ArticleFrontiers in aging neuroscience2021
Computer-Aided Classification Framework of Parkinsonian Disorders Using
Article 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 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.
- 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
- Comparison betweenQuantitative imaging in medicine and surgery · 2026Article
- Q.Clear reconstruction shortening the scanning time forEJNMMI physics · 2026Article
- The value of PET/CT in the diagnosis and differential diagnosis of Parkinson's disease: a dual-tracer study.NPJ Parkinson's disease · 2024Article
- Evaluation of damage discrimination in dopaminergic neurons using dopamine transporter PET tracer [EJNMMI research · 2024Article
- Toward AI-driven neuroepigenetic imaging biomarker for alcohol use disorder: A proof-of-concept study.iScience · 2024Article
- Diagnostic performance of artificial intelligence-assisted PET imaging for Parkinson's disease: a systematic review and meta-analysis.NPJ digital medicine · 2024Article
- Identification of Parkinson's disease and multiple system atrophy using multimodal PET/MRI radiomics.European radiology · 2024Article
- Auto-Classification of Parkinson's Disease with Different Motor Subtypes Using Arterial Spin Labelling MRI Based on Machine Learning.Brain sciences · 2023Article
Corrections and comments
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Authors and funding
11 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo investigate the usefulness of a novel computer-aided classification framework for the differential diagnosis of parkinsonian disorders (PDs) based on
methodsPatients with different forms of PDs-including Parkinson's disease (PD), multiple system atrophy (MSA) and progressive supranuclear palsy (PSP)-underwent dopamine transporter (DAT) imaging with
resultsAccuracy values for identifying PD, MSA, and PSP were 85.0, 82.2, and 89.7%, respectively-with an overall accuracy of 80.4%. The caudate and putamen provided the highest diagnostic relevance to the proposed classification framework, whereas the contribution of midbrain was negligible. With the exception of sensitivity for diagnosing PSP, the strategy comprising both early and advanced disease stages performed better in terms of sensitivity, specificity, positive predictive value, and negative predictive value within each PDs subtype.
conclusionsThe proposed computer-aided classification framework based on
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Registered trials
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