April 2011
Volume 52, Issue 14
Free
ARVO Annual Meeting Abstract  |   April 2011
Personalized Pseudophakic Model For Refractive Assessment
Author Affiliations & Notes
  • Filomena J. ribeiro
    Visual Sciences Research Centre, Lisbon, Portugal
    GoLP/Plasmas and Nuclear Fusion Institute, Instituto Superior Tecnico, Lisbon, Portugal
  • Antonio Castanheira-Diniz
    Visual Sciences Research Centre, Lisbon, Portugal
  • Joao M. Dias
    GoLP/Plasmas and Nuclear Fusion Institute, Instituto Superior Tecnico, Lisbon, Portugal
  • Footnotes
    Commercial Relationships  Filomena J. ribeiro, None; Antonio Castanheira-Diniz, None; Joao M. Dias, None
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science April 2011, Vol.52, 6178. doi:
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      Filomena J. ribeiro, Antonio Castanheira-Diniz, Joao M. Dias; Personalized Pseudophakic Model For Refractive Assessment. Invest. Ophthalmol. Vis. Sci. 2011;52(14):6178.

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

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Abstract

Purpose: : To create a pseudophakic eye model that will allow a personalized refractive assessment

Methods: : Population Sample: 50 participants (50 eyes) who underwent cataract surgery, 22 men (M) and 28 women (W) with an average age of 66.5 ± 4.7 years and 66.1 ± 4.2 years, respectively, were assessed before and after surgery, and post-surgery subjective refraction was determined. Modeling: The Liou-Brennan eye model was used as a starting point, and biometric values were replaced by individual measurements. Detailed corneal surface data were obtained from topography, and a full 59x59 grid of values was used to define corneal surfaces. Anterior chamber depth (acd) was determined based on preoperatory anterior chamber, lens thickness, and lens equator definition from the Liou-Brennan model. For the refractive assessment, optimization criteria were applied based on values of MTF weighted with CSF.Validation of the model: refractive assessment determined by this personalized model (PM) was compared with subjective assessment determined by the patient after cataract surgery.

Results: : Comparison of corneal power estimation using this PM with values obtained by keratometry showed a very strong correlation (r = 0.844, p < 0.0001). Comparison of acd estimation using this PM with the Olsen formula showed a very strong correlation (r = 0.738, p < 0.0001). Comparison of post-operative refractive assessment obtained using this PM with subjective refractive assessment showed a very strong correlation (r = 0.789, p < 0.0001), with average values (-1.20 ± 0.75 vs -0.65 ± 0.84, p <0.0001).

Conclusions: : The results suggest that this personalized model, which uses individual biometric values, is a valuable tool for an accurate refractive estimation.

Keywords: computational modeling • refraction • intraocular lens 
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