  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.
