In a new study, researchers found that using artificial intelligence (AI) to analyze retinal images could one day help doctors select the best treatment for patients with vision loss from diabetic macular edema.
This diabetes complication is a major cause of vision loss among working-age adults.
The research was conducted by a team from Duke University.
Anti-vascular endothelial growth factor (VEGF) agents are widely used as the first line of therapy for diabetic macular edema, but they don’t work for everyone.
There’s a need to identify who would benefit from the therapy because it requires multiple injections that are costly and burdensome for both patients and physicians.
In the study, the team developed an algorithm that can be used to automatically analyze optical coherence tomography (OCT) images of the retina to predict whether a patient is likely to respond to anti-VEGF treatments.
The researchers tested their new 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 analyze 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 percent chance of correctly predicting who would respond to treatment.
It exhibited an average precision and specificity of 85% and a sensitivity of 80%.
The findings showed that the new algorithm can analyze just one pre-treatment volumetric scan to accurately predict whether a patient is likely to respond to anti-VEGF therapy.
The new 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.
The algorithm could also be adapted to predict therapy response for many other eye diseases, including neovascular age-related macular degeneration.
The lead author of the study is Sina Farsiu from Duke University.
The study is published in Biomedical Optics Express.
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