Application of Deep Neural Networks in the Manufacturing Process of Mesenchymal Stem Cells Therapeutics
Dat Ngo1,*, Jeongmin Lee2,3,*, Sun Jae Kwon2, Jin Hun Park4,5, Baek Hwan Cho5,6, Jong Wook Chang2,3,7
1Department of Computer Engineering, Korea National University of Transportation, Chungju, Korea
2CDMO Technology Institute, ENCell Co., Ltd., Seoul, Korea
3Cell and Gene Therapy Institute, Samsung Medical Center, Seoul, Korea
4Department of Media and Communication, College of Future Convergence Division of Healthcare Sciences, CHA University, Seongnam, Korea
5Department of Biomedical Informatics, School of Medicine, CHA University, Seongnam, Korea
6Institute of Biomedical Informatics, School of Medicine, CHA University, Seongnam, Korea
7Department of Health Sciences and Technology, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Korea
2CDMO Technology Institute, ENCell Co., Ltd., Seoul, Korea
3Cell and Gene Therapy Institute, Samsung Medical Center, Seoul, Korea
4Department of Media and Communication, College of Future Convergence Division of Healthcare Sciences, CHA University, Seongnam, Korea
5Department of Biomedical Informatics, School of Medicine, CHA University, Seongnam, Korea
6Institute of Biomedical Informatics, School of Medicine, CHA University, Seongnam, Korea
7Department of Health Sciences and Technology, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, Seoul, Korea
Correspondence to: Jong Wook Chang
Department of Health Sciences and Technology, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, 115 Irwon-ro, Gangnam-gu, Seoul 06355, Korea
E-mail: jongwook.chang@samsung.com
Baek Hwan Cho
Department of Biomedical Informatics, School of Medicine, CHA University, 335 Pangyo-ro, Bundang-gu, Seongnam 13488, Korea
E-mail: baekhwan.cho@cha.ac.kr
Jin Hun Park
Department of Media and Communication, College of Future Convergence Division of Healthcare Sciences, CHA University, 335 Pangyo-ro, Bundang-gu, Seongnam 13488, Korea
E-mail: parkjinhun@cha.ac.kr
*These authors contributed equally to this work.
Department of Health Sciences and Technology, Samsung Advanced Institute for Health Sciences & Technology, Sungkyunkwan University, 115 Irwon-ro, Gangnam-gu, Seoul 06355, Korea
E-mail: jongwook.chang@samsung.com
Baek Hwan Cho
Department of Biomedical Informatics, School of Medicine, CHA University, 335 Pangyo-ro, Bundang-gu, Seongnam 13488, Korea
E-mail: baekhwan.cho@cha.ac.kr
Jin Hun Park
Department of Media and Communication, College of Future Convergence Division of Healthcare Sciences, CHA University, 335 Pangyo-ro, Bundang-gu, Seongnam 13488, Korea
E-mail: parkjinhun@cha.ac.kr
*These authors contributed equally to this work.
Received June 14, 2024; Revised August 26, 2024; Accepted September 2, 2024.
This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
- Abstract
- Current image-based analysis methods for monitoring cell confluency and status depend on individual interpretations, which can lead to wide variations in the quality of cell therapeutics. To overcome these limitations, images of mesenchymal stem cells cultured adherently in various types of culture vessels were captured and analyzed using a deep neural network. Among the various deep learning methods, a classification and detection algorithm was selected to verify cell confluency and status. We confirmed that the image classification algorithm demonstrates significant accuracy for both single- and multistack images. Abnormal cells could be detected exclusively in single-stack images, as multistack culture was performed only when abnormal cells were absent in the single-stack culture. This study is the first to analyze cell images based on a deep learning method that directly impacts yield and quality, which are important product parameters in stem cell therapeutics.
- Keywords : Mesenchymal stem cells, Deep learning, Digital image processing, Cell proliferation, Neural network models
- November 2024, 17 (4)
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