揭示的类器官:具有人工智能的深层下一代体外模型的形态学分析。

IF 8.1 1区 医学 Q1 ENGINEERING, BIOMEDICAL
Xuan Du, Zaozao Chen, Qiwei Li, Sheng Yang, Lincao Jiang, Yi Yang, Yanhui Li, Zhongze Gu
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引用次数: 4

摘要

在现代术语中,“类器官”指的是在体外特定的三维(3D)环境中生长的细胞,它们与其来源器官或组织具有相似的结构。通过显微镜观察类器官的形态或生长特征是一种常用的类器官分析方法。然而,仅靠人工筛选和分析类器官是困难的,耗时的,不准确的,这是传统技术无法轻易解决的问题。人工智能(AI)技术已被证明在许多生物和医学研究领域是有效的,特别是在分析单细胞或苏木精/伊红染色的组织切片方面。当用于分析类器官时,人工智能也应该提供更高效、定量、准确和快速的解决方案。在本文中,我们将首先简要概述类器官的应用领域,然后讨论传统的类器官测量和分析方法的不足。其次,我们将总结从机器学习到深度学习的发展以及后者的优势,然后描述如何利用卷积神经网络来解决类器官观察和分析中的挑战。最后,我们将讨论目前人工智能在类器官研究中的局限性,以及未来的研究方向和机会。图形抽象:
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Organoids revealed: morphological analysis of the profound next generation in-vitro model with artificial intelligence.

Organoids revealed: morphological analysis of the profound next generation in-vitro model with artificial intelligence.

Organoids revealed: morphological analysis of the profound next generation in-vitro model with artificial intelligence.

Organoids revealed: morphological analysis of the profound next generation in-vitro model with artificial intelligence.

In modern terminology, "organoids" refer to cells that grow in a specific three-dimensional (3D) environment in vitro, sharing similar structures with their source organs or tissues. Observing the morphology or growth characteristics of organoids through a microscope is a commonly used method of organoid analysis. However, it is difficult, time-consuming, and inaccurate to screen and analyze organoids only manually, a problem which cannot be easily solved with traditional technology. Artificial intelligence (AI) technology has proven to be effective in many biological and medical research fields, especially in the analysis of single-cell or hematoxylin/eosin stained tissue slices. When used to analyze organoids, AI should also provide more efficient, quantitative, accurate, and fast solutions. In this review, we will first briefly outline the application areas of organoids and then discuss the shortcomings of traditional organoid measurement and analysis methods. Secondly, we will summarize the development from machine learning to deep learning and the advantages of the latter, and then describe how to utilize a convolutional neural network to solve the challenges in organoid observation and analysis. Finally, we will discuss the limitations of current AI used in organoid research, as well as opportunities and future research directions.

Graphic abstract:

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来源期刊
Bio-Design and Manufacturing
Bio-Design and Manufacturing Materials Science-Materials Science (miscellaneous)
CiteScore
13.30
自引率
7.60%
发文量
148
期刊介绍: Bio-Design and Manufacturing reports new research, new technology and new applications in the field of biomanufacturing, especially 3D bioprinting. Topics of Bio-Design and Manufacturing cover tissue engineering, regenerative medicine, mechanical devices from the perspectives of materials, biology, medicine and mechanical engineering, with a focus on manufacturing science and technology to fulfil the requirement of bio-design.
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