Application of AI in cardiovascular multimodality imaging

Area of Science:

• Cardiovascular Imaging
• Artificial Intelligence in Medicine
• Medical Report Generation

Background:

• Cardiac imaging techniques like CCTA, CMR, and echocardiography are crucial for diagnosing heart conditions.
• Current interpretation workflows can be time-consuming and prone to variability.
• Artificial intelligence (AI) offers potential solutions to enhance efficiency and reproducibility.

Purpose of the Study:

• To provide a comprehensive overview of current AI applications in multimodality cardiac imaging.
• To highlight how AI improves the speed and accuracy of cardiac image analysis.
• To discuss AI's role in CCTA, CMR, and echocardiography for clinical practice.

Main Methods:

• Review of existing literature on AI applications in cardiac computed tomography angiography (CCTA).
• Review of existing literature on AI applications in cardiac magnetic resonance (CMR).
• Review of existing literature on AI applications in echocardiography.

Main Results:

• AI in CCTA aids in detecting stenosis, characterizing plaques, and identifying myocardial ischemia.
• AI in CMR focuses on post-processing, particularly cardiac chamber segmentation during late gadolinium enhancement.
• AI in echocardiography assists with chamber segmentation, valvular function assessment, and wall motion abnormality detection.

Conclusions:

• AI significantly improves the reproducibility and reduces interpretation time in cardiac imaging.
• AI applications across CCTA, CMR, and echocardiography aim to streamline reporting without information loss.
• AI is a transformative technology in multimodality cardiac imaging, enhancing clinical workflows.