Libera Kacper, Schut Dirk, Kritsi Effrosyni, van Steijn Louis, Dallman Timothy, Lipman Len
Institute for Risk Assessment Sciences (IRAS), Utrecht University, Yalelaan 2, 3584 CM Utrecht, the Netherlands.
Computational Imaging Group, Centrum Wiskunde & Informatica (CWI), Science Park 123, 1098 XG Amsterdam, the Netherlands.
Poult Sci. 2025 May 5;104(8):105264. doi: 10.1016/j.psj.2025.105264.
Animal welfare monitoring is a key part of veterinary surveillance in every poultry slaughterhouse. Among the animal welfare indicators routinely inspected, the prevalence of wing fractures and soft tissues injuries (e.g. bruises) is particularly relevant, because it is related to acute pain and suffering in injured birds. According to current practice, assessment corresponds to visual examination by animal welfare officers. However, taking into consideration the speed of the production line and limitations associated with human inspection (e.g. different visual perception, subjectivism and fatigue), new more objective and automated techniques are desirable. Therefore, the aim of this study was to assess the applicability of three deep learning classification models to detect fractures and/or bruises based on computed tomography (CT) scans and photographs of the wings. Namely, 1. Model_CT (two categories: 1.BROKEN and 2.NON_BROKEN) detecting fractures based on CT scans, 2.Model_Photo_Fractures (1.FRACTURES and 2.NO_FRACTURES) detecting fractures based on photographs and 3.Model_Photo_Bruises (1.BRUISES and 2.NO_BRUISES) detecting bruises based on photographs. To train, validate and test these models 306 CT scans and 285 photographs were collected. The 3D ResNet34 and 2D EfficientNetV2_s architectures were used for the CT and Photo_Models, respectively. The models reached an accuracy of 98 % (Model_CT), 96 % (Model_Photo_Fractures) and 82 % (Model_Photo_Bruises). All in all, applying deep learning to the combination of CT scanning and photography can help to objectively recognize wing fractures and bruises. Consequently, it might lead to more accurate and objective animal welfare monitoring and ultimately to raised animal welfare standards.
动物福利监测是每个家禽屠宰场兽医监督的关键部分。在常规检查的动物福利指标中，翅膀骨折和软组织损伤（如瘀伤）的发生率尤为重要，因为这与受伤禽类的急性疼痛和痛苦有关。根据目前的做法，评估由动物福利官员进行目视检查。然而，考虑到生产线的速度以及与人工检查相关的局限性（如不同的视觉感知、主观性和疲劳），需要更新的、更客观和自动化的技术。因此，本研究的目的是评估三种深度学习分类模型基于翅膀的计算机断层扫描（CT）图像和照片检测骨折和/或瘀伤的适用性。具体而言，1. Model_CT（两类：1. 骨折和2. 未骨折）基于CT图像检测骨折；2. Model_Photo_Fractures（1. 骨折和2. 无骨折）基于照片检测骨折；3. Model_Photo_Bruises（1. 瘀伤和2. 无瘀伤）基于照片检测瘀伤。为了训练、验证和测试这些模型，收集了306张CT图像和285张照片。3D ResNet34和2D EfficientNetV2_s架构分别用于CT模型和照片模型。这些模型的准确率分别达到了98%（Model_CT）、96%（Model_Photo_Fractures）和82%（Model_Photo_Bruises）。总而言之，将深度学习应用于CT扫描和摄影的组合有助于客观地识别翅膀骨折和瘀伤。因此，这可能会带来更准确、客观的动物福利监测，并最终提高动物福利标准。
