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Geometrical custom modeling of human cornea in vivo and its use for the diagnosis of corneal ectasia.

Cavas-Martínez F, Fernández-Pacheco DG, De la Cruz-Sánchez E, Nieto Martínez J, Fernández Cañavate FJ, Vega-Estrada A, Plaza-Puche AB, Alió JL - PLoS ONE (2014)

Bottom Line: Later, geometric variables are extracted from the model obtained and statistically analyzed to detect deformations of the cornea.The 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).Also, from a clinical point of view, the procedure described has established a new approach for the study of eye-related diseases.

View Article: PubMed Central - PubMed

Affiliation: Department of Graphical Expression, Technical University of Cartagena, Cartagena, Spain.

ABSTRACT

Aim: To 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.

Method: The 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.

Results: The 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).

Conclusion: Geometric 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.

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Related in: MedlinePlus

Analysis of the point-surface deviation for the anterior surface reconstruction of: a) a healthy cornea, b) a cornea with advanced keratoconus.
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pone-0110249-g002: Analysis of the point-surface deviation for the anterior surface reconstruction of: a) a healthy cornea, b) a cornea with advanced keratoconus.

Mentions: The surface that best fits the point cloud was generated with the Rhinoceros’s patch surface function (Fig. 1-B), a reconstruction software option that fits a surface through given curves, meshes, point objects, and point clouds [44]. For this research, this function tried to minimize the nominal distance between the 3D point cloud and the solution surface. For this objective, the function was configured by setting the sample point spacing at 256 (number of points for each data ring), the surface span planes at 255 for both u and v directions (the maximum number of span planes that the software permitted), and the stiffness of the solution surface at 10−3 [mm]. This last parameter provides information on how much the best fit plane can be deformed in order to match the input points. This deviation can be calculated later by the software, providing a mean value of the distance error for the solution surface. This can be seen in Figure 2a, where the top view of the point cloud for the anterior surface of a healthy cornea is represented and a mean distance error of 7.23×10−6±1.536×10−5 [mm] (mean ± standard deviation) is obtained. Figure 2b shows the deviation error for the anterior surface of a cornea with advanced keratoconus, obtaining in this case a mean distance error of 3.54×10−4±6.36×10−4 [mm] (mean ± standard deviation). In both figures the same good/bad threshold values have been configured: 10−3 [mm] for bad points (in red) and 10−4 [mm] for good points (in blue). These figures show how the points are distributed in perfect circular rings from r = 0 mm to r = 4 mm in steps of 0.2 mm. This is because, as previously mentioned, the Sirius device gives the 3D points in polar coordinates, and once converted into Cartesian format, they are distributed in a circular map.


Geometrical custom modeling of human cornea in vivo and its use for the diagnosis of corneal ectasia.

Cavas-Martínez F, Fernández-Pacheco DG, De la Cruz-Sánchez E, Nieto Martínez J, Fernández Cañavate FJ, Vega-Estrada A, Plaza-Puche AB, Alió JL - PLoS ONE (2014)

Analysis of the point-surface deviation for the anterior surface reconstruction of: a) a healthy cornea, b) a cornea with advanced keratoconus.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4201525&req=5

pone-0110249-g002: Analysis of the point-surface deviation for the anterior surface reconstruction of: a) a healthy cornea, b) a cornea with advanced keratoconus.
Mentions: The surface that best fits the point cloud was generated with the Rhinoceros’s patch surface function (Fig. 1-B), a reconstruction software option that fits a surface through given curves, meshes, point objects, and point clouds [44]. For this research, this function tried to minimize the nominal distance between the 3D point cloud and the solution surface. For this objective, the function was configured by setting the sample point spacing at 256 (number of points for each data ring), the surface span planes at 255 for both u and v directions (the maximum number of span planes that the software permitted), and the stiffness of the solution surface at 10−3 [mm]. This last parameter provides information on how much the best fit plane can be deformed in order to match the input points. This deviation can be calculated later by the software, providing a mean value of the distance error for the solution surface. This can be seen in Figure 2a, where the top view of the point cloud for the anterior surface of a healthy cornea is represented and a mean distance error of 7.23×10−6±1.536×10−5 [mm] (mean ± standard deviation) is obtained. Figure 2b shows the deviation error for the anterior surface of a cornea with advanced keratoconus, obtaining in this case a mean distance error of 3.54×10−4±6.36×10−4 [mm] (mean ± standard deviation). In both figures the same good/bad threshold values have been configured: 10−3 [mm] for bad points (in red) and 10−4 [mm] for good points (in blue). These figures show how the points are distributed in perfect circular rings from r = 0 mm to r = 4 mm in steps of 0.2 mm. This is because, as previously mentioned, the Sirius device gives the 3D points in polar coordinates, and once converted into Cartesian format, they are distributed in a circular map.

Bottom Line: Later, geometric variables are extracted from the model obtained and statistically analyzed to detect deformations of the cornea.The 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).Also, from a clinical point of view, the procedure described has established a new approach for the study of eye-related diseases.

View Article: PubMed Central - PubMed

Affiliation: Department of Graphical Expression, Technical University of Cartagena, Cartagena, Spain.

ABSTRACT

Aim: To 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.

Method: The 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.

Results: The 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).

Conclusion: Geometric 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.

Show MeSH
Related in: MedlinePlus