Physiologic pacing in congenitally corrected transposition of the great arteries with electroanatomic mapping guidance: a case report.

Background: This case report details the application of left bundle branch pacing in a patient with congenitally corrected transposition of the great arteries (cc-TGA), a rare congenital heart defect characterized by anatomical complexities that pose unique challenges in the management of device-related complications and heart failure. The patient's history is notable for complex anatomical considerations, cardiovascular implantable electronic device (CIED) infection, and heart failure.

Case Summary: The patient underwent a series of interventions, including treatment for pocket-site infections, abandonment of epicardial leads, and an unsuccessful attempt at trans-catheter leadless pacemaker implantation.

Multipolar Electrograms: A New Configuration That Increases the Measurement Accuracy of Intracardiac Signals.

Background: Accurate measurements of intracardiac electrograms (EGMs) remain a clinical challenge because of the suboptimal attenuation of far-field potentials. Multielectrode mapping catheters provide an opportunity to construct multipolar instead of bipolar EGMs for rejecting common far-field potentials recorded from a multivectorial space.

Objectives: The purpose of this study was to develop a multipolar EGM and compare its characteristics to those of bipolar EGMs METHODS: Using a 36-electrode array catheter (Optrell-36, Biosense Webster), a far-field component was mathematically constructed from clusters of electrodes surrounding each inspected electrode.

Deep learning in magnetic resonance enterography for Crohn's disease assessment: a systematic review.

Crohn's disease (CD) poses significant morbidity, underscoring the need for effective, non-invasive inflammatory assessment using magnetic resonance enterography (MRE). This literature review evaluates recent publications on the role of deep learning in improving MRE for CD assessment. We searched MEDLINE/PUBMED for studies that reported the use of deep learning algorithms for assessment of CD activity.