Feighelstein Marcelo, Luna Stelio P, Silva Nuno O, Trindade Pedro E, Shimshoni Ilan, van der Linden Dirk, Zamansky Anna
Department of Information Systems, University of Haifa, Haifa, Israel.
School of Veterinary Medicine and Animal Science, Sao Paolo State University (Unesp), São Paulo, Brazil.
Sci Rep. 2025 Jan 3;15(1):626. doi: 10.1038/s41598-024-83950-y.
This study explores the question whether Artificial Intelligence (AI) can outperform human experts in animal pain recognition using sheep as a case study. It uses a dataset of N = 48 sheep undergoing surgery with video recordings taken before (no pain) and after (pain) surgery. Four veterinary experts used two types of pain scoring scales: the sheep facial expression scale (SFPES) and the Unesp-Botucatu composite behavioral scale (USAPS), which is the 'golden standard' in sheep pain assessment. The developed AI pipeline based on CLIP encoder significantly outperformed human facial scoring (AUC difference = 0.115, p < 0.001) when having access to the same visual information (front and lateral face images). It further effectively equaled human USAPS behavioral scoring (AUC difference = 0.027, p = 0.163), but the small improvement was not statistically significant. The fact that the machine can outperform human experts in recognizing pain in sheep when exposed to the same visual information has significant implications for clinical practice, which warrant further scientific discussion.
本研究以绵羊为案例，探讨人工智能（AI）在动物疼痛识别方面是否能超越人类专家。该研究使用了一个数据集，其中有48只绵羊接受手术，并在手术前（无疼痛）和手术后（疼痛）进行了视频记录。四位兽医专家使用了两种疼痛评分量表：绵羊面部表情量表（SFPES）和圣保罗大学 - 博图卡图综合行为量表（USAPS），后者是绵羊疼痛评估的“金标准”。当获取相同的视觉信息（正面和侧面面部图像）时，基于CLIP编码器开发的AI管道在面部评分方面显著优于人类（AUC差异 = 0.115，p < 0.001）。它在USAPS行为评分方面也有效等同于人类（AUC差异 = 0.027，p = 0.163），但这种小幅度的提升在统计学上并不显著。当机器接触相同视觉信息时，在识别绵羊疼痛方面能超越人类专家这一事实对临床实践具有重要意义，值得进一步进行科学探讨。
