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Future stem cell analysis: progress and challenges towards state-of-the art approaches in automated cells analysis
BACKGROUND AND AIMS: A microscopic image has been used in cell analysis for cell type identification and classification, cell counting and cell size measurement. Most previous research works are tedious, including detailed understanding and time-consuming. The scientists and researchers are seeking...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9789697/ https://www.ncbi.nlm.nih.gov/pubmed/36573241 http://dx.doi.org/10.7717/peerj.14513 |
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author | Mohamad Zamani, Nurul Syahira Wan Zaki, Wan Mimi Diyana Abd Hamid, Zariyantey Baseri Huddin, Aqilah |
author_facet | Mohamad Zamani, Nurul Syahira Wan Zaki, Wan Mimi Diyana Abd Hamid, Zariyantey Baseri Huddin, Aqilah |
author_sort | Mohamad Zamani, Nurul Syahira |
collection | PubMed |
description | BACKGROUND AND AIMS: A microscopic image has been used in cell analysis for cell type identification and classification, cell counting and cell size measurement. Most previous research works are tedious, including detailed understanding and time-consuming. The scientists and researchers are seeking modern and automatic cell analysis approaches in line with the current in-demand technology. OBJECTIVES: This article provides a brief overview of a general cell and specific stem cell analysis approaches from the history of cell discovery up to the state-of-the-art approaches. METHODOLOGY: A content description of the literature study has been surveyed from specific manuscript databases using three review methods: manuscript identification, screening, and inclusion. This review methodology is based on Prism guidelines in searching for originality and novelty in studies concerning cell analysis. RESULTS: By analysing generic cell and specific stem cell analysis approaches, current technology offers tremendous potential in assisting medical experts in performing cell analysis using a method that is less laborious, cost-effective, and reduces error rates. CONCLUSION: This review uncovers potential research gaps concerning generic cell and specific stem cell analysis. Thus, it could be a reference for developing automated cells analysis approaches using current technology such as artificial intelligence and deep learning. |
format | Online Article Text |
id | pubmed-9789697 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-97896972022-12-25 Future stem cell analysis: progress and challenges towards state-of-the art approaches in automated cells analysis Mohamad Zamani, Nurul Syahira Wan Zaki, Wan Mimi Diyana Abd Hamid, Zariyantey Baseri Huddin, Aqilah PeerJ Bioinformatics BACKGROUND AND AIMS: A microscopic image has been used in cell analysis for cell type identification and classification, cell counting and cell size measurement. Most previous research works are tedious, including detailed understanding and time-consuming. The scientists and researchers are seeking modern and automatic cell analysis approaches in line with the current in-demand technology. OBJECTIVES: This article provides a brief overview of a general cell and specific stem cell analysis approaches from the history of cell discovery up to the state-of-the-art approaches. METHODOLOGY: A content description of the literature study has been surveyed from specific manuscript databases using three review methods: manuscript identification, screening, and inclusion. This review methodology is based on Prism guidelines in searching for originality and novelty in studies concerning cell analysis. RESULTS: By analysing generic cell and specific stem cell analysis approaches, current technology offers tremendous potential in assisting medical experts in performing cell analysis using a method that is less laborious, cost-effective, and reduces error rates. CONCLUSION: This review uncovers potential research gaps concerning generic cell and specific stem cell analysis. Thus, it could be a reference for developing automated cells analysis approaches using current technology such as artificial intelligence and deep learning. PeerJ Inc. 2022-12-21 /pmc/articles/PMC9789697/ /pubmed/36573241 http://dx.doi.org/10.7717/peerj.14513 Text en © 2022 Mohamad Zamani et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. |
spellingShingle | Bioinformatics Mohamad Zamani, Nurul Syahira Wan Zaki, Wan Mimi Diyana Abd Hamid, Zariyantey Baseri Huddin, Aqilah Future stem cell analysis: progress and challenges towards state-of-the art approaches in automated cells analysis |
title | Future stem cell analysis: progress and challenges towards state-of-the art approaches in automated cells analysis |
title_full | Future stem cell analysis: progress and challenges towards state-of-the art approaches in automated cells analysis |
title_fullStr | Future stem cell analysis: progress and challenges towards state-of-the art approaches in automated cells analysis |
title_full_unstemmed | Future stem cell analysis: progress and challenges towards state-of-the art approaches in automated cells analysis |
title_short | Future stem cell analysis: progress and challenges towards state-of-the art approaches in automated cells analysis |
title_sort | future stem cell analysis: progress and challenges towards state-of-the art approaches in automated cells analysis |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9789697/ https://www.ncbi.nlm.nih.gov/pubmed/36573241 http://dx.doi.org/10.7717/peerj.14513 |
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