ArticleNPJ digital medicine2024
Diagnostic performance of artificial intelligence-assisted PET imaging for Parkinson's disease: a systematic review and meta-analysis.
Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 4 of them syntheses that pooled it.
What it found
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Who cites it
20 citing papers in PubMed, 4 syntheses or guidelines pooled it, 35 citations in OpenAlex.
- Detection of parkinson's disease with neuroimaging modalities using machine learning and artificial intelligence: a systematic review.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026Pooled 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
- Pooled it
- Stage-stratified benefits of AI-radiomics PET in early Alzheimer's disease: a systematic review and meta-analysis.Frontiers in neurologyPooled it
- A review of FDG-PET biomarker analysis methods in Parkinson's disease.Journal of Parkinson's disease · 2026Review
- Advances in biomarkers for Parkinson's disease: from molecular pathology to precision diagnostics.Journal of Zhejiang University. Science. B · 2026Review
- Current Trends in AI Gait Analysis for the Detection and Assessment of Parkinson's Disease Severity: Systematic Review and Meta-Analysis of Performance Using Logit Transformation.Healthcare (Basel, Switzerland) · 2026Review
- Enhancing interpretability of AI with radiomics-based deep neural network: proof of concept in the classification of Parkinsonian syndromes withEuropean journal of nuclear medicine and molecular imaging · 2026Article
- Multimodal brain network topology and enhanced computer-aided diagnosis in Parkinson's Disease: a systematic review and meta-analysis.NPJ digital medicine · 2026Article
- Multimodal data-driven eye-movement subtypes and their cerebral glucose metabolic patterns in Parkinson's disease.Frontiers in aging neuroscience · 2026Article
- Enhancing 3D dopamine transporter imaging as a biomarker for Parkinson's disease via self-supervised learning with diffusion models.Cell reports. Medicine · 2025Article
- Baseline [NPJ Parkinson's disease · 2025Article
- Cross-modality PET image synthesis for Parkinson's Disease diagnosis: a leap from [European journal of nuclear medicine and molecular imaging · 2025Article
- Applications of machine learning for computer-aided diagnosis of Parkinson's disease: progress and benchmark case study.Artificial intelligence review · 2025Article
- Explainable graph neural network based on metabolic brain imaging for differential diagnosis of parkinsonism.Frontiers in aging neuroscience · 2025Article
- Biomarker-Based Approach to α-Synucleinopathies: Lessons from Neuropathology.Movement disorders : official journal of the Movement Disorder Society · 2024Article
- Recent Breakthroughs in PET-CT Multimodality Imaging: Innovations and Clinical Impact.Bioengineering (Basel, Switzerland) · 2024Review
- Radiomics-Guided Deep Learning Networks Classify Differential Diagnosis of Parkinsonism.Brain sciences · 2024Article
- Gastric Emptying Scintigraphy Protocol Optimization Using Machine Learning for the Detection of Delayed Gastric Emptying.Diagnostics (Basel, Switzerland) · 2024Article
- A brief reference to AI-driven audible reality (AuRa) in open world: potential, applications, and evaluation.Frontiers in artificial intelligence · 2024Article
Corrections and comments
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Authors and funding
16 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI)-assisted PET imaging is emerging as a promising tool for the diagnosis of Parkinson's disease (PD). We aim to systematically review the diagnostic accuracy of AI-assisted PET in detecting PD. The Ovid MEDLINE, Ovid Embase, Web of Science, and IEEE Xplore databases were systematically searched for related studies that developed an AI algorithm in PET imaging for diagnostic performance from PD and were published by August 17, 2023. Binary diagnostic accuracy data were extracted for meta-analysis to derive outcomes of interest: area under the curve (AUC). 23 eligible studies provided sufficient data to construct contingency tables that allowed the calculation of diagnostic accuracy. Specifically, 11 studies were identified that distinguished PD from normal control, with a pooled AUC of 0.96 (95% CI: 0.94-0.97) for presynaptic dopamine (DA) and 0.90 (95% CI: 0.87-0.93) for glucose metabolism (
Identifiers
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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.