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Several scientific equine projects are scheduled for our new purpose built Equine Research & Development Centre on Epsom Ave, Ascot. Western Australia.

Collaborating with leading equine veterinarians and universities both here and abroad we seek to bring together the best minds and state-of-the-art imaging technology to get a better understanding of the pathophysiological processes that lead to lameness and fatigue fractures.

The research projects include:

AI-Based Lameness Predictor for Thoroughbred Horses.

The aim of this project is to investigate the feasibility of using artificial intelligence – specifically, deep neural networks – applied to video of horse movement to quantify lameness and injury risk. Working in collaboration with Dr Andre Kyme of the Biomedical Engineering Faculty, The University of Sydney our first objective is to develop a convolutional neural network trained on consensus 5-point grading scores from multiple experienced observers, and to validate the accuracy of this model on unseen data. A subsequent objective is to refine the model training and validation using the ground-truth findings from clinical imaging.

Ethics Approved  In compliance with Section 27 of the NSW Animal Research Act 1985, this Animal Research Authority (ARA) remains in force for a period of 12 months, unless cancelled sooner. Project number: 2020/1846

Relationship between serum biomarkers and equine nuclear medicine radionomics in the prediction of lameness and fracture.

The purpose of this study is to evaluate the usefulness of measuring serum BCM in risk stratifying ENM scans in association with radiomic analysis. This will be achieved with the following research objectives:


  • Examine the strength of the association of serum bone and cartilage biomarker (BCM) concentrations with increased radionuclide uptake in racing/training thoroughbreds.

  • To evaluate the prognostic value of concomitant BCM levels in the risk stratification of lameness and spontaneous bone failure.

  • Develop a radiomic platform to identify high-risk bone lesions, which may serve as a clinically useful predictive tool.

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