[HTML][HTML] Deep learning-based quality control of cultured human-induced pluripotent stem cell-derived cardiomyocytes
Using bright-field images of cultured human-induced pluripotent stem cell-derived
cardiomyocytes (hiPSC-CMs), we trained a convolutional neural network (CNN), a machine
learning technique, to decide whether the qualities of cell cultures are suitable for
experiments. VGG16, an open-source CNN framework, resulted in a mean F1 score of 0.89
and judged the cell qualities at a speed of approximately 2000 images per second when run
on a commercially available laptop computer equipped with Core i7. Thus, CNNs provide a …
cardiomyocytes (hiPSC-CMs), we trained a convolutional neural network (CNN), a machine
learning technique, to decide whether the qualities of cell cultures are suitable for
experiments. VGG16, an open-source CNN framework, resulted in a mean F1 score of 0.89
and judged the cell qualities at a speed of approximately 2000 images per second when run
on a commercially available laptop computer equipped with Core i7. Thus, CNNs provide a …
Abstract
Using bright-field images of cultured human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs), we trained a convolutional neural network (CNN), a machine learning technique, to decide whether the qualities of cell cultures are suitable for experiments. VGG16, an open-source CNN framework, resulted in a mean F1 score of 0.89 and judged the cell qualities at a speed of approximately 2000 images per second when run on a commercially available laptop computer equipped with Core i7. Thus, CNNs provide a useful platform for the high-throughput quality control of hiPSC-CMs.
Elsevier
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