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

• Published in: Scientific reports Vol. 14; no. 1; p. 1782
• Main Authors: Burattini, Margherita, Lo Muzio, Francesco Paolo, Hu, Mirko, Bonalumi, Flavia, Rossi, Stefano, Pagiatakis, Christina, Salvarani, Nicolò, Fassina, Lorenzo, Luciani, Giovanni Battista, Miragoli, Michele
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 20-01-2024; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/1305; 631/114/1564; 631/114/794; 631/532; 639/166/985; 692/4019; Algorithms; Animals; Biomechanical Phenomena; Cardiomyocytes; Computer vision; Depolarization; Heart; Humanities and Social Sciences; Kinematics; Learning algorithms; Machine Learning; Mice; multidisciplinary; Myocytes, Cardiac - physiology; Open source software; Science; Science (multidisciplinary); Software; Ventricle
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-52081-9

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.