Prediction of Stem Cell State Using Cell Image-Based Deep Learning

Minjae Kim, Yong Namkung, Donghun Hyun, Sunghoi Hong

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)


Stem cells represent an ideal source for regenerative medicine; however, longitudinal assessment of stem cell phenotype and function is challenging. Contrastingly, a convolutional neural network (CNN) algorithm can automatically extract the image features and produce highly accurate image recognition. Thus, this study implements CNN to establish stable and reproducible cell culture experiments by predicting a unique morphology of pluripotent stem cell (PSC) lines. Interestingly, the algorithm distinguishes the PSC lines cultured in the different cell culture conditions, such as the presence or absence of small molecules and/or the long- or short-term culture in our induced PSC (iPSC) models, which include iPSC lines with abnormal gene expression patterns and genomic abnormalities. Our deep learning technology accurately classifies the various cell lines with or without genetic defects using only the cell images, without any labeling process. This suggests that the CNN system may simplify the various tasks involving stable cell cultures and their differentiation.

Original languageEnglish
Article number2300017
JournalAdvanced Intelligent Systems
Issue number7
Publication statusPublished - 2023 Jul

Bibliographical note

Funding Information:
This work was supported by the Ministry of Science and ICT (2019M3E5D5065399), the National Research Foundation of Korea (NRF-2020R1A2C1101294), and the Ministry of Health and Welfare (RS-2022-00060247) of the government of the Republic of Korea.

Publisher Copyright:
© 2023 The Authors. Advanced Intelligent Systems published by Wiley-VCH GmbH.


  • cell culture
  • cell morphology
  • convolutional neural networks
  • deep learning
  • prediction
  • stem cells

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction
  • Mechanical Engineering
  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Materials Science (miscellaneous)


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