ArticleRadiology advances2024
Deep learning-based amyloid PET harmonization to predict cognitive decline in non-demented elderly.
Article in Radiology advances, 2024. 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.
- SPECT and PET imaging of Alzheimer's disease revisited: from biomarkers to artificial intelligence-based prediction.Annals of nuclear medicine · 2026Review
- Predicting future cognitive impairment in preclinical Alzheimer's disease using multimodal imaging: a multisite machine learning study.medRxiv : the preprint server for health sciences · 2025Article
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Authors and funding
9 authors.
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Abstract
Background: The robustness of conventional amyloid PET harmonization across tracers has been questioned. Purpose: To evaluate deep learning-based harmonization of amyloid PET in predicting conversion from cognitively unimpaired (CU) to mild cognitive impairment (MCI) and MCI to Alzheimer's disease (AD). Materials and Methods: We developed an amyloid PET-based deep-learning model to classify participants with a clinical diagnosis of AD-dementia vs CU across different tracers from the Alzheimer's Disease Neuroimaging Initiative (ADNI), Japanese ADNI, and Australian Imaging, Biomarker, and Lifestyle cohorts ( Results: DL-ADprob was independently prognostic in both ADNI-MCI ( Conclusion: Deep learning-based harmonization of amyloid PET improves cognitive decline prediction in non-demented elderly, suggesting it could complement conventional amyloid PET measures.
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