ArticleAlzheimer's & dementia : the journal of the Alzheimer's Association2015
Imaging-based enrichment criteria using deep learning algorithms for efficient clinical trials in mild cognitive impairment.
Article in Alzheimer's & dementia : the journal of the Alzheimer's Association, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
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Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review and Implementation of IoT-Based Pervasive Sensor-Enabled Tracking System for Dementia Patients.Journal of medical systems · 2019Pooled it
- Predicting categorical and continuous Alzheimer's disease outcomes from a single MRI scan.Nature aging · 2026Article
- Harnessing artificial intelligence to improve clinical trial design.Communications medicine · 2023Article
- Article
- Artificial intelligence for reducing the radiation burden of medical imaging for the diagnosis of coronavirus disease.European physical journal plus · 2023Review
- Dimension constraints improve hypothesis testing for large-scale, graph-associated, brain-image data.Biostatistics (Oxford, England) · 2022Article
- Limitations of clinical trial sample size estimate by subtraction of two measurements.Statistics in medicine · 2022Article
- A Deep Spatiotemporal Attention Network for Mild Cognitive Impairment Identification.Frontiers in aging neuroscience · 2022Article
- A SuperLearner Approach to Predict Run-In Selection in Clinical Trials.Computational and mathematical methods in medicine · 2022Article
- Recent Technical Advances in Accelerating the Clinical Translation of Small Animal Brain Imaging: Hybrid Imaging, Deep Learning, and Transcriptomics.Frontiers in medicine · 2022Review
- Predicting amyloid positivity in patients with mild cognitive impairment using a radiomics approach.Scientific reports · 2021Article
- Application and Construction of Deep Learning Networks in Medical Imaging.IEEE transactions on radiation and plasma medical sciences · 2021Article
- Automatic Prediction of Cognitive and Functional Decline Can Significantly Decrease the Number of Subjects Required for Clinical Trials in Early Alzheimer's Disease.Journal of Alzheimer's disease : JAD · 2021Article
- Machine-learning classification of texture features of portable chest X-ray accurately classifies COVID-19 lung infection.Biomedical engineering online · 2020Article
- A Gaussian-based model for early detection of mild cognitive impairment using multimodal neuroimaging.Journal of neuroscience methods · 2020Article
- A 5-min Cognitive Task With Deep Learning Accurately Detects Early Alzheimer's Disease.Frontiers in aging neuroscience · 2020Article
- Cox Proportional Hazard Regression Versus a Deep Learning Algorithm in the Prediction of Dementia: An Analysis Based on Periodic Health Examination.JMIR medical informatics · 2019Article
- A deep learning model for early prediction of Alzheimer's disease dementia based on hippocampal magnetic resonance imaging data.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2019Article
- Deep learning only by normal brain PET identify unheralded brain anomalies.EBioMedicine · 2019Article
- Recent Advances of Deep Learning in Bioinformatics and Computational Biology.Frontiers in genetics · 2019Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
The mild cognitive impairment (MCI) stage of Alzheimer's disease (AD) may be optimal for clinical trials to test potential treatments for preventing or delaying decline to dementia. However, MCI is heterogeneous in that not all cases progress to dementia within the time frame of a trial and some may not have underlying AD pathology. Identifying those MCIs who are most likely to decline during a trial and thus most likely to benefit from treatment will improve trial efficiency and power to detect treatment effects. To this end, using multimodal, imaging-derived, inclusion criteria may be especially beneficial. Here, we present a novel multimodal imaging marker that predicts future cognitive and neural decline from [F-18]fluorodeoxyglucose positron emission tomography (PET), amyloid florbetapir PET, and structural magnetic resonance imaging, based on a new deep learning algorithm (randomized denoising autoencoder marker, rDAm). Using ADNI2 MCI data, we show that using rDAm as a trial enrichment criterion reduces the required sample estimates by at least five times compared with the no-enrichment regime and leads to smaller trials with high statistical power, compared with existing methods.
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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.