Current Areas of Study
Huge research has been taking place over the year in regards to machine learning applied to musculoskeletal imaging. Our research group has focused on five main research areas; namely scoliosis, Zebrafish vertebrae segmentation, knee injuries and hamstring muscles tear.
Cardiovascular imaging has largely taken advantage of recent achievements in Machine Learning. We are focusing our research on semi-supervised segmentation of MRI scans, particularly by doing label propagation using image registration of the short axis view.
Cancer remains one of the leading causes of mortality globally despite the growing availability of high efficacy treatment options. The standard clinical approach to identify the best treatment is to iteratively trial treatments until a durable clinical benefit is achieved. This poses challenge, as clinicians race to find optimal treatments. A clear clinical need exists for predictive tools which can provide insight into the most likely treatments to provide durable clinical benefit.
Machine Learning and AI have the potential to revolutionise the clinical approach to selecting appropriate treatments by providing more accurate, early predictions of successful treatment for a given patient. We are leveraging both radiomic and deep learning approaches on multiple timepoint scans to deliver higher accuracy, early predictions and metrics which quantify response to treatment on a continuous basis.