Enhanced stroke risk prediction in hypertensive patients through deep learning integration of imaging and clinical data.

ABSTRACT:

Background

Stroke is one of the leading causes of death and disability worldwide, with a significantly elevated incidence among individuals with hypertension. Conventional risk assessment methods primarily rely on a limited set of clinical parameters and often exclude imaging-derived structural features, resulting in suboptimal predictive accuracy.

Objective

This study aimed to develop a deep learning-based multimodal stroke risk prediction model by integrating carotid ultrasound imaging with multidimensional clinical data to enable precise identification of high-risk individuals among hypertensive patients.

Methods

A total of 2,176 carotid artery ultrasound images from 1,088 hypertensive patients were collected. ResNet50 was employed to automatically segment the caro

SUBMITTER: Li H 

PROVIDER: S-EPMC12315267 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature