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Keratoconus: A Probe Into the Refractive Symmetry
Sankhajyoti Saha1, Moubani Dutta1, Soumendra Nath Bandyopadhyay1
1Department of Health Science, NSHM Knowledge Campus, Kolkata, West Bengal, India.
Background:
A debilitating corneal ectasia, designated as keratoconus, often leads to distorted and obscured vision, and greater reactivity to light. Inevitably, the cornea becomes thinner and protrudes outward forming a cone-like configuration. The research hypothesis is initiated to estimate the reliability of the mirror symmetry and the dimensions of keratoconus severity, prospecting that mirror octant possessed a significant impact on the trajectory of the disorder.
Methods:
This study included patients with clinically diagnosed with bilateral keratoconus and bilateral astigmatism. Mirror symmetry or enantiomorphism was quantified employing the refractive cylindrical notations of yoke eye. Pentacam enable the observation of keratoconus severity utilizing corneal thickness, average corneal thinning, and asphericity coefficient. Multiple R was performed to analyze the model fit along with descriptive statistics. One-way ANOVA, guided by F-statistics, was solicited to analyze group variability, while a scatter plot was exploited to forecast the direction of mirror octant association of the variables. SPSS 29.0 software was utilized to perform the statistical analysis, with a p value of less than 0.05 was considered statistically significant.
Result:
With a significant (p < 0.05) F-statistic, the mirror symmetry remains the statistically significant predictor in the regression model. Average corneal diopter for both eyes exhibit a positive correlation. Conversely, corneal thickness and asphericity coefficient for both eyes demonstrate a negative correlation with mirror symmetry.
Conclusions:
Mirror symmetry's applicability may be inadequate by its subservience on corneal contour analysis, which, although obliging, may abstruse other salient clinical considerations. With 35.6% of the model variability, it indicates a room for improvement by adding nonlinear predictors to enhance the model.
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