# Bridging Species with AI: A Cross-Species Deep Learning Model for Fracture Detection and Beyond

## TL;DR

This paper introduces an AI model that detects fractures in horses by adapting human medical data, with potential applications across species.

## Contribution

A cross-species deep learning model for fracture detection using transfer learning and advanced imaging techniques.

## Key findings

- The model achieved 96.7% accuracy for modality classification and 97.2% for projection recognition.
- Fracture localization had intersection over union values of 0.71–0.84 across equine datasets.
- The model can be adapted for use in other veterinary species and human healthcare applications.

## Abstract

Fractures are a leading cause of morbidity and mortality in Thoroughbred racehorses, posing a significant threat to their welfare and careers. This study introduces a deep learning model specifically designed to facilitate fracture detection in equine athletes. By leveraging extensive training on human fracture data and refining the model with equine imaging, it highlights the transformative potential of transfer learning across species and medical contexts. This approach is not limited to equine fractures but could be adapted for use in detecting injuries or conditions in other veterinary species and even human healthcare applications. A comprehensive databank of radiographs, sourced from public archives and equine hospitals, was curated to encompass diverse conditions (fracture and non-fracture), ensuring robust pattern recognition. The architecture integrates a Vision Transformer for global context modelling with a ResNet backbone and loss function to optimize local feature extraction and cross-species adaptability. The pipeline achieved 96.7% accuracy for modality classification, 97.2% accuracy for projection recognition, and fracture localization intersection over union values of 0.71–0.84 across equine datasets. This work bridges advancements in human and veterinary medicine, opening pathways for AI-driven solutions that extend beyond fractures, fostering improved diagnostic precision and broader applications across species (felines, canines, etc.). By integrating advanced imaging techniques with AI, this study aims to set a foundation for more comprehensive and versatile health monitoring systems.