Citation: Vellara HR, Ali NQ, Gokul A, Turuwhenua J, Patel DV, McGhee CNJ. Quantitative analysis of corneal energy dissipation and corneal and orbital deformation in response to an airpulse in healthy eyes. Invest Ophthalmol Vis Sci. 2015;56:6941-6947. DOI:10.1167/iovs.15-17396 PURPOSE. To examine and evaluate ocular biomechanical metrics and additionally derived corneal and orbital components using a noncontact Scheimpflug-based tonometer (CorVis ST) in a population of healthy eyes. METHODS.A total of 152 eyes of 152 participants were examined by slit-lamp biomicroscopy, corneal tomography, and the CorVis ST (CST). This determined the distribution of outputs from the CST, such as deformation amplitude (DA), and additionally derived parameters, including maximum corneal deformation (MCD), maximum orbital deformation (MOD), and corneal energy dissipation (CED).RESULTS. The mean age of participants was 35.88 6 13.8 years. Deformation amplitude significantly correlated with age (r ¼ 0.24, P ¼ 0.002) but not sex or ethnicity (P > 0.05). Multiple linear regression analysis revealed significant correlations between DA and age (r ¼ 0.19, P ¼ 0.006) and DA and IOP (r ¼ À0.59, P < 0.001). Age correlated with MCD (r ¼ 0.20, P ¼ 0.01), MOD (r ¼ 0.18, P ¼ 0.03), and CED (r ¼ 0.39, P < 0.001). Males had a lower MOD than females (0.24 vs. 0.26 mm, respectively, P ¼ 0.01); however, there were no differences in MCD or CED between sexes (P > 0.05). There were no significant differences between ethnicities for MCD, MOD, and CED (P > 0.05). Multiple linear regression analysis revealed significant correlations between MCD and IOP (r ¼ À0.65, P < 0.001), CED and age (r ¼ 0.41, P < 0.001), CED and IOP (r ¼ 0.28, P ¼ 0.001), and between CED and central corneal thickness (CCT) (r ¼ À0.36, P < 0.001).CONCLUSIONS. The isolation of the corneal component (MCD) should be used when analyzing deformation characteristics in diseases that only affect the cornea. This study establishes a baseline for a population of healthy eyes. Future publications will identify differences in MCD, MOD, and CED between healthy and diseased populations.
Advances in anterior segment imaging have enhanced our ability to detect keratoconus in its early stages and characterize the pathologic changes that occur. Computerized corneal tomography has elucidated the alterations in shape of the anterior and posterior corneal surfaces and alterations in thickness as the disease progresses. Automated screening indices such as the keratoconus screening index were developed to assist in detecting keratoconus in suspicious cases. In vivo assessment of keratoconic corneas has revealed that compromised corneal biomechanics can now be measured clinically. Optical coherence tomography has demonstrated alterations in corneal epithelial thickness and distribution in keratoconus, has a role in assessing Descemet's membrane detachment in acute corneal hydrops (ACH) and the depth of the demarcation line following corneal collagen cross-linking. In vivo confocal microscopy has exhibited cellular changes that occur in keratoconus and provided insight into cellular events that may be related to the development of neovascularization in ACH.
Keratoconus is an ectatic disorder with highly complex and varied causes including genetic variations and environmental factors. Its prevalence varies widely between regions and countries. Many environmental factors have been proposed to be associated with keratoconus, but the interpretation of their individual contributions is difficult due to the presence of many confounding variables. The current literature was reviewed to evaluate the strength of the associations and the causative effects of environmental factors on keratoconus. Ethnicity and consanguinity have been revealed as important determinants for geographical variations in keratoconus prevalence. Eye rubbing, atopy, floppy eyelid syndrome, contact lens wear, pregnancy, and thyroid hormone disturbances are likely associated with keratoconus. The first 4 factors can induce ocular surface inflammation, matrix metalloproteinase release, and keratocyte apoptosis, consistent with the postulated etiology of keratoconus. The associations of keratoconus with UV exposure, cigarette smoking, personality, and sex were less convincing once confounding factors were considered. Future studies powered for multivariate analysis of factors discussed will hopefully shed light on what is truly important in the development and progression of keratoconus.
ObjectiveTo evaluate the accuracy of convolutional neural networks technique (CNN) in detecting keratoconus using colour-coded corneal maps obtained by a Scheimpflug camera.DesignMulticentre retrospective study.Methods and analysisWe included the images of keratoconic and healthy volunteers’ eyes provided by three centres: Royal Liverpool University Hospital (Liverpool, UK), Sedaghat Eye Clinic (Mashhad, Iran) and The New Zealand National Eye Center (New Zealand). Corneal tomography scans were used to train and test CNN models, which included healthy controls. Keratoconic scans were classified according to the Amsler-Krumeich classification. Keratoconic scans from Iran were used as an independent testing set. Four maps were considered for each scan: axial map, anterior and posterior elevation map, and pachymetry map.ResultsA CNN model detected keratoconus versus health eyes with an accuracy of 0.9785 on the testing set, considering all four maps concatenated. Considering each map independently, the accuracy was 0.9283 for axial map, 0.9642 for thickness map, 0.9642 for the front elevation map and 0.9749 for the back elevation map. The accuracy of models in recognising between healthy controls and stage 1 was 0.90, between stages 1 and 2 was 0.9032, and between stages 2 and 3 was 0.8537 using the concatenated map.ConclusionCNN provides excellent detection performance for keratoconus and accurately grades different severities of disease using the colour-coded maps obtained by the Scheimpflug camera. CNN has the potential to be further developed, validated and adopted for screening and management of keratoconus.
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