AI-based screening and clinical decision support for orthopaedic fracture patients’ diagnosis and treatment planning

Chief Investigators: Dr Beat Schmutz, Dr Jason Dowling, A/Prof Kevin Tetsworth, Prof Michael Schuetz 

Trauma patients presenting to an emergency department (ED) undergo initial standard X-ray imaging and, if required, subsequent computed tomography (CT) scanning. The obtained medical images are then utilised by clinicians for diagnosis and treatment planning.

This research aims to develop automated tools for clinical diagnosis and decision support, utilising artificial intelligence (AI). The AI tool will detect and map the bone fractures on the medical images, followed by classification into standard fracture types. Once integrated in the hospital system, such a screening tool will streamline and standardise patient management, reduce cost, reduce rate of early misdiagnosis and patient radiation.

In the longer term, combining this AI-based tool with big data (patient demographics, mechanism of injury, diagnosis, treatment, patient outcomes) will enable the tool to generate patient-specific and evidence-based treatment recommendations for surgeons.