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可以解释的猫的自动疼痛识别.

Marcelo Feighelstein1, Lea Henze2, Sebastian Meller2

  • 1Information Systems Department, University of Haifa, Haifa, Israel.

Scientific reports
|June 2, 2023
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使用人工智能 (AI) 对猫的自动疼痛识别显示出有希望. 一种基于里程碑的AI方法在检测不同猫种群的疼痛方面取得了超过77%的准确性,超过了深度学习模型.

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科学领域:

  • 兽医医学 兽医医学 兽医医学
  • 动物福利科学 动物福利科学
  • 医疗保健中的人工智能

背景情况:

  • 手动评估猫的疼痛是主观的,需要专业知识.
  • 目前正在为包括猫在内的各种物种开发自动疼痛识别系统.
  • 以前的研究表明,深度学习和基于里程碑的方法的准确性相似,但使用的是同质的数据集.

研究的目的:

  • 评估AI模型来对异质猫群的疼痛进行分类.
  • 为了比较深度学习与基于里程碑的方法在现实环境中的通用性.
  • 为了研究AI疼痛识别在猫的可解释性.

主要方法:

  • 在84只客户猫的面部图像上使用两种方法 (基于深度学习和基准) 训练人工智能模型.
  • 使用基于格拉斯哥综合测量疼痛量表和临床病史的专家兽医评分.
  • 分析了人工智能疼痛分类中重要的面部特征.

主要成果:

  • 基于里程碑的方法在疼痛检测方面取得了超过77%的准确性.
  • 深度学习方法实现了超过65%的准确性.
  • 鼻子和口腔区域被确定为比耳朵区域更重要的疼痛分类区域.

结论:

  • 一种基于里程碑的AI方法在各种猫种群中展示了自动疼痛识别的卓越性能.
  • 人工智能模型可以识别猫的关键面部疼痛指标.
  • 需要进一步的研究来完善人工智能,以客观地评估动物的疼痛.