ArticleJournal of Alzheimer's disease : JAD2020
Predicting Amyloid-β Levels in Amnestic Mild Cognitive Impairment Using Machine Learning Techniques.
Article in Journal of Alzheimer's disease : JAD, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed, 38 citations in OpenAlex.
- Trial
- Diagnostic and prognostic multimodal prediction models in Alzheimer's disease: A scoping review.Journal of Alzheimer's disease : JAD · 2025Article
- Development of Simple Risk Scores for Prediction of Brain β-Amyloid and Tau Status in Older Adults With Mild Cognitive Impairment: A Machine Learning Approach.The journals of gerontology. Series B, Psychological sciences and social sciences · 2025Article
- A Machine Learning Approach to Predict Cognitive Decline in Alzheimer Disease Clinical Trials.Neurology · 2025Article
- Predicting amyloid beta accumulation in cognitively unimpaired older adults: Cognitive assessments provide no additional utility beyond demographic and genetic factors.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Machine learning models for dementia screening to classify brain amyloid positivity on positron emission tomography using blood markers and demographic characteristics: a retrospective observational study.Alzheimer's research & therapy · 2025Observational
- Development and assessment of algorithms for predicting brain amyloid positivity in a population without dementia.Alzheimer's research & therapy · 2024Article
- Contributions of the ADNI Biostatistics Core.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Review
- Brain structural indicators of β-amyloid neuropathology.Neurobiology of aging · 2024Article
- Amyloid-β prediction machine learning model using source-based morphometry across neurocognitive disorders.Scientific reports · 2024Article
- Comparison of deep learning architectures for predicting amyloid positivity in Alzheimer's disease, mild cognitive impairment, and healthy aging, from T1-weighted brain structural MRI.Frontiers in neuroscience · 2024Article
- Examining the diagnostic value of the mnemonic discrimination task for classification of cognitive status and amyloid-beta burden.Neuropsychologia · 2023Article
- Predicting positron emission tomography brain amyloid positivity using interpretable machine learning models with wearable sensor data and lifestyle factors.Alzheimer's research & therapy · 2023Observational
- Predicting amyloid-beta pathology in the general population.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Article
- Review
- PRedicting the EVolution of SubjectIvE Cognitive Decline to Alzheimer's Disease With machine learning: the PREVIEW study protocol.BMC neurology · 2023Article
- Do radiomics or diffusion-tensor images provide additional information to predict brain amyloid-beta positivity?Scientific reports · 2023Article
- Personalized Explanations for Early Diagnosis of Alzheimer's Disease Using Explainable Graph Neural Networks with Population Graphs.Bioengineering (Basel, Switzerland) · 2023Article
- Improved Prediction of Amyloid-β and Tau Burden Using Hippocampal Surface Multivariate Morphometry Statistics and Sparse Coding.Journal of Alzheimer's disease : JAD · 2023Article
- Predicting Amyloid Positivity in Cognitively Unimpaired Older Adults: A Machine Learning Approach Using A4 Data.Neurology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 5 institutions in 1 country.
Funding
Abstract
backgroundAmyloid-β positivity (Aβ+) based on PET imaging is part of the enrollment criteria for many of the clinical trials of Alzheimer's disease (AD), particularly in trials for amyloid-targeted therapy. Predicting Aβ positivity prior to PET imaging can decrease unnecessary patient burden and costs of running these trials.
objectiveThe aim of this study was to evaluate the performance of a machine learning model in estimating the individual risk of Aβ+ based on gold-standard of PET imaging.
methodsWe used data from an amnestic mild cognitive impairment (aMCI) subset of the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort to develop and validate the models. The predictors of Aβ status included demographic and ApoE4 status in all models plus a combination of neuropsychological tests (NP), MRI volumetrics, and cerebrospinal fluid (CSF) biomarkers.
resultsThe models that included NP and MRI measures separately showed an area under the receiver operating characteristics (AUC) of 0.74 and 0.72, respectively. However, using NP and MRI measures jointly in the model did not improve prediction. The models including CSF biomarkers significantly outperformed other models with AUCs between 0.89 to 0.92.
conclusionsPredictive models can be effectively used to identify persons with aMCI likely to be amyloid positive on a subsequent PET scan.
Indexed as
Identifiers
What OpenQuestion holds
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.