SynthesisFrontiers in aging neuroscience2021
Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature.
Synthesis in Frontiers in aging neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 102 papers, 5 of them syntheses that pooled it.
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
102 citing papers in PubMed, 5 syntheses or guidelines pooled it, 360 citations in OpenAlex.
- Machine learning and wearable sensors for automated Parkinson's disease diagnosis aid: a systematic review.Journal of neurology · 2024Pooled it
- The Role of Deep Learning and Gait Analysis in Parkinson's Disease: A Systematic Review.Sensors (Basel, Switzerland) · 2024Pooled it
- Machine learning for the detection and diagnosis of cognitive impairment in Parkinson's Disease: A systematic review.PloS one · 2024Pooled it
- The differential diagnosis value of radiomics-based machine learning in Parkinson's disease: a systematic review and meta-analysis.Frontiers in aging neuroscience · 2023Pooled it
- Internet of Things Technologies and Machine Learning Methods for Parkinson's Disease Diagnosis, Monitoring and Management: A Systematic Review.Sensors (Basel, Switzerland) · 2022Pooled it
- A Platform for Automatic Extraction of Imaging Biomarkers in CT Scans from Ischemic and Hemorrhagic Stroke Patients.Journal of imaging informatics in medicine · 2026Article
- Bio-Inspired Deep Learning for Parkinson's Disease Detection: A Comparative Study Based on Vocal Biomarkers and Archimedean Spiral Analysis.Biomimetics (Basel, Switzerland) · 2026Article
- Surface Electromyography for Parkinson's Disease Monitoring: A Review of Machine and Deep Learning Techniques.Sensors (Basel, Switzerland) · 2026Review
- Gradient boosting machine model predicts psychiatric complications after deep brain stimulation in Parkinson's disease.World journal of psychiatry · 2026Article
- Toward future diagnostics of Parkinson's disease: a perspective on multimodal motor assessment and personalized digital twins.Frontiers in aging neuroscience · 2026Article
- Novel applications of machine learning and computational neuroscience models to neuroimaging in Parkinson's disease and related disorders.Frontiers in aging neuroscience · 2026Review
- Mitochondria as a Therapeutic Target in Neurodegeneration: Strategies for Restoring Cellular Homeostasis.Current neuropharmacology · 2026Review
- Multi-omics identification of novel biomarkers and therapeutic targets for Parkinson's disease: from transcriptome to drug interaction.Frontiers in aging neuroscience · 2026Article
- Subitem-level multi-scale assessment and machine learning for three-class cognitive status classification in Parkinson's disease.NPJ Parkinson's disease · 2025Article
- Applications of Artificial Intelligence in Neurological Voice Disorders.World journal of otorhinolaryngology - head and neck surgery · 2025Review
- Voice-Based Detection of Parkinson's Disease Using Machine and Deep Learning Approaches: A Systematic Review.Bioengineering (Basel, Switzerland) · 2025Review
- Bridging Clinical Expertise and Digital Innovation: Toward a Modern Phenomenology of Parkinsonism.Movement disorders clinical practice · 2025Article
- Multi-modal deep learning framework for early detection of Parkinson's disease using neurological and physiological data for high-fidelity diagnosis.Scientific reports · 2025Article
- Multi-Channel Spectro-Temporal Representations for Speech-Based Parkinson's Disease Detection.Journal of imaging · 2025Article
- Transcriptomic Analysis Reveals the Molecular Relationship Between Common Respiratory Infections and Parkinson's Disease.Current issues in molecular biology · 2025Article
42 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diagnosis of Parkinson's disease (PD) is commonly based on medical observations and assessment of clinical signs, including the characterization of a variety of motor symptoms. However, traditional diagnostic approaches may suffer from subjectivity as they rely on the evaluation of movements that are sometimes subtle to human eyes and therefore difficult to classify, leading to possible misclassification. In the meantime, early non-motor symptoms of PD may be mild and can be caused by many other conditions. Therefore, these symptoms are often overlooked, making diagnosis of PD at an early stage challenging. To address these difficulties and to refine the diagnosis and assessment procedures of PD, machine learning methods have been implemented for the classification of PD and healthy controls or patients with similar clinical presentations (e.g., movement disorders or other Parkinsonian syndromes). To provide a comprehensive overview of data modalities and machine learning methods that have been used in the diagnosis and differential diagnosis of PD, in this study, we conducted a literature review of studies published until February 14, 2020, using the PubMed and IEEE Xplore databases. A total of 209 studies were included, extracted for relevant information and presented in this review, with an investigation of their aims, sources of data, types of data, machine learning methods and associated outcomes. These studies demonstrate a high potential for adaptation of machine learning methods and novel biomarkers in clinical decision making, leading to increasingly systematic, informed diagnosis of 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.