ReviewFrontiers in aging neuroscience2023
Machine learning models for diagnosis and prognosis of Parkinson's disease using brain imaging: general overview, main challenges, and future directions.
Review in Frontiers in aging neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
What it found
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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
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Performance of machine learning algorithms in diffusion tensor imaging of movement disorders: an exploratory meta-analysis.Biomedical engineering online · 2026Pooled it
- Molecularly Engineered Phenoxazinone-Skeleton Cascade-Activated NIR Probes for Monitoring FeAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- AGMNet: an explainable gated multimodal framework integrating MRI heterogeneity and dopaminergic asymmetry for Parkinson's disease classification.Frontiers in artificial intelligence · 2026Article
- Machine learning identifies lactate metabolism biomarkers and deciphers immune infiltration landscapes in Parkinson's disease.Scientific reports · 2025Article
- Predicting Parkinson's disease and its progression based on radiomics in T1-weight images and α‑synuclein in cerebrospinal fluid.NPJ Parkinson's disease · 2025Article
- Artificial Intelligence-Driven Neuromodulation in Neurodegenerative Disease: Precision in Chaos, Learning in Loss.Biomedicines · 2025Review
- Review
- Machine Learning Predicts Risk of Falls in Parkison's Disease Patients in a Multicenter Observational Study.European journal of neurology · 2025Observational
- Explainable artificial intelligence to diagnose early Parkinson's disease via voice analysis.Scientific reports · 2025Article
- Integrating Wearable Sensor Signal Processing with Unsupervised Learning Methods for Tremor Classification in Parkinson's Disease.Bioengineering (Basel, Switzerland) · 2025Article
- Research hotspots and future trends of insomnia in Parkinson's disease: a bibliometric and visualization analysis from 1973 to 2024.Frontiers in aging neuroscience · 2025Article
- Diagnosis and Severity Rating of Parkinson's Disease Based on Multimodal Gait Signal Analysis With GLRT and ST-CNN-Transformer Networks.IEEE journal of translational engineering in health and medicine · 2025Article
- Review
- Language Modeling Screens Parkinson's Disease with Self-reported Questionnaires.medRxiv : the preprint server for health sciences · 2024Article
- Alpha-Synuclein Gene Alterations Modulate Tyrosine Hydroxylase in Human iPSC-Derived Neurons in a Parkinson's Disease Animal Model.Life (Basel, Switzerland) · 2024Article
- Distributed Harmonization: Federated Clustered Batch Effect Adjustment and Generalization.KDD : proceedings. International Conference on Knowledge Discovery & Data Mining · 2024Article
- Exploring the application and challenges of fNIRS technology in early detection of Parkinson's disease.Frontiers in aging neuroscience · 2024Article
- Article
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Parkinson's disease (PD) is a progressive and complex neurodegenerative disorder associated with age that affects motor and cognitive functions. As there is currently no cure, early diagnosis and accurate prognosis are essential to increase the effectiveness of treatment and control its symptoms. Medical imaging, specifically magnetic resonance imaging (MRI), has emerged as a valuable tool for developing support systems to assist in diagnosis and prognosis. The current literature aims to improve understanding of the disease's structural and functional manifestations in the brain. By applying artificial intelligence to neuroimaging, such as deep learning (DL) and other machine learning (ML) techniques, previously unknown relationships and patterns can be revealed in this high-dimensional data. However, several issues must be addressed before these solutions can be safely integrated into clinical practice. This review provides a comprehensive overview of recent ML techniques analyzed for the automatic diagnosis and prognosis of PD in brain MRI. The main challenges in applying ML to medical diagnosis and its implications for PD are also addressed, including current limitations for safe translation into hospitals. These challenges are analyzed at three levels: disease-specific, task-specific, and technology-specific. Finally, potential future directions for each challenge and future perspectives are discussed.
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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.