Abstract: Autoimmune retinopathy (AIR) refers to both paraneoplastic and non-paraneoplastic forms of a rare, acquired retinal degeneration thought to be mediated by the production of antiretinal antibodies. However, the mechanisms underlying AIR pathogenesis are incompletely understood, and it remains a diagnosis of exclusion given the lack of definitive testing as well as its protean clinical presentation. This review summarizes the current literature on the epidemiology, diagnosis, and management of AIR, with a focus on non-paraneoplastic disease and the potential role of immunomodulatory therapy. A recent expert consensus statement on diagnosis and management of non-paraneoplastic AIR served as a framework for interpreting the limited data available, a process that was complicated by the small sample sizes, heterogeneity, and retrospective nature of these studies. Additional work is needed to characterize AIR patients on the basis of cytokine and immunogenetic profiling; to establish the pathogenicity of antiretinal antibodies; and to standardize treatment regimens as well as assessment of clinical outcomes.
Abstract: Artificial intelligence (AI) methods have become a focus of intense interest within the eye care community. This parallels a wider interest in AI, which has started impacting many facets of society. However, understanding across the community has not kept pace with technical developments. What is AI, and how does it relate to other terms like machine learning or deep learning? How is AI currently used within eye care, and how might it be used in the future? This review paper provides an overview of these concepts for eye care specialists. We explain core concepts in AI, describe how these methods have been applied in ophthalmology, and consider future directions and challenges. We walk through the steps needed to develop an AI system for eye disease, and discuss the challenges in validating and deploying such technology. We argue that among medical fields, ophthalmology may be uniquely positioned to benefit from the thoughtful deployment of AI to improve patient care.
Abstract: Several factors drive the need for increased efficiency in telemedicine screening programs directed toward diabetic retinopathy: continually increasing prevalence of diabetes worldwide, growing awareness among physicians and patients of the importance of early detection of retinal damage, and emerging technology in artificial intelligence that enables rapid identification of vision-threatening fundus features. In this context, optimizing workflows in teleretinopathy programs becomes a priority. Recent work has revealed opportunities for improvement in areas of logistics, in particular in finding the best way to get diabetic patients in front of screening cameras as conveniently as possible, as this improves compliance and, ultimately, achieves the widest reach for detection programs. The present review discusses particular aspects of mobile screening programs in which specialized retinal cameras are deployed in a van or similar type of vehicle so that they can reach patients anywhere in order to reduce barriers to access. The rationale for implementing such programs and practical considerations are presented, along with a view toward future expansion of screening and integration with artificial intelligence platforms. Lacking standardization of format and quality control among smartphone-linked approaches at present, translation of eye clinic-based photographic techniques to community-based screening offers a means of expanding the scope of impactful screening programs without the need for adoption of significantly new technology.
Abstract: The most prominent causes of loss of vision in individuals over 50 years include age-related macular degeneration (AMD), glaucoma, and diabetic retinopathy (DR). While it is important to screen for these diseases effectively, current eye care is not properly doing so for much of the population, resulting in unfortunate visual disability and high costs for patients. Innovative functional testing can be unified with other screening methods for a more robust and safer screening and prediction of disease. The goal in the creation of functional testing modalities is to develop highly sensitive screening tests that are easy to use, accessible to all users, and inexpensive. The tests herein are deployed on an iPad with easily understood and intuitive instructions for rapid, streamlined, and automatic administration. These testing modalities could become highly sensitive screenings for early detection of potentially blinding diseases. The applications from our collaborators at AMA Optics include a cone photostress recovery test for detection of AMD and diabetic macular edema (DME), brightness balance perception for optic nerve dysfunction and especially glaucoma, color vision testing which is a broad screening tool, and visual acuity test. Machine learning with the combined structural and functional data will optimize identification of disease and prediction of outcomes. Here, we review and assess various tests of visual function that are easily administered on a tablet for screening in primary care. These user-friendly and simple screening tests allow patients to be identified in the early stages of disease for referral to specialists, proper assessment and treatment.
