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相关概念视频

Phases of Wound Repair01:28

Phases of Wound Repair

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Following injury, the integrity of the injured tissues must be reestablished. For example, in skin tissue, wound repair involves coordination among resident skin cells, blood mononuclear cells, extracellular matrix, growth factors, and cytokines to complete the healing cascade.
Formation of Blood Clot
In case of deep injuries, trauma to blood vessels results in blood loss. In the meantime, phospholipids released from the ruptured endothelial cellular membrane are converted into arachidonic...
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相关实验视频

Updated: May 21, 2025

Digital Planimetry for Assessing Wound Closure Kinetics in a Mouse Model
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Digital Planimetry for Assessing Wound Closure Kinetics in a Mouse Model

Published on: January 10, 2025

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使用AI进行复杂的伤口分析.

Connor J Robinson1, Bruce Dickie1, Claudia Lindner2

  • 1Division of Cell Matrix Biology & Regenerative Medicine, School of Biological Sciences, FBMH, The University of Manchester, M13 9PT, UK.

Computers in biology and medicine
|March 19, 2025
PubMed
概括
此摘要是机器生成的。

本研究引入了一种自动深度学习 (DL) 方法,用于分析伤口愈合图像. 这种人工智能方法客观地评估复杂的人类伤口和小鼠模型,提高诊断准确度.

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Murine Excisional Wound Healing Model and Histological Morphometric Wound Analysis
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Murine Excisional Wound Healing Model and Histological Morphometric Wound Analysis

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Generation of a Three-dimensional Full Thickness Skin Equivalent and Automated Wounding
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Generation of a Three-dimensional Full Thickness Skin Equivalent and Automated Wounding

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相关实验视频

Last Updated: May 21, 2025

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Digital Planimetry for Assessing Wound Closure Kinetics in a Mouse Model

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Murine Excisional Wound Healing Model and Histological Morphometric Wound Analysis
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Murine Excisional Wound Healing Model and Histological Morphometric Wound Analysis

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Generation of a Three-dimensional Full Thickness Skin Equivalent and Automated Wounding
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Generation of a Three-dimensional Full Thickness Skin Equivalent and Automated Wounding

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科学领域:

  • 生物医学工程 生物医学工程
  • 计算病理学计算病理学
  • 伤口治愈研究研究 伤口治愈研究

背景情况:

  • 伤口愈合障碍是一个重大的临床挑战.
  • 目前的组织学伤口评估是主观的,耗时的,并且经常被遗漏.
  • 宏观和有限的组织学方法缺乏对伤口活检的全面分析.

研究的目的:

  • 开发一种自动化的深度学习 (DL) 方法,用于对组织学伤口图像进行客观和全面的分析.
  • 将这种DL模型应用于分析复杂的人类伤口和小鼠伤口愈合模型.
  • 为了提高伤口特征细分和量化的准确性和效率.

主要方法:

  • 开发了一个深度神经网络 (DNN) 架构,优化了对H&E染色图像中特征性伤口特征的细分.
  • 训练和验证DL模型使用小鼠伤口活检图像在四个愈合时间点.
  • 对DL模型的修订和应用,用于人类复杂的伤口活检的细胞水平分析.

主要成果:

  • 在分析小鼠伤口愈合时,DL模型实现了89%的平均测试集准确度.
  • 在细胞层面分析人类复杂伤口时,模型性能提高到97%的平均测试集准确度.
  • 该方法使人类伤口活检的综合分析和小鼠伤口愈合的准确形态分析成为可能.

结论:

  • 自动化DL方法提供了对H&E染色伤口段的客观和全面分析.
  • 这种方法促进了对小鼠伤口愈合的深入分析和准确的形态计量量化.
  • DL模型有助于分析和量化免疫细胞透,用于临床诊断人体复杂伤口.