Using simulation to calibrate real data acquisition in veterinary medicine

TL;DR

This study demonstrates how simulation environments like Blender can generate synthetic gait data for dogs, improving machine learning diagnostics in veterinary medicine by combining simulated and real data.

Contribution

It introduces a novel simulation-based method for data acquisition in veterinary diagnostics, specifically for gait analysis, enhancing model training and accuracy.

Findings

Synthetic datasets improve gait classification accuracy.

Camera angle influences model performance.

Simulation enhances data diversity for machine learning.

Abstract

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.