基于深度学习从人诱导多能干细胞分化出的SHOX2/HCN4双阳性细胞中识别窦房结样起搏细胞。
Deep learning-based identification of sinoatrial node-like pacemaker cells from SHOX2/HCN4 double-positive cells differentiated from human iPS cells.
作者信息
Wakimizu Takayuki, Naito Junpei, Ishida Manabu, Kurata Yasutaka, Tsuneto Motokazu, Shirayoshi Yasuaki, Hisatome Ichiro
机构信息
Division of Regenerative Medicine and Therapeutics, Department of Genetic Medicine and Regenerative Therapeutics Tottori University Graduate School of Medical Science Yonago Japan.
ERISA Corporation Matsue Japan.
出版信息
J Arrhythm. 2023 Jun 16;39(4):664-668. doi: 10.1002/joa3.12883. eCollection 2023 Aug.
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.
背景
源自人诱导多能干细胞（hiPSC）的心肌细胞包括显示窦房结型和非窦房结型自发动作电位的细胞。
目的
研究深度学习技术能否通过形态识别源自hiPSC的显示窦房结型动作电位的类窦房结样细胞。
方法
我们获取了源自hiPSC的SHOX2/HCN4双阳性类窦房结样细胞和非类窦房结样细胞的相差图像，并制作了基于VGG16的卷积神经网络（CNN）模型，以将输入图像分类为类窦房结样或非类窦房结样细胞，并与人类辨别能力进行比较。
结果
从训练后的CNN模型获得的所有参数值，如准确率、召回率、特异性和精确率，均高于人类分类的参数值。
结论
深度学习技术能够以相当高的准确率识别源自hiPSC的类窦房结样细胞。
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