Machine Learning in Invasive and Noninvasive Coronary Angiography

Area of Science:

• Cardiovascular Medicine
• Medical Imaging
• Artificial Intelligence

Background:

• Coronary artery disease (CAD) remains a significant health challenge, often underdiagnosed and undertreated despite advancements in imaging.
• Machine learning (ML) offers powerful capabilities to enhance the analysis of cardiovascular imaging and data.

Purpose of the Study:

• To review the transformative potential of machine learning (ML) in coronary angiography.
• To explore current ML applications in coronary angiography and their implications for cardiovascular medicine.

Main Methods:

• Review of existing literature on ML applications in coronary angiography.
• Identification and discussion of key use cases for ML in the field.

Main Results:

• ML improves image analysis, hemodynamic assessment, lesion detection, and predictive modeling in coronary angiography.
• ML streamlines workflows, reduces the need for invasive procedures, and enhances diagnostic accuracy.
• Five key use cases for ML in coronary angiography are identified: quality improvement, plaque characterization, hemodynamic assessment, outcome prediction, and diagnosis of non-atherosclerotic disease.

Conclusions:

• The integration of ML with coronary angiography represents a new era in cardiovascular medicine.
• ML advancements are crucial for improving the prevention, diagnosis, and treatment of CAD.
• Further research and integration of ML will optimize patient outcomes in cardiovascular care.