ArticleJapanese journal of radiology2025
Evaluation of amyloid PET positivity using machine learning on
Article in Japanese journal of radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Brain PET in the era of anti-amyloid-β antibody therapy for Alzheimer disease.Japanese journal of radiology · 2026Review
- Correlation and Interchangeability of Amyloid, Tau, and Glucose Metabolism PET in Mild Cognitive Impairment and Alzheimer: A Review.Brain sciences · 2025Review
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSince the approval of disease-modifying drugs for Alzheimer's disease, the demand for amyloid positron emission tomography (PET) scans, which are crucial for determining treatment eligibility, is expected to increase significantly. We thus investigated the ability of an algorithm to predict amyloid accumulation from
methodsWe analyzed the images of 194 subjects with cognitive disorders who had undergone brain FDG-PET, amyloid PET using Pittsburgh compound-B (
resultsWe observed 81.5% accuracy, 78.5% sensitivity, 84.6% specificity, and an area under the curve (AUC) of 0.846 during training. The validation results for the trained model revealed 85.9% accuracy, 88.4% sensitivity, 81.0% specificity, and an AUC of 0.918.
conclusionThese results indicate that the performance of our algorithm to predict amyloid accumulation from
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