Detection of subclinical keratoconus using biometric parameters

Abstract

The validation of innovative methodologies for diagnosing keratoconus in its earliest stages is of major interest in ophthalmology. So far, subclinical keratoconus diagnosis has been made by combining several clinical criteria that allowed the definition of indices and decision trees, which proved to be valuable diagnostic tools. However, further improvements need to be made in order to reduce the risk of ectasia in patients who undergo corneal refractive surgery. The purpose of this work is to report a new subclinical keratoconus detection method based in the analysis of certain biometric parameters extracted from a custom 3D corneal model. This retrospective study includes two groups: the first composed of 67 patients with healthy eyes and normal vision, and the second composed of 24 patients with subclinical keratoconus and normal vision as well. The proposed detection method generates a 3D custom corneal model using computer-aided graphic design (CAGD) tools and corneal surfaces’ data provided by a corneal tomographer. Defined bio-geometric parameters are then derived from the model, and statistically analysed to detect any minimal corneal deformation. The metric which showed the highest area under the receiver-operator curve (ROC) was the posterior apex deviation. This new method detected differences between healthy and sub-clinical keratoconus corneas by using abnormal corneal topography and normal spectacle corrected vision, enabling an integrated tool that facilitates an easier diagnosis and follow-up of keratoconus.