학술논문
Deep learning‐based identification of sinoatrial node‐like pacemaker cells from SHOX2/HCN4 double‐positive cells differentiated from human iPS cells
Document Type
Report
Author
Source
Journal of Arrhythmia. August 2023, Vol. 39 Issue 4, p664, 5 p.
Subject
Language
English
ISSN
1880-4276
Abstract
INTRODUCTION Sinoatrial node (SAN) cells exhibit automaticity.[sup.1] The dysfunction of SAN cells causes bradyarrhythmia. Because of the medical shortcomings of artificial pacemakers, the biological pacemaker has been proposed.[sup.2] We reported [...]
: 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.
: 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.