Artificial intelligence to improve medical imaging for patients with brain ailments
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.
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