An Integrated Visual Analytics System for Studying Clinical Carotid Artery Plaques

TL;DR

This paper presents an integrated visual analytics system that combines visualization, physiological data analysis, and machine learning to assist clinicians in studying and diagnosing carotid artery plaques more effectively.

Contribution

The system uniquely integrates visualization, physiological indicators, and machine learning for comprehensive carotid plaque analysis, aiding clinical decision-making.

Findings

System effectively visualizes correlations between plaques and factors.

Machine learning enhances analysis of plaque components.

Case studies demonstrate clinical usefulness.

Abstract

Carotid artery plaques can cause arterial vascular diseases such as stroke and myocardial infarction, posing a severe threat to human life. However, the current clinical examination mainly relies on a direct assessment by physicians of patients' clinical indicators and medical images, lacking an integrated visualization tool for analyzing the influencing factors and composition of carotid artery plaques. We have designed an intelligent carotid artery plaque visual analysis system for vascular surgery experts to comprehensively analyze the clinical physiological and imaging indicators of carotid artery diseases. The system mainly includes two functions: First, it displays the correlation between carotid artery plaque and various factors through a series of information visualization methods and integrates the analysis of patient physiological indicator data. Second, it enhances the interface guidance analysis of the inherent correlation between the components of carotid artery plaque through machine learning and displays the spatial distribution of the plaque on medical images. Additionally, we conducted two case studies on carotid artery plaques using real data obtained from a hospital, and the results indicate that our designed carotid analysis system can effectively provide clinical diagnosis and treatment guidance for vascular surgeons.