ArticleNPJ digital medicine2024
Constructing personalized characterizations of structural brain aberrations in patients with dementia using explainable artificial intelligence.
Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Personalized single-cell transcriptomics reveals molecular diversity in Alzheimer's disease.Nature communications · 2026Article
- NeuroFM: Toward Precision Neuroimaging with Foundation Models for Individualized Brain Health Estimation.medRxiv : the preprint server for health sciences · 2026Article
- Investigating the Utility of Explainable Artificial Intelligence for Neuroimaging-Based Dementia Diagnosis and Prognosis.Human brain mapping · 2026Article
- Brain age trajectories and cognition after stroke in two longitudinal cohorts.Brain communications · 2026Article
- Skull-stripping induces shortcut learning in MRI-based Alzheimer's disease classification.Insights into imaging · 2025Article
- Computational whole-body-exposome models for global precision brain health.Nature communications · 2025Article
- An unsupervised XAI framework for dementia detection with context enrichment.Scientific reports · 2025Article
- Predicting Mental and Neurological Illnesses Based on Cerebellar Normative Features.Biological psychiatry global open science · 2025Article
- Contrastive self-supervised learning for neurodegenerative disorder classification.Frontiers in neuroinformatics · 2025Article
- Review
- Deep learning interpretability in neuroimaging: A comprehensive survey and methodological recommendations.Imaging neuroscience (Cambridge, Mass.)Article
Corrections and comments
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Authors and funding
16 authors.
Funding
Abstract
Deep learning approaches for clinical predictions based on magnetic resonance imaging data have shown great promise as a translational technology for diagnosis and prognosis in neurological disorders, but its clinical impact has been limited. This is partially attributed to the opaqueness of deep learning models, causing insufficient understanding of what underlies their decisions. To overcome this, we trained convolutional neural networks on structural brain scans to differentiate dementia patients from healthy controls, and applied layerwise relevance propagation to procure individual-level explanations of the model predictions. Through extensive validations we demonstrate that deviations recognized by the model corroborate existing knowledge of structural brain aberrations in dementia. By employing the explainable dementia classifier in a longitudinal dataset of patients with mild cognitive impairment, we show that the spatially rich explanations complement the model prediction when forecasting transition to dementia and help characterize the biological manifestation of disease in the individual brain. Overall, our work exemplifies the clinical potential of explainable artificial intelligence in precision medicine.
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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.