Tag: deep learning

April 14, 2019September 26, 2020

Optical coherence tomography (OCT) is one of the most rapidly-evolving imaging technologies used in ophthalmology. OCT enables visualization of detailed anatomical structures in the eye. Advances in artificial intelligence are simultaneously enabling technology that can learn to read these images and recognize features that are hallmarks for various diseases. Deep learning with artificial multi-layer neural networks has become very successful in these kinds of visual learning tasks.

Forecasting future Humphrey Visual Fields using deep learning

April 5, 2019April 15, 2020

Glaucoma is a leading cause of blindness worldwide, and the ability to quickly anticipate future disease progression may prevent unnecessary vision loss. Humphrey visual field testing is the standard method used to evaluate disease progression in glaucoma patients. This test uses linear regression of certain global eye measurements to monitor damage and predict risk for disease progression. It has some shortcomings in that does not take into account the spatial nature of visual field loss in glaucoma and relies on global indices rather than more focal measurements. Newer models have been developed that are better at detecting subtle changes and can identify disease progression earlier, but they require a multiple vision field tests to achieve accurate results. In this study, the authors aimed to develop a deep learning generative model to predict disease progression (to predict future visual fields with preserved spatial information) using minimal baseline visual field testing as input.

Generating retinal flow maps from structural optical coherence tomography with artificial intelligence

December 21, 2017September 27, 2020

Advances in both imaging technology and artificial intelligence are enabling many new developments in the field of ophthalmology. Optical coherence tomography angiography (OCTA) is one such development. OCTA measures blood flow in retinal microvasculature and can visualize the superficial and deep capillary plexus of the retinal vasculature without the need for dye. But the technology is also expensive and has a limited field of view. In this study, we sought to develop a deep learning model that could address this issue by first learning to infer between structure and retinal vascular function from structural OCT images and then generating an OCTA-like image. This would allow for accurate objectively-generated annotations, eliminating the need for expert-defined labels, and potentially enable the acquisition of new information about retinal blood circulation from preexisting OCT databases.

Fully automated, deep learning segmentation of oxygen-induced retinopathy images