3D Myocardial Scar Prediction Model Derived from Multimodality Analysis of Electromechanical Mapping and Magnetic Resonance Imaging

Nuclear Cardiac Imaging in the Interventional Suite

PURPOSE OF REVIEW

This review presents the current state of imaging approaches that enable real-time molecular imaging in the interventional suite and discusses the potential future use of integrated nuclear imaging and fluoroscopy for intraprocedural guidance in the evaluation and treatment of both cardiovascular and oncological diseases.

RECENT FINDINGS

Although there are no commercially available real-time hybrid nuclear imaging devices that are approved for use in the interventional suite, prototype open gantry hybrid nuclear imaging and x-ray c-arm imaging systems and theranostic catheter for location radiotracer detection are currently undergoing development and testing by multiple groups. The integration of physiological and molecular targeted nuclear imaging for real-time delivery of targeted theranostics in the interventional laboratory may enable more personalized care for a wide variety of cardiovascular procedures and improve patient outcomes.

Big Data and Artificial Intelligence: Opportunities and Threats in Electrophysiology

The combination of big data and artificial intelligence (AI) is having an increasing impact on the field of electrophysiology. Algorithms are created to improve the automated diagnosis of clinical ECGs or ambulatory rhythm devices. Furthermore, the use of AI during invasive electrophysiological studies or combining several diagnostic modalities into AI algorithms to aid diagnostics are being investigated. However, the clinical performance and applicability of created algorithms are yet unknown. In this narrative review, opportunities and threats of AI in the field of electrophysiology are described, mainly focusing on ECGs. Current opportunities are discussed with their potential clinical benefits as well as the challenges. Challenges in data acquisition, model performance, (external) validity, clinical implementation, algorithm interpretation as well as the ethical aspects of AI research are discussed. This article aims to guide clinicians in the evaluation of new AI applications for electrophysiology before their clinical implementation.