猪肺图像的评分:计算机视觉系统和人类评估器之间的比较
Robert Valeris-Chacin1, Beatriz Garcia-Morante2,3,4, Marina Sibila2,3,4
1Veterinary Education, Research, and Outreach (VERO), Department of Veterinary Pathobiology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, Canyon, TX, USA.
Veterinary research
|January 13, 2025
概括
计算机视觉系统 (CVS) 提供了一种一致的替代方案,用于对猪的中肺结合 (CVPC) 进行评分. 虽然人类的评分显示了变化,但AI DIAGNOS系统为CVPC检测提供了可靠,可重复的结果.
科学领域:
- 兽医病理学 兽医病理学
- 农业技术 农业技术
- 猪健康管理 猪健康管理
背景情况:
- 中肺结合 (CVPC) 是屠宰猪中普遍存在的肺病变,经常与Mycoplasma hyopneumoniae有关.
- 目前的CVPC手动评分方法缺乏简单性,速度和一致的验证.
- 对CVPC的客观评估对于群体健康监测和猪生产经济影响评估至关重要.
研究的目的:
- 评估计算机视觉系统 (CVS) 的性能,AI DIAGNOS,在猪肺中的CVPC得分.
- 为了比较CVS得分与人类评估者的准确性和一致性.
- 评估CVPC评分中的评估者内部和评估者间的变化.
主要方法:
- 使用AI DIAGNOS计算机视觉系统对1050张背部视图猪肺图像进行分析.
- 将CVS生成的CVPC分数与人类评估人员分配的分数进行比较.
- 评估总肺损伤得分,叶片特异性损伤得分,以及受影响肺部区域的百分比.
- 使用类内相关系数 (ICC) 评估评估者内部和评估者间的变化.
主要成果:
- CVS在二元分类中显示了中度的准确性 (62-71%) 损伤与非损伤的肺叶,不包括隔膜叶.
- 在肺叶水平上观察到低的多类分类准确性 (24-36%).
- 在人类得分者中,观察到中度至高的评估者间变异性 (ICC:0.29-0.6).
- CVS表现出完美的一致性,在所有图像中为每个肺叶提供相同的分数.
结论:
- 在屠宰检查中,AI DIAGNOS计算机视觉系统显示出作为手动CVPC评分的一致替代方案的潜力.
- 该系统的二进制分类准确性和完美的一致性支持其用于检测肺病变的实用性.
- 可能需要进一步细化,以提高肺叶水平的多类分类准确性.
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