The study was led by Siamak Yousefi, PhD, an assistant professor from University of Tennessee’s department of ophthalmology in Memphis.
Researchers used an AI visual field analyzer that looked for vision loss patterns typically indicating glaucoma in 176 participants and compared results to an assessment by a traditional ocular hypertension treatment study-certified visual field reader over the course of 16 years.
According to the study’s researchers, results showed that AI-based automated machine learning systems can identify early signs of visual field loss and recognize early signs of glaucoma in participants with the same accuracy as ophthalmology experts.
