Evaluating retinal image quality for myopia control lenses in a synthetic accommodative wavefront model

在合成调节波前模型中评估近视控制镜片的视网膜成像质量

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Abstract

This study uses a wavefront model to evaluate the effects of contact lenses on retinal image quality (RIQ) as a function of target vergence (TV). Three hundred synthetic eyes were generated using an accommodative wavefront model to simulate the changes in RIQ with accommodation. The synthetic eyes wavefronts were computationally combined using direct wavefront summation with two myopia control CLs. One has a +2.00D treatment zone dual-focus (DF) design (MiSight), and the other has an extended depth of focus (EDOF) design (Mylo). Peak image quality was calculated for the naked eye, with CLs in the relaxed state, and for an accommodative demand of -2.5D. Additionally, the predicted accommodative response (change in accommodative response that maximizes RIQ) was calculated. The mean RIQ (normalized between 0 and 1) for the relaxed eye was 0.42 ± 0.10 in the naked eye. It decreased to 0.26 ± 0.05 with the DF and threefold to 0.15 ± 0.04 with the EDOF. For a TV = -2.5D, the RIQ was 0.34 ± 0.09 in the naked eye condition, 0.24 ± 0.07 with the DF, and 0.19 ± 0.03 with the EDOF. For the TV, the CL's effective add power, the accommodative RIQ showed a secondary peak, with RIQs of 0.08 ± 0.03 (DF) and 0.12 ± 0.04 (EDOF). The theoretical accommodative response (AR) of the naked eye was 2.33 ± 0.20 D, while with the DF and EDOF, it was 2.23 ± 0.39 D and 1.75 ± 0.22 D, respectively. Both myopia control CLs reduced RIQ at a TV of 0 D. At a TV = -2.5D; both lenses caused a secondary peak in the RIQ through focus. With EDOF, the secondary peak was less than 0.1 D from the maximum peak, likely due to its EDOF design. The theoretical AR was reduced with both myopia control CLs, 0.27 D for DF CLs and 0.75 D for EDOF CLs. These results indicate that both lenses impose significant limitations on RIQ and theoretical AR, align with findings from previous studies on subjective optical performance, and validate the model as a useful testing tool.

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