The use of artificial intelligence in induced pluripotent stem cell-based technology over 10-year period: A systematic scoping review.
<h4>Background</h4>Stem cell research, particularly in the domain of induced pluripotent stem cell (iPSC) technology, has shown significant progress. The integration of artificial intelligence (AI), especially machine learning (ML) and deep learning (DL), has played a pivotal role in ref...
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2024-01-01
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| author | Quan Duy Vo Yukihiro Saito Toshihiro Ida Kazufumi Nakamura Shinsuke Yuasa |
| author_facet | Quan Duy Vo Yukihiro Saito Toshihiro Ida Kazufumi Nakamura Shinsuke Yuasa |
| author_sort | Quan Duy Vo |
| collection | DOAJ |
| description | <h4>Background</h4>Stem cell research, particularly in the domain of induced pluripotent stem cell (iPSC) technology, has shown significant progress. The integration of artificial intelligence (AI), especially machine learning (ML) and deep learning (DL), has played a pivotal role in refining iPSC classification, monitoring cell functionality, and conducting genetic analysis. These enhancements are broadening the applications of iPSC technology in disease modelling, drug screening, and regenerative medicine. This review aims to explore the role of AI in the advancement of iPSC research.<h4>Methods</h4>In December 2023, data were collected from three electronic databases (PubMed, Web of Science, and Science Direct) to investigate the application of AI technology in iPSC processing.<h4>Results</h4>This systematic scoping review encompassed 79 studies that met the inclusion criteria. The number of research studies in this area has increased over time, with the United States emerging as a leading contributor in this field. AI technologies have been diversely applied in iPSC technology, encompassing the classification of cell types, assessment of disease-specific phenotypes in iPSC-derived cells, and the facilitation of drug screening using iPSC. The precision of AI methodologies has improved significantly in recent years, creating a foundation for future advancements in iPSC-based technologies.<h4>Conclusions</h4>Our review offers insights into the role of AI in regenerative and personalized medicine, highlighting both challenges and opportunities. Although still in its early stages, AI technologies show significant promise in advancing our understanding of disease progression and development, paving the way for future clinical applications. |
| format | Article |
| id | doaj-art-73083dd9c4b9495ba96b8209867e9e15 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Public Library of Science (PLoS) |
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| series | PLoS ONE |
| spelling | doaj-art-73083dd9c4b9495ba96b8209867e9e152025-02-05T05:32:26ZengPublic Library of Science (PLoS)PLoS ONE1932-62032024-01-01195e030253710.1371/journal.pone.0302537The use of artificial intelligence in induced pluripotent stem cell-based technology over 10-year period: A systematic scoping review.Quan Duy VoYukihiro SaitoToshihiro IdaKazufumi NakamuraShinsuke Yuasa<h4>Background</h4>Stem cell research, particularly in the domain of induced pluripotent stem cell (iPSC) technology, has shown significant progress. The integration of artificial intelligence (AI), especially machine learning (ML) and deep learning (DL), has played a pivotal role in refining iPSC classification, monitoring cell functionality, and conducting genetic analysis. These enhancements are broadening the applications of iPSC technology in disease modelling, drug screening, and regenerative medicine. This review aims to explore the role of AI in the advancement of iPSC research.<h4>Methods</h4>In December 2023, data were collected from three electronic databases (PubMed, Web of Science, and Science Direct) to investigate the application of AI technology in iPSC processing.<h4>Results</h4>This systematic scoping review encompassed 79 studies that met the inclusion criteria. The number of research studies in this area has increased over time, with the United States emerging as a leading contributor in this field. AI technologies have been diversely applied in iPSC technology, encompassing the classification of cell types, assessment of disease-specific phenotypes in iPSC-derived cells, and the facilitation of drug screening using iPSC. The precision of AI methodologies has improved significantly in recent years, creating a foundation for future advancements in iPSC-based technologies.<h4>Conclusions</h4>Our review offers insights into the role of AI in regenerative and personalized medicine, highlighting both challenges and opportunities. Although still in its early stages, AI technologies show significant promise in advancing our understanding of disease progression and development, paving the way for future clinical applications.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0302537&type=printable |
| spellingShingle | Quan Duy Vo Yukihiro Saito Toshihiro Ida Kazufumi Nakamura Shinsuke Yuasa The use of artificial intelligence in induced pluripotent stem cell-based technology over 10-year period: A systematic scoping review. PLoS ONE |
| title | The use of artificial intelligence in induced pluripotent stem cell-based technology over 10-year period: A systematic scoping review. |
| title_full | The use of artificial intelligence in induced pluripotent stem cell-based technology over 10-year period: A systematic scoping review. |
| title_fullStr | The use of artificial intelligence in induced pluripotent stem cell-based technology over 10-year period: A systematic scoping review. |
| title_full_unstemmed | The use of artificial intelligence in induced pluripotent stem cell-based technology over 10-year period: A systematic scoping review. |
| title_short | The use of artificial intelligence in induced pluripotent stem cell-based technology over 10-year period: A systematic scoping review. |
| title_sort | use of artificial intelligence in induced pluripotent stem cell based technology over 10 year period a systematic scoping review |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0302537&type=printable |
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