Deep learning‐based identification of sinoatrial node‐like pacemaker cells from SHOX2/HCN4 double‐positive cells differentiated from human iPS cells

BACKGROUND: Cardiomyocytes derived from human iPS cells (hiPSCs) include cells showing SAN‐ and non‐SAN‐type spontaneous APs. OBJECTIVES: To examine whether the deep learning technology could identify hiPSC‐derived SAN‐like cells showing SAN‐type‐APs by their shape. METHODS: We acquired phase‐contrast images for hiPSC‐derived SHOX2/HCN4 double‐positive SAN‐like and non‐SAN‐like cells and made a VGG16‐based CNN model to classify an input image as SAN‐like or non‐SAN‐like cell, compared to human discriminability. RESULTS: All parameter values such as accuracy, recall, specificity, and precision obtained from the trained CNN model were higher than those of human classification. CONCLUSIONS: Deep learning technology could identify hiPSC‐derived SAN‐like cells with considerable accuracy.