ArticleEJNMMI research2021
Deep learning-based amyloid PET positivity classification model in the Alzheimer's disease continuum by using 2-[
Article in EJNMMI research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Hybrid CNN-Transformer Framework for Molecular Imaging-based Early Alzheimer's Disease Detection.Molecular imaging and biology · 2026Article
- Brain PET in the era of anti-amyloid-β antibody therapy for Alzheimer disease.Japanese journal of radiology · 2026Review
- RDoC-informed explainable AI as a paradigm for multilevel Alzheimer's disease diagnosis and progression prediction: a systematic review.Brain informatics · 2026Review
- Alzheimer's disease detection using a quantum deep neural network with Haralick feature extraction and simulated annealing optimization.PeerJ. Computer science · 2026Article
- The gut-brain axis in Alzheimer's and Parkinson's diseases: a comparative perspective.Frontiers in microbiomes · 2026Review
- Correlation and Interchangeability of Amyloid, Tau, and Glucose Metabolism PET in Mild Cognitive Impairment and Alzheimer: A Review.Brain sciences · 2025Review
- A Robust Residual Three-dimensional Convolutional Neural Networks Model for Prediction of Amyloid-β Positivity by Using FDG-PET.Clinical nuclear medicine · 2025Article
- Machine learning model for predicting Amyloid-β positivity and cognitive status using early-phaseScientific reports · 2025Article
- Multimodal MRI accurately identifies amyloid status in unbalanced cohorts in Alzheimer's disease continuum.Network neuroscience (Cambridge, Mass.) · 2025Article
- Review
- Toward AI-driven neuroepigenetic imaging biomarker for alcohol use disorder: A proof-of-concept study.iScience · 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
- Application of Deep Learning for Prediction of Alzheimer's Disease in PET/MR Imaging.Bioengineering (Basel, Switzerland) · 2023Review
- Generation of ConventionalMedicina (Kaunas, Lithuania) · 2023Article
- Classification of Alzheimer's Disease Using Maximal Information Coefficient-Based Functional Connectivity with an Extreme Learning Machine.Brain sciences · 2023Article
- Visualizing reactive astrocyte-neuron interaction in Alzheimer's disease using 11C-acetate and 18F-FDG.Brain : a journal of neurology · 2023Article
- Personalized Explanations for Early Diagnosis of Alzheimer's Disease Using Explainable Graph Neural Networks with Population Graphs.Bioengineering (Basel, Switzerland) · 2023Article
- Automatic Detection of Alzheimer's Disease using Deep Learning Models and Neuro-Imaging: Current Trends and Future Perspectives.Neuroinformatics · 2023Review
- Introducing a gatekeeping system for amyloid status assessment in mild cognitive impairment.European journal of nuclear medicine and molecular imaging · 2022Article
- Quantification of amyloid PET for future clinical use: a state-of-the-art review.European journal of nuclear medicine and molecular imaging · 2022Review
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13 authors.
Funding
Abstract
backgroundConsidering the limited accessibility of amyloid position emission tomography (PET) in patients with dementia, we proposed a deep learning (DL)-based amyloid PET positivity classification model from PET images with 2-deoxy-2-[fluorine-18]fluoro-D-glucose (2-[
methodsWe used 2-[
resultsThere were 686 out of 1433 (47.9%) and 50 out of 100 (50%) amyloid PET-positive participants in the training and internal validation datasets and the external validation datasets, respectively. With 50 times iterations of model training and validation, the model achieved an AUC of 0.811 (95% confidence interval (CI) of 0.803-0.819) and 0.798 (95% CI, 0.789-0.807) on the internal and external validation datasets, respectively. The area under the curve (AUC) was 0.860 when tested with the model with the highest value (0.864) on the external validation dataset. Moreover, it had 75.0% accuracy, 76.0% sensitivity, 74.0% specificity, and 75.0% F1-score. We found an overlap between the regions within the default mode network, thus generating high classification values.
conclusionThe proposed model based on the 2-[
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