ArticleBrain communications2024
Deep learning-based patient stratification for prognostic enrichment of clinical dementia trials.
Article in Brain communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
17 citing papers in PubMed.
- Dead-Space Microdomains as Explicit Barriers to Extracellular Drug Transport in Alzheimer's Disease.Pharmaceutical research · 2026Article
- A dual space MRI radiomic network signature for risk stratification and subtyping of mild cognitive impairment.iScience · 2026Article
- Current progress in pathway-targeted therapeutics for Alzheimer's disease: mechanistic insights and windows of opportunity.Molecular and cellular biochemistry · 2026Review
- Stratification of Alzheimer's disease patients using knowledge-guided unsupervised latent factor clustering with electronic health record data.Communications medicine · 2026Article
- Clinical impact of the Alzheimer's Disease Neuroimaging Initiative: A review of studies using ADNI data (2023 to June 2025).Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Review
- Article
- Real-World Multimodal Machine Learning for Risk Enrichment Across the Alzheimer's Disease Spectrum.Journal of clinical medicine · 2026Article
- Machine-learning prediction and risk stratification of 12-month cognitive decline in Alzheimer's disease using routine clinical and MRI data.Scientific reports · 2026Article
- Should all MCI with Alzheimer's biological diagnosis receive anti-amyloid therapy?Cell death & disease · 2026Review
- The roles of biomarkers in Alzheimer's disease clinical trials.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2026Review
- FUSION-AD: interpretable AI framework for risk assessment and subgroup discovery in Alzheimer's disease.Frontiers in neuroinformatics · 2026Article
- Brain Latent Progression: Individual-based spatiotemporal disease progression on 3D Brain MRIs via latent diffusion.Medical image analysis · 2025Article
- Predicting future cognitive impairment in preclinical Alzheimer's disease using multimodal imaging: a multisite machine learning study.medRxiv : the preprint server for health sciences · 2025Article
- BrainAge moderates associations between Alzheimer's disease biomarkers and cognitive decline: a meta-analysis across A4/LEARN, HABS and ADNI cohorts.medRxiv : the preprint server for health sciences · 2025Article
- Deep representation learning for clustering longitudinal survival data from electronic health records.Nature communications · 2025Article
- Rethinking the residual approach: leveraging statistical learning to operationalize cognitive resilience in Alzheimer's disease.Brain informatics · 2025Article
- Rethinking the residual approach: Leveraging machine learning to operationalize cognitive resilience in Alzheimer's disease.medRxiv : the preprint server for health sciences · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Dementia probably due to Alzheimer's disease is a progressive condition that manifests in cognitive decline and impairs patients' daily life. Affected patients show great heterogeneity in their symptomatic progression, which hampers the identification of efficacious treatments in clinical trials. Using artificial intelligence approaches to enable clinical enrichment trials serves a promising avenue to identify treatments. In this work, we used a deep learning method to cluster the multivariate disease trajectories of 283 early dementia patients along cognitive and functional scores. Two distinct subgroups were identified that separated patients into 'slow' and 'fast' progressing individuals. These subgroups were externally validated and independently replicated in a dementia cohort comprising 2779 patients. We trained a machine learning model to predict the progression subgroup of a patient from cross-sectional data at their time of dementia diagnosis. The classifier achieved a prediction performance of 0.70 ± 0.01 area under the receiver operating characteristic curve in external validation. By emulating a hypothetical clinical trial conducting patient enrichment using the proposed classifier, we estimate its potential to decrease the required sample size. Furthermore, we balance the achieved enrichment of the trial cohort against the accompanied demand for increased patient screening. Our results show that enrichment trials targeting cognitive outcomes offer improved chances of trial success and are more than 13% cheaper compared with conventional clinical trials. The resources saved could be redirected to accelerate drug development and expand the search for remedies for cognitive impairment.
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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.