Current status and future directions in artificial intelligence for nuclear cardiology

Area of Science:

• Nuclear Cardiology
• Medical Imaging
• Artificial Intelligence

Background:

• Myocardial perfusion imaging (MPI) is a common cardiac test requiring expert manual processing for accurate results.
• Technical challenges in motion correction, image registration, and reconstruction impact MPI quality.
• Accurate MPI processing integrates diverse data for patient management.

Purpose of the Study:

• To outline the role of artificial intelligence (AI) in nuclear cardiology.
• To review AI applications in MPI motion correction, registration, and reconstruction.
• To discuss AI's potential in extracting anatomic data and integrating information for improved diagnosis and risk stratification.

Main Methods:

• Literature review of AI in nuclear cardiology (2020-2024) on PubMed and Google Scholar.
• Identification of AI solutions for MPI image acquisition and processing.
• Analysis of AI methods for data integration and clinical application.

Main Results:

• AI shows promise in automating and enhancing MPI image acquisition and reconstruction.
• AI can extract neglected anatomic data for hybrid MPI.
• AI facilitates the integration of comprehensive data for improved clinical decision-making.

Conclusions:

• AI is poised to transform MPI performance through automation and improved image quality.
• Understanding AI's strengths is crucial for physicians and researchers to maximize MPI's clinical utility.
• AI integration in MPI offers significant potential for advancing cardiac diagnostics and patient care.