AI and Retinal Imaging to Transform Chronic Disease Detection
A new research initiative spearheaded by the Digital Health Cooperative Research Centre (DHCRC), Monash University, and Optain Health is set to redefine how chronic diseases are detected in Australia using artificial intelligence (AI) and retinal imaging to enable earlier, non-invasive diagnosis.
The collaborative project aims to develop a foundational AI model capable of identifying a wide range of systemic diseases, including cardiovascular disease (CVD) and chronic kidney disease (CKD), directly from retinal images. By combining these images with large-scale linked health datasets, the research team hopes to create accurate and scalable screening tools to support early diagnosis, treatment, and prevention.
Chronic diseases such as CVD and CKD affect nearly half of Australians aged over 65, posing a significant burden on individuals, families, and the health system. Because symptoms often appear late in the disease trajectory, early detection remains a major challenge. Existing diagnostic tools are frequently invasive, expensive, or lack the precision required for widespread clinical use.
This new research leverages oculomics, the emerging science of identifying systemic disease biomarkers through the eye. Often described as a “window to whole-body health,” the retina can reveal early signs of a range of diseases long before symptoms appear elsewhere in the body.
“Research has shown that the retina provides a unique, non-invasive glimpse into the body’s vascular and neural system,” said Associate Professor Zongyuan Ge, from the AIM for Health Lab at Monash University’s Faculty of Information Technology. “We hypothesise that oculomics will help us develop rapid, non-invasive, cost-effective biomarkers to detect systemic diseases and predict future risks to prioritise treatment. In fact, the project promises to revolutionise systemic disease screening by providing a highly accurate and accessible diagnostic tool of oculomics.”
While traditional oculomic approaches rely on manual image analysis, the DHCRC-led project will apply advanced AI techniques to de-identified, longitudinal data from hundreds of thousands of participants. The goal is to build a multimodal AI model that can detect multiple diseases simultaneously, offering a more comprehensive and efficient approach than current single-disease tools.
According to the project partners, the initiative represents a major step toward integrating AI-powered retinal screening into routine healthcare, potentially transforming population-level prevention strategies.
“Our goal is to develop a foundational AI model capable of detecting a broad range of systemic diseases, including cardiovascular and chronic kidney disease, all from a simple retinal image. Early detection of these conditions can empower patients to take greater control of their own health, enabling earlier intervention, and helping shift healthcare towards prevention rather than treatment,” said Dr Zachary Tan, President of Optain Health.
Jeff Dunkel, CEO of Optain Health, added: “This project highlights the strength of Australia’s research ecosystem – uniting clinical expertise, advanced AI research, and translational capability to deliver innovations that improve patient care on a global scale.”
DHCRC CEO Annette Schmiede said the project has the potential to enhance the cost-effectiveness of healthcare, ensuring that each health dollar is spent wisely and efficiently. “By integrating ocular imaging with AI, the project tailors healthcare to individual needs, allowing for early detection of chronic diseases, which are becoming an increasing health and economic burden as our population ages,” Ms Schmiede said. “Innovative solutions like this that enable early detection and intervention, effectively deliver better health outcomes, and ultimately lower health care costs.”
If successful, the project could pave the way for AI-assisted retinal imaging to become a cornerstone of preventive healthcare in Australia offering clinicians a faster, cheaper, and non-invasive tool to identify patients at risk of systemic diseases before they progress.