ArticleTranslational psychiatry2024
A multimodal deep learning approach for the prediction of cognitive decline and its effectiveness in clinical trials for Alzheimer's disease.
Article in Translational psychiatry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
What it found
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Who cites it
16 citing papers in PubMed, 28 citations in OpenAlex.
- Multimodal Assessment of Consciousness with Brain-Computer Interfaces and Artificial Intelligence: From Acquired Brain Injury to Neurodegenerative Disease.Journal of clinical medicine · 2026Review
- Predicting categorical and continuous Alzheimer's disease outcomes from a single MRI scan.Nature aging · 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
- Machine-learning prediction and risk stratification of 12-month cognitive decline in Alzheimer's disease using routine clinical and MRI data.Scientific reports · 2026Article
- Integrating anatomical priors and clinical semantics for MRI-based diagnosis and care support in Alzheimer's disease.Frontiers in neuroscience · 2026Article
- Multimodal neuroimaging and AI integration in cognitive disorders: advances, challenges, and future directions for precision medicine.Psychoradiology · 2026Review
- Flexible Multimodal Neuroimaging Fusion for Alzheimer's Disease Progression Prediction.Applications of medical artificial intelligence. AMAI (Workshop) (4th : 2024 : Taejon-si, Korea) · 2026Article
- 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
- 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
- Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions.Acta pharmaceutica Sinica. B · 2025Review
- Attention-driven hybrid deep learning and SVM model for early Alzheimer's diagnosis using neuroimaging fusion.BMC medical informatics and decision making · 2025Article
- Limitations of Correlation Coefficients in Research on Functional Connectomes and Psychological Processes.Human brain mapping · 2025Review
- Regression Trees With Fused Leaves.Statistics in medicine · 2024Article
- Rationale and design of the BeyeOMARKER study: prospective evaluation of blood- and eye-based biomarkers for early detection of Alzheimer's disease pathology in the eye clinic.Alzheimer's research & therapy · 2024Observational
- Investigating the amyloid-tau-neurodegeneration framework in Alzheimer's disease using semi-supervised multimodal imaging data fusion.Alzheimer's & dementia (Amsterdam, Netherlands)Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Alzheimer's disease is one of the most important health-care challenges in the world. For decades, numerous efforts have been made to develop therapeutics for Alzheimer's disease, but most clinical trials have failed to show significant treatment effects on slowing or halting cognitive decline. Among several challenges in such trials, one recently noticed but unsolved is biased allocation of fast and slow cognitive decliners to treatment and placebo groups during randomization caused by the large individual variation in the speed of cognitive decline. This allocation bias directly results in either over- or underestimation of the treatment effect from the outcome of the trial. In this study, we propose a stratified randomization method using the degree of cognitive decline predicted by an artificial intelligence model as a stratification index to suppress the allocation bias in randomization and evaluate its effectiveness by simulation using ADNI data set.
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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.