Deep learning methods for cardiomyocyte motion analysis

Abstract

Heart failure is a leading cause of death and forms a growing health concern. The development of novel drugs is however hampered by the absence of adequate screening methods and disease models. Cardiomyocytes derived from patients could assist in the development of a patient specific drug screen method to test the efficacy and safety of putative drugs. Simultaneously, deep learning has been applied to a variety of biomedical datasets, achieving state-of-the-art performance. Previous methods for the classification of cardiomyocytes as healthy or diseased only focused on machine learning methods. We present the first deep learning approach to perform this classification task together with a novel artificial intelligence interpretability method called Contraction Analysis Local Interpretable model-agnostic explanations (CA-LIME), able to explain the predictions made by the classifier. The proposed classifier is shown to outperform previously developed methods to classify cardiomyocytes, obtaining 97.5% accuracy. Our results indicate this classifier could aid in the development of a high throughput drug screening system for cardiac drug development. The explanations made by CA-LIME are in correspondence with previous observations of drugs with known effects, verifying the effectiveness of our approach. Together with CA-LIME, the processing pipeline could lead to the discovery of new differences between the motion of healthy and aberrant beating cardiomyocytes.