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.

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The aim of this study was to describe the application of a new bioengineering graphical technique based on geometric custom modelling capable to detect and to discriminate small variations in the morphology of the corneal surface. Methods: A virtual 3D solid custom model of the cornea was obtained employing Computer Aided Geometric Design tools, 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. Geometric reconstruction was performed using B-Spline functions, defining and calculating the representative geometric variables of the corneal morphology of patients under clinical diagnosis of keratoconus. Results: At least four variables could be used in order to classify corneal abnormalities related to keratoconus disease: anterior corneal surface area (ROC 0.853; p < 0.0001), posterior corneal surface area (ROC 0.813; p < 0.0001), anterior apex deviation (ROC 0.742; p < 0.0001) and posterior apex deviation (ROC 0.899; p < 0.0001). Conclusions: Custom geometric modelling enables an accurate characterization of the human cornea based on untreated raw data from the corneal topographer and the calculation of morphological variables of the cornea, which permits the clinical diagnosis of keratoconus disease.

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