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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~3 min • Beginner • English

Index

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.

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

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