%0 Journal Article
%A Gatinel, Damien
%A Debellemanière, Guillaume
%A Saad, Alain
%A Dubois, Mathieu
%A Rampat, Radhika
%T Determining the Theoretical Effective Lens Position of Thick Intraocular Lenses for Machine Learning–Based IOL Power Calculation and Simulation
%B Translational Vision Science & Technology
%D 2021
%R 10.1167/tvst.10.4.27
%J Translational Vision Science & Technology
%V 10
%N 4
%P 27-27
%@ 2164-2591
%X To describe a formula to back-calculate the theoretical position of the principal object plane of an intraocular lens (IOL), as well as the theoretical anatomic position in a thick lens eye model. A study was conducted to ascertain the impact of variations in design and IOL power, on the refractive outcomes of cataract surgery. A schematic eye model was designed and manipulated to reflect changes in the anterior and posterior radii of an IOL, while keeping the central thickness and paraxial powers static. Modifications of the shape factor (X) of the IOL affects the thick lens estimated effective lens position (ELP). Corresponding postoperative spherical equivalent (SE) were computed for different IOL powers (–5 diopters [D], 5 D, 15 D, 25 D, and 35 D) with X ranging from –1 to +1 by 0.1. The impact of the thick lens estimated effective lens position shift on postoperative refraction was highly dependent on the optical power of the IOL and its thickness. Design modifications could theoretically induce postoperative refraction variations between approximately 0.50 and 3.0 D, for implant powers ranging from 15 D to 35 D. This work could be of interest for researchers involved in the design of IOL power calculation formulas. The importance of IOL geometry in refractive outcomes, especially for short eyes, should challenge the fact that these data are not usually published by IOL manufacturers. The back-calculation of the estimated effective lens position is central to intraocular lens calculation formulas, especially for artificial intelligence–based optical formulas, where the algorithm can be trained to predict this value.
%[ 7/30/2021
%U https://doi.org/10.1167/tvst.10.4.27