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

Abstract

Abstract 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.

Keywords

• automaticity
• CNN model
• deep learning
• human iPS cells
• SAN‐like cells