Video analysis of ex vivo beating hearts during preservation on the TransMedics® organ care system

Unlocking cardiac motion: assessing software and machine learning for single-cell and cardioid kinematic insights

The heart coordinates its functional parameters for optimal beat-to-beat mechanical activity. Reliable detection and quantification of these parameters still represent a hot topic in cardiovascular research. Nowadays, computer vision allows the development of open-source algorithms to measure cellular kinematics. However, the analysis software can vary based on analyzed specimens. In this study, we compared different software performances in in-silico model, in-vitro mouse adult ventricular cardiomyocytes and cardioids. We acquired in-vitro high-resolution videos during suprathreshold stimulation at 0.5-1-2 Hz, adapting the protocol for the cardioids. Moreover, we exposed the samples to inotropic and depolarizing substances. We analyzed in-silico and in-vitro videos by (i) MUSCLEMOTION, the gold standard among open-source software; (ii) CONTRACTIONWAVE, a recently developed tracking software; and (iii) ViKiE, an in-house customized video kinematic evaluation software. We enriched the study with three machine-learning algorithms to test the robustness of the motion-tracking approaches. Our results revealed that all software produced comparable estimations of cardiac mechanical parameters. For instance, in cardioids, beat duration measurements at 0.5 Hz were 1053.58 ms (MUSCLEMOTION), 1043.59 ms (CONTRACTIONWAVE), and 937.11 ms (ViKiE). ViKiE exhibited higher sensitivity in exposed samples due to its localized kinematic analysis, while MUSCLEMOTION and CONTRACTIONWAVE offered temporal correlation, combining global assessment with time-efficient analysis. Finally, machine learning reveals greater accuracy when trained with MUSCLEMOTION dataset in comparison with the other software (accuracy > 83%). In conclusion, our findings provide valuable insights for the accurate selection and integration of software tools into the kinematic analysis pipeline, tailored to the experimental protocol.

Biorobotic hybrid heart as a benchtop cardiac mitral valve simulator

In this work, we developed a high-fidelity beating heart simulator that provides accurate mitral valve pathophysiology. The benchtop platform is based on a biorobotic hybrid heart that combines preserved intracardiac tissue with soft robotic cardiac muscle providing dynamic left ventricular motion and precise anatomical features designed for testing intracardiac devices, particularly for mitral valve repair. The heart model is integrated into a mock circulatory loop, and the active myocardium drives fluid circulation producing physiological hemodynamics without an external pulsatile pump. Using biomimetic soft robotic technology, the heart can replicate both ventricular and septal wall motion, as well as intraventricular pressure-volume relationships. This enables the system to recreate the natural motion and function of the mitral valve, which allows us to demonstrate various surgical and interventional techniques. The biorobotic cardiovascular simulator allows for real-time hemodynamic data collection, direct visualization of the intracardiac procedure, and compatibility with clinical imaging modalities.