Applying deep learning in glaucoma screening

Glaucoma is usually asymptomatic in the early stage with unnoticeable peripheral vision loss. Therefore, developing a sensitive glaucoma screening tool is crucial for early detection and prompt treatment. Previous studies showed that the application of deep learning (DL), a subset of artificial intelligence, on optical coherence tomography (OCT) for glaucoma assessment was efficient, accurate, and promising. In this review, a summary of deployment of the DL models and future research directions have been discussed. (Eye (Lond). 2021 Jan;35(1):188-201. doi: 10.1038/s41433-020-01191-5.)

Anyone interested in future collaboration in this field of research is welcome to contact our key investigator Dr Carol CHEUNG of the Department of Ophthalmology and Visual Sciences, CUHK. Dr Cheung’s research focuses on using ocular imaging technology and artificial intelligence to study human circulation and nervous systems, and link with eye, brain and cardiovascular diseases, in particular, diabetic eye disease, glaucoma, Alzheimer’s disease and stroke.