Artificial intelligence for a cardiac drug study

Abstract

Machine learning that is the vital part of modern artificial intelligence was used to classify the potential effects of two drugs on induced pluripotent stem cell-derived cardiomyocytes (iPSC-CM). Peak data were detected from calcium transient signals of cycling iPSC-CMs first at baseline, second exposed to adrenaline and third exposed with one of the drugs either flecainide or carvedilol. Various machine learning classification tests were executed to evaluate whether a drug did or did not affect signal shape after exposing adrenaline or the drugs. According to our classification results, machine learning can be applied to analyze possible effects of drugs on induced pluripotent stem cell-derived cardiomyocytes derived from the catecholaminergic polymorphic ventricular tachycardia patients’ cell samples.

Keywords

• Classification
• Machine learning
• Peak data
• Cardiology
• Drug effect
• Induced pluripotent cardiomyocyte