Early Keratoconus Detection Enhanced by Modern Diagnostic Technology

This chapter describes how to employ a geometrical analysis of the human cornea in order to detect differences in a sample with healthy corneas and corneas with abnormal corneal topography. We obtained geometrical data from a total of 120 patients divided into two groups: healthy (89) and subclinical keratoconic (31) corneas. The proposed detection method generates a virtual 3D solid custom model of the cornea employing Computer-Aided Geometric Design tools and using raw data from a discrete and finite set of spatial points representative of both sides of the corneal surface provided by a corneal topographer. Determined geometric variables are then extracted from the model and statistically analyzed to detect any corneal deformation. The predictive value of the modeled variables was established through a ROC analysis, achieving the Posterior apex deviation variable the best results. This accurate characterization of the human cornea enables new paths in the detection of this type of corneal pathology and offers a new and more integrated tool that facilitates a better diagnosis and follow-up of keratoconus.