Geometrical Custom Modeling of Human Cornea In Vivo and Its Use for the Diagnosis of Corneal Ectasia

Abstract

AimTo establish a new procedure for 3D geometric reconstruction of the human cornea to obtain a solid model that represents a personalized and in vivo morphology of both the anterior and posterior corneal surfaces. This model is later analyzed to obtain geometric variables enabling the characterization of the corneal geometry and establishing a new clinical diagnostic criterion in order to distinguish between healthy corneas and corneas with keratoconus.MethodThe method for the geometric reconstruction of the cornea consists of the following steps: capture and preprocessing of the spatial point clouds provided by the Sirius topographer that represent both anterior and posterior corneal surfaces, reconstruction of the corneal geometric surfaces and generation of the solid model. Later, geometric variables are extracted from the model obtained and statistically analyzed to detect deformations of the cornea.ResultsThe variables that achieved the best results in the diagnosis of keratoconus were anterior corneal surface area (ROC area: 0.847, p<0.000, std. error: 0.038, 95% CI: 0.777 to 0.925), posterior corneal surface area (ROC area: 0.807, p<0.000, std. error: 0.042, 95% CI: 0,726 to 0,889), anterior apex deviation (ROC area: 0.735, p<0.000, std. error: 0.053, 95% CI: 0.630 to 0.840) and posterior apex deviation (ROC area: 0.891, p<0.000, std. error: 0.039, 95% CI: 0.8146 to 0.9672).ConclusionGeometric modeling enables accurate characterization of the human cornea. Also, from a clinical point of view, the procedure described has established a new approach for the study of eye-related diseases.

A Study for Parametric Morphogeometric Operators to Assist the Detection of Keratoconus

“…These data also presented the previously-mentioned extrinsic errors. However, the authors adopted a design protocol based on geometrical and clinical principles in which no interpolation was performed, using only real data [33]. Thus, the geometric model generated was authentic and completely personalized of the morphological biometry of the cornea, with a high sensitivity for the diagnosis and posterior analysis of the progression of the disease.…”

“…For this study, the following variables from the posterior surface of the cornea were analysed [32,33] (Figure 4): (i) the sagittal plane apex area, defined as the area of the cornea within the sagittal plane that passes through the highest point (apex, maximum curvature) of the posterior corneal surface and the Z-axis; (ii) the posterior apex deviation, defined as the average distance from the highest point (apex, maximum curvature) of the posterior corneal surface to the Z-axis; (iii) the sagittal plane area at the minimum thickness point, defined as the area of the cornea within the sagittal plane that passes through the minimum thickness point of the posterior corneal surface and the Z-axis; and (iv) the posterior minimum thickness point deviation, described as the average distance in the XY plane from the minimum thickness point of the posterior corneal surface to the Z-axis.…”

Distinction between Early Keratoconus with Normal Vision and Normal Cornea based on Pattern Recognition Analysis

“…In a recent study conducted by our research group, the difference between normal and mild keratoconic patients by using a three-dimensional computer modeling was analyzed. 19 In that study, it was observed that one of the variables under analysis, the posterior apex deviation, showed a high discrimination capability between study groups with an AUC of 0.89.…”

Modelado geométrico personalizado de la córnea humana y su aplicación a la detección de ectasias corneales

“… Una primera etapa que corresponde al proceso de adquisición y tratamiento interno de los datos. Es en esta etapa donde los topógrafos obtienen los datos altimétricos en forma de matriz de elevaciones sobre un conjunto discreto y finito de puntos representativo de la superficie corneal, denominados datos en bruto (Cavas et al, 2014).…”

“…Este método de reconstrucción se caracteriza porque presenta flexibilidad y precisión en el ajuste al considerar datos dentro de una zona delimitada para el cálculo de un punto de la superficie. Esto significa que la representación es local, y por tanto, las irregularidades locales no afectan a la representación global de la superficie, en contraste con las funciones globales adoptadas en la reconstrucción modal, como superficies cuadráticas o polinomios de alto orden que carecen de la información a nivel local y por tanto, las irregularidades locales o defectos de la superficie no se pueden representar adecuadamente mediante estos enfoques (Zhu et al, 2010;Cavas-Martínez et al, 2014).…”