Diabetic Retinopathy
Our digital health research includes • the development and testing of software 'CREDIS' to facilitate the assessment of monitoring of diabetic eye disease and communication between multi-disciplinary care team members. The software can also be used in the training of retinal graders.
Artificial Intelligence in Detecting Diabetic Eye Disease
The evaluation of artificial intelligence (AI) in detecting the presence of and severity of diabetic eye disease (retinopathy). Evaluation of the level of retinopathy in retinal photos taken by health workers.and graded by both trained human graders and AI has demonstrated comparable performance of a novel AI program (manuscript in preparation).
Surveys into Diabetes Care
The development and use of surveys, usually via REDCap, to assess diabetes care. Current studies in progress include the assessment of Type 1 diabetes care in the Western Pacific Region, blood fat (lipid) care in adults with Type 1 diabetes (the ENACT1D Study) and glucose control during pregnancy in women with diabetes.
Decision Support for Clinicians
Development of electronic clinical decision support tools to deliver relevant, up-to-date guidelines based on patient lab results and presenting condition.
