The University of Sydney’s Brain and Mind
Centre will partner with the Sydney Neuroimaging Analysis Centre to
improve diagnostic neuroimaging of brain ailments such as multiple sclerosis
and dementia.
The Brain and Mind Centre is an institute
within the University researching and developing treatments for conditions of
the brain and mind.
According to the report
released by the University, funding amounting to A$ 2.36 million will be
awarded to the project through the government’s Cooperative
Research Centre-Project (CRC-P) Program as announced by Assistant
Minister for Science, Jobs and Innovation the Hon Zed Seselja.
The CRC-P Program is a competitive
merit-based program supporting industry-led, outcomes-focused partnerships
between industry, researchers and the community.
The investment made by the government is
matched by nearly A$ 2.8 million of cash and in-kind contributions by the
project partners of both the University and the Brain and Mind Centre,
including Sydney Neuroimaging Analysis Centre (SNAC) and the I-MED Radiology
Network.
Brain and Mind Centre’s Professor of
Neurology, Dr Michael Barnett said that they are aiming to transform the
delivery of neuro-radiology services across Australia.
Dr Barnett, who is also a consultant
neurologist at Royal Prince Alfred Hospital in Sydney, added that they are
planning to do this project by developing novel, automated algorithms that aid
in both the diagnosis and monitoring of brain diseases using magnetic resonance
images and CT scans.
It is estimated that clinicians
misinterpret up to 4% of medical images, a figure that is likely to be higher
in demanding subspecialties such as neuro-imaging.
Dr Barnett explained that when these
algorithms are built they will be deployed on an artificial intelligence (AI)
platform that integrates with routine clinical radiology workflows to
dramatically improve productivity, enhance reporting accuracy and rapidly
identify critical imaging abnormalities.
The commercial application of AI in the
medical imaging industry is currently in its infancy, driven by independent
technology companies targeting individual patients, rather than enhancing
innovation in the radiology and research-imaging industries.
Moreover, tech companies do not have access
to well-characterised clinical populations need to derive the development of
accurate algorithms.
The Sydney Neuroimaging Analysis Centre
(SNAC) is a state-of-the-art facility established at the Brain and Mind Centre
in 2012.
It facilitates novel imaging biomarker
research and makes quantitative analysis of magnetic resonance imaging (MRI)
images available to the pharmaceutical industry and researchers undertaking
Phase 2-4 clinical trials.
SNAC, together with the University’s
experts, will lead the project’s three-year implementation to develop an
artificial intelligence platform and neuro-imaging algorithms based on deep
learning ‘artificial neural networks’.
Deep learning is a collection of machine or
computer learning algorithms capable of recognising patterns in data. The data,
in this case, are brain images without manual labelling or identification of
their features.
The University’s project team includes top
AI scientist Professor Dacheng Tao; neurologist and academic lead for the
biomedical data initiative, Professor Michael Barnett; and multimodal imaging
expert Professor Tom (Weidong) Cai.
I-MED, a project partner, is a national
radiology provider that processes 4.2 million clinical images annually at more
than 200 clinics across Australia.
It will be supplying the bulk of the
project’s de-identified imaging and reporting data to inform algorithm
development and validation.
The University’s Deputy Vice Chancellor,
Research Professor Duncan Ivison said the project was a benchmark for how to
improve health outcomes. He commended the government and project partners for
funding this effort to improve diagnostic neuro-imaging for the benefit of
people with degenerative brain disorders.
He concluded that collaboration and
multidisciplinary research hold the key to solving the biggest healthcare
challenges and this project is a great example of this approach.
