A Deep Learning Model for the Identification of Active Contraction Properties of the Myocardium Using Limited Clinical Metrics

Abstract

Technological breakthroughs have enhanced our understanding of myocardial mechanics and physiological responses to detect early disease indicators. Using constitutive models to represent myocardium structure is critical for understanding the intricacies of such complex tissues. Several models have been developed to depict both passive response and active contraction of myocardium, however they require careful adjustment of material parameters for patient-specific scenarios and substantial time and computing resources. Thus, most models are unsuitable for employment outside of research. Deep learning (DL) has sparked interest in data-driven computational modeling for complex system analysis. We developed a DL model for assessing and forecasting the behavior of an active contraction model of the left ventricular (LV) myocardium under a patient-specific clinical setting. Our original technique analyzes a context in which clinical measures are limited: as model input, just a handful of clinical parameters and a pressure-volume (PV) loop are required. This technique aims to bridge the gap between theoretical calculations and clinical applications by allowing doctors to use traditional metrics without administering additional data and processing resources. Our DL model's main objectives are to produce a waveform of active contraction property that properly portrays patient-specific data during a cardiac cycle and to estimate fiber angles at the endocardium and epicardium. Our model accurately represented the mechanical response of the LV myocardium for various PV curves, and it applies to both idealized and patient-specific geometries. Integrating artificial intelligence with constitutive-based models allows for the autonomous selection of hidden model parameters and facilitates their application in clinical settings.