ArticleAlzheimer's & dementia : the journal of the Alzheimer's Association2019
A deep learning model for early prediction of Alzheimer's disease dementia based on hippocampal magnetic resonance imaging data.
Article in Alzheimer's & dementia : the journal of the Alzheimer's Association, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 106 papers, 3 of them syntheses that pooled it.
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Who cites it
106 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Multimodal AI for Alzheimer Disease Diagnosis: Systematic Review of Datasets, Models, and Modalities.Journal of medical Internet research · 2026Pooled it
- Deep neural network heatmaps capture Alzheimer's disease patterns reported in a large meta-analysis of neuroimaging studies.NeuroImage · 2023Pooled it
- Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer's disease dementia: a systematic review.Alzheimer's research & therapy · 2021Pooled it
- Advancing fair and explainable machine learning for neuroimaging dementia pattern classification in multi-racial and multi-ethnic populations.Nature communications · 2026Article
- Predicting categorical and continuous Alzheimer's disease outcomes from a single MRI scan.Nature aging · 2026Article
- 3D foundation model for generalizable disease detection in head computed tomography.Nature biomedical engineering · 2026Article
- Imaging-genetics-based dementia risk prediction using deep survival neural networks in the Rotterdam Study.Scientific reports · 2026Article
- Next generation preventive neurology: how artificial intelligence and machine learning are reshaping Alzheimer's disease research.Behavioral and brain functions : BBF · 2026Review
- Bidirectional prognostic predictions of conversion and reversion in amnestic mild cognitive impairment.GeroScience · 2026Article
- A physics-informed graph neural network to approximate docking-based binding affinity for DYRK2 in Alzheimer's drug repurposing.Scientific reports · 2026Article
- A multimodal sleep foundation model for disease prediction.Nature medicine · 2026Article
- Clinical translation of fluid, imaging, and digital biomarkers for Alzheimer's disease.Alzheimer's research & therapy · 2026Review
- A token efficient vision framework using patch residual transformer for Alzheimer's disease diagnosis.Scientific reports · 2026Article
- Normalizing flow based neural processes for Alzheimer's disease progression prediction.PloS one · 2026Article
- Forecasting individualized progression of Alzheimer's disease using structural MRI and population spatiotemporal priors.Frontiers in aging neuroscience · 2026Article
- Deep learning for Alzheimer's disease: advances in classification, segmentation, subtyping, and explainability.Biomedical engineering online · 2025Review
- Investigating the Amyloid-Tau-Neurodegeneration Framework in Alzheimer's Disease Using Semi-Supervised Multimodal Imaging Data Fusion.medRxiv : the preprint server for health sciences · 2025Article
- ConvNeXt-Driven Detection of Alzheimer's Disease: A Benchmark Study on Expert-Annotated AlzaSet MRI Dataset Across Anatomical Planes.Diagnostics (Basel, Switzerland) · 2025Article
- Comparative analysis of multiple deep learning models with mitigation-driven approaches for enhanced Alzheimer's disease classification.Scientific reports · 2025Article
- Imaging-genetics-based dementia risk prediction using deep survival neural networks in the Rotterdam Study.Research square · 2025Article
46 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
introductionIt is challenging at baseline to predict when and which individuals who meet criteria for mild cognitive impairment (MCI) will ultimately progress to Alzheimer's disease (AD) dementia.
methodsA deep learning method is developed and validated based on magnetic resonance imaging scans of 2146 subjects (803 for training and 1343 for validation) to predict MCI subjects' progression to AD dementia in a time-to-event analysis setting.
resultsThe deep-learning time-to-event model predicted individual subjects' progression to AD dementia with a concordance index of 0.762 on 439 Alzheimer's Disease Neuroimaging Initiative testing MCI subjects with follow-up duration from 6 to 78 months (quartiles: [24, 42, 54]) and a concordance index of 0.781 on 40 Australian Imaging Biomarkers and Lifestyle Study of Aging testing MCI subjects with follow-up duration from 18 to 54 months (quartiles: [18, 36, 54]). The predicted progression risk also clustered individual subjects into subgroups with significant differences in their progression time to AD dementia (P < .0002). Improved performance for predicting progression to AD dementia (concordance index = 0.864) was obtained when the deep learning-based progression risk was combined with baseline clinical measures. DISCUSSION: Our method provides a cost effective and accurate means for prognosis and potentially to facilitate enrollment in clinical trials with individuals likely to progress within a specific temporal period.
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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 OpenQuestion graph.