ArticleJournal of neurology2019
Classification of degenerative parkinsonism subtypes by support-vector-machine analysis and striatal
Article in Journal of neurology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
What it found
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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.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it, 40 citations in OpenAlex.
- Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature.Frontiers in aging neuroscience · 2021Pooled it
- Imaging of dopamine transporters in Parkinson disease: a meta-analysis ofAnnals of clinical and translational neurology · 2020Pooled it
- Semiquantitative [¹²³I]FP-CIT SPECT metrics combined with machine learning improve clinical differentiation of Parkinson's disease and atypical parkinsonian syndrome.European journal of nuclear medicine and molecular imaging · 2026Article
- Characterization of open-source BSREM reconstruction forPhysical and engineering sciences in medicine · 2026Article
- Feasibility Study of Deep Learning-Driven Image Restoration for Fast, Low-Count [Diagnostics (Basel, Switzerland) · 2026Article
- Performance evaluation of a clinical SPECT system with multifocal collimator for brain studies.Medical physics · 2026Article
- Molecular Imaging in Parkinsonian Disorders-What's New and Hot?Brain sciences · 2022Review
- Machine Learning for Early Parkinson's Disease Identification within SWEDD Group Using Clinical and DaTSCAN SPECT Imaging Features.Journal of imaging · 2022Article
- Explainable AI to improve acceptance of convolutional neural networks for automatic classification of dopamine transporter SPECT in the diagnosis of clinically uncertain parkinsonian syndromes.European journal of nuclear medicine and molecular imaging · 2022Article
- Differentiation of Parkinson's disease and Parkinsonism predominant multiple system atrophy in early stage by morphometrics in susceptibility weighted imaging.Frontiers in human neuroscience · 2022Article
- Temporal Progression Patterns of Brain Atrophy in Corticobasal Syndrome and Progressive Supranuclear Palsy Revealed by Subtype and Stage Inference (SuStaIn).Frontiers in neurology · 2022Article
- Single Photon Emission Computed Tomography/Positron Emission Tomography Molecular Imaging for Parkinsonism: A Fast-Developing Field.Annals of neurology · 2021Review
- Artificial intelligence for molecular neuroimaging.Annals of translational medicine · 2021Review
- Computer-Aided Classification Framework of Parkinsonian Disorders UsingFrontiers in aging neuroscience · 2021Article
- Using Artificial Neural Network to Discriminate Parkinson's Disease from Other Parkinsonisms by Focusing on Putamen of Dopamine Transporter SPECT Images.Biomedicines · 2020Article
- Appropriate assessment method ofJournal of neurology · 2020Article
- A Shape Approximation for Medical Imaging Data.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 3 countries.
Funding
Abstract
objectivesTo provide an automated classification method for degenerative parkinsonian syndromes (PS) based on semiquantitative
methods
resultsUnivariate analyses showed significantly lower VOIs uptake, higher striatal AI and C/P ratio for each PS in comparison to controls (all p < 0.001). Among PS, higher degree of striatal impairment was observed in MSA-P and PSP, while CBS showed moderate uptake reduction and higher AI. Binary SVM classification showed 92.9% accuracy in distinguishing PS from controls. Classification based on each binary combination of PS ranged 62.9-83.7% accuracy with the most satisfactory results when separating CBS from the other PS. Sensitivity and specificity values were high and balanced ranging from 60 to 80% for all analyses with > 70% accuracy. Overall, striatal AI and C/P ratio on the more affected side had the highest weighting factors.
conclusionSemiquantitative
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.