Efficacy of Morpho-Geometrical Analysis of the Corneal Surfaces in Keratoconus Disease According to Moderate Visual Limitation

  • J. S. Velázquez-Blázquez
  • D. G. Fernández-Pacheco
  • J. Alió del Barrio
  • J. L. Alió
  • F. Cavas-MartínezEmail author
Conference paper
Part of the Lecture Notes in Mechanical Engineering book series (LNME)


The cornea is a complex hemispheric structure, made of collagen fibres that provide it a homogenous and stable geometry. During keratoconus disease, a loss of tenacity takes place in the collagen fibres that form the corneal structure, producing an alteration of its geometry, this is, a change of its curvature, and therefore, a loss of visual quality of patients. The geometric characterization of the hemispheric structure by means of biometric parameters is a very solid technique of diagnosis, based in a virtual 3D model, which has already been validated for several degrees of severity of keratoconus pathology. In this prospective comparative study, 93 corneas (50 healthy subjects and 43 patients with keratoconus with moderate visual limitation) were geometrically modelled. The results obtained in this work suggest that the best predictive biometric parameters are anterior corneal surface area and posterior apex deviation, and that the strongest correlation is produced between sagittal plane apex area in minimum thickness point and sagittal plane apex area. The studied biometric parameters have shown significant differences between groups. Therefore, the analysis of the biometric parameters that register the geometric decompensation that locally appear in a corneal region, as a response to the asymmetry produced during the development of keratoconus disease with a moderate visual impairment, is a new approach that may lead to a better understanding of the disease with this degree of optical limitation.


Computer-Aided Geometric Design (CAGD) Optical Aberrometry Scheimpflug 3D modelling 



This publication has been carried out in the framework of the Thematic Network for Co-Operative Research in Health (RETICS) reference number RD16/0008/0012 financed by the Carlos III Health Institute-General Subdirection of Networks and Cooperative Investigation Centres (R&D&I National Plan 2013–2016) and the European Regional Development Fund (FEDER).

Conflict of Interest

The authors have no conflict of interest to declare.

Financial Disclosure.

Neither author has a financial or proprietary interest in any material or method mentioned.


  1. 1.
    Albertazzi, R.: Queratocono: pautas para su diagnóstico y tratamiento. Buenos Aires: Ediciones Científicas Argentinas 6, 123–135 (2010)Google Scholar
  2. 2.
    Buey Salas, M., Peris, M.: Biomecánica y arquitectura corneal. Elsevier, Spain (2014)Google Scholar
  3. 3.
    Cavas-Martínez, F., De la Cruz Sánchez, E., Martínez, J.N., Cañavate, F.F., Fernández-Pacheco, D.: Corneal topography in keratoconus: state of the art. Eye Vis. 3(1), 5 (2016)CrossRefGoogle Scholar
  4. 4.
    Piñero, D.P., Alió, J.L., Alesón, A., Vergara, M.E., Miranda, M.: Corneal volume, pachymetry, and correlation of anterior and posterior corneal shape in subclinical and different stages of clinical keratoconus. J. Cataract Refract. Surg. 36(5), 814–825 (2010)CrossRefGoogle Scholar
  5. 5.
    Zadnik, K., Barr, J.T., Edrington, T.B., Everett, D.F., Jameson, M., McMahon, T.T., Shin, J.A., Sterling, J.L., Wagner, H., Gordon, M.O.: Baseline findings in the Collaborative Longitudinal Evaluation of Keratoconus (CLEK) study. Invest. Ophthalmol. Vis. Sci. 39(13), 2537–2546 (1998)Google Scholar
  6. 6.
    McMahon, T.T., Szczotka-Flynn, L., Barr, J.T., Anderson, R.J., Slaughter, M.E., Lass, J.H., Iyengar, S.K., Group CS: A new method for grading the severity of keratoconus: the Keratoconus Severity Score (KSS). Cornea 25(7), 794–800 (2006)CrossRefGoogle Scholar
  7. 7.
    Ukwatta, E., Arevalo, H., Rajchl, M., White, J., Pashakhanloo, F., Prakosa, A., Herzka, D.A., McVeigh, E., Lardo, A.C., Trayanova, N.A., Vadakkumpadan, F.: Image-based reconstruction of three-dimensional myocardial infarct geometry for patient-specific modeling of cardiac electrophysiology. Med. Phys. 42(8), 4579–4590 (2015). Scholar
  8. 8.
    Henckel, J., Holme, T.J., Radford, W., Skinner, J.A., Hart, A.J.: 3D-printed patient-specific guides for hip arthroplasty. J. Am. Acad. Orthop. Surg. 26(16), e342–e348 (2018). Scholar
  9. 9.
    VanKoevering, K.K., Zopf, D.A., Hollister, S.J.: Tissue engineering and 3-dimensional modeling for facial reconstruction. Facial Plast. Surg. Clin. North Am. 27(1), 151–161 (2019). Scholar
  10. 10.
    Liew, Y.M., Khalid, A., Tan, L.K., Lim, E., Chan, B.T., Md Sari, N.A.B., Chee, K.H., Abdul Aziz, Y.F.: Spatial cardiac dysfunction assessment via personalized modelling from MRI. In: 2018 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2018 - Proceedings, pp. 69–74 (2019).
  11. 11.
    Naser, M.A., Sayed, A.M., Wahba, A.A., Eldosoky, M.A.A.: Modeling procedures for breast cancer diagnosis based on clinical elastography images. In: Proceedings - 2018 13th International Conference on Computer Engineering and Systems, ICCES 2018, pp. 671–677 (2019).
  12. 12.
    Cavas-Martínez, F., Bataille, L., Fernández-Pacheco, D.G., Cañavate, F.J.F., Alio, J.L.: Keratoconus detection based on a new corneal volumetric analysis. Sci. Rep. 7(1) (2017).
  13. 13.
    Cavas-Martínez, F., Bataille, L., Fernández-Pacheco, D.G., Cañavate, F.J.F., Alió, J.L.: A new approach to keratoconus detection based on corneal morphogeometric analysis. PLoS One 12(9) (2017). Scholar
  14. 14.
    Cavas-Martínez, F., Fernández-Pacheco, D.G., De La Cruz-Sánchez, E., Nieto Martínez, J., Fernández Cañavate, F.J., Vega-Estrada, A., Plaza-Puche, A.B., Alió, J.L.: Geometrical custom modeling of human cornea in vivo and its use for the diagnosis of corneal ectasia. PLoS One 9(10) (2014). Scholar
  15. 15.
    Alio, J.L., Pinero, D.P., Aleson, A., Teus, M.A., Barraquer, R.I., Murta, J., Maldonado, M.J., Castro de Luna, G., Gutierrez, R., Villa, C., Uceda-Montanes, A.: Keratoconus-integrated characterization considering anterior corneal aberrations, internal astigmatism, and corneal biomechanics. J. Cataract Refract. Surg. 37(3), 552–568 (2011). Scholar
  16. 16.
    Cavas-Martínez, F., Fernández-Pacheco, D.G., Cañavate, F.J.F., Velázquez-Blázquez, J.S., Bolarín, J.M., Alió, J.L.: Study of morpho-geometric variables to improve the diagnosis in Keratoconus with mild visual limitation. Symmetry 10(8) (2018). Scholar
  17. 17.
    Cavas Martinez, F., Garcia Fernandez-Pacheco, D., Fernandez Cañavate, F.J., Velázquez Blázquez, J.S., Bolarin, J.M., Tiveron, M., Alio, J.L.: Detección De Queratocono Temprano Mediante Modelado 3D Personalizado Y Análisis De Sus Parámetros Geométricos. Dyna Ingenieria E Industria 94(1), 175–181 (2019). Scholar
  18. 18.
    Huseynli, S., Salgado-Borges, J., Alio, J.L.: Comparative evaluation of Scheimpflug tomography parameters between thin non-keratoconic, subclinical keratoconic, and mild keratoconic corneas. Eur. J. Ophthalmol. 28(5), 521–534 (2018). Scholar
  19. 19.
    Cavas-Martínez, F., Fernández-Pacheco, D.G., Parras, D., Cañavate, F.J.F., Bataille, L., Alió, J.: Study and characterization of morphogeometric parameters to assist diagnosis of keratoconus. BioMed. Eng. Online 17 (2018).
  20. 20.
    Lasko, T.A., Bhagwat, J.G., Zou, K.H., Ohno-Machado, L.: The use of receiver operating characteristic curves in biomedical informatics. J. Biomed. Inform. 38(5), 404–415 (2005). Scholar
  21. 21.
    Pepe, M.S.: The Statistical Evaluation of Medical Tests for Classification and Prediction. Medicine (2003)Google Scholar
  22. 22.
    Piñero, D.P.: Technologies for anatomical and geometric characterization of the corneal structure and anterior segment: a review. Semin. Ophthalmol. 30 (2015). Scholar
  23. 23.
    Anderson, K., El-Sheikh, A., Newson, T.: Application of structural analysis to the mechanical behaviour of the cornea. J. R. Soc. Interface 1(1), 3–15 (2004). Scholar
  24. 24.
    Gefen, A., Shalom, R., Elad, D., Mandel, Y.: Biomechanical analysis of the keratoconic cornea. J. Mech. Behav. Biomed. Mater. 2(3), 224–236 (2009). Scholar
  25. 25.
    Montalbán, R., Alio, J.L., Javaloy, J., Piñero, D.P.: Correlation of anterior and posterior corneal shape in keratoconus. Cornea 32 (2013). Scholar
  26. 26.
    Cavas-Martínez, F., Fernández-Pacheco, D.G., Cruz-Sánchez, E., Nieto Martínez, J., Fernández Cañavate, F.J., Vega-Estrada, A.: Geometrical custom modeling of human cornea in vivo and its use for the diagnosis of corneal ectasia. PLoS One 9 (2014). Scholar
  27. 27.
    Tomidokoro, A., Oshika, T., Amano, S., Higaki, S., Maeda, N., Miyata, K.: Changes in anterior and posterior corneal curvatures in keratoconus. Ophthalmology 107(7), 1328–1332 (2000)CrossRefGoogle Scholar
  28. 28.
    Piñero, D.P., Alió, J.L., Aleson, A., Escaf Vergara, M., Miranda, M.: Corneal volume, pachymetry, and correlation of anterior and posterior corneal shape in subclinical and different stages of clinical keratoconus. J. Cataract Refract. Surg. 36 (2010). Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Structures, Construction and Graphical ExpressionTechnical University of CartagenaCartagenaSpain
  2. 2.Keratoconus Unit of Vissum Corporation AlicanteAlicanteSpain
  3. 3.Department of OphthalmologyMiguel Hernández University of ElcheAlicanteSpain

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