Journal of neuro-ophthalmology : the official journal of the North American Neuro-Ophthalmology Society
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Telehealth provides health care to a patient from a provider at a distant location. Before the COVID-19 pandemic, adoption of telehealth modalities was increasing slowly but steadily. During the public health emergency, rapid widespread telehealth implementation has been encouraged to promote patient and provider safety and preserve access to health care. ⋯ Telehealth is an increasingly recognized means of health care delivery. Tele-Neuro-Ophthalmology adoption is necessary for the sake of our patients, the survival of our subspecialty, and the education of our trainees and students. Telehealth does not supplant but supplements and complements in-person neuro-ophthalmologic care. Innovations in digital optical fundus photography, mobile vision testing applications, artificial intelligence, and principles of channel management will facilitate further adoption of tele-neuro-ophthalmology and bring the specialty to the leading edge of health care delivery.
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During the COVID-19 pandemic, telehealth modalities have come to prominence as a strategy for providing patient care when in-person care provision opportunities are limited. The degree of adoption by neuro-ophthalmologists has not been quantified. ⋯ Telehealth modality usage by neuro-ophthalmologists increased during the COVID-19 pandemic. Identified benefits have relevance both during and beyond COVID-19. Further work is needed to address barriers in their current and future states to maintain these modalities as viable care delivery options.
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Deep learning (DL) has demonstrated human expert levels of performance for medical image classification in a wide array of medical fields, including ophthalmology. In this article, we present the results of our DL system designed to determine optic disc laterality, right eye vs left eye, in the presence of both normal and abnormal optic discs. ⋯ Small data sets can be used to develop high-performing DL systems for semantic labeling of neuro-ophthalmology images, specifically in distinguishing between right and left optic discs, even in the presence of neuro-ophthalmological pathologies. Although this may seem like an elementary task, this study demonstrates the power of transfer learning and provides an example of a DCNN that can help curate large medical image databases for machine-learning purposes and facilitate ophthalmologist workflow by automatically labeling images according to laterality.
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In a patient with an optic tract syndrome, we describe the loss of retinal nerve fiber layer and retinal microvasculature using enface and optical coherence tomography angiography image analyses.