Prediction of Effective Lens Position Using Multiobjective Evolutionary Algorithm - Tamaoki, Akeno; Kojima, Takashi; Tanaka, Yoshiki; Hasegawa, Asato; Kaga, Tatsushi; Ichikawa, Kazuo; Tanaka, Kiyoshi (Translational Vision Science & Technology, 2019)
The purpose of this study was to evaluate the prediction accuracy of effective lens position (ELP) after cataract surgery using a multiobjective evolutionary algorithm (MOEA). Ninety-six eyes of 96 consecutive patients (aged 73.9 ± 8.6 years) who underwent cataract surgery were retrospectively studied; the eyes were randomly distributed to a prediction group (55 eyes) and a verification group (41 eyes). The procedure was repeated randomly 30 times to create 30 data sets for both groups. In the prediction group, based on the parameters of preoperative optical coherence tomography (OCT), biometry, and anterior segment (AS)-OCT, the prediction equation of ELP was created using MOEA and stepwise multiple regression analysis (SMR). Subsequently, the prediction accuracy of ELPs was evaluated and compared with conventional formulas, including SRK/T and the Haigis formula. The rate of mean absolute prediction error of 0.3 mm or higher was significantly lower in MOEA (mean 4.9% ± 3.2%, maximum 9.8%) than SMR (mean 7.3% ± 4.8%, maximum 24.4%) ( = 0.0323). The median of the correlation coefficient ( = 0.771) between the MOEA predicted and measured ELP was higher than the SRK/T ( = 0.412) and Haigis ( = 0.438) formulas. The study demonstrated that ELP prediction by MOEA was more accurate and was a method of less fluctuation than that of SMR and conventional formulas. MOEA is a promising method for solving clinical problems such as prediction of ocular biometry values by simultaneously optimizing several conditions for subjects affected by various complex factors.