Machine learning adaptation of intraocular lens power calculation for a patient group

Abstract

Background To examine the effectiveness of the use of machine learning for adapting an intraocular lens (IOL) power calculation for a patient group. Methods In this retrospective study, the clinical records of 1,611 eyes of 1,169 Japanese patients who received a single model of monofocal IOL (SN60WF, Alcon) at Miyata Eye Hospital were reviewed and analyzed. Using biometric metrics and postoperative refractions of 1211 eyes of 769 patients, constants of the SRK/T and Haigis formulas were optimized. The SRK/T formula was adapted using a support vector regressor. Prediction errors in the use of adapted formulas as well as the SRK/T, Haigis, Hill-RBF and Barrett Universal II formulas were evaluated with data from 395 eyes of 395 distinct patients. Mean prediction errors, median absolute errors, and percentages of eyes within ± 0.25 D, ± 0.50 D, and ± 1.00 D, and over + 0.50 D of errors were compared among formulas. Results The mean prediction errors in the use of the SRT/K and adapted formulas were smaller than the use of other formulas (P < 0.001). In the absolute errors, the Hill-RBF and adapted methods were better than others. The performance of the Barrett Universal II was not better than the others for the patient group. There were the least eyes with hyperopic refractive errors (16.5%) in the use of the adapted formula. Conclusions Adapting IOL power calculations using machine learning technology with data from a particular patient group was effective and promising.

Investigation of the Myopic Outcomes of the Newer Intraocular Lens Power Calculation Formulas in Korean Patients with Long Eyes

“…To the best of our knowledge, this study represents the first attempt to describe the mechanisms of how ethnic differences in ocular geometry can specifically influence the outcomes of IOL power calculation formulas. While there have been suggestions that variations in ocular characteristics among different ethnic groups can impact refractive outcomes in cataract surgery [27,32], detailed descriptions of the mechanisms by which these variations influence the outcomes have not been reported. By reasonably employing available methods in a predominantly independent manner, this study proposes a plausible hypothesis for the underlying causes in the myopic outcomes of newer IOL calculation formulas in long Korean eyes.…”

“…With the ACD becoming the primary factor governing ELP prediction, we investigated the ACD change based on AL, anticipating differences between the two population groups. Numerous studies have indicated that East Asian populations tend to have smaller anterior segments, including shallower ACDs than Caucasians [27,28].…”

Advances in artificial intelligence models and algorithms in the field of optometry

“…Using AI in designing the IOL calculation formula can improve the accuracy of the calculation according to the characteristics of different patients so that better visual quality can be obtained following surgery. Mori et al (2021) constructed an ML model based on a support vector regression algorithm to adapt the diopter calculation method for intraocular lens in a specific patient population to improve the accuracy of the calculation. By analyzing the clinical data for 11 611 eyes with a single monofocal IOL implantation model, the constants of the SRK/T and Haigis formulas were optimized and the support vector regression algorithm was used to adapt the SRK/T, Haigis, Hill-RBF, and Barrett Universal II formulas.…”