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基于人工智能的AI分析在介质细胞干细胞成像中的当前趋势和未来机会:一个范围审查

Maksim Solopov¹, Elizaveta Chechekhina², Viktor Turchin¹

¹V.K. Gusak Institute of Emergency and Reconstructive Surgery, 283045 Donetsk, Russia.

Journal of imaging

|October 28, 2025

概括

人工智能 (AI) 显著增强了介质干细胞 (MSC) 图像分析,提高了准确性和速度. 卷积神经网络 (CNN) 是关键的,但数据标准化等挑战需要解决临床使用.

科学领域:

生物技术是生物技术.
医疗成像医学成像
人工智能的人工智能

背景情况:

介质细胞干细胞 (MSC) 在再生医学中至关重要.
分析MSC图像对于质量控制和治疗疗效至关重要.
传统的图像分析方法在准确性和客观性方面存在局限性.

研究的目的:

在MSC图像分析中对AI应用进行范围审查.
识别使用的AI技术,它们的有效性和挑战.
突出未来AI在MSC研究和生物制造中的方向.

主要方法:

在2014-2024年期间对25项研究进行了范围审查.
搜索了六个主要的科学数据库.
专注于基于AI的图像处理,用于MSC分析.

主要成果:

人工智能方法,特别是机器学习,优于传统技术 (精度高达97.5%).
卷积神经网络 (CNN) 是占主导地位的人工智能方法 (64%的研究).
关键应用包括细胞分类,细分,分化评估和衰老分析.

结论:

人工智能提供自动化,客观和非侵入性的MSC图像分析.
挑战包括MSC异质性,缺乏标准化的协议和有限的数据集.
未来的工作需要可解释的人工智能,标准化验证和开放的数据集用于临床翻译.

关键词:

人工智能的人工智能是人工智能.深度学习是一种深度学习.影像成像技术影像成像技术机器学习是机器学习.介质细胞干细胞介质细胞干细胞

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Mesenchymal Stem Cells

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Mesenchymal stem cells (MSCs) are adult stem cells that can differentiate into most connective tissue cell types, except for hematopoietic cells, depending upon the source of MSCs. For example, bone-marrow-derived MSCs (BM-MSCs) can differentiate into osteocytes, hepatocytes, and pancreatic and neuronal cells. MSCs can be isolated from various sources such as bone marrow, placenta, adipose tissue, teeth, and Wharton’s jelly, a gelatinous substance in the umbilical cord. The ease of their...