ArticleFrontiers in neuroscience2020
Deep Learning-Based Classification and Voxel-Based Visualization of Frontotemporal Dementia and Alzheimer's Disease.
Article in Frontiers in neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 3 of them syntheses that pooled it.
What it found
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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.
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Who cites it
27 citing papers in PubMed, 3 syntheses or guidelines pooled it, 68 citations in OpenAlex.
- Frontotemporal dementia: a systematic review of artificial intelligence approaches in differential diagnosis.Frontiers in aging neuroscience · 2025Pooled it
- Predictive modelling of brain disorders with magnetic resonance imaging: A systematic review of modelling practices, transparency, and interpretability in the use of convolutional neural networks.Human brain mapping · 2023Pooled it
- Interpretable machine learning for dementia: A systematic review.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Pooled it
- Structural MRI in frontotemporal dementia and Alzheimer's disease: stage-dependent atrophy patterns.Journal of neural transmission (Vienna, Austria : 1996) · 2026Review
- Cogninet: an explainable deep learning model for multi-class MRI-based Alzheimer's disease staging.International journal of computer assisted radiology and surgery · 2026Article
- Multimodal neuroimaging and AI integration in cognitive disorders: advances, challenges, and future directions for precision medicine.Psychoradiology · 2026Review
- The potential role of machine learning and deep learning in differential diagnosis of Alzheimer's disease and FTD using imaging biomarkers: A review.The neuroradiology journal · 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
- Prediction of difficulty in cryoballoon ablation with a three-dimensional deep learning model using polygonal mesh representation.Journal of arrhythmia · 2025Article
- Contrastive self-supervised learning for neurodegenerative disorder classification.Frontiers in neuroinformatics · 2025Article
- An Evolutionary Federated Learning Approach to Diagnose Alzheimer's Disease Under Uncertainty.Diagnostics (Basel, Switzerland) · 2025Article
- EEG-based classification of Alzheimer's disease and frontotemporal dementia: a comprehensive analysis of discriminative features.Cognitive neurodynamics · 2024Article
- AI-based differential diagnosis of dementia etiologies on multimodal data.Nature medicine · 2024Article
- AI-based differential diagnosis of dementia etiologies on multimodal data.medRxiv : the preprint server for health sciences · 2024Article
- Differential diagnosis of frontotemporal dementia subtypes with explainable deep learning on structural MRI.Frontiers in neuroscience · 2024Article
- Predicting Antiseizure Medication Treatment in Children with Rare Tuberous Sclerosis Complex-Related Epilepsy Using Deep Learning.AJNR. American journal of neuroradiology · 2023Article
- Deep grading for MRI-based differential diagnosis of Alzheimer's disease and Frontotemporal dementia.Artificial intelligence in medicine · 2023Article
- Diagnostic Efficacy and Clinical Relevance of Artificial Intelligence in Detecting Cognitive Decline.Cureus · 2023Review
- XGBoost-SHAP-based interpretable diagnostic framework for alzheimer's disease.BMC medical informatics and decision making · 2023Article
- Three-round learning strategy based on 3D deep convolutional GANs for Alzheimer's disease staging.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
Abstract
Frontotemporal dementia (FTD) and Alzheimer's disease (AD) have overlapping symptoms, and accurate differential diagnosis is important for targeted intervention and treatment. Previous studies suggest that the deep learning (DL) techniques have the potential to solve the differential diagnosis problem of FTD, AD and normal controls (NCs), but its performance is still unclear. In addition, existing DL-assisted diagnostic studies still rely on hypothesis-based expert-level preprocessing. On the one hand, it imposes high requirements on clinicians and data themselves; On the other hand, it hinders the backtracking of classification results to the original image data, resulting in the classification results cannot be interpreted intuitively. In the current study, a large cohort of 3D T1-weighted structural magnetic resonance imaging (MRI) volumes (
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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.