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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
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Imaging-based enrichment criteria using deep learning algorithms for efficient clinical trials in mild cognitive
Vamsi K Ithapu1, Vikas Singh2, Ozioma C Okonkwo3
1Department of Computer Sciences, University of Wisconsin Madison, Madison, WI, USA; Wisconsin Alzheimer's Disease Research Center, University of Wisconsin Madison, Madison, WI, USA.
Summary
A new deep learning marker accurately predicts cognitive decline in mild cognitive impairment (MCI) patients with Alzheimer's disease (AD). This improves clinical trial efficiency by identifying individuals most likely to benefit from treatments.
Area of Science:
- Neuroscience
- Medical Imaging
- Artificial Intelligence
Background:
- Mild cognitive impairment (MCI) is a heterogeneous stage of Alzheimer's disease (AD).
- Identifying MCI patients likely to progress to dementia is crucial for efficient clinical trials.
- Current methods for patient selection in AD trials have limitations.
Purpose of the Study:
- To develop and validate a novel multimodal imaging marker for predicting cognitive and neural decline in MCI.
- To assess the marker's utility in improving clinical trial efficiency for AD treatments.
Main Methods:
- Utilized a deep learning algorithm (randomized denoising autoencoder marker, rDAm) on multimodal imaging data.
- Included [F-18]fluorodeoxyglucose positron emission tomography (PET), amyloid florbetapir PET, and structural MRI from the ADNI2 MCI cohort.
- Evaluated rDAm as a trial enrichment criterion.
Main Results:
- The rDAm marker effectively predicts future cognitive and neural decline in MCI patients.
- Employing rDAm as an enrichment criterion reduced required sample size by over fivefold compared to no enrichment.
- rDAm-enriched trials demonstrated high statistical power with smaller sample sizes.
Conclusions:
- A novel multimodal imaging marker (rDAm) can significantly enhance the efficiency of Alzheimer's disease clinical trials.
- rDAm facilitates the identification of MCI individuals most likely to progress, optimizing trial design.
- This approach promises smaller, more powerful trials for AD therapeutics.
