ReviewNPJ Parkinson's disease2022
Mining imaging and clinical data with machine learning approaches for the diagnosis and early detection of Parkinson's disease.
Review in NPJ Parkinson's disease, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 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
25 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Diagnostic performance of PET-based artificial intelligence for differentiating Parkinson's disease from normal controls or atypical parkinsonism: a systematic review and meta-analysis.Journal of neurology · 2025Pooled 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
- Towards real-time additive-free dopamine detection at 10Nature communications · 2026Article
- Artificial intelligence and multiomics integration for Parkinson's disease drug development.Molecules and cells · 2026Review
- Quantitative susceptibility mapping and MRS-based multimodal machine learning for early Parkinson's disease classification.NPJ Parkinson's disease · 2026Article
- Habitat-based MRI radiomics for enhanced parkinson's diagnosis.Scientific reports · 2026Article
- Structural and Statistical Knowledge-Enhanced Attention Network for early Parkinson's disease diagnosis.Quantitative imaging in medicine and surgery · 2026Article
- Deep Learning to Differentiate Parkinsonian Syndromes Using Multimodal Magnetic Resonance Imaging: A Proof-of-Concept Study.Movement disorders : official journal of the Movement Disorder Society · 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
- Parkinson's Disease: Bridging Gaps, Building Biomarkers, and Reimagining Clinical Translation.Cells · 2025Review
- Brain structural features with functional priori to classify Parkinson's disease and multiple system atrophy using diagnostic MRI.Scientific reports · 2025Article
- Using machine learning to identify Parkinson's disease severity subtypes with multimodal data.Journal of neuroengineering and rehabilitation · 2025Article
- Article
- Applications of machine learning for computer-aided diagnosis of Parkinson's disease: progress and benchmark case study.Artificial intelligence review · 2025Article
- Machine learning-guided analysis of metabolomic alterations in Parkinson's disease with comorbid symptoms.Frontiers in aging neuroscience · 2025Article
- Hot Topics and Frontiers of Resting-State fMRI in Parkinson's Disease: Research Trends and Paradigm Shifts From a Bibliometric Perspective.Parkinson's disease · 2025Review
- Artificial General Intelligence for the Detection of Neurodegenerative Disorders.Sensors (Basel, Switzerland) · 2024Review
- Machine Learning Models for Parkinson Disease: Systematic Review.JMIR medical informatics · 2024Review
- Exploiting macro- and micro-structural brain changes for improved Parkinson's disease classification from MRI data.NPJ Parkinson's disease · 2024Article
- Detecting Parkinson's disease from shoe-mounted accelerometer sensors using convolutional neural networks optimized with modified metaheuristics.PeerJ. Computer science · 2024Article
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Parkinson's disease (PD) is a common, progressive, and currently incurable neurodegenerative movement disorder. The diagnosis of PD is challenging, especially in the differential diagnosis of parkinsonism and in early PD detection. Due to the advantages of machine learning such as learning complex data patterns and making inferences for individuals, machine-learning techniques have been increasingly applied to the diagnosis of PD, and have shown some promising results. Machine-learning-based imaging applications have made it possible to help differentiate parkinsonism and detect PD at early stages automatically in a number of neuroimaging studies. Comparative studies have shown that machine-learning-based SPECT image analysis applications in PD have outperformed conventional semi-quantitative analysis in detecting PD-associated dopaminergic degeneration, performed comparably well as experts' visual inspection, and helped improve PD diagnostic accuracy of radiologists. Using combined multi-modal (imaging and clinical) data in these applications may further enhance PD diagnosis and early detection. To integrate machine-learning-based diagnostic applications into clinical systems, further validation and optimization of these applications are needed to make them accurate and reliable. It is anticipated that machine-learning techniques will further help improve differential diagnosis of parkinsonism and early detection of PD, which may reduce the error rate of PD diagnosis and help detect PD at pre-motor stage to make it possible for early treatments (e.g., neuroprotective treatment) to slow down PD progression, prevent severe motor symptoms from emerging, and relieve patients from suffering.
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.