The purpose of this review is to provide a comprehensive and updated overview of the clinical features, imaging modalities, differential diagnosis, diagnostic criteria, and treatment options for Vogt-Koyanagi-Harada (VKH) syndrome, a rare progressive inflammatory condition characterized by bilateral granulomatous panuveitis and systemic manifestations. While the clinical features and disease course of VKH syndrome are well-characterized in the literature, its diagnosis is challenging due to a broad differential that include infectious and noninfectious causes of uveitis and rare inflammatory conditions, as well as a lack of a single diagnostic finding on exam, laboratory testing, or imaging. The evolution of the diagnostic criteria for VKH syndrome reflects the growing understanding of the disease by the ophthalmic community and advancement of imaging technology. Findings on enhanced depth imaging (EDI) optical coherence tomography (OCT) and indocyanine green angiography (ICGA) help detect subtle inflammation of the choroid and were incorporated into new diagnostic criteria developed in the last few years. There is limited research on the treatment for acute VKH, but results of studies to date support the early initiation of immunomodulatory therapy (IMT) due to a high recurrence rate and progression to chronic disease in patients treated with monotherapy with high-dose systemic corticosteroids. This review will provide an in-depth summary of recent literature on advanced imaging modality and IMT to guide clinicians in their management of patients with VKH syndrome.
Background: The oxygen induced retinopathy rodent model is widely used, notably for the assessment of developmental dystrophies in preclinical studies of vascular retinal diseases. Typically, the quantification of vessel tufts and avascular regions is computed manually from flat mounted retinas imaged using fluorescent probes that highlight the vascular network. However, such manual measurements are time-consuming and hampered by user variability and bias, thus a rapid and objective alternative is required.
Methods: We employ a machine learning approach to segment and characterize vascular tufts. The proposed quantitative retinal vascular assessment (QuRVA) technique uses quadratic discrimination analysis and morphological techniques to provide reliable measurements of vascular density and pathological vascular tuft regions, devoid of user intervention within seconds. Our algorithms allow also delineating the whole vasculature network, and identifying and analyzing avascular regions.
Results: Our first experiment shows the high degree of error and variability of manual segmentations. In consequence, we developed a set of algorithms to perform this task automatically. We benchmark and validate the results of our analysis pipeline using the consensus of several manually curated segmentations using commonly used computer tools. We describe the method, provide details for reproducing the algorithm, and validate all aspects of the analysis.
Conclusions: Manual and semi-automated procedures for tuft detection present strong fluctuations among users, demonstrating the need for fast and unbiased tools in this highly active research field with tremendous implications for basic research and industry.
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: In this investigation, we explore the literature regarding neuroregeneration from the 1700s to the present. The regeneration of central nervous system neurons or the regeneration of axons from cell bodies and their reconnection with other neurons remains a major hurdle. Injuries relating to war and accidents attracted medical professionals throughout early history to regenerate and reconnect nerves. Early literature till 1990 lacked specific molecular details and is likely provide some clues to conditions that promoted neuron and/or axon regeneration. This is an avenue for the application of natural language processing (NLP) to gain actionable intelligence. Post 1990 period saw an explosion of all molecular details. With the advent of genomic, transcriptomics, proteomics, and other omics—there is an emergence of big data sets and is another rich area for application of NLP. How the neuron and/or axon regeneration related keywords have changed over the years is a first step towards this endeavor.
Methods: Specifically, this article curates over 600 published works in the field of neuroregeneration. We then apply a dynamic topic modeling algorithm based on the Latent Dirichlet allocation (LDA) algorithm to assess how topics cluster based on topics.
Results: Based on how documents are assigned to topics, we then build a recommendation engine to assist researchers to access domain-specific literature based on how their search text matches to recommended document topics. The interface further includes interactive topic visualizations for researchers to understand how topics grow closer and further apart, and how intra-topic composition changes over time.
Conclusions: We present a recommendation engine and interactive interface that enables dynamic topic modeling for neuronal regeneration.
