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A multimodal deep learning approach for the prediction of cognitive decline and its effectiveness in clinical trials for Alzheimer’s disease

Medicine and Health

A multimodal deep learning approach for the prediction of cognitive decline and its effectiveness in clinical trials for Alzheimer’s disease

C. Wang, H. Tachimori, et al.

This innovative study, conducted by Caihua Wang, Hisateru Tachimori, Hiroyuki Yamaguchi, Atsushi Sekiguchi, Yuanzhong Li, and Yuichi Yamashita, presents a groundbreaking AI-driven method to enhance the randomization process in Alzheimer's disease clinical trials, significantly reducing participant allocation bias and trial size. Discover how they utilized a multimodal deep learning model to predict cognitive decline accurately!

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Playback language: English
Abstract
Clinical trials for Alzheimer's disease (AD) often fail due to biased allocation of participants with varying cognitive decline rates. This study proposes a stratified randomization method using an AI model to predict cognitive decline, aiming to reduce allocation bias and improve trial efficiency. A multimodal deep learning model predicts CDR-SB changes using brain images and clinical data. Simulations using the ADNI dataset show that AI-based randomization reduces allocation bias by approximately 22%, leading to a nearly 37% reduction in sample size.
Publisher
Translational Psychiatry
Published On
Feb 21, 2024
Authors
Caihua Wang, Hisateru Tachimori, Hiroyuki Yamaguchi, Atsushi Sekiguchi, Yuanzhong Li, Yuichi Yamashita
DOI
https://doi.org/10.1038/s41398-024-02819-w
Tags
Alzheimer's disease
clinical trials
AI model
stratified randomization
cognitive decline
deep learning
allocation bias

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