AI improves medical imaging to benefit people with brain ailments
Announced this week by , the funding comes via the government鈥檚 , a competitive, merit-based program supporting industry-led, outcomes-focused partnerships between industry, researchers and the community.
The government鈥檚 investment is matched by nearly $2.8 million of cash and in-kind contributions by the University and Brain and Mind Centre鈥檚 project partners, including (SNAC) and the .
We aim to transform the delivery of neuro-radiology services across Australia
鈥淲e aim to transform the delivery of neuro-radiology services across Australia,鈥� says the , who is also a consultant neurologist at Royal Prince Alfred Hospital in Sydney.
鈥淲e plan to do this by developing novel, automated algorithms that aid in both the diagnosis and monitoring of brain diseases using and .鈥�
It鈥檚 estimated that clinicians misinterpret up to four percent of medical images, a figure likely to be even higher in demanding subspecialties such as neuro-imaging.
鈥淲hen 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,鈥� Professor Barnett says.
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.
These tech-companies also lack access to well-characterised clinical populations needed to drive the development of accurate algorithms.聽
It鈥檚 estimated that clinicians misinterpret up to four percent of medical images, a figure likely to be even higher in demanding subspecialties such as neuro-imaging
(SNAC) was established at the Brain and Mind Centre in 2012 to facilitate novel imaging biomarker research and make quantitative analysis of magnetic resonance imaging (MRI) images available to the pharmaceutical industry and researchers undertaking Phase 2-4 clinical trials.
Together with leading University of Sydney experts, SNAC will lead the project鈥檚 three-year implementation to develop what Professor Barnett describes as 鈥渁n artificial intelligence platform and neuro-imaging algorithms based on deep learning 鈥榓rtificial neural networks鈥欌��.
Deep learning is a collection of machine or computer learning algorithms capable of recognising patterns in data, in this case brain images, without manual labelling or identification of their features.
The University鈥檚 project team includes top AI scientist , neurologist and academic lead for the biomedical data initiative, , and multimodal imaging expert .
Project partner, I-MED, is a national radiology provider that processes 4.2 million clinical images annually at more than 200 clinics across Australia. It will supply the bulk of the project鈥檚 de-identified imaging and reporting data to inform algorithm development and validation.
I commend the government and project partners for funding this effort to improve diagnostic neuro-imaging for the benefit of people with degenerative brain disorders
The University of Sydney鈥檚 said the project was a benchmark for how to improve health outcomes.
鈥淚 commend the government and project partners for funding this effort to improve diagnostic neuro-imaging for the benefit of people with degenerative brain disorders,鈥� said Professor Ivison.
鈥淐ollaboration and multidisciplinary research hold the key to solving our biggest healthcare challenges and this project is a great example of this approach.鈥�
