ArticleAlzheimer's & dementia (Amsterdam, Netherlands)2021
Deep learning improves utility of tau PET in the study of Alzheimer's disease.
Article in Alzheimer's & dementia (Amsterdam, Netherlands), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 16 citations in OpenAlex.
- SPECT and PET imaging of Alzheimer's disease revisited: from biomarkers to artificial intelligence-based prediction.Annals of nuclear medicine · 2026Review
- Deep Learning-Based Classification of Temporal Stages of AT8-Labeled Tau Pathology After Experimental Traumatic Brain Injury.Neuroinformatics · 2026Article
- LNODE: Uncovering the Latent Dynamics ofMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2026Article
- Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions.Acta pharmaceutica Sinica. B · 2025Review
- Brain tau PET-based identification and characterization of subpopulations in patients with Alzheimer's disease using deep learning-derived saliency maps.EJNMMI physics · 2025Article
- A review of the flortaucipir literature for positron emission tomography imaging of tau neurofibrillary tangles.Brain communications · 2024Review
- Advancing Tau-PET quantification in Alzheimer's disease with machine learning: introducing THETA, a novel tau summary measure.Research square · 2023Article
- Application of Deep Learning for Prediction of Alzheimer's Disease in PET/MR Imaging.Bioengineering (Basel, Switzerland) · 2023Review
- Improved interpretation ofiScience · 2023Article
- Deep learning application for the classification of Alzheimer's disease usingScientific reports · 2023Article
- Deep learning improves utility of tau PET in the study of Alzheimer's disease.Alzheimer's & dementia (Amsterdam, Netherlands) · 2021Article
- Classifying mild cognitive impairment from normal cognition: fMRI complexity matches tau PET performance.Alzheimer's & dementia (Amsterdam, Netherlands)Article
- Deep learning interpretability in neuroimaging: A comprehensive survey and methodological recommendations.Imaging neuroscience (Cambridge, Mass.)Article
Corrections and comments
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Authors and funding
13 authors at 3 institutions in 1 country.
Funding
Abstract
introductionPositron emission tomography (PET) imaging targeting neurofibrillary tau tangles is increasingly used in the study of Alzheimer's disease (AD), but its utility may be limited by conventional quantitative or qualitative evaluation techniques in earlier disease states. Convolutional neural networks (CNNs) are effective in learning spatial patterns for image classification.
methods18F-MK6240 (n = 320) and AV-1451 (n = 446) PET images were pooled from multiple studies. We performed iterations with differing permutations of radioligands, heuristics, and architectures. Performance was compared to a standard region of interest (ROI)-based approach on prediction of memory impairment. We visualized attention of the network to illustrate decision making.
resultsOverall, models had high accuracy (> 80%) with good average sensitivity and specificity (75% and 82%, respectively), and had comparable or higher accuracy to the ROI standard. Visualizations of model attention highlight known characteristics of tau radioligand binding. DISCUSSION: CNNs could improve tau PET's role in early disease and extend the utility of tau PET across generations of radioligands.
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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.