Future stem cell analysis: progress and challenges towards state-of-the art approaches in automated cells analysis

Author:

Mohamad Zamani Nurul Syahira1,Wan Zaki Wan Mimi Diyana1ORCID,Abd Hamid Zariyantey2,Baseri Huddin Aqilah1ORCID

Affiliation:

1. Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Department of Electrical, Electronic and Systems Engineering, UKM Bangi, Selangor, Malaysia

2. Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Biomedical Science Programme and Centre for Diagnostic, Therapeutic and Investigative Science, Kuala Lumpur, W. P. Kuala Lumpur, Malaysia

Abstract

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.

Funder

Research University Grant from Universiti Kebangsaan Malaysia

Publisher

PeerJ

Subject

General Agricultural and Biological Sciences,General Biochemistry, Genetics and Molecular Biology,General Medicine,General Neuroscience

Reference101 articles.

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