Cardiac arrhythmias are a significant risk factor for sudden cardiac death, accounting for 15% to 20% of all deaths worldwide. Causes of arrhythmias are diverse and include genetic factors, patients’ physical and mental condition, and certain types of medications. The onset and severity of arrhythmic events in patients are notoriously unpredictable, especially in younger people and patients without heart disease. To solve this problem, Boon-Seng Soh, Jeremy Kah Sheng Pang and colleagues from the Institute of Molecular and Cell Biology, Singapore combined stem cell technology with machine learning, enabling them to predict with a high degree of accuracy arrhythmias in the laboratory. The results of the research were recently published in the journal Stem cell reports.
In their research, the team used human heart muscle cells, so-called cardiomyocytes, which were made from pluripotent stem cells in the laboratory. The different cardiomyocyte cultures used in this study had varying propensities for arrhythmias due to genetic mutations or drug treatment. Using video data from more than 3,000 “healthy” and arrhythmia-prone cardiomyocytes, the researchers trained a machine learning program on cultures’ specific stroke behaviors using a visible indicator of changes in calcium concentrations in the cells as a measure of heart function. Using this system, the computer algorithms achieved over 90% accuracy in predicting the occurrence of drug- or genetically induced arrhythmias and identified distinct patterns that predict arrhythmias.
This research lays the foundation for machine learning-based patient risk profiling and drug toxicity testing in patient-derived cardiomyocytes, which can help generate safer and more effective drugs.
Stem cell reports
Characterization of arrhythmia using machine learning analysis of Ca2 + cycling in human cardiomyocytes
Article Release Date
July 14, 2022
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