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.