PURPOSE: To predict clinical defocus curve performance of the PanOptix intraocular lens (IOL) model TFNT00, a population-based image quality metric was applied to a pseudophakic eye model. METHODS: Visual acuity (VA) was simulated using a 2-surface reduced eye model. For each virtual eye, the derived corneal surface was combined with scaled IOL surface. Corneal power and aberration, anterior chamber depth, and pupil size were iterated using a Monte-Carlo approach. Image quality of the IOLs was assessed using the total aberration map to compute the amplitude point spread function. A diffraction-normalized light-in-the-bucket metric was calculated for each virtual eye for defocuses from -3.5 D to +1.0 D (step size 0.25 D) and transformed to VAs and defocus curves. Simulated VA for the ReSTOR +3.0 D lens was used to generate a calibration function by linear regression correlation of simulated data with clinical VA data. Simulated TFNT00 VA was then validated by comparing defocus curves to clinical TFNT00 data. RESULTS: From -3.5 D to +1.0 D, the simulated defocus curve was generally consistent with the defocus curve from the TFNT00 clinical trial. The mean absolute difference was 0.022 logMAR (~1 letter) for simulated VA versus clinical trial VA. CONCLUSION: IOL image quality can be assessed using a population-based virtual eye model to simulate VA and predict clinical performance. Computational modeling and simulation can be applied to future IOL development before clinical trials are conducted.
PURPOSE: To predict clinical defocus curve performance of the PanOptix intraocular lens (IOL) model TFNT00, a population-based image quality metric was applied to a pseudophakic eye model. METHODS: Visual acuity (VA) was simulated using a 2-surface reduced eye model. For each virtual eye, the derived corneal surface was combined with scaled IOL surface. Corneal power and aberration, anterior chamber depth, and pupil size were iterated using a Monte-Carlo approach. Image quality of the IOLs was assessed using the total aberration map to compute the amplitude point spread function. A diffraction-normalized light-in-the-bucket metric was calculated for each virtual eye for defocuses from -3.5 D to +1.0 D (step size 0.25 D) and transformed to VAs and defocus curves. Simulated VA for the ReSTOR +3.0 D lens was used to generate a calibration function by linear regression correlation of simulated data with clinical VA data. Simulated TFNT00 VA was then validated by comparing defocus curves to clinical TFNT00 data. RESULTS: From -3.5 D to +1.0 D, the simulated defocus curve was generally consistent with the defocus curve from the TFNT00 clinical trial. The mean absolute difference was 0.022 logMAR (~1 letter) for simulated VA versus clinical trial VA. CONCLUSION: IOL image quality can be assessed using a population-based virtual eye model to simulate VA and predict clinical performance. Computational modeling and simulation can be applied to future IOL development before clinical trials are conducted.
Authors: Thomas Kohnen; Michael Herzog; Eva Hemkeppler; Sabrina Schönbrunn; Nina De Lorenzo; Kerstin Petermann; Myriam Böhm Journal: Am J Ophthalmol Date: 2017-09-18 Impact factor: 5.258
Authors: Marjan D Nijkamp; Maria G T Dolders; John de Brabander; Bart van den Borne; Fred Hendrikse; Rudy M M A Nuijts Journal: Ophthalmology Date: 2004-10 Impact factor: 12.079
Authors: Yong Woo Lee; Chul Young Choi; Kun Moon; Yong Jin Jeong; Sang Il An; Je Myung Lee; Jong Ho Lee; Min Cheol Seong Journal: BMC Ophthalmol Date: 2022-09-21 Impact factor: 2.086