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      Deep Learning Predicts OCT Measures of Diabetic Macular Thickening From Color Fundus Photographs

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          Automated Identification of Diabetic Retinopathy Using Deep Learning

          Diabetic retinopathy (DR) is one of the leading causes of preventable blindness globally. Performing retinal screening examinations on all diabetic patients is an unmet need, and there are many undiagnosed and untreated cases of DR. The objective of this study was to develop robust diagnostic technology to automate DR screening. Referral of eyes with DR to an ophthalmologist for further evaluation and treatment would aid in reducing the rate of vision loss, enabling timely and accurate diagnoses.
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            Deep Learning Is Effective for Classifying Normal versus Age-Related Macular Degeneration OCT Images

            The advent of Electronic Medical Records (EMR) with large electronic imaging databases along with advances in deep neural networks with machine learning has provided a unique opportunity to achieve milestones in automated image analysis. Optical coherence tomography (OCT) is the most commonly obtained imaging modality in ophthalmology and represents a dense and rich dataset when combined with labels derived from the EMR. We sought to determine if deep learning could be utilized to distinguish normal OCT images from images from patients with Age-related Macular Degeneration (AMD).
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              Detecting Preperimetric Glaucoma with Standard Automated Perimetry Using a Deep Learning Classifier.

              To differentiate the visual fields (VFs) of preperimetric open-angle glaucoma (OAG) patients from the VFs of healthy eyes using a deep learning (DL) method.
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                Author and article information

                Journal
                Investigative Opthalmology & Visual Science
                Invest. Ophthalmol. Vis. Sci.
                Association for Research in Vision and Ophthalmology (ARVO)
                1552-5783
                March 01 2019
                March 01 2019
                : 60
                : 4
                : 852
                Affiliations
                [1 ]Pharma Research and Early Development (pRED), Roche Innovation Center Basel, Basel, Switzerland
                [2 ]Genentech, Inc., San Francisco, California, United States
                [3 ]School of Pharmaceutical Sciences, University of Geneva, Switzerland
                Article
                10.1167/iovs.18-25634
                30821810
                5994e658-86ff-48e2-b2ac-a1ab989f7b1b
                © 2019

                http://creativecommons.org/licenses/by-nc-nd/4.0/

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