Intraoperative characterization of cardiac tissue: the potential of light scattering spectroscopy.

Significance: Damage to the cardiac conduction system remains one of the most significant risks associated with surgical interventions to correct congenital heart disease. This work demonstrates how light-scattering spectroscopy (LSS) can be used to non-destructively characterize cardiac tissue regions.

Aim: To present an approach for associating tissue composition information with location-specific LSS data and further evaluate an LSS and machine learning system as a method for non-destructive tissue characterization.

3D models of the cardiac conduction system in healthy neonatal human hearts.

Iatrogenic damage to the cardiac conduction system (CCS) remains a significant risk during congenital heart surgery. Current surgical best practice involves using superficial anatomical landmarks to locate and avoid damaging the CCS. Prior work indicates inherent variability in the anatomy of the CCS and supporting tissues.

Toward cardiac tissue characterization using machine learning and light-scattering spectroscopy.

Significance: The non-destructive characterization of cardiac tissue composition provides essential information for both planning and evaluating the effectiveness of surgical interventions such as ablative procedures. Although several methods of tissue characterization, such as optical coherence tomography and fiber-optic confocal microscopy, show promise, many barriers exist that reduce effectiveness or prevent adoption, such as time delays in analysis, prohibitive costs, and limited scope of application. Developing a rapid, low-cost non-destructive means of characterizing cardiac tissue could improve planning, implementation, and evaluation of cardiac surgical procedures.