Artificial-Intelligence-Based Imaging Analysis of Stem Cells: A Systematic Scoping Review
This systematic scoping review aims to map and identify the available artificial-intelligence-based techniques for imaging analysis, the characterization of stem cell differentiation, and trans-differentiation pathways. On the ninth of March 2022, data were collected from five electronic databases (...
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| Format: | Article |
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MDPI AG
2022-09-01
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| Series: | Biology |
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| Online Access: | https://www.mdpi.com/2079-7737/11/10/1412 |
| _version_ | 1797475177475342336 |
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| author | Julien Issa Mazen Abou Chaar Bartosz Kempisty Lukasz Gasiorowski Raphael Olszewski Paul Mozdziak Marta Dyszkiewicz-Konwińska |
| author_facet | Julien Issa Mazen Abou Chaar Bartosz Kempisty Lukasz Gasiorowski Raphael Olszewski Paul Mozdziak Marta Dyszkiewicz-Konwińska |
| author_sort | Julien Issa |
| collection | DOAJ |
| description | This systematic scoping review aims to map and identify the available artificial-intelligence-based techniques for imaging analysis, the characterization of stem cell differentiation, and trans-differentiation pathways. On the ninth of March 2022, data were collected from five electronic databases (PubMed, Medline, Web of Science, Cochrane, and Scopus) and manual citation searching; all data were gathered in Zotero 5.0. A total of 4422 articles were collected after deduplication; only twenty-seven studies were included in this systematic scoping review after a two-phase screening against inclusion criteria by two independent reviewers. The amount of research in this field is significantly increasing over the years. While the current state of artificial intelligence (AI) can tackle a multitude of medical problems, the consensus amongst researchers remains that AI still falls short in multiple ways that investigators should examine, ranging from the quality of images used in training sets and appropriate sample size, as well as the unexpected events that may occur which the algorithm cannot predict. |
| first_indexed | 2024-03-09T20:41:23Z |
| format | Article |
| id | doaj.art-f9565ba346da48449ecee0ef80a6d553 |
| institution | Directory Open Access Journal |
| issn | 2079-7737 |
| language | English |
| last_indexed | 2024-03-09T20:41:23Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biology |
| spelling | doaj.art-f9565ba346da48449ecee0ef80a6d5532023-11-23T22:59:11ZengMDPI AGBiology2079-77372022-09-011110141210.3390/biology11101412Artificial-Intelligence-Based Imaging Analysis of Stem Cells: A Systematic Scoping ReviewJulien Issa0Mazen Abou Chaar1Bartosz Kempisty2Lukasz Gasiorowski3Raphael Olszewski4Paul Mozdziak5Marta Dyszkiewicz-Konwińska6Department of Diagnostics, Poznań University of Medical Sciences, Bukowska 70, 60-812 Poznań, PolandDepartment of Anatomy, Poznan University of Medical Sciences, 60-701 Poznan, PolandDepartment of Anatomy, Poznan University of Medical Sciences, 60-701 Poznan, PolandDepartment of Medical Simulation, Poznan University of Medical Sciences, 60-701 Poznan, PolandDepartment of Oral and Maxillofacial Surgery, Cliniques Univeristaires Saint-Luc, UCLouvain, 1200 Brussels, BelgiumPrestage Department of Poultry Sciences, North Carolina State University, Raleigh, NC 27695, USADepartment of Diagnostics, Poznań University of Medical Sciences, Bukowska 70, 60-812 Poznań, PolandThis systematic scoping review aims to map and identify the available artificial-intelligence-based techniques for imaging analysis, the characterization of stem cell differentiation, and trans-differentiation pathways. On the ninth of March 2022, data were collected from five electronic databases (PubMed, Medline, Web of Science, Cochrane, and Scopus) and manual citation searching; all data were gathered in Zotero 5.0. A total of 4422 articles were collected after deduplication; only twenty-seven studies were included in this systematic scoping review after a two-phase screening against inclusion criteria by two independent reviewers. The amount of research in this field is significantly increasing over the years. While the current state of artificial intelligence (AI) can tackle a multitude of medical problems, the consensus amongst researchers remains that AI still falls short in multiple ways that investigators should examine, ranging from the quality of images used in training sets and appropriate sample size, as well as the unexpected events that may occur which the algorithm cannot predict.https://www.mdpi.com/2079-7737/11/10/1412artificial intelligencestem cellsinduced pluripotent stem cellsembryonic stem cellsadult stem cellsimaging |
| spellingShingle | Julien Issa Mazen Abou Chaar Bartosz Kempisty Lukasz Gasiorowski Raphael Olszewski Paul Mozdziak Marta Dyszkiewicz-Konwińska Artificial-Intelligence-Based Imaging Analysis of Stem Cells: A Systematic Scoping Review Biology artificial intelligence stem cells induced pluripotent stem cells embryonic stem cells adult stem cells imaging |
| title | Artificial-Intelligence-Based Imaging Analysis of Stem Cells: A Systematic Scoping Review |
| title_full | Artificial-Intelligence-Based Imaging Analysis of Stem Cells: A Systematic Scoping Review |
| title_fullStr | Artificial-Intelligence-Based Imaging Analysis of Stem Cells: A Systematic Scoping Review |
| title_full_unstemmed | Artificial-Intelligence-Based Imaging Analysis of Stem Cells: A Systematic Scoping Review |
| title_short | Artificial-Intelligence-Based Imaging Analysis of Stem Cells: A Systematic Scoping Review |
| title_sort | artificial intelligence based imaging analysis of stem cells a systematic scoping review |
| topic | artificial intelligence stem cells induced pluripotent stem cells embryonic stem cells adult stem cells imaging |
| url | https://www.mdpi.com/2079-7737/11/10/1412 |
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