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Two innovative projects come to life at Queen’s University

Updated: Feb 27, 2023

Explore fascinating news from Queen’s University with remote-controlled slit lamps and diabetic eye screening



The research program at Queen University's Department of Ophthalmology has three broad themes: basic science research, knowledge translation and medical education, and health policy and health services research. Here are two innovative projects we are focused on in the health services domain.


Remote-controlled slit lamp

Queen's Ophthalmology teamed up with the Bascom Palmer Eye Institute and the University of Miami Bio-Engineering department to deploy a remote control "drone" slit lamp in Moose Factory, Ontario, as part of the Weeneebayko Area Health Authority’s efforts to provide comprehensive eye care to remote communities in the James Bay and Hudson's Bay coasts in Northern Ontario.


This device is the first of its kind in use in North America; it consolidates an array of technologies and tools to realize a dynamic remote eye exam. While another telemedicine infrastructure allows sharing diagnostic imagery and communication channels between healthcare providers and patients, we can now perform a complete anterior segment exam—requiring active direction from a specialist—at a distance.


Diabetic eye screening

In partnership with Vision Loss Rehabilitation Ontario and the Ontario Ministry of Health, the Queen's Department of Ophthalmology has embarked on an innovative vision assessment program to screen for diabetic eye disease in remote parts of the province.


The program employs portable fundus cameras requiring limited training in the hands of remote health clinics or mobile medical teams; the images captured are evaluated using an artificial intelligence algorithm to identify signs of concern warranting further investigation.


In the early phases of this program, flagged imagery is directed to our centre in Kingston for further evaluation by an ophthalmologist, who then directs appropriate follow-up. The project is evaluating the capacity of an AI algorithm to effectively screen for eye disease in the absence of specialized ophthalmic facilities, with the aim of identifying early-stage eye disease when it can be most effectively managed.

1 Fundus image showing severe retinopathy, taken in the field by EMS Paramedic with no special ophthalmic training, flagged by AI for referral.

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