ArticleJournal of digital imaging2023
An Explainable Convolutional Neural Network for the Early Diagnosis of Alzheimer's Disease from 18F-FDG PET.
Article in Journal of digital imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 36 citations in OpenAlex.
- From Single-Modal to Multi-Modal Artificial Intelligence in Alzheimer's Disease: A Systematic Review of Databases, Modalities, Diagnostic Performance, and Clinical Translation Challenges.Sensors (Basel, Switzerland) · 2026Pooled it
- RDoC-informed explainable AI as a paradigm for multilevel Alzheimer's disease diagnosis and progression prediction: a systematic review.Brain informatics · 2026Review
- Integration of AI diagnostic tools into clinical practice for Alzheimer's disease: barriers and solutions.Annals of medicine and surgery (2012) · 2026Review
- Feature Integration of [medRxiv : the preprint server for health sciences · 2026Article
- Use of explainable AI (xAI) in dementia detection and prognosis: a scoping review.BMC medical informatics and decision making · 2026Article
- Feature integration of [18F]FDG PET brain imaging using deep learning for sensitive cognitive decline detection.PloS one · 2026Article
- A deep learning-based computational pipeline predicts developmental outcome in retinal organoids.PLoS biology · 2026Article
- AI-enabled multimodal neuroimaging for neurotransmitter mapping in normal aging and age-related disease.Frontiers in aging neuroscience · 2026Review
- An unsupervised XAI framework for dementia detection with context enrichment.Scientific reports · 2025Article
- Diagnosis of Alzheimer's disease using brain [Formula: see text]-FDG PET imaging based on a state space model.Scientific reports · 2025Article
- Explainable Artificial Intelligence in Neuroimaging of Alzheimer's Disease.Diagnostics (Basel, Switzerland) · 2025Review
- Highly sensitive early-onset Alzheimer's disease: a case report.Frontiers in psychology · 2025Article
- An Evolutionary Federated Learning Approach to Diagnose Alzheimer's Disease Under Uncertainty.Diagnostics (Basel, Switzerland) · 2025Article
- Enhancing early detection of Alzheimer's disease through hybrid models based on feature fusion of multi-CNN and handcrafted features.Scientific reports · 2024Article
- Explainable Machine Learning Models for Brain Diseases: Insights from a Systematic Review.Neurology international · 2024Review
- An Explainable AI Paradigm for Alzheimer's Diagnosis Using Deep Transfer Learning.Diagnostics (Basel, Switzerland) · 2024Article
- Deep learning combining FDG-PET and neurocognitive data accurately predicts MCI conversion to Alzheimer's dementia 3-year post MCI diagnosis.Neurobiology of disease · 2023Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
Convolutional Neural Networks (CNN) which support the diagnosis of Alzheimer's Disease using 18F-FDG PET images are obtaining promising results; however, one of the main challenges in this domain is the fact that these models work as black-box systems. We developed a CNN that performs a multiclass classification task of volumetric 18F-FDG PET images, and we experimented two different post hoc explanation techniques developed in the field of Explainable Artificial Intelligence: Saliency Map (SM) and Layerwise Relevance Propagation (LRP). Finally, we quantitatively analyze the explanations returned and inspect their relationship with the PET signal. We collected 2552 scans from the Alzheimer's Disease Neuroimaging Initiative labeled as Cognitively Normal (CN), Mild Cognitive Impairment (MCI), and Alzheimer's Disease (AD) and we developed and tested a 3D CNN that classifies the 3D PET scans into its final clinical diagnosis. The model developed achieves, to the best of our knowledge, performances comparable with the relevant literature on the test set, with an average Area Under the Curve (AUC) for prediction of CN, MCI, and AD 0.81, 0.63, and 0.77 respectively. We registered the heatmaps with the Talairach Atlas to perform a regional quantitative analysis of the relationship between heatmaps and PET signals. With the quantitative analysis of the post hoc explanation techniques, we observed that LRP maps were more effective in mapping the importance metrics in the anatomic atlas. No clear relationship was found between the heatmap and the PET signal.
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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.