Artificial Intelligence Improves OCT Image Analysis Of The Eye

Researchers at the Queensland University of Technology (QUT; Brisbane, Australia) applied artificial intelligence (AI) deep learning techniques to develop a more accurate and detailed method for analyzing optical coherence tomography (OCT) images of the back of the eye to help clinicians better detect and track eye diseases, such as glaucoma and age-related macular degeneration.

OCT imaging, commonly used in optometry and ophthalmology, takes cross-sectional, high-resolution images of the eye, showing the different tissue layers. These images measure around 4 µm. Using OCT scanning to map and monitor the thickness of the tissue layers in the eye can help clinicians to detect eye diseases, says David Alonso-Caneiro, Senior Research Fellow from the Faculty of Health, School of Optometry and Vision Science at QUT, the study’s lead author.

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