A new approach that uses an algorithm to analyse retinal images could help practitioners select the best treatment for patients with vision loss from diabetic macular edema.
Research leader Associate Professor Sina Farsiu from Duke University said the team developed an algorithm that can be used to automatically analyse OCT images of the retina to predict whether a patient is likely to respond to anti-VEGF treatments.
They are now planning a larger observational trial of patients who have not yet undergone treatment to confirm and extend the findings from their pilot study.
The research team recognised a need to identify patients that would benefit from anti-VEGF therapy because it requires multiple injections that are costly and burdensome for both patients and physicians, but isn’t effective for every patient.
“This research represents a step toward precision medicine, in which such predictions help clinicians better select first-line therapies for patients based on specific disease conditions,” Farsiu said.
In their paper, Farsiu and colleagues showed the new algorithm can analyse just one pre-treatment volumetric scan to accurately predict whether a patient is likely to respond to anti-VEGF therapy.
“Our approach could potentially be used in eye clinics to prevent unnecessary and costly trial-and-error treatments and thus alleviate a substantial treatment burden for patients,” Farsiu told The Optical Society.
“The algorithm could also be adapted to predict therapy response for many other eye diseases, including neovascular age-related macular degeneration.”
The researchers tested the algorithm with OCT images from 127 patients who had been treated for diabetic macular edema with three consecutive injections of anti-VEGF agents.
They applied the algorithm to analyse OCT images taken before the anti-VEGF injections, then compared the algorithm’s predictions to OCT images taken after anti-VEGF therapy to confirm whether the therapy improved the condition.
Based on the results, the researchers calculated that the algorithm would have an 87% chance of correctly predicting who would respond to treatment.
