This paper explores the innovative use of simulation environments to enhance data acquisition and diagnostics in veterinary medicine, focusing specifically on gait analysis in dogs. The study harnesses the power of Blender and the Blenderproc library to generate synthetic datasets that reflect diverse anatomical, environmental, and behavioral conditions. The generated data, represented in graph form and standardized for optimal analysis, is utilized to train machine learning algorithms for identifying normal and abnormal gaits. Two distinct datasets with varying degrees of camera angle granularity are created to further investigate the influence of camera perspective on model accuracy. Preliminary results suggest that this simulation-based approach holds promise for advancing veterinary diagnostics by enabling more precise data acquisition and more effective machine learning models. By integrating synthetic and real-world patient data, the study lays a robust foundation for improving overall effectiveness and efficiency in veterinary medicine.

翻译：本文探讨了仿真环境在兽医领域中用于增强数据采集和诊断的创新应用，重点关注犬类步态分析。研究利用Blender和Blenderproc库生成反映多样化解剖结构、环境条件和行为状态的合成数据集。生成的数据以图结构形式呈现并进行标准化处理，用于训练机器学习算法以识别正常与异常步态。通过创建两种不同相机角度粒度的数据集，进一步探究了相机视角对模型精度的影响。初步结果表明，这种基于仿真的方法通过实现更精确的数据采集和更高效的机器学习模型，有望推动兽医诊断技术的发展。通过整合合成数据与真实患者数据，该研究为提升兽医医学的整体效能和效率奠定了坚实基础。