Health and Biosecurity

AI for Early Detection of Neurodegenerative Disorder in Elderly

This Project aims to develop outcome measures for early detection of neurodegenerative disorders using artificial intelligence (AI) and various sensing modalities, offering personalised support to older adults. The expected outcome is the creation of AI algorithms to detect early signs of neurodegenerative disorders in older adults living independently at home. The potential benefit is early detection of neurodegenerative disorders in older adults, improving quality of life and effective disease management.

Prediction of molecular interactions with RNA using AI

This Project will develop deep-learning models to predict interactions of ribonucleic acid (RNA) with other molecules. The expected outcomes are to improve prediction capabilities to decode RNA interactions in disease mechanisms, identify novel therapeutic modalities, and improve existing therapies for targeting RNA. This could result in enhanced capacity to design new therapies and potential to optimise RNA targeting molecules for therapeutic applications. 

Applying imaging methods and data analytics to explore the listening brain

This Project aims to understand brain circuits and processes supporting communication in individuals with hearing problems, including those who use devices such as hearing aids and cochlear implants. The potential benefits are that individualised strategies based on real-time brain states estimate algorithms to empower listening and support effective communication. The Project will use brain-imaging techniques‚ including those compatible with listening technologies, including electroencephalogram (EEG), to explore the listening brain. The Project will explore brain changes that arise from hearing loss, how changes in brain function – within and beyond the auditory brain – arise to support listening when hearing is impaired, and how these findings can be used as a part of devices such as cochlear implants that engage the rest of the brain to support an individual's listening. 

Synthetic CT via Generative AI for MR-guided Radiotherapy Planning in the Abdomen and Lungs

This Project will leverage artificial intelligence to develop and validate synthetic computed tomography (CT) from magnetic resonance imaging (MRI) in the abdominal and lung regions. The expected outcomes are an AI-based synthetic CT model, thorough technical and clinical validation and potential patent/licensing opportunities. This may reduce unnecessary ionising radiation of CT in patients and improve treatment efficiency during radiotherapy planning.

Data analytics for enhanced hearing health

This project aims to consolidate hearing health data with other datasets to unlock valuable insights and drive personalised hearing healthcare interventions. The expected outcomes are to streamline access to hearing health data, improve data connectivity, and enhance treatment outcomes. The potential benefit is to facilitate informed decision-making for healthcare providers to enable better patient care.

Communicating in the real world

This project aims to utilise existing sensor technologies to evaluate the impact of hearing loss when communicating in noisy environments. The expected outcome is the discovery of novel analysis methods that utilise multi-sensor fusion techniques. The potential benefit is improved outcomes for individuals with hearing loss.