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A new deep learning approach can better discern changes in the eyes of glaucoma patients, according to a new study in Biomedical Optics Express.
Glaucoma is a group of diseases that damage the eye’s optic nerve and can cause vision loss, including blindness. Although there is no cure, early detection and treatment can delay its progression. The progression is marked by complex structural changes in the optic nerve head tissues, such as the thinning of retinal nerve fiber layers and the width of membranes.
Current deep learning methods applied to optical coherence tomography, which uses light to take cross-section images, can detect these changes automatically, but existing methods require a different tissue-specific algorithm to examine each type of tissue. This is also computationally expensive and prone to segmentation errors.