CERA

Science and Research

Ophthalmic epidemiology research

CERA’s population health research is focused on understanding blinding eye disease in the community and developing innovative strategies to reduce its impact, using advanced technology and data science.

Overview

Big data and advanced artificial intelligence technologies are a key focus of our ophthalmic epidemiology research. In one major project, our world-leading scientists have developed a deep learning system that can screen for common blinding eye diseases. This technology has been deployed in studies in clinical scenarios such as GP clinics, endocrinology clinics and primary health facilities in remote and Aboriginal regions in Australia. We have also developed prediction models based on deep learning for diabetic retinopathy, age-related macular degeneration and glaucoma, run large data linkage projects, and established an electronic capture system for the collection of clinical data from medical instruments such as fundus cameras. Other key population health projects at CERA include the first National Eye Health Survey – a study into the prevalence and causes of vision impairment in non-Indigenous and Indigenous Australians – and initiatives to increase participation in diabetic eye disease screening, such as KeepSight. CERA is proud to be a designated WHO Collaborating Centre for the Prevention of Blindness, the only such centre in Australia.

Why this research is important

Our research is developing efficient, accurate and convenient ways of delivering eye screening services, improving patient outcomes and reducing the burden and cost of eye disease to the community.

The success of big data and smart clinical inquiry system projects will result in new databases and collaborative networks that will benefit both deep learning research and clinical research such as real-world treatment outcome studies.

Key research questions

  • How can we increase participation in screening for eye disease such as diabetic retinopathy?
  • How can artificial intelligence tools improve the diagnosis and treatment of eye disease?
  • How can these screening systems be applied in different care models in non-ophthalmology settings, like GP clinics, endocrinology clinics and Indigenous health services?
  • What is the accuracy, cost-efficiency and acceptancy of these care models in real-world practice?
  • How can we integrate existing and further-evolved deep learning technology to develop and validate a clinical decision system that is able to predict disease outcomes and prognosis?

Researchers