Keratoconus Diagnosis by Patient-Specific 3D Modelling and Geometric Parameters Analysis

The aim of this study is to describe a new technique for diagnosing keratoconus based on Patient-specific 3D modelling. This procedure can diagnose small variations in the morphology of the cornea due to keratoconus disease. The posterior corneal surface was analysed using an optimised computational geometric procedure and raw data provided by a corneal tomographer. A retrospective observational case series study was carried out. A total of 86 eyes from 86 patients were obtained and divided into two groups: one group composed of 43 healthy eyes and the other of 43 eyes diagnosed with keratoconus. The predictive value of each morphogeometric variable was established through a receiver operating characteristic (ROC) analysis. The posterior apex deviation variable showed the best keratoconus diagnosis capability (area: 0.9165, p < 0.000, std. error: 0.035, 95% CI: 0.846-0.986), with a cut-off value of 0.097 mm and an associated sensitivity and specificity of 89% and 88%, respectively. Patient-specific geometric models of the cornea can provide accurate quantitative information about the morphogeometric properties of the cornea on several singular points of the posterior surface and describe changes in the corneal anatomy due to keratoconus disease. This accurate characterisation of the cornea enables new evaluation criteria in the diagnosis of this type of ectasia and demonstrates that a device-independent approach to the diagnosis of keratoconus is feasible.