At ARVO, IBM researchers presented new techniques in deep learning that could help unlock earlier glaucoma detection. IBM and NYU’s research demonstrates how deep learning models can be trained to learn from and analyze easily-obtained retina images, and then use this analysis to directly estimate visual function. Traditional methods to assess visual function rely almost exclusively on patient feedback, which can make results subjective and inaccurate, but this new deep learning technique could uncover valuable information in a non-invasive manner and lay the groundwork for new and much more rapid glaucoma testing.
IBM Research, in collaboration with New York University, has conducted a study that estimates the visual field index (VFI) from a single 3D raw OCT image of the optic nerve with unprecedented accuracy, with Pearson correlation of 0.88. VFI is a global metric that represents the entire visual field, and accurately capturing that with AI offers to lay the groundwork for future technologies that can potentially use this analysis to quickly estimate a patient’s visual function. This could give professionals access to precise information—without the need for multiple and time-intensive tests—when gathering data for a glaucoma diagnosis.
More information on the research can be found on IBM’s website by clicking here.