ArticleEJNMMI physics2023
Estimation of brain amyloid accumulation using deep learning in clinical [
Article in EJNMMI physics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 8 citations in OpenAlex.
- Hybrid CNN-Transformer Framework for Molecular Imaging-based Early Alzheimer's Disease Detection.Molecular imaging and biology · 2026Article
- SPECT and PET imaging of Alzheimer's disease revisited: from biomarkers to artificial intelligence-based prediction.Annals of nuclear medicine · 2026Review
- Inflammasomes meet organoids and artificial intelligence: unraveling the complexity of gynecological inflammation.Frontiers in immunology · 2026Review
- Influence of ethnicity on the diagnostic accuracy of plasma p-tau217 for the identification of Alzheimer's disease: A consecutive mixed memory clinic cohort study.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- The cerebral blood flow response to neuroactivation is reduced in cognitively normal men with β-amyloid accumulation.Alzheimer's research & therapy · 2025Article
- Multimodal MRI accurately identifies amyloid status in unbalanced cohorts in Alzheimer's disease continuum.Network neuroscience (Cambridge, Mass.) · 2025Article
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionEstimation of brain amyloid accumulation is valuable for evaluation of patients with cognitive impairment in both research and clinical routine. The development of high throughput and accurate strategies for the determination of amyloid status could be an important tool in patient selection for clinical trials and amyloid directed treatment. Here, we propose the use of deep learning to quantify amyloid accumulation using standardized uptake value ratio (SUVR) and classify amyloid status based on their PET images.
methodsA total of 1309 patients with cognitive impairment scanned with [
resultsThe prevalence of amyloid positive patients was 61%. The amyloid status classification model reproduced the expert reader's classification with 99% accuracy. There was a high correlation between reference and predicted SUVR (R
conclusionThe proposed CNN models reproduced both the expert classification and quantitative measure of amyloid accumulation in a large local dataset. This method has the potential to replace or simplify existing clinical routines and can facilitate fast and accurate classification well-suited for a high throughput pipeline.
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