Using AI-generated digital twins to boost clinical trial efficiency in Alzheimer's disease.

ABSTRACT:

Introduction

Machine learning models leverage baseline data to create artificial intelligence (AI)-generated digital twins (DTs)-individualized predictions of each participant's clinical outcomes if they had received placebo. Incorporating DTs may increase statistical power or reduce required sample sizes in Phase 2 or 3 trials, and therefore improve efficiency in Alzheimer's disease (AD) trials. Here we demonstrate these properties using data from an AD Phase 2 clinical trial (AWARE, NCT02880956).

Methods

A conditional restricted Boltzmann machine (CRBM) model was trained on historical clinical trials and observational data from 6736 unique subjects after data harmonization to generate DTs of participants from the AWARE trial. The AWARE trial enrolled 453 subjects with mild

SUBMITTER: Wang D 

PROVIDER: S-EPMC12639399 | biostudies-literature | 2025 Oct-Dec

REPOSITORIES: biostudies-literature