Using simulation to calibrate real data acquisition in veterinary medicine

Document detail

ID

oai:arXiv.org:2307.11695

Topic

Computer Science - Machine Learnin...

Author

Strzałka, Krystian Mazurek, Szymon Wielgosz, Maciej Russek, Paweł Caputa, Jakub Łukasik, Daria Krupiński, Jan Grzeszczyk, Jakub Karwatowski, Michał Frączek, Rafał Jamro, Ernest Pietroń, Marcin Koryciak, Sebastian Dąbrowska-Boruch, Agnieszka Wiatr, Kazimierz

Year

2023

listing date

7/26/2023

Keywords

acquisition medicine veterinary

Metrics

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.

Strzałka, Krystian,Mazurek, Szymon,Wielgosz, Maciej,Russek, Paweł,Caputa, Jakub,Łukasik, Daria,Krupiński, Jan,Grzeszczyk, Jakub,Karwatowski, Michał,Frączek, Rafał,Jamro, Ernest,Pietroń, Marcin,Koryciak, Sebastian,Dąbrowska-Boruch, Agnieszka,Wiatr, Kazimierz, 2023, Using simulation to calibrate real data acquisition in veterinary medicine