Apollo Medical Imaging Technology Pty. Ltd.

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Pictured: CT perfusion imaging software tool to guide acute stroke therapy

Victoria

Project title: Artificial Intelligence-based Clinical Decision Support Software for Guiding Acute Stroke Therapy

BMTH Round: Third

Apollo is developing an artificial intelligence-based clinical decision support system (AICDSS) for predicting good and poor patient outcomes of acute stroke therapy.

Apollo is developing an artificial intelligence-based clinical decision support system (AICDSS) for predicting good and poor patient outcomes of acute stroke therapy.

Stroke is the leading cause of adult disability in the world. With one new stroke occurs every 10 mins and >50,000 strokes per year in Australia, stroke is the leading cause of adult disability and costs the Australian health system an estimated $5 billion per year. Ischaemic stroke is caused by a clot blocking a brain vessel, so identifying and removing the clot is the key of acute stroke therapy.

In collaboration with Prof Mark Parsons' group involving the University of Newcastle, the Melbourne Brain Centre, Ingham Institute for Applied Medical Research and the University of NSW, Apollo has partnered in several NHMRC projects with the establishment of the International Stroke Perfusion Imaging Registry (INSPIRE). With funding support of Accelerating Commercialisation from AusIndustry, Apollo has developed and commercialised a software tool (AutoMIStar) to aid doctors making diagnostic and treatment decision.

There are two treatment approaches currently available including thrombolysis (clot dissolving) and thrombectomy (clot retrieval). Making decisions about how to treat a patient is a challenging task for clinicians, and current clinical guidelines provide limited assistance to clinicians without effective tools to predict how an individual patient will respond to each treatment.

In collaboration with Prof Mark Parsons' group, the project will aim to develop an artificial intelligence-based clinical decision support system (AICDSS) for predicting good and poor patient outcomes for stroke patients if treated by each therapy respectively.

Project Partner: Ingham Institute for Applied Medical Research, University of New South Wales (UNSW).

Contact: Qing Yang, PhD