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Multimodal Study of Murine Cardiovascular Remodeling: Four-Dimensional Ultrasound and Mass Spectrometry Imaging

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Published on: January 10, 2025

Non-invasive Multimodal Cardiovascular Disease Detection Method Based on Comprehensive View Analysis.

Yining Xie¹, Kaiwen Zhang², Jun Long²

¹College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin, China. yiningxie@nefu.edu.cn.

Journal of Imaging Informatics in Medicine

|July 7, 2026

Summary

This study introduces a novel multimodal approach for cardiovascular disease (CVD) detection using retinal images and clinical data. The method enhances diagnostic accuracy by integrating multi-modal information for comprehensive CVD progression prediction.

Keywords:

Cardiovascular disease (CVD)Clinical indicator Comprehensive view analysis Multi-order belief interaction fusion Retinal fundus images

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Multimodal Study of Murine Cardiovascular Remodeling: Four-Dimensional Ultrasound and Mass Spectrometry Imaging

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Published on: September 20, 2024

Area of Science:

Ophthalmology
Cardiology
Medical Imaging
Artificial Intelligence

Background:

Cardiovascular disease (CVD) diagnosis is increasingly linked to retinal fundus images and clinical indicators.
Current diagnostic methods often rely on single-modal data, limiting comprehensive CVD progression prediction.
There is a need for non-invasive methods that integrate multi-modal information for improved CVD detection.

Purpose of the Study:

To develop and validate a non-invasive multimodal method for cardiovascular disease (CVD) detection.
To enhance CVD diagnosis by integrating retinal fundus images and clinical indicators.
To improve the prediction of CVD progression through comprehensive multi-modal analysis.

Main Methods:

A two-branch architecture was designed to extract features from retinal fundus images and clinical indicators separately.
A comprehensive view analysis incorporating vascular segmentation maps was used for retinal image feature extraction.
A multi-order belief interaction feature fusion method, utilizing basic belief assignment and Dempster-Shafer theory, was employed for two-stage fusion of multi-modal features.

Main Results:

The developed multimodal model achieved an Area Under the Curve (AUC) of 0.873 (95% CI, 0.855-0.892).
The model obtained a Precision-Recall (PR) score of 0.896 (95% CI, 0.882-0.91).
Performance metrics were superior to single-modality methods and traditional fusion techniques, demonstrating the efficacy of the proposed approach.

Conclusions:

The proposed non-invasive multimodal CVD detection method effectively integrates retinal fundus images and clinical indicators.
The comprehensive view analysis and multi-order belief interaction fusion enhance the complementarity of multimodal information.
This approach offers a promising tool for more accurate and comprehensive cardiovascular disease diagnosis and progression prediction.