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      Feasibility of a Nonmydriatic Ocular Fundus Camera in an Outpatient Neurology Clinic

      , , , , ,
      The Neurologist
      Ovid Technologies (Wolters Kluwer Health)

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          Artificial intelligence and deep learning in ophthalmology

          Artificial intelligence (AI) based on deep learning (DL) has sparked tremendous global interest in recent years. DL has been widely adopted in image recognition, speech recognition and natural language processing, but is only beginning to impact on healthcare. In ophthalmology, DL has been applied to fundus photographs, optical coherence tomography and visual fields, achieving robust classification performance in the detection of diabetic retinopathy and retinopathy of prematurity, the glaucoma-like disc, macular oedema and age-related macular degeneration. DL in ocular imaging may be used in conjunction with telemedicine as a possible solution to screen, diagnose and monitor major eye diseases for patients in primary care and community settings. Nonetheless, there are also potential challenges with DL application in ophthalmology, including clinical and technical challenges, explainability of the algorithm results, medicolegal issues, and physician and patient acceptance of the AI ‘black-box’ algorithms. DL could potentially revolutionise how ophthalmology is practised in the future. This review provides a summary of the state-of-the-art DL systems described for ophthalmic applications, potential challenges in clinical deployment and the path forward.
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            Fundus Photography in the 21st Century—A Review of Recent Technological Advances and Their Implications for Worldwide Healthcare

            The introduction of fundus photography has impacted retinal imaging and retinal screening programs significantly.
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              The Current State of Teleophthalmology in the United States

              Telemedicine services facilitate the evaluation, diagnosis, and management of the remote patient. Telemedicine has rapidly flourished in the United States and has improved access to care, outcomes, and patient satisfaction. However, the use of telemedicine in ophthalmology is currently in its infancy and has yet to gain wide acceptance. Current models of telemedicine in ophthalmology are largely performed via “store and forward” methods, but remote monitoring and interactive modalities exist. Although studies have examined the effects of telemedicine, few reports have characterized its current status. We perform a descriptive analysis of the current state of teleophthalmology in the United States. We describe the use of teleophthalmology in the hospital and outpatient settings. We also review the applications to retinopathy of prematurity, diabetic retinopathy, age-related macular degeneration, and glaucoma, as well as anticipated barriers and hurdles for the future adoption of teleophthalmology. With ongoing advances in teleophthalmology, these models may provide earlier detection and more reliable monitoring of vision-threatening diseases.
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                Author and article information

                Journal
                The Neurologist
                Ovid Technologies (Wolters Kluwer Health)
                1074-7931
                2020
                March 2020
                March 5 2020
                : 25
                : 2
                : 19-23
                Article
                10.1097/NRL.0000000000000259
                6fbd60e7-2d81-4512-a21c-3f03d0ec8faf
                © 2020
                History

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