The morphological characterization of the cornea using corneal topographers is a widespread clinical practice that is essential for the diagnosis of keratoconus. The current state of this non-invasive exploratory technique has evolved with the possibility of achieving a great number of measuring points of both anterior and posterior corneal surfaces, which is possible due to the development of new and advanced measurement devices. All these data are later used to extract a series of topographic valuation indices that permit to offer the most exact and reliable clinical diagnosis. This paper describes the technologies in which current corneal topographers are based on, being possible to define the main morphological characteristics that the keratoconus pathology produces on corneal surface. Finally, the main valuation indices, which are provided by the corneal topographers and used for the diagnosis of keratoconus, are also defined.

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.

There are numerous tomographic indices for the detection of keratoconus risk. When the indexes based on corneal volume are analyzed, two problems are presented: on the one hand, they are not very sensitive to the detection of incipient cases of keratoconus because they are not locally defined in the primary developmental region of the structural abnormalities; and on the other hand, they do not register the geometric decompensation driven by the asymmetry present during the disease progression. This work performed a morphogeometric modeling of the cornea by the aid of CAD tools and using raw topographic data (Sirius system, CSO, Firenze). For this method, four singular points present on the corneal surfaces were located and the following parameters based on corneal volume were calculated: VOLmct, defined by the points of minimal thickness; VOLaap, defined by the anterior corneal apex, and VOLpap, defined by the posterior corneal apex. The results demonstrate that a further reduction of corneal volume in keratoconus happens and significantly progresses along the disease severity level. The combination of optical and volumetric data, that collect the sensitivity of the asymmetry generated by the disease, allows an accurate detection of incipient cases and follow up of the disease progression.

PurposeTo characterize corneal structural changes in keratoconus using a new morphogeometric approach and to evaluate its potential diagnostic ability.MethodsComparative study including 464 eyes of 464 patients (age, 16 and 72 years) divided into two groups: control group (143 healthy eyes) and keratoconus group (321 keratoconus eyes). Topographic information (Sirius, CSO, Italy) was processed with SolidWorks v2012 and a solid model representing the geometry of each cornea was generated. The following parameters were defined: anterior (Aant) and posterior (Apost) corneal surface areas, area of the cornea within the sagittal plane passing through the Z axis and the apex (Aapexant, Aapexpost) and minimum thickness points (Amctant, Amctpost) of the anterior and posterior corneal surfaces, and average distance from the Z axis to the apex (Dapexant, Dapexpost) and minimum thickness points (Dmctant, Dmctpost) of both corneal surfaces.ResultsSignificant differences among control and keratoconus group were found in Aapexant, Aapexpost, Amctant, Amctpost, Dapexant, Dapexpost (all p<0.001), Apost (p = 0.014), and Dmctpost (p = 0.035). Significant correlations in keratoconus group were found between Aant and Apost (r = 0.836), Amctant and Amctpost (r = 0.983), and Dmctant and Dmctpost (r = 0.954, all p<0.001). A logistic regression analysis revealed that the detection of keratoconus grade I (Amsler Krumeich) was related to Apost, Atot, Aapexant, Amctant, Amctpost, Dapexpost, Dmctant and Dmctpost (Hosmer-Lemeshow: p>0.05, R2 Nagelkerke: 0.926). The overall percentage of cases correctly classified by the model was 97.30%.ConclusionsOur morphogeometric approach based on the analysis of the cornea as a solid is useful for the characterization and detection of keratoconus.

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