Artificial Intelligence-assisted Retinal Photography

Project Details

fundus image feautre extraction

Our Artificial Intelligence-driven retinal feature extraction algorithm is applied to live fundus photography to enable computer-aided image aquisition for more effective diabetic retinopathy screening.

This is used to identify potentially ungradable images at the time of aquisition allowing for recapture prior to uploading to a retinopathy grading service or a machine learning retinopathy grading algorithm.

Collaborators

Dr Laima Brazionis, The University of Melbourne

Research Group

Digital Health Diabetic Retinopathy Research Group


School Research Themes

Cardiometabolic



Key Contact

For further information about this research, please contact the research group leader.

Department / Centre

Medicine

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