Machine Learning Techniques to Classify Healthy and Diseased Cardiomyocytes by Contractility Profile.

ABSTRACT: Cardiomyocytes derived from human induced pluripotent stem (iPS) cells enable the study of cardiac physiology and the developmental testing of new therapeutic drugs in a human setting. In parallel, machine learning methods are being applied to biomedical science in unprecedented ways. Machine learning has been used to distinguish healthy from diseased cardiomyocytes using calcium (Ca²⁺) transient signals. Most Ca²⁺ transient signals are obtained via terminal assays that do not permit longitudinal studies, although some recently developed options can circumvent these concerns. Here, we describe the use of machine learning to identify healthy and diseased cardiomyocytes according to their contractility profiles, which are derived from brightfield videos. This noncontact

SUBMITTER: Teles D 

PROVIDER: S-EPMC9188827 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature