[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124755-en":3,"doc-seo-124755-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124755,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Unlocking cardiac motion - assessing software and machine learning for single-cell and cardioid kinematic insights","Cardiac motion parameters underpin beat-to-beat mechanical activity, yet reliable detection and quantification remain challenging across specimen types. This work compares open-source motion-tracking software and machine-learning robustness across in-silico models, in-vitro adult mouse ventricular cardiomyocytes, and cardioids. High-resolution videos from suprathreshold stimulation at 0.5–1–2 Hz were analyzed using MUSCLEMOTION, CONTRACTIONWAVE, and ViKiE, with additional testing under inotropic and depolarizing compounds. Software outputs showed comparable parameter estimates; ViKiE improved sensitivity, while ML accuracy exceeded 83% when trained on MUSCLEMOTION.","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nUnlocking cardiac motion: assessing software and machine learning for single‑cell and cardioid kinematic insights  \nMargherita Burattini1,2, Francesco Paolo Lo Muzio2,3, Mirko Hu2, Flavia Bonalumi2  \n,  \nStefano Rossi2, Christina Pagiatakis4,5, Nicolò Salvarani4,6, Lorenzo Fassina7, Giovanni Battista Luciani1 & Michele Miragoli2,4*  \nThe 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.  \nThe beating heart is the result of optimal electromechanical synchronization. Due to the high interconnection between electrical and mechanical activities, researchers developed multiple types of models trying to unveil and quantify both from cellular to whole organ levels1–5. Extensive and exhaustive studies have been performed on the electrical counterpart in both physiological and pathological conditions, starting from single cells and organ slices6–8, and broadening to ex-vivo9 and in-vivo models10, 11. Thanks to the recently improved computation ability, both heart functions were studied with in-silico simulations, as an alternative to animal experimentation12–14. When purely measuring the mechanical activity of the heart, in-vitro studies face multiple challenges compared to ex-vivo and in-vivo. Specifically, kinematics and contraction force measured in-vitro need to address the micrometric cell dimension and forces expressed and require specific and expensive instrumentation 15. The advent of computer vision technologies in biology brought alternative solutions to cell movement tracking and  \n1Department of Surgery, Dentistry and Maternity, University of Verona, Verona, Italy. 2Department of Medicine and Surgery, University of Parma, Parma, Italy. 3Deutsches Herzzentrum Der Charité, Department of Cardiology, Angiology and Intensive Care Medicine, Berlin, Germany. 4Humanitas Research Hospital, IRCCS, Rozzano (Milan), Italy. 5Department of Biotechnology and Life Sciences, University of Insubria, Varese, Italy. 6Institute of Genetic and Biomedical Research (IRGB), UOS of Milan, National Research Council of Italy, Milan, Italy. 7Depa","cbCaiv9HNk6ZA2jl","https://ap.wps.com/l/cbCaiv9HNk6ZA2jl","pdf",4008494,1,15,"English","en",105,"# Introduction\n## Electromechanical synchronization and modeling\n## Challenges in in-vitro mechanical measurements\n## Computer vision approaches for motion tracking\n# Methods\n## Experimental video acquisition and stimulation protocol\n## Software tools compared: MUSCLEMOTION, CONTRACTIONWAVE, ViKiE\n## Machine-learning models for robustness testing\n# Results\n## Agreement of cardiac mechanical parameter estimations\n## Sensitivity differences across software and localized analysis\n## Machine-learning training effects and accuracy","[{\"question\":\"Which software tools were compared for cardiac motion analysis?\",\"answer\":\"The study compared MUSCLEMOTION, CONTRACTIONWAVE, and ViKiE to analyze in-silico and in-vitro videos and extract cardiac kinematic parameters.\"},{\"question\":\"How were the samples stimulated and what video data were collected?\",\"answer\":\"High-resolution in-vitro videos were acquired during suprathreshold stimulation at 0.5–1–2 Hz, with protocol adaptation for cardioids. Samples were also exposed to inotropic and depolarizing substances.\"},{\"question\":\"What key performance differences emerged between the software packages?\",\"answer\":\"All software produced comparable estimations of cardiac mechanical parameters. ViKiE showed higher sensitivity due to localized kinematic analysis, while MUSCLEMOTION and CONTRACTIONWAVE provided temporal correlation with global and time-efficient assessment.\"}]","Unlocking cardiac motion - assessing software and machine learning for single-cell and cardioid kinematic insights | PDF",1785894315,38,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"unlocking-cardiac-motion-assessing-software-and-machine-learning-for-single-cell-and-cardioid-kinematic-insights","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/unlocking-cardiac-motion-assessing-software-and-machine-learning-for-single-cell-and-cardioid-kinematic-insights/124755/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Which software tools were compared for cardiac motion analysis?","Question",{"text":75,"@type":76},"The study compared MUSCLEMOTION, CONTRACTIONWAVE, and ViKiE to analyze in-silico and in-vitro videos and extract cardiac kinematic parameters.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were the samples stimulated and what video data were collected?",{"text":80,"@type":76},"High-resolution in-vitro videos were acquired during suprathreshold stimulation at 0.5–1–2 Hz, with protocol adaptation for cardioids. Samples were also exposed to inotropic and depolarizing substances.",{"name":82,"@type":73,"acceptedAnswer":83},"What key performance differences emerged between the software packages?",{"text":84,"@type":76},"All software produced comparable estimations of cardiac mechanical parameters. ViKiE showed higher sensitivity due to localized kinematic analysis, while MUSCLEMOTION and CONTRACTIONWAVE provided temporal correlation with global and time-efficient assessment.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]