Seminar: Jonathan Henderson, QUB, on early detection of Glaucoma

Glaucoma, a leading cause of irreversible blindness, is characterized by the neurodegenerative loss of retinal ganglion cells (RGCs). While a definitive cure remains elusive, clinical techniques offer avenues to mitigate the impact, particularly when the condition is diagnosed early. Recent strides in Adaptive Optics confocal Scanning Laser Ophthalmoscopy are beginning to allow clinicians to visualize a small subset of the RGC population approaching the single cell resolution. Distance-based metrics such as the nearest neighbour distance have been applied to these datasets, but have proven unreliable for early disease detection, due to point pattern heterogeneities and the presence of noise. To extract more reliable information from these observations, techniques from the field of point pattern analysis can be used. This study explores the potential of fractal geometry within the context of point patterns, showcasing their efficacy in identifying glaucoma in its incipient stages using rodent disease models. The use of these fractal measures is further considered in constructing model parameters for the disease process based on an approximate Bayesian computation algorithm. This allows for a change-point detection algorithm to be applied that can be used to identify regions of the retina at risk of future loss. This can be used to inform clinical practice through earlier diagnosis of disease and better prediction of therapeutic intervention efficacy.