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未来的干细胞分析:在自动化细胞分析的先进方法方面的进展和挑战。

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

机构信息

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

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.

出版信息

PeerJ. 2022 Dec 21;10:e14513. doi: 10.7717/peerj.14513. eCollection 2022.

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.

摘要

背景与目的

微观图像已被用于细胞分析,以进行细胞类型识别和分类、细胞计数和细胞大小测量。之前的大多数研究工作都很繁琐,包括详细的理解和耗时。科学家和研究人员正在寻求符合当前需求的现代和自动细胞分析方法。

目的

本文简要概述了从细胞发现史到最新方法的一般细胞和特定干细胞分析方法。

方法

使用三种综述方法(手稿识别、筛选和纳入),从特定手稿数据库中对文献研究进行内容描述。该综述方法基于 Prism 指南,用于搜索与细胞分析相关的研究中的原创性和新颖性。

结果

通过分析通用细胞和特定干细胞分析方法,当前技术在使用一种不那么繁琐、具有成本效益且降低错误率的方法协助医学专家进行细胞分析方面具有巨大潜力。

结论

本综述揭示了通用细胞和特定干细胞分析方面的潜在研究空白。因此,它可以为使用人工智能和深度学习等当前技术开发自动化细胞分析方法提供参考。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5990/9789697/de8123262114/peerj-10-14513-g001.jpg

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