ArticleAlzheimer's research & therapy2022
Predicting conversion of brain β-amyloid positivity in amyloid-negative individuals.
Article in Alzheimer's research & therapy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed, 16 citations in OpenAlex.
- Donanemab exposure-response in early symptomatic Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Trial
- Double cutoff strategies for plasma pTau217 to predict Tau PET positivity across multiple assay platforms: Tau-enriched and Tau-scarce cohorts for cost-effective clinical use.Alzheimer's research & therapy · 2026Article
- Prognostic Value of A/T/N Biomarkers: Comparing Plasma and Imaging Modalities in Alzheimer Disease.Clinical nuclear medicine · 2025Article
- Diagnostic and prognostic multimodal prediction models in Alzheimer's disease: A scoping review.Journal of Alzheimer's disease : JAD · 2025Article
- Establishing Regional Aβ Cutoffs and Exploring Subgroup Prevalence Across Cognitive Stages Using BeauBrain AmyloDementia and neurocognitive disorders · 2025Article
- Alzheimer's Prediction Methods with Harris Hawks Optimization (HHO) and Deep Learning-Based Approach Using an MLP-LSTM Hybrid Network.Diagnostics (Basel, Switzerland) · 2025Article
- Predicting future amyloid conversion: the role of baseline amyloid distribution, genetic and cognitive resilience.NPJ dementia · 2025Article
- A Comparative Analysis of Two Automated Quantification Methods for Regional Cerebral Amyloid Retention: PET-Only and PET-and-MRI-Based Methods.International journal of molecular sciences · 2024Article
- Machine learning prediction of future amyloid beta positivity in amyloid-negative individuals.Alzheimer's research & therapy · 2024Article
- Deep learning-based quantification of brain atrophy using 2D T1-weighted MRI for Alzheimer's disease classification.Frontiers in aging neuroscience · 2024Article
- Identification of preclinical dementia according to ATN classification for stratified trial recruitment: A machine learning approach.PloS one · 2023Article
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCortical deposition of β-amyloid (Aβ) plaque is one of the main hallmarks of Alzheimer's disease (AD). While Aβ positivity has been the main concern so far, predicting whether Aβ (-) individuals will convert to Aβ (+) has become crucial in clinical and research aspects. In this study, we aimed to develop a classifier that predicts the conversion from Aβ (-) to Aβ (+) using artificial intelligence.
methodsData were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort regarding patients who were initially Aβ (-). We developed an artificial neural network-based classifier with baseline age, gender, APOE ε4 genotype, and global and regional standardized uptake value ratios (SUVRs) from positron emission tomography. Ten times repeated 10-fold cross-validation was performed for model measurement, and the feature importance was assessed. To validate the prediction model, we recruited subjects at the Samsung Medical Center (SMC).
resultsA total of 229 participants (53 converters) from the ADNI dataset and a total of 40 subjects (10 converters) from the SMC dataset were included. The average area under the receiver operating characteristic values of three developed models are as follows: Model 1 (age, gender, APOE ε4) of 0.674, Model 2 (age, gender, APOE ε4, global SUVR) of 0.814, and Model 3 (age, gender, APOE ε4, global and regional SUVR) of 0.841. External validation result showed an AUROC of 0.900.
conclusionWe developed prediction models regarding Aβ positivity conversion. With the growing recognition of the need for earlier intervention in AD, the results of this study are expected to contribute to the screening of early treatment candidates.
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