May 2006
Volume 47, Issue 13
Free
ARVO Annual Meeting Abstract  |   May 2006
Dynamic Performance of Accommodating–Intraocular Lenses: A Simulation–Based Study
Author Affiliations & Notes
  • C.M. Schor
    School of Optometry, Univ of California–Berkeley, Berkeley, CA
  • S.R. Bharadwaj
    School of Optometry, Univ of California–Berkeley, Berkeley, CA
  • C.D. Burns
    School of Optometry, Univ of California–Berkeley, Berkeley, CA
  • Footnotes
    Commercial Relationships  C.M. Schor, None; S.R. Bharadwaj, None; C.D. Burns, None.
  • Footnotes
    Support  NIH EY03532
Investigative Ophthalmology & Visual Science May 2006, Vol.47, 5843. doi:
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      C.M. Schor, S.R. Bharadwaj, C.D. Burns; Dynamic Performance of Accommodating–Intraocular Lenses: A Simulation–Based Study . Invest. Ophthalmol. Vis. Sci. 2006;47(13):5843.

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

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Abstract

Purpose: : A dynamic model of accommodation was developed using Matlab & Simulink that simulates stability & dynamic performance of accommodating intraocular lenses (A–IOL’s) when they are controlled under negative feedback of the accommodation system. The model simulates first– & second–order dynamics of accommodation that result from interactions between the accommodative neural controller of an older eye & the A–IOL material with biomechanical properties of a younger eye. An interactive web model of A–IOL is available at http://schorlab.berkeley.edu/.

Methods: : The model predicts static & dynamic properties of the accommodative step response based on age–dependent biomechanics of accommodative plant & neural control signals. The model independently controls the dynamic & static characteristics of the response with phasic–velocity & tonic–position signals respectively. Three A–IOL simulations were performed. In simulation I, the visco–elasticity of a 45–yr old matrix was replaced with that of a 25–yr old matrix. In simulation II, only elasticity of a 45–yr old matrix was replaced with that of a 25–yr old matrix while the matrix viscosity was retained. In simulation III, the visco–elasticity of a 45–yr old matrix was replaced with that of a polymer that was used previously as an A–IOL material (Koopmans et al., 2003; 2004). The 45–yr old lens capsule elasticity was retained in all simulations. The simulations illustrate the influence of decreased visco–elasticity of the A–IOL on dynamic accommodation & how adaptation of phasic & tonic signals can restore normal response dynamics.

Results: : Reducing the visco–elasticity of A–IOL to equal a younger lens increased the static amplitude of accommodation but with marked overshoots & oscillations in all simulations. However, the dynamic response could be stabilized by decreasing the amplitudes of phasic & tonic neural control signals.

Conclusions: : The model is a tool to simulate static & dynamic properties of A–IOL materials with visco–elastic properties of a young lens & how adjustments of neural control properties of the older eye can restore stable dynamics. Simulations indicate that neural control must be recalibrated to avoid unstable dynamic accommodation with the A–IOL.

Keywords: optical properties • plasticity • treatment outcomes of cataract surgery 
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