Application of Artificial Intelligence to Cardiovascular Computed Tomography

Area of Science:

• Radiology and Medical Imaging
• Cardiology
• Artificial Intelligence in Medicine

Background:

• Cardiovascular computed tomography (CT) is a rapidly advancing field with continuous innovation in image acquisition and analysis.
• Artificial intelligence (AI) offers significant potential for integration into clinical cardiovascular CT applications.

Purpose of the Study:

• To review the latest research on the application of deep learning (DL) in cardiovascular CT.
• To cover DL applications from image quality enhancement to automated image analysis.

Main Methods:

• Review of recent scientific literature on deep learning in cardiovascular CT.
• Focus on DL methods for image quality improvement.
• Analysis of DL for automated tasks including calcium scoring, image segmentation, and coronary artery evaluation.

Main Results:

• Deep learning demonstrates significant potential in improving cardiovascular CT image quality.
• AI-driven tools are emerging for automated analysis of cardiovascular CT data.
• Applications span from basic measurements to complex evaluations of cardiac structures and function.

Conclusions:

• Deep learning is a transformative technology for cardiovascular CT.
• AI integration promises more efficient and accurate cardiovascular CT interpretations.
• Future research will likely focus on further clinical validation and broader application of DL in cardiology.