Bonafini Beatriz Leandro, Breuer Lukas, Ernst Lisa, Tolba René, Oliveira Lucas Ferrari de, Abreu de Souza Mauren, Czaplik Michael, Pereira Carina Barbosa
Post Graduate Program in Technology in Health, Polytechnique School, Pontifical Catholic University of Paraná, Curitiba 80215-901, Brazil.
Department of Anesthesiology, Faculty of Medicine, RWTH Aachen University, 52074 Aachen, Germany.
Animals (Basel). 2024 Nov 25;14(23):3398. doi: 10.3390/ani14233398.
The validation of methods for understanding the effects of many diseases and treatments requires the use of animal models in translational research. In this context, sheep have been employed extensively in scientific studies. However, the imposition of experimental conditions upon these animals may result in the experience of discomfort, pain, and stress. The ethical debates surrounding the use of animals in research have resulted in the adoption of Directive 2010/63/EU. The present study proposes a non-contact method for monitoring the respiration rate of sheep based on video processing. The Detecron2 model was trained to segment the sheep's body, abdominal, and facial regions in the video frames. A motion-tracking algorithm was developed to assess abdominal movement associated with the sheep's respiratory cycle. The method was applied to videos of Rhön sheep under experimental and housing conditions, utilising two types of cameras to assess the effectiveness of the proposed approach. The mean average error (MAE) obtained was 0.79 breaths/minute for the visible and 1.83 breaths/minute for the near-infrared (NIR) method. This study demonstrates the feasibility of video technology for simultaneous and non-invasive respiration monitoring, being a crucial parameter for assessing the health deterioration of multiple laboratory animals.
理解多种疾病和治疗效果的方法验证需要在转化研究中使用动物模型。在此背景下,绵羊已被广泛应用于科学研究。然而,对这些动物施加实验条件可能会导致它们感到不适、疼痛和压力。围绕在研究中使用动物的伦理辩论导致了欧盟第2010/63/EU号指令的通过。本研究提出了一种基于视频处理的非接触式监测绵羊呼吸频率的方法。对Detecron2模型进行训练,以在视频帧中分割绵羊的身体、腹部和面部区域。开发了一种运动跟踪算法来评估与绵羊呼吸周期相关的腹部运动。该方法应用于罗恩绵羊在实验和饲养条件下的视频,使用两种类型的相机来评估所提方法的有效性。对于可见光方法,获得的平均绝对误差(MAE)为0.79次呼吸/分钟,对于近红外(NIR)方法为1.83次呼吸/分钟。本研究证明了视频技术用于同时进行非侵入性呼吸监测的可行性,呼吸监测是评估多种实验动物健康恶化的关键参数。