Deep Learning-based Cardiac Microphysiological Systems For Studying Reentry Arrhythmia

Arrhythmias can be caused by reentry and focal activities, leading to a disruption of the normal sinus rhythm. Understanding these mechanisms and their contributions to arrhythmias in individual patients is crucial for developing effective treatments. To achieve this, in vitro human models based on induced pluripotent stem cell-derived cardiomyocytes have been developed to replicate arrhythmogenic activities and identify pathophysiological mechanisms specific to patients. However, the current methods for analyzing electrophysiology (EP) videos are limited and subjective, making the interpretation of clinical EP videos challenging. To address these limitations, we have developed a deep cardiac EP system called cEP-Net. The system comprises a cardiac microphysiological system designed for reproducing various forms of reentry and focal activities in vitro and a deep learning-based EP video classifier with a spatial and temporal streams architecture. The deep EP classifier analyzes cardiac EP videos, identifies arrhythmogenic activities, and categorizes videos into four categories based on their characteristics: (1) localized activities, (2) normal linear propagation, (3) focal activity, and (4) reentrant activity. We pre-trained cEP-Net using 111,110 annotated in vitro EP videos produced by a cardiac microphysiological system and evaluated its performance for in vitro and in vivo clinical settings. Our pre-trained cEP-Net demonstrated high Kappa (91.0%) and concordance rate (94.7%) for all categories. cEP-Net visualized in vitro EP videos in UMAP domains and successfully clustered them into four categories. Additionally, cEP-Net successfully captured the development and evolution of reentry during early in vitro cardiac tissue formation and the initiation of focal activities in the HCN4 (a pacemaker protein)-expressing tissues. We also used cEP-Net to quantify the arrhythmogenic risks of cardiac agents and found that cEP-Net could capture Flecainide-induced vulnerability to reentry. Lastly, we applied cEP-Net to analyze electroanatomical mapping videos in vivo. The cEP-Net demonstrated its capability to identify phase singularities in hearts with atrial fibrillation (AF) and capture the EP patterns of persistent AF hearts.