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J Pharmacol Sci. 2019 May 4. pii: S1347-8613(19)31053-9. doi: 10.1016/j.jphs.2019.04.008. [Epub ahead of print]

Deep learning-based quality control of cultured human-induced pluripotent stem cell-derived cardiomyocytes.

Author information

1
Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo, 113-0033, Japan.
2
Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo, 113-0033, Japan; Center for Information and Neural Networks, National Institute of Information and Communications Technology, Suita City, Osaka, 565-0871, Japan. Electronic address: yuji@ikegaya.jp.

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.

KEYWORDS:

Heart; Machine learning; iPSC

PMID:
31113731
DOI:
10.1016/j.jphs.2019.04.008
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