Associate Professor Antonio Di leva, pictured, and his team have pioneered the use of AI-driven technology to improve the accuracy of brain images and in turn improve patient diagnosis and treatment.
Original Published Date: 
Friday, June 26, 2020

Full article issued by Macquarie University (MQ).

A team of Macquarie University neurosurgery and computer science researchers, including ARC Future Fellow, Associate Professor Antonio Di Ieva, is investigating the use of Artificial Intelligence and other computer tools to improve the study of brain disease in the University’s world-first Computational NeuroSurgery (CNS) Laboratory, with results already indicating far-reaching impact for disease diagnosis and treatment.

The CNS Lab which was recently founded by Associate Professor Antonio Di Ieva, who is also a practising neurosurgeon at Macquarie University Hospital, is focused on developing computerised tools to produce more accurate images of the brain. The research focus of the lab is to develop new computer methods to identify novel diagnostic, prognostic and therapeutic markers of brain diseases, such as brain tumours and cerebrovascular diseases. 

In their latest research, led by Di Ieva and collaborator, Dr Sidong Liu, an NHMRC Early Career Fellow from the Australian Institute of Health Innovation, and in partnership with several centres at Macquarie University and overseas (Universities of Istanbul and Doha), the team used an AI method called Deep Learning to analyse surgical samples of gliomas—the most common primary brain tumours—and to predict patient outcome and treatment. 

By using Deep Learning to analyse surgical samples of gliomas, this allows a much faster and cheaper way to predict the presence of a important DNA marker for the disease, improving and speeding up the treatment of patients affected by brain cancer.

 

Photo credit: 

Associate Professor Antonio Di leva, pictured, and his team have pioneered the use of AI-driven technology to improve the accuracy of brain images and in turn improve patient diagnosis and treatment. Credit: MQ.