Artificial intelligence in nuclear cardiology: Enhancing diagnostic accuracy and efficiency

Area of Science:

• Cardiovascular Imaging
• Artificial Intelligence
• Nuclear Cardiology

Background:

• Cardiovascular imaging is complex, posing challenges in image acquisition, reconstruction, and interpretation.
• Artificial intelligence (AI) offers solutions to enhance efficiency and accuracy in medical imaging.

Purpose of the Study:

• To review recent advances in AI applications within nuclear cardiology.
• To outline the potential of AI to transform clinical workflows.
• To discuss future directions for AI integration in routine practice.

Main Methods:

• Review of recent literature on AI in nuclear cardiology.
• Analysis of AI's role in image optimization, virtual attenuation correction, and automated quantification.
• Exploration of machine learning and deep learning for diagnosis and risk stratification.

Main Results:

• AI enhances image quality, reduces radiation exposure, and improves efficiency in nuclear cardiology.
• AI enables automated quantification of risk markers and integration of multimodal data for diagnosis.
• Deep learning models provide direct diagnostic and risk stratification estimates from images.

Conclusions:

• AI is poised to revolutionize nuclear cardiology by optimizing image analysis and risk assessment.
• Integration of AI tools can significantly improve clinical workflows and patient outcomes.
• Further research and development are crucial for widespread AI adoption in cardiovascular imaging.