Deep Learning-Based In Vitro Detection Method for Cellular Impurities in Human Cell-Processed Therapeutic Products
Automated detection of impurities is in demand for evaluating the quality and safety of human cell-processed therapeutic products in regenerative medicine. Deep learning (DL) is a powerful method for classifying and recognizing images in cell biology, diagnostic medicine, and other fields because it...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/20/9755 |
| _version_ | 1797515329619886080 |
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| author | Yasunari Matsuzaka Shinji Kusakawa Yoshihiro Uesawa Yoji Sato Mitsutoshi Satoh |
| author_facet | Yasunari Matsuzaka Shinji Kusakawa Yoshihiro Uesawa Yoji Sato Mitsutoshi Satoh |
| author_sort | Yasunari Matsuzaka |
| collection | DOAJ |
| description | Automated detection of impurities is in demand for evaluating the quality and safety of human cell-processed therapeutic products in regenerative medicine. Deep learning (DL) is a powerful method for classifying and recognizing images in cell biology, diagnostic medicine, and other fields because it automatically extracts the features from complex cell morphologies. In the present study, we construct prediction models that recognize cancer-cell contamination in continuous long-term (four-day) cell cultures. After dividing the whole dataset into Early- and Late-stage cell images, we found that Late-stage images improved the DL performance. The performance was further improved by optimizing the DL hyperparameters (batch size and learning rate). These findings are first report for the implement of DL-based systems in disease cell-type classification of human cell-processed therapeutic products (hCTPs), that are expected to enable the rapid, automatic classification of induced pluripotent stem cells and other cell treatments for life-threatening or chronic diseases. |
| first_indexed | 2024-03-10T06:43:58Z |
| format | Article |
| id | doaj.art-025766a86f5848ea9aed766f4aae37a9 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T06:43:58Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-025766a86f5848ea9aed766f4aae37a92023-11-22T17:23:51ZengMDPI AGApplied Sciences2076-34172021-10-011120975510.3390/app11209755Deep Learning-Based In Vitro Detection Method for Cellular Impurities in Human Cell-Processed Therapeutic ProductsYasunari Matsuzaka0Shinji Kusakawa1Yoshihiro Uesawa2Yoji Sato3Mitsutoshi Satoh4Department of Medical Molecular Informatics, Meiji Pharmaceutical University, Kiyose 204-8588, Tokyo, JapanDivision of Cell-Based Therapeutic Producers, National Institute of Health Sciences, Kawasaki 201-9501, Kanagawa, JapanDepartment of Medical Molecular Informatics, Meiji Pharmaceutical University, Kiyose 204-8588, Tokyo, JapanDivision of Cell-Based Therapeutic Producers, National Institute of Health Sciences, Kawasaki 201-9501, Kanagawa, JapanDepartment of Toxicology and Pharmacology, Faculty of Pharmaceutical Sciences, Meiji Pharmaceutical University, Kiyose 204-8588, Tokyo, JapanAutomated detection of impurities is in demand for evaluating the quality and safety of human cell-processed therapeutic products in regenerative medicine. Deep learning (DL) is a powerful method for classifying and recognizing images in cell biology, diagnostic medicine, and other fields because it automatically extracts the features from complex cell morphologies. In the present study, we construct prediction models that recognize cancer-cell contamination in continuous long-term (four-day) cell cultures. After dividing the whole dataset into Early- and Late-stage cell images, we found that Late-stage images improved the DL performance. The performance was further improved by optimizing the DL hyperparameters (batch size and learning rate). These findings are first report for the implement of DL-based systems in disease cell-type classification of human cell-processed therapeutic products (hCTPs), that are expected to enable the rapid, automatic classification of induced pluripotent stem cells and other cell treatments for life-threatening or chronic diseases.https://www.mdpi.com/2076-3417/11/20/9755human cell-processed therapeutic productsdeep learningconvolutional neural networkshyperparametersimage classification |
| spellingShingle | Yasunari Matsuzaka Shinji Kusakawa Yoshihiro Uesawa Yoji Sato Mitsutoshi Satoh Deep Learning-Based In Vitro Detection Method for Cellular Impurities in Human Cell-Processed Therapeutic Products Applied Sciences human cell-processed therapeutic products deep learning convolutional neural networks hyperparameters image classification |
| title | Deep Learning-Based In Vitro Detection Method for Cellular Impurities in Human Cell-Processed Therapeutic Products |
| title_full | Deep Learning-Based In Vitro Detection Method for Cellular Impurities in Human Cell-Processed Therapeutic Products |
| title_fullStr | Deep Learning-Based In Vitro Detection Method for Cellular Impurities in Human Cell-Processed Therapeutic Products |
| title_full_unstemmed | Deep Learning-Based In Vitro Detection Method for Cellular Impurities in Human Cell-Processed Therapeutic Products |
| title_short | Deep Learning-Based In Vitro Detection Method for Cellular Impurities in Human Cell-Processed Therapeutic Products |
| title_sort | deep learning based in vitro detection method for cellular impurities in human cell processed therapeutic products |
| topic | human cell-processed therapeutic products deep learning convolutional neural networks hyperparameters image classification |
| url | https://www.mdpi.com/2076-3417/11/20/9755 |
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