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Artificial-Intelligence-Based Imaging Analysis of Stem Cells: A Systematic Scoping Review.
Julien Issa1,2, Mazen Abou Chaar3, Bartosz Kempisty3,4,5,6
1Department of Diagnostics, Poznań University of Medical Sciences, Bukowska 70, 60-812 Poznań, Poland.
Biology
|October 27, 2022
Summary
This review maps artificial intelligence (AI) techniques for stem cell imaging analysis. While AI shows promise, current methods need improvement in image quality, sample size, and handling unpredictable events for reliable stem cell characterization.
Area of Science:
- Biomedical Engineering
- Computational Biology
- Regenerative Medicine
Background:
- Artificial intelligence (AI) is increasingly applied in medical imaging analysis.
- Stem cell differentiation and trans-differentiation are crucial for regenerative medicine.
- Standardized AI approaches for analyzing stem cell imaging are lacking.
Purpose of the Study:
- To systematically map and identify AI-based techniques for stem cell imaging analysis.
- To characterize AI applications in stem cell differentiation and trans-differentiation.
- To highlight current limitations and future research directions in AI for stem cell research.
Main Methods:
- Systematic scoping review methodology.
- Data collection from five electronic databases (PubMed, Medline, Web of Science, Cochrane, Scopus) and manual citation searching.
- Two-phase screening of 4422 retrieved articles, with 27 studies included.
Main Results:
- Significant increase in research on AI for stem cell imaging analysis over the years.
- Identified various AI techniques applied to stem cell imaging.
- Highlighted limitations including image quality, sample size, and algorithm predictability.
Conclusions:
- AI holds significant potential for advancing stem cell imaging analysis and understanding differentiation pathways.
- Further research is needed to address limitations in AI model training and validation.
- Improved AI methodologies are essential for reliable clinical translation of stem cell therapies.
