ArticleAnnals of nuclear medicine2022
Diagnosis of Parkinson syndrome and Lewy-body disease using
Article in Annals of nuclear medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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
4 citing papers in PubMed.
- Accelerating imaging: deep learning for enhancedJapanese journal of radiology · 2026Article
- Examination of simple artificial intelligence-based analysis of dopamine transporter scintigraphy for supporting a diagnosis of Parkinson's disease.Annals of nuclear medicine · 2026Article
- Review
- Specific Binding Ratio Estimation of [Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
objectives
methodsWe assessed
resultsThe AUC was high with all ML methods (0.92-0.96) for high or low uptake. The AUC was the highest for symmetry or asymmetry with the kNN method (AUC 0.75) and the comma-dot feature with the GBT method (AUC 0.94). Based on the test data set, the diagnostic accuracy for a diagnosis of PS/PD/DLB was 0.86 ± 0.04 (SE), 0.87 ± 0.04, and 0.93 ± 0.02 for Models 1, 2 and 3, respectively. The AUC was optimal for Model 3, and significantly differed between Models 3 and 1 (p = 0.027), and 3 and 2 (p = 0.029).
conclusionsImage features such as high or low uptake, symmetry or asymmetry, and comma- or dot-like profiles can be determined using ML. The diagnostic accuracy of differentiating PS/PD/DLB was the highest for the multivariate model with three features and age compared with the conventional ROI-based method.
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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.