement battery a comprehensive test set to evaluate of motor abilities in persons with Parkinson's disease.Scientific reports · 2025Article
- Modernizing the Staging of Parkinson Disease Using Digital Health Technology.Journal of medical Internet research · 2025Article
- Wearable Sensors in Other Medical Domains with Application Potential for Orthopedic Trauma Surgery-A Narrative Review.Journal of clinical medicine · 2024Review
- Generalizing Parkinson's disease detection using keystroke dynamics: a self-supervised approach.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Identifying Subtle Motor Deficits Before Parkinson's Disease is Diagnosed: What to Look for?Journal of Parkinson's disease · 2024Review
- The use of wearables for the diagnosis and treatment of Parkinson's disease.Journal of neural transmission (Vienna, Austria : 1996) · 2023Review
- Smartphone-derived Virtual Keyboard Dynamics Coupled with Accelerometer Data as a Window into Understanding Brain Health: Smartphone Keyboard and Accelerometer as Window into Brain Health.Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2023Article
- A scoping review of neurodegenerative manifestations in explainable digital phenotyping.NPJ Parkinson's disease · 2023Article
- Remote Assessments of Hand Function in Neurological Disorders: Systematic Review.JMIR rehabilitation and assistive technologies · 2022Review
- Rapid development of an integrated remote programming platform for neuromodulation systems through the biodesign process.Scientific reports · 2022Article
- The state of telemedicine for persons with Parkinson's disease.Current opinion in neurology · 2021Review
- Detection of Mild Cognitive Impairment Through Natural Language and Touchscreen Typing Processing.Frontiers in digital health · 2020Article
Corrections and comments
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Authors and funding
10 authors at 6 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Fine-motor impairment (FMI) is progressively expressed in early Parkinson's Disease (PD) patients and is now known to be evident in the immediate prodromal stage of the condition. The clinical techniques for detecting FMI may not be robust enough and here, we show that the subtle FMI of early PD patients can be effectively estimated from the analysis of natural smartphone touchscreen typing via deep learning networks, trained in stages of initialization and fine-tuning. In a validation dataset of 36,000 typing sessions from 39 subjects (17 healthy/22 PD patients with medically validated UPDRS Part III single-item scores), the proposed approach achieved values of area under the receiver operating characteristic curve (AUC) of 0.89 (95% confidence interval: 0.80-0.96) with sensitivity/specificity: 0.90/0.83. The derived estimations result in statistically significant ([Formula: see text]) correlation of 0.66/0.73/0.58 with the clinical standard UPDRS Part III items 22/23/31, respectively. Further validation analysis on 9 de novo PD patients vs. 17 healthy controls classification resulted in AUC of 0.97 (0.93-1.00) with 0.93/0.90. For 253 remote study participants, with self-reported health status providing 252.000 typing sessions via a touchscreen typing data acquisition mobile app (iPrognosis), the proposed approach predicted 0.79 AUC (0.66-0.91) with 0.76/0.71. Remote and unobtrusive screening of subtle FMI via natural smartphone usage, may assist in consolidating early and accurate diagnosis of PD.
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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.