Spatial Modelling of Retinal Thickness in Images from Patients with Diabetic Macular Oedema

For the diagnosis and monitoring of retinal diseases, the spatial context of retinal thickness is highly relevant but often under-utilised. Despite the data being spatially collected, current approaches are not spatial: they involve analysing each location separately, or they analyse all image sectors together but they ignore the possible spatial correlations such as linear models, and multivariate analysis of variance (MANOVA). We propose spatial statistical inference framework for retinal images, which is based on a linear mixed effect model and which models the spatial topography via fixed effect and spatial error structures. We compare our method with MANOVA in analysis of spatial retinal thickness data from a prospective observational study, the Early Detection of Diabetic Macular Oedema (EDDMO) study involving 89 eyes with maculopathy and 168 eyes without maculopathy from 149 diabetic participants. Heidelberg Optical Coherence Tomography (OCT) is used to measure retinal thickness. MANOVA analysis suggests that the overall retinal thickness of eyes with maculopathy are not significantly different from the eyes with no maculopathy (p = 0.11), while our spatial framework can detect the difference between the two disease groups (p = 0.02). We also evaluated our spatial statistical model framework on simulated data whereby we illustrate how spatial correlations can affect the inferences about fixed effects. Our model addresses the need of correct adjustment for spatial correlations in ophthalmic images and to improve the precision of association in clinical studies. This model can be potentially extended into disease monitoring and prognosis in other diseases or imaging technologies.