To develop a predictive optical modeling process that utilizes individual computer eye models along with a novel through-focus image quality metric.
Individual eye models were implemented in optical design software (Zemax, Bellevue, WA) based on evaluation of ocular aberrations, pupil diameter, visual acuity, and accommodative response of 90 subjects (180 eyes; 24–63 years of age). Monocular high-contrast minimum angle of resolution (logMAR) acuity was assessed at 6 m, 2 m, 1 m, 67 cm, 50 cm, 40 cm, 33 cm, 28 cm, and 25 cm. While the subject fixated on the lowest readable line of acuity, total ocular aberrations and pupil diameter were measured three times each using the Complete Ophthalmic Analysis System (COAS HD VR) at each distance. A subset of 64 mature presbyopic eyes was used to predict the clinical logMAR acuity performance of five novel multifocal contact lens designs. To validate predictability of the design process, designs were manufactured and tested clinically on a population of 24 mature presbyopes (having at least +1.50 D spectacle add at 40 cm). Seven object distances were used in the validation study (6 m, 2 m, 1 m, 67 cm, 50 cm, 40 cm, and 25 cm) to measure monocular high-contrast logMAR acuity.
Baseline clinical through-focus logMAR was shown to correlate highly (R2 = 0.85) with predicted logMAR from individual eye models. At all object distances, each of the five multifocal lenses showed less than one line difference, on average, between predicted and clinical normalized logMAR acuity. Correlation showed R2 between 0.90 and 0.97 for all multifocal designs.
Computer-based models that account for patient’s aberrations, pupil diameter changes, and accommodative amplitude can be used to predict the performance of contact lens designs. With this high correlation (R2 ≥ 0.90) and high level of predictability, more design options can be explored in the computer to optimize performance before a lens is manufactured and tested clinically.
*BS, MS, CCOA
†BOptom, PhD, FAAO
Bausch and Lomb, Rochester, New York (ACK, IGC); and University of Rochester, Rochester, New York (ACK).
Amanda C. Kingston Bausch and Lomb 1400 N. Goodman Street Rochester, NY 14609 e-mail: Amanda.C.Kingston@bausch.com