ArticleEBioMedicine2023
Visual deep learning of unprocessed neuroimaging characterises dementia subtypes and generalises across non-stereotypic samples.
Article in EBioMedicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic review: fluid biomarkers and machine learning methods to improve the diagnosis from mild cognitive impairment to Alzheimer's disease.Alzheimer's research & therapy · 2023Pooled it
- Hybrid multimodal late fusion frameworks for bvFTD classification in imbalanced dementia datasets.Frontiers in aging neuroscience · 2026Article
- Computational whole-body-exposome models for global precision brain health.Nature communications · 2025Article
- A computational ontology framework for the synthesis of multi-level pathology reports from brain MRI scans.Journal of Alzheimer's disease : JAD · 2025Article
- Creative experiences and brain clocks.Nature communications · 2025Article
- Social exposome and brain health outcomes of dementia across Latin America.Nature communications · 2025Article
- Structural inequality linked to brain volume and network dynamics in aging and dementia across the Americas.Nature aging · 2025Article
- Contrastive self-supervised learning for neurodegenerative disorder classification.Frontiers in neuroinformatics · 2025Article
- Predictive and Explainable Artificial Intelligence for Neuroimaging Applications.Diagnostics (Basel, Switzerland) · 2024Review
- Biomarkers of neurodegeneration across the Global South.The lancet. Healthy longevity · 2024Review
- Comparison of Explainable AI Models for MRI-based Alzheimer's Disease Classification.bioRxiv : the preprint server for biology · 2024Article
- A structural MRI marker predicts individual differences in impulsivity and classifies patients with behavioral-variant frontotemporal dementia from matched controls.bioRxiv : the preprint server for biology · 2024Article
- Educational disparities in brain health and dementia across Latin America and the United States.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- Robust and interpretable AI-guided marker for early dementia prediction in real-world clinical settings.EClinicalMedicine · 2024Article
- Computer-aided prognosis of tuberculous meningitis combining imaging and non-imaging data.Scientific reports · 2024Article
- Advancements in dementia research, diagnostics, and care in Latin America: Highlights from the 2023 Alzheimer's Association International conference satellite symposium in Mexico City.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Review
- A synergetic turn in cognitive neuroscience of brain diseases.Trends in cognitive sciences · 2024Review
- AI-based differential diagnosis of dementia etiologies on multimodal data.medRxiv : the preprint server for health sciences · 2024Article
- Walking the talk for dementia: A unique immersive, embodied, and multi-experiential initiative.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- Automated Detection of Cervical Spinal Stenosis and Cord Compression via Vision Transformer and Rules-Based Classification.AJNR. American journal of neuroradiology · 2024Article
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Authors and funding
6 authors.
Funding
Abstract
backgroundDementia's diagnostic protocols are mostly based on standardised neuroimaging data collected in the Global North from homogeneous samples. In other non-stereotypical samples (participants with diverse admixture, genetics, demographics, MRI signals, or cultural origins), classifications of disease are difficult due to demographic and region-specific sample heterogeneities, lower quality scanners, and non-harmonised pipelines.
methodsWe implemented a fully automatic computer-vision classifier using deep learning neural networks. A DenseNet was applied on raw (unpreprocessed) data from 3000 participants (behavioural variant frontotemporal dementia-bvFTD, Alzheimer's disease-AD, and healthy controls; both male and female as self-reported by participants). We tested our results in demographically matched and unmatched samples to discard possible biases and performed multiple out-of-sample validations.
findingsRobust classification results across all groups were achieved from standardised 3T neuroimaging data from the Global North, which also generalised to standardised 3T neuroimaging data from Latin America. Moreover, DenseNet also generalised to non-standardised, routine 1.5T clinical images from Latin America. These generalisations were robust in samples with heterogenous MRI recordings and were not confounded by demographics (i.e., were robust in both matched and unmatched samples, and when incorporating demographic variables in a multifeatured model). Model interpretability analysis using occlusion sensitivity evidenced core pathophysiological regions for each disease (mainly the hippocampus in AD, and the insula in bvFTD) demonstrating biological specificity and plausibility.
interpretationThe generalisable approach outlined here could be used in the future to aid clinician decision-making in diverse samples.
fundingThe specific funding of this article is provided in the acknowledgements section.
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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 Socratic graph.