July 2019
Volume 60, Issue 9
Open Access
ARVO Annual Meeting Abstract  |   July 2019
Optical Simulation Approach to Compare Visual Outcomes from Different IOLs under Same Testing Conditions
Author Affiliations & Notes
  • Kevin Baker
    Alcon, Texas, United States
  • Jeff Lutkenhaus
    Alcon, Texas, United States
  • Zaiwei Xu
    Alcon, Texas, United States
  • Jessie Lemp-Hull
    Alcon, Texas, United States
  • Footnotes
    Commercial Relationships   Kevin Baker, Alcon (E); Jeff Lutkenhaus, Alcon (E); Zaiwei Xu, Alcon (E); Jessie Lemp-Hull, Alcon (E)
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science July 2019, Vol.60, 3718. doi:
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      Kevin Baker, Jeff Lutkenhaus, Zaiwei Xu, Jessie Lemp-Hull; Optical Simulation Approach to Compare Visual Outcomes from Different IOLs under Same Testing Conditions. Invest. Ophthalmol. Vis. Sci. 2019;60(9):3718.

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

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Purpose : To design and power clinical trial objectives appropriately it is useful to have comparable performance metrics when comparing different intraocular lenses (IOLs), which is not always available. Creating representative optical eye models unique to an IOL allows simulations to run with the same test conditions, so that performance comparison predictions can be made with good confidence.

Methods : Anterior and posterior IOL cross section surface profiles using white light interferometry and optical data (refractive index, Abbe #) were measured experimentally for several monofocal, multifocal and extended depth of focus IOLs. Custom signal pre-processing algorithms (Matlab) were applied to each profile to reduce noise and ensure that each profile is symmetric about the center. The processed profile’s base shape was estimated by fitting the data to a 6th order even asphere function. The remaining fitting residual was used to create a 2-D grid of surface sag values. Simulation results were generated using optical raytracing software (Zemax) by replacing the Navarro optical eye model’s lens data with the IOL’s base asphere shape, grid sag, and optical data. IOL eye models were validated by comparing simulation results with analogous bench measures and clinic outcomes.

Results : Fitting each IOL’s surface profile to the asphere function provides an accurate representation of the base shape; and the residual sag profile uncovers any underlying structure (diffraction patterns are now revealed). Bench and simulated modulation transfer function (MTF) curves show similar characteristics (shapes and magnitudes) and both highlight unique IOL differences. Optical metrics describing the simulations (e.g., area under MTF curve) are correlated with clinical visual performance outcomes illustrating their direct relationship to one another. Badal bench and simulation images are comparable to one another further validating each IOL model’s effectiveness. Simulated defocus curves generated for the IOL models under the same test conditions emphasize predicted visual performance differences at specific defocus levels that could aid in new IOL product development and testing.

Conclusions : IOL model simulations generated using the same test conditions demonstrate that this approach is useful when predicting visual outcomes and comparing different IOLs.

This abstract was presented at the 2019 ARVO Annual Meeting, held in Vancouver, Canada, April 28 - May 2, 2019.


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