By harnessing large amounts of complex medical data more efficiently, artificial intelligence (AI) has the potential to help discover new disease mechanisms, facilitate early diagnosis, and optimise treatment strategies for chronic conditions such as MS.
In this project, Dr Chenyu Wang and Professor Michael Barnett will leverage the power of AI to develop comprehensive tools for MRI (Magnetic Resonance Imaging) scan analysis that are specifically designed to uncover and measure progression in MS before it manifests clinically, enabling early intervention to prevent future disability. These tools will transform traditional qualitative radiology reporting with accurate, quantitative measures of disease progression.
The team’s second objective is to engineer a system capable of interpreting AI-generated MRI analyses in the appropriate clinical context by benchmarking against people living without MS, as well as other people living with MS with a comparable disease phenotype.
Dr Wang and Professor Barnett aim to utilise these systems, which will support precision management of people living with MS, integrating them into research community platforms such as the MSBase Imaging Repository (MSBIR), a unique, global multi-site image analysis platform that is accelerating clinical-imaging MS research in conjunction with MSBase. In conjunction with their industry partner, Sydney Neuroimaging Analysis Centre, the team will translate these systems into practical clinical applications.
In their pursuit of this goal, the Dr Wang and Professor Barnett intend to conduct a ‘virtual clinical trial’, utilising data derived from MSBase and MSBIR, to enhance their understanding of the effectiveness of current MS treatments on disease progression in a real-world dataset.
Over the past year, Associate Professor Wang and Professor Barnett have made significant progress in developing advanced MRI tools to detect early changes in the brains of people with MS. They focused on creating automated methods that identify subtle brain changes (imaging biomarkers) before major symptoms appear.
A key achievement was developing the Grade-Adjusted Decay (GRADE) algorithm. GRADE helps distinguish slow, chronic MS lesion expansion (that is likely due to a low grade of inflammation) from newly formed lesions (that are likely due to acute inflammation). By doing so, it offers a more precise way to monitor the ongoing, low-grade inflammation that drives long-term disability in MS.
Associate Professor Wang and Professor Barnett have collected and processed thousands of MRI scans from hundreds of people with MS across different centres around Australia. Their strict data collection, image processing, and expert manual validation ensures their methods are robust and accurate and can be deployed widely later.
Working with MSBase/MSBase Imaging Repository (MSBIR) and Sydney Neuroimaging Analysis Centre, Associate Professor Wang and Professor Barnett have started integrating their imaging biomarkers into MSBIR. They will continue this work over the next year to make their tools freely available to the broad MS research community via MSBIR.
Overall, Associate Professor Wang and Professor Barnett’s research supports transforming complex imaging data into practical tools using AI and advanced digital solutions that can predict disease progression and support clinical decision-making for MS. Associate Professor Wang and Professor Barnett have submitted a paper based on their research for publication in a peer-reviewed scientific journal.
Last updated 31 March 2025
$750,000
2024
3 years
Current project
