April 2011
Volume 52, Issue 14
ARVO Annual Meeting Abstract  |   April 2011
Keratoconus Eye Modeling
Author Affiliations & Notes
  • Ying-Ling Chen
    Univ of Tennessee Space Inst, Tullahoma, Tennessee
  • Lei Shi
    Univ of Tennessee Space Inst, Tullahoma, Tennessee
  • J W L. Lewis
    E-Vision Technologies, Inc, Tullahoma, Tennessee
  • Ming Wang
    Wang Vision Institute, Nashville, Tennessee
  • Ryan Vida
    Wang Vision Institute, Nashville, Tennessee
  • Footnotes
    Commercial Relationships  Ying-Ling Chen, None; Lei Shi, None; J W L. Lewis, E-Vision Tech (P); Ming Wang, E-Vision Tech (P); Ryan Vida, None
  • Footnotes
    Support  NIH Grants EY018935 and EY018385
Investigative Ophthalmology & Visual Science April 2011, Vol.52, 2811. doi:
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    • Get Citation

      Ying-Ling Chen, Lei Shi, J W L. Lewis, Ming Wang, Ryan Vida; Keratoconus Eye Modeling. Invest. Ophthalmol. Vis. Sci. 2011;52(14):2811.

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      © ARVO (1962-2015); The Authors (2016-present)

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To construct a pool of customized keratoconic, ametropic, and emmetropic eye models that are optically functional and analytical and can be extensively used in contemporary optical design programs for various applications.


We collected clinical data from 30 mild to moderate keratoconic and healthy ametropic and emmetropic eyes. The Navarro emmetropic model and age-related lens parameters [Chen ARVO 2010] are used as the base model. The clinical data used in the modeling include 2 corneal topographies, central corneal thickness, ACD, and related position to center of pupil from Pentacam, axial measurement from IOL Master, and wavefront aberration from WaveScan. Maximum usable corneal area and entrance pupil diameter and wavefront are incorporated in each model. The precision of Pentacam topographic data is enhanced from 1 um to 0.01 um by Zernike reconstruction up to 25th order. Optical optimization is performed in ZEMAX under conditions of WaveScan measurement to achieve minimum RMS error between the model and the measured wavefront. Optimization is performed on biconic-Zernike surfaces that define the lens.


15 keratoconic and 15 normal eyes were successfully constructed. All lens parameters are within reasonable statistical range. The RMS error between final models and clinic data are between 0.0019 to 0.0154 wave. As shown in the figure, no difference in the optimization convergence was observed between the keratoconus and normal groups of model, and pupil size appears to be the most influential factor.


With the eye models, we also successfully predicted the fundus camera measurement and retinoscopy measurement for various eye conditions. The modeling result can be extended to include cataract, tear film properties, and more complicated conditions in the future.  

Keywords: computational modeling • keratoconus • aberrations 

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