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.
Highlights
Ocular inflammation is an important medical concern with a wide range of manifestations from the treatable to sight threatening
The eye offers unique advantages for imaging studies of the autoimmune process in a target tissue, permitting serial assessment, and sophisticated quantification of different parameters of inflammation that go beyond more general clinical scores used in models such as experimental autoimmune encephalomyelitis
There is a dramatic reduction in function, that accompanies early disease [103], presenting before morphologic changes. These findings indicate that functional loss could be mediated by inflammation rather than just physical damage, and that retinal function is potentially a sensitive early indicator [59, 66]
Summary
Ocular inflammation is an important medical concern with a wide range of manifestations from the treatable to sight threatening. The eye offers unique advantages for imaging studies of the autoimmune process in a target tissue, permitting serial assessment, and sophisticated quantification of different parameters of inflammation that go beyond more general clinical scores used in models such as experimental autoimmune encephalomyelitis. There is potential for automatic segmentation of structures (in which the boundaries between, for example, different layers of the retina are identified in an unsupervised process), quantification of infiltration and disease classification by machine learning, which can be used to support unsupervised clinical assessment [25, 26].
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