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: The objective of the paper is to provide a general view for automatic cup to disc ratio (CDR) assessment in fundus images. As for the cause of blindness, glaucoma ranks as the second in ocular diseases. Vision loss caused by glaucoma cannot be reversed, but the loss may be avoided if screened in the early stage of glaucoma. Thus, early screening of glaucoma is very requisite to preserve vision and maintain quality of life. Optic nerve head (ONH) assessment is a useful and practical technique among current glaucoma screening methods. Vertical CDR as one of the clinical indicators for ONH assessment, has been well-used by clinicians and professionals for the analysis and diagnosis of glaucoma. The key for automatic calculation of vertical CDR in fundus images is the segmentation of optic cup (OC) and optic disc (OD). We take a brief description of methodologies about the OC and disc optic segmentation and comprehensively presented these methods as two aspects: hand-craft feature and deep learning feature. Sliding window regression, super-pixel level, image reconstruction, super-pixel level low-rank representation (LRR), deep learning methodologies for segmentation of OD and OC have been shown. It is hoped that this paper can provide guidance and bring inspiration to other researchers. Every mentioned method has its advantages and limitations. Appropriate method should be selected or explored according to the actual situation. For automatic glaucoma screening, CDR is just the reflection for a small part of the disc, while utilizing comprehensive factors or multimodal images is the promising future direction to furthermore enhance the performance.
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.
Background: Using a randomized controlled trial (RCT), to assess the efficacy of the folded technique of self-adherent wrap to eyes after orbital tumour extirpation and compare it with the classic technique.
Methods: A single-centre, prospective, randomized, controlled study was conducted among 128 patients who underwent orbital tumour extirpation in this study. The folded and classic techniques of applying self-adherent wraps were randomly allocated to patients (1:1). The primary endpoint was the interface pressure on the affected eye. Secondary efficacy endpoints were the interface pressure above and below the ear of the affected side, above the ear of the non-affected side, and discomfort scores. Postoperative complications were observed for 24 hours.
Results: The interface pressure with the folded technique on the affected eye was neither inferior nor superior to the classic technique (1.33±0.07 vs. 1.41±0.09 mmHg, P=0.480). Most importantly, the pressure at three other points outside of the affected eye, including above and below the ear of the affected side, and above the ear of the non-affected side, were significantly higher when using the classic technique than when using the folded technique (P=0.041, 0.019, and 0.047, respectively). Discomfort scores were higher in the classic technique group than in the folded technique group (2.93±0.30 vs.1.52±0.19, P≤0.001).
Conclusions: Findings demonstrated the advantages of using folded technique to apply self-adherent wrap for wounds after orbital tumour extirpation with lower interface pressure outside of the affected eye and patient discomfort scores, without influencing pressure on the affected eye comparing with the classic technique.
