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Updated: Jan 15, 2026

A Porcine Model of Acute Autologous Pulmonary Embolism
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A Porcine Model of Acute Autologous Pulmonary Embolism

Published on: September 6, 2024

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计算机视觉模型的比较,用于检测猪慢性肺炎.

Daniel Hjorth Lund1, Lis Alban2, Matt Denwood3

  • 1Section of Animal Welfare, Danish Technological Institute, Taastrup, Denmark.

Preventive veterinary medicine
|October 7, 2025
PubMed
概括

计算机视觉系统 (CVS) 在检测猪慢性肺炎方面表现出很高的准确性,在灵敏度上优于传统的肉类检查. 这项技术为肉类检查框架中的质量保证提供了准确,统一的注册.

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科学领域:

  • 兽医诊断 兽医诊断 兽医诊断 兽医诊断
  • 农业中的人工智能
  • 食品安全技术 食品安全技术

背景情况:

  • 慢性肺炎对猪生产率产生负面影响,对屠宰场质量保证具有重要意义.
  • 慢性肺炎的官方注册不再是丹麦当局的优先事项.
  • 精确检测慢性肺炎对于了解其患病率和经济影响至关重要.

研究的目的:

  • 评估计算机视觉系统 (CVS) 中三个卷积神经网络 (CNN) 模型的诊断性能,用于检测猪尸体中的慢性肺炎.
  • 将CVS诊断性能与官方肉类检查方法进行比较.
  • 评估CVS技术在基于风险的肉类检查方面的潜力.

主要方法:

  • 在15个屠宰日内分析了85,413头猪尸体的数据.
  • 使用传统协议统计和贝叶斯潜伏类模型的评估,独立于黄金标准.
  • 使用科恩卡帕和流行率和偏差调整的卡帕,CVS模型和人肉检查员之间的灵敏度和特异性的比较.

主要成果:

  • CVS模型显示,与肉类检查员的一致程度中等到近乎完美 (kappa值为0.72-0.89).
  • 与肉类检查员 (79.4-83.0%) 相比,CVS模型表现出更高的灵敏度 (84.7-90.3%).
关键词:
尸体的评估和评价隐性类型建模的模型.机器视觉 机器视觉 机器视觉肉类的安全保证

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  • 肉类检查员保持了比CVS模型 (97.6-98.8%) 略高的特异性 (99.7-99.9%).
  • ResNeXt-101架构 (CVS-Complex-HighRes) 的表现最好;来自多个模型的共识改善了特异性.
  • 除了轻微的缺陷,CVS检测与其他肉类检查结果之间没有发现显著的关联.

    • 结论:

    • 计算机视觉系统可以精确和统一地检测慢性淋巴结膜炎,补充传统的肉类检查.
    • 在基于风险的肉类检查框架中,CVS技术显示出实施的前景.
    • 需要进一步的工作来确定性能门,并解决商业采用监管方面的问题.