Romano Anastasia, De Camillis Antonio, Sciota Domenico, Baghini Simona, Di Provvido Andrea, Rosamilia Alfonso, Capobianco Dondona Andrea, Bernabò Nicola, Vaccarelli Francesca, Corradi Attilio, Marruchella Giuseppe
Department of Veterinary Medicine, University of Teramo, Teramo, Italy.
Local Health Unit Authority, Teramo, Italy.
Front Vet Sci. 2025 May 21;12:1591032. doi: 10.3389/fvets.2025.1591032. eCollection 2025.
Abattoir-based data are widely regarded as suitable tools to estimate farm animals' health and welfare during the entire lifecycles. However, the systematic detection and recording of lesions at postmortem inspection are expensive, time consuming, somewhat biased by inter- and/or intra-observers' variability. Artificial intelligence could solve the above issues, and it could be particularly well-suited for solving repetitive tasks, by automating workflows and improving their efficiency. This study aims to assess whether a CNN, previously trained to score pneumonia in slaughtered pigs, is likewise capable of solving this task in a different animal species (i.e., in lambs). A total of 229 lamb lungs were photographed at postmortem inspection under different field conditions. Picture were evaluated by 5 independent veterinarians with different professional background, who scored each lung as healthy or diseased. The same pictures were scored by the CNN, which highlighted the lung profile, the bent over lobe (if any), and the lesion (if any). Finally, all veterinarians critically rated CNN's assessments. Overall, the CNN was able to solve that task, showing a substantial agreement (Cohen's kappa coefficient between 0.65-0.71) and high level of sensitivity (0.87-0.88), specificity (0.88-0.91), and accuracy (0.87-0.88) when compared to skilled investigators. Shifting CNN to different animal species could facilitate and fasten the adoption of such tools, which could benefit veterinarians and auxiliary staff, mainly where sheep farming is more widespread and economically relevant.
基于屠宰场的数据被广泛认为是评估农场动物在整个生命周期内健康和福利的合适工具。然而，在宰后检查中系统地检测和记录病变既昂贵又耗时，并且在一定程度上受到观察者间和/或观察者内变异性的影响而存在偏差。人工智能可以解决上述问题，并且通过自动化工作流程和提高效率，它可能特别适合解决重复性任务。本研究旨在评估之前经过训练以对屠宰猪的肺炎进行评分的卷积神经网络（CNN）是否同样能够在不同动物物种（即羔羊）中完成这项任务。在宰后检查中，在不同现场条件下对总共229个羔羊肺进行了拍照。由5名具有不同专业背景的独立兽医对照片进行评估，他们将每个肺评为健康或患病。这些相同的照片由CNN进行评分，该网络突出显示了肺的轮廓、弯曲的叶（如果有的话）和病变（如果有的话）。最后，所有兽医对CNN的评估进行了严格评级。总体而言，CNN能够完成该任务，与熟练的调查人员相比，显示出高度一致性（科恩kappa系数在0.65 - 0.71之间）以及高水平的敏感性（0.87 - 0.88）、特异性（0.88 - 0.91）和准确性（0.87 - 0.88）。将CNN应用于不同动物物种可以促进并加速此类工具的采用，这将使兽医和辅助人员受益，主要是在养羊更为普遍且具有经济意义的地方。
