Science & Technology, Canada (Commonwealth Union) – The University of Toronto’s research team, led by Professor Willy Wong and PhD candidate Yan Li from the Faculty of Applied Science & Engineering, has developed an online simulator offering a more accurate visual depiction of the effects of glaucoma-induced deterioration.
This innovative simulator provides a unique perspective on the disease, allowing viewers to experience it from the patient’s point of view. Professor Wong notes that most other representations of glaucoma found on the internet are notably inaccurate.
“If you do an internet search for what glaucoma looks like, the images returned are tunnel vision with the periphery blacked out. There’s very little truth to this.” explained Wong of the Edward S. Rogers Sr. department of electrical and computer engineering. “What’s really happening is patches of your visual field are losing their spatial integrity – more what you might see when you just wake up, when you’re not really focused in.”
Wong and Li’s online simulator is underpinned by a data-driven model they meticulously developed to quantify glaucoma measurements. Their model, which takes into account the intricate physiological workings of the eye, has recently been documented in a paper featured in the journal Translational Vision Science & Technology.
Li indicated that they worked closely collaborated with glaucoma specialists, two of whom are co-authors of the paper. Their guidance enabled them to grasp the intricacies of the eye’s pathophysiology. By working alongside them, they gained invaluable firsthand insights into the technological needs of clinicians.
Glaucoma, typically painless and more prevalent among older individuals, is characterized by increased pressure within the eye’s fluid, which in turn exerts pressure on and harms the nerve endings responsible for transmitting signals to the brain.
While glaucoma stands as a leading cause of blindness, its progression can be substantially slowed through medication and other interventions. To determine the timing for these treatments, doctors typically engage in proactive monitoring, which encompasses various qualitative assessments, including the examination of the optic nerve and the retinal layer. These assessments often rely on numerical measurements to achieve the utmost precision.
Wong indicated that however, even with these measurements, doctors don’t always have a clear picture of whether the condition is worsening, improving, or remaining stable.
This uncertainty arises from the inherent noise in many physiological measurements. For instance, visual field tests depend on measuring visual thresholds—the minimum energy required for sight—yet these thresholds can be unreliable. The noisiness is further compounded by the extended timeframe over which these tests are conducted and the inevitable gaps caused by missed appointments.
Li pointed out that what made this model unique is its technique of combining data from these tests with information regarding the biology of the eye.
“You have to be mindful of how the nerves go from the eye itself – from the visual field into the optic disc,” said Li. “If you know the relationship between the two and add that causality to the clinical data, you have a much better prediction tool.”
Wong and Li’s online simulator empowers users to specify the patient’s age range and exercise control over the disease’s progression rates, beginning at mild, moderate, or severe stages. Subsequently, the simulator realistically portrays the annual advancement of the condition through a series of images featuring people, landscapes, and urban scenes.
Wong shares an encouraging response from an ophthalmologist who had pointed out that it was precisely what was needed. Given the gradual nature of glaucoma’s development, convincing patients to adhere to medication can be challenging, as the changes are often imperceptible to them.
As the project continues, Li’s next endeavor involves integrating various glaucoma testing methodologies and harnessing machine learning (ML) techniques to expedite the reliable detection of the disease’s onset. Accelerating detection will facilitate earlier intervention, offering individuals a greater opportunity to halt the irreversible loss of vision.
