Machine learning-enhanced screening funnel for clinical trials in Alzheimer's disease.

ABSTRACT:

Introduction

Alzheimer's disease (AD) clinical trials with therapeutic interventions require hundreds of subjects to be studied over many months/years due to variable and slow disease progression. This article presents a novel screening paradigm integrating disease progression models to improve trial efficiency by identifying appropriate candidates for early phase clinical studies.

Methods

A traditional screening funnel is enhanced using machine learning models, including 3D convolutional neural networks and ensemble models, which integrate neuroimaging, demographic, genetic, and clinical data.

Results

This approach predicts clinical progression (2-year Clinical Dementia Rating Sum of Boxes change > 1) with an area under the curve of 0.836. Incorporating it into trial

SUBMITTER: Gladstein S 

PROVIDER: S-EPMC12019303 | biostudies-literature | 2025 Apr-Jun

REPOSITORIES: biostudies-literature