Study and characterization of morphogeometric parameters to assist diagnosis of keratoconus

Abstract

BackgroundIn case of significant imperfections on the cornea, data acquisition is difficult and a significant level of missing data could require the interpolation of important areas of the cornea, resulting in a very ambiguous model. The development of methods to define in vivo customised geometric properties of the cornea based only on real raw data is extremely useful to diagnose and assess the progression of diseases directly related to the corneal architecture. The present work tries to improve the prognostic of corneal ectasia creating a 3D customised model of the cornea and analysing different geometric variables from this model to determine which variables or combination of them could be defined as an indicator of susceptibility to develop keratoconus.MethodsA corneal geometric reconstruction was performed using zonal functions and retrospective Scheimpflug tomography data from 187 eyes of 187 patients. Morphology of healthy and keratoconic corneas was characterized by means of geometric variables. The performance of these variables as predictors of a new geometric marker was assessed and their correlations were analysed.ResultsThe more representative variable to classify the corneal anomalies related to keratoconus was posterior apex deviation (area under receiver operating characteristic curve > 0.899; p < 0.0001). However, the strongest correlations in both healthy and pathological corneas were provided by the metrics directly related to the thickness, as deviations of the anterior/posterior minimum thickness points.ConclusionsThe presented morphogeometric approach based on the analysis and custom geometric modelling of the cornea demonstrates to be useful for the characterization and diagnosis of keratoconus disease, stating that geometrical deformation is an effective marker of the ectatic disease’s progression.

Changes in the 3D Corneal Structure and Morphogeometric Properties in Keratoconus after Corneal Collagen Crosslinking

“…Our research group developed a new method to perform a more complete morphogeometric analysis of the cornea on the basis of the data obtained from a Scheimpflug imaging-based tomography system [8]. This advanced corneal analysis has been demonstrated to be useful for characterizing the corneal structure in patients with keratoconus [9,10] for subclinical and clinical keratoconus detection [11][12][13], patient education [14], and even for characterizing the symmetry of the corneal profile between both eyes of healthy patients [15]. The aim of the current study was to conduct this 3D morphogeometric analysis to evaluate corneal changes after CXL in keratoconus patients and during a 6 month follow-up period.…”

“…A custom-made script programmed in the Matlab R2017b (Mathworks, Natick, USA) software was created to transform each point of the cloud provided by the tomographer in polar coordinates into the Cartesian format. This procedure has been meticulously described in several previous research works [8][9][10][11][12][13][14][15]. The program provides a. CSV file containing data of the points of clouds that describe both the anterior and posterior corneal surfaces for the area comprised between the geometric center of the cornea (r = 0 mm) and the mid-peripheral area (r = 4 mm).…”

“…The program provides a. CSV file containing data of the points of clouds that describe both the anterior and posterior corneal surfaces for the area comprised between the geometric center of the cornea (r = 0 mm) and the mid-peripheral area (r = 4 mm). This zone usually contains most corneal anomalies for not only diseased, but also healthy eyes [8][9][10][11][12][13][14][15]. Afterward, both surfaces were imported to the Rhinoceros V 5.0 (MCNeel and Associates, Seattle, USA) software, and the "patch" function was used to find the surfaces that better fitted the clouds of points.…”

“…• Solid modeling and morphogeometric analysis. Finally, a customized three-dimensional model of the patient's cornea was created from the surfaces previously generated in Rhinoceros by importing them to the SolidWorks V2018 (Dassault Systèmes, Vélizy-Villacoublay, France) software [8][9][10][11][12][13][14][15]. This model was then analyzed, and several morphogeometrical parameters were established.…”

A Machine-Learning Model Based on Morphogeometric Parameters for RETICS Disease Classification and GUI Development

“…The product of this procedure is a patient-specific 3D custom corneal model, which can be studied to determine several morpho-geometric parameters that have already been described and used in a previous study [37]. Of them all, anterior corneal surface area (anterior surface area), posterior corneal surface area (posterior surface area) and total corneal volume (total volume) were selected to be used, along with demographic, pachymetry and clinical parameters ( Figure 1).…”