ArticleFrontiers in neuroscience2023
Broad learning for early diagnosis of Alzheimer's disease using FDG-PET of the brain.
Article in Frontiers in neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Alzheimer's disease recognition via long-range state space model using multi-modal brain images.Frontiers in neuroscience · 2025Article
- Assessing the Diagnostic Utility of FDG-PET and HPLC for Detecting Microglia Activation and Inflammation in Alzheimer's Disease.Advanced biomedical research · 2025Review
- The Diagnostic Value of Cerebrospinal Fluid Neurogranin in Neurodegenerative Diseases.International journal of molecular sciences · 2024Review
- Article
- Explainable Vision Transformer with Self-Supervised Learning to Predict Alzheimer's Disease Progression Using 18F-FDG PET.Bioengineering (Basel, Switzerland) · 2023Article
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Authors and funding
6 authors.
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Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disease, and the development of AD is irreversible. However, preventive measures in the presymptomatic stage of AD can effectively slow down deterioration. Fluorodeoxyglucose positron emission tomography (FDG-PET) can detect the metabolism of glucose in patients' brains, which can help to identify changes related to AD before brain damage occurs. Machine learning is useful for early diagnosis of patients with AD using FDG-PET, but it requires a sufficiently large dataset, and it is easy for overfitting to occur in small datasets. Previous studies using machine learning for early diagnosis with FDG-PET have either involved the extraction of elaborately handcrafted features or validation on a small dataset, and few studies have explored the refined classification of early mild cognitive impairment (EMCI) and late mild cognitive impairment (LMCI). This article presents a broad network-based model for early diagnosis of AD (BLADNet) through PET imaging of the brain; this method employs a novel broad neural network to enhance the features of FDG-PET extracted
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