Quantitative Assessment of Experimental Ocular Inflammatory Disease

Abstract

Ocular inflammation imposes a high medical burden on patients and substantial costs on the health-care systems that mange these often chronic and debilitating diseases. Many clinical phenotypes are recognized and classifying the severity of inflammation in an eye with uveitis is an ongoing challenge. With the widespread application of optical coherence tomography in the clinic has come the impetus for more robust methods to compare disease between different patients and different treatment centers. Models can recapitulate many of the features seen in the clinic, but until recently the quality of imaging available has lagged that applied in humans. In the model experimental autoimmune uveitis (EAU), we highlight three linked clinical states that produce retinal vulnerability to inflammation, all different from healthy tissue, but distinct from each other. Deploying longitudinal, multimodal imaging approaches can be coupled to analysis in the tissue of changes in architecture, cell content and function. This can enrich our understanding of pathology, increase the sensitivity with which the impacts of therapeutic interventions are assessed and address questions of tissue regeneration and repair. Modern image processing, including the application of artificial intelligence, in the context of such models of disease can lay a foundation for new approaches to monitoring tissue health.

Long‐lived autoreactive memory CD4+ T cells mediate the sustained retinopathy in chronic autoimmune uveitis

“…Eyes were kept moist by application of ocular lubricant (Genteal® gel, Alcon, Fort Worth, TX) and were examined for optic disc inflammation, retinal vessel changes, retinal infiltrates, and structural damage. Fundus images were taken and scored by a masked grader using a valid and reproducible grading system that provides a more detailed and refined approach by assessing the aforementioned clinical parameters on a scale of 0–4 for each of them with sum of them reported as the summary score of clinical disease, as described previously 12,15,19–21 …”

Space-Variant Image Reconstruction Via Cauchy Regularisation: Application to Optical Coherence Tomography

“…Specifically, we performed experiments on real data, in the form of OCT images of murine retinas acquired by our collaborators within the AIR Lab at the University of Bristol. The images were acquired using a Micron IV system for imaging rodent eyes (Phoenix technologies, CA) and previously employed in [37,5].…”

A machine learning framework for the quantification of experimental uveitis in murine OCT

“…It is widely accepted that OCT is the most promising approach for quantification of uveitis and useful attempts to define the characteristics of active disease have been reported [ 20 ]. Nevertheless, most current assessments remain subjective and automated algorithms are found in some cases to be less robust in the presence of inflammation [ 21 ]. The development of automated tools to objectively and precisely characterize the pathological changes induced by uveitis is the subject of this work.…”