Human induced pluripotent stem cell (hiPSC)-derived cardiomyocytes
(hiPSC-CMs) offer versatile applications in tissue engineering and
drug screening. To facilitate the monitoring of hiPSC cardiac differentiation,
a noninvasive approach using convolutional neural networks (CNNs)
was explored. HiPSCs were differentiated into cardiomyocytes and analyzed
using the quantitative real-time polymerase chain reaction (qRT-PCR).
The bright-field images of the cells at different time points were
captured to create the dataset. Six pretrained models (AlexNet, GoogleNet,
ResNet 18, ResNet 50, DenseNet 121, VGG 19-BN) were employed to identify
different stages in differentiation. VGG 19-BN outperformed the other
five CNNs and exhibited remarkable performance with 99.2% accuracy,
recall, precision, and F1 score and 99.8% specificity. The pruning
process was then applied to the optimal model, resulting in a significant
reduction of model parameters while maintaining high accuracy. Finally,
an automation application using the pruned VGG 19-BN model was developed,
facilitating users in assessing the cell status during the myocardial
differentiation of hiPSCs.