Deep learning-based multimodal risk stratification for atherosclerosis management.

ABSTRACT:

Introduction

Atherosclerosis is a leading cause of cardiovascular events, requiring accurate risk stratification. Traditional methods rely on subjective imaging and clinical scores, limiting precision.

Material and methods

We developed a deep learning (DL) model combining U-Net for lesion segmentation, ResNet for classification, and an attention mechanism to enhance detection of high-risk plaques. Multimodal data - including ultrasound, CTA, and clinical variables - underwent standard preprocessing. The dataset was split (8 : 1 : 1) and evaluated using 5-fold cross-validation.

Results

The U-Net achieved a Dice coefficient of 0.88. The ResNet, integrated with clinical features, reached 92% classification accuracy and an AUC of 0.97. The attention mechanism improved vul

SUBMITTER: Xu M 

PROVIDER: S-EPMC12703483 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature