ArticleCommunications biology2022
Heterogeneous digital biomarker integration out-performs patient self-reports in predicting Parkinson's disease.
Article in Communications biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 12 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.
Kintsugi Voice Device Study
Kintsugi Voice Device SCID-5-CT Pilot Study
Who cites it
12 citing papers in PubMed, 33 citations in OpenAlex.
- Smartphone-Based Multimodal Digital Biomarker Integration for Parkinson's Disease Screening and Diagnostic Support.Neurology and therapy · 2026Article
- From phenotype to biology: a multi-modal roadmap of biofluid, tissue, imaging, and digital biomarkers in Parkinson's disease.Translational neurodegeneration · 2026Review
- A framework of digital biomarkers for neurodegenerative diseases.Nature reviews bioengineering · 2026Article
- Objective monitoring of motor symptom severity and their progression in Parkinson's disease using a digital gait device.Scientific reports · 2025Article
- Smartphone-derived multidomain features including voice, finger-tapping movement and gait aid early identification of Parkinson's disease.NPJ Parkinson's disease · 2025Article
- Digital gait biomarkers in Parkinson's disease: susceptibility/risk, progression, response to exercise, and prognosis.NPJ Parkinson's disease · 2025Review
- Development of Neurodegenerative Disease Diagnosis and Monitoring from Traditional to Digital Biomarkers.Biosensors · 2025Review
- Transformer-based transfer learning on self-reported voice recordings for Parkinson's disease diagnosis.Scientific reports · 2024Article
- Parkinson Disease Recognition Using a Gamified Website: Machine Learning Development and Usability Study.JMIR formative research · 2023Article
- Toward Personalized Medicine Approaches for Parkinson Disease Using Digital Technologies.JMIR formative research · 2023Review
- Classification of Parkinson's disease and its stages using machine learning.Scientific reports · 2022Article
- Telemedicine in neurology: advances and possibilities.Arquivos de neuro-psiquiatria · 2022Review
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 one of the first diseases where digital biomarkers demonstrated excellent performance in differentiating disease from healthy individuals. However, no study has systematically compared and leveraged multiple types of digital biomarkers to predict PD. Particularly, machine learning works on the fine-motor skills of PD are limited. Here, we developed deep learning methods that achieved an AUC (Area Under the receiver operator characteristic Curve) of 0.933 in identifying PD patients on 6418 individuals using 75048 tapping accelerometer and position records. Performance of tapping is superior to gait/rest and voice-based models obtained from the same benchmark population. Assembling the three models achieved a higher AUC of 0.944. Notably, the models not only correlated strongly to, but also performed better than patient self-reported symptom scores in diagnosing PD. This study demonstrates the complementary predictive power of tapping, gait/rest and voice data and establishes integrative deep learning-based models for identifying PD.
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.