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

BackgroundCardiomyocytes derived from human iPS cells (hiPSCs) include cells showing SAN- and non-SAN-type spontaneous APs. ObjectivesTo examine whether the deep learning technology could identify hiPSC-derived SAN-like cells showing SAN-type-APs by their shape. MethodsWe 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. ResultsAll parameter values such as accuracy, recall, specificity, and precision obtained from the trained CNN model were higher than those of human classification. ConclusionsDeep learning technology could identify hiPSC-derived SAN-like cells with considerable accuracy.