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Home
Artificial Intelligence In Image-based Cardiovascular Disease Analysis.

Home
Artificial Intelligence In Image-based Cardiovascular Disease Analysis.

Related Experiment Video

Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis

Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis

Published on: September 26, 2018

Artificial Intelligence in Image-Based Cardiovascular Disease Analysis.

Xin Wang¹, Mingcheng Hu², Connie W Tsao³

¹1Department of Epidemiology and Biostatistics, College of Integrated Health Sciences and AI Plus Institute, University at Albany, SUNY, Albany, New York, USA;

Annual Review of Biomedical Data Science

|May 28, 2026

View abstract on PubMed

Summary

This summary is machine-generated.

Artificial intelligence (AI) is revolutionizing cardiovascular disease (CVD) analysis using medical imaging. This review explores AI

Related Experiment Videos

Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis

Hydra, a Computer-Based Platform for Aiding Clinicians in Cardiovascular Analysis and Diagnosis

Published on: September 26, 2018

Area of Science:

Cardiovascular Disease (CVD) Analysis
Artificial Intelligence (AI) in Medical Imaging
Diagnostic Technologies

Background:

Cardiovascular diseases (CVDs) remain a leading cause of mortality worldwide.
Traditional CVD analysis methods often rely on subjective interpretation of medical images.
Emerging AI technologies offer potential for more objective and efficient CVD assessment.

Purpose of the Study:

To provide a comprehensive review of current Artificial Intelligence (AI) applications in image-based cardiovascular disease (CVD) analysis.
To systematically categorize AI applications based on anatomical structures (nonvessel and vessel) and imaging modalities.
To identify challenges and future research directions in AI-driven CVD diagnostics.

Main Methods:

Systematic literature review of AI applications in cardiovascular imaging.

Categorization of studies based on anatomical focus: nonvessel structures (ventricles, atria) and vessel structures (aorta, coronary arteries).

Inclusion of various imaging modalities such as computed tomography (CT) and magnetic resonance imaging (MRI).

Main Results:

AI demonstrates significant influence in image-based cardiovascular disease (CVD) analysis.
Diverse AI applications identified across different anatomical structures and imaging techniques.
Review highlights the integration of AI with modalities like CT and MRI for enhanced CVD insights.

Conclusions:

AI holds substantial promise for advancing cardiovascular disease (CVD) diagnostics through medical image analysis.
Current AI methods face challenges including data variability and interpretability.
Future research should focus on addressing limitations to fully realize AI's potential in CVD care.