Application of Deep Learning for the rapid and accurate diagnostic of bone fractures in dogs using radiographic images
Abstract
Radiography remains the most widely used diagnostic tool in veterinary medicine for detecting bone fractures in dogs. However, manual interpretation can be affected by the clinician’s level of experience, as well as by factors such as fatigue or excessive workload. In this context, the present study evaluated the performance of a deep learning model based on the YOLOv5 architecture, aimed at diagnostic canine radiographic images divided into two categories: presence or absence of fracture. The model achieved an accuracy of 83.3%, significantly outperforming three general practice veterinarians, whose accuracy ranged from 40% to 70%. Furthermore, the automated system reduced the average diagnostic time by 40%, delivering classifications within seconds. These results highlight the feasibility of artificial intelligence as a tool to enhance diagnostic precision, speed, and consistency in veterinary medicine, especially in resource- constrained environments. Further dataset expansion and clinical validation are recommended.
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References
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