Evaluation of the Nallasamy Formula: A Stacking Ensemble Machine Learning Method for Refraction Prediction in Cataract Surgery

Abstract Aims To develop a new intraocular lens (IOL) power selection method with improved accuracy for general cataract patients receiving Alcon SN60WF lenses. Methods and Analysis A total of 5016 patients (6893 eyes) who underwent cataract surgery at University of Michigan’ s Kellogg Eye Center and received the Alcon SN60WF lens were included in the study. A machine learning-based method was developed using a training dataset of 4013 patients (5890 eyes), and evaluated on a testing dataset of 1003 patients (1003 eyes). Each eye had a complete profile of preoperative biometry, the implanted IOL power, and postoperative refraction. The performance of our method was compared to that of Barrett Universal II, Haigis, Hoffer Q, Holladay 1, and SRK/T. Results MAE of the Nallasamy formula in the testing dataset was 0.312 Diopters (MedAE = 0.242 D). Performance of existing methods were as follows: Barrett Universal II MAE = 0.328 D (MedAE = 0.256 D), Haigis MAE = 0.363 D (MedAE = 0.289 D), Hoffer Q MAE = 0.404 D (MedAE = 0.331 D), Holladay 1 MAE = 0.371 D (MedAE = 0.298 D) and SRK/T MAE = 0.376 D (MedAE = 0.300 D). The Nallasamy formula performed significantly better than all five existing methods based on the paired Wilcoxon test with Bonferroni correction (p-value < 0.05). Conclusions Nallasamy formula outperformed the five methods studied (including Barrett Universal II) on overall MAE and MedAE, percentage of eyes within 0.5 D of prediction, as well as MAE in short, medium, and long axial length eyes. SYNOPSIS Nallasamy formula, a novel machine learning-based IOL power calculation formula developed based on a dataset of 6893 eyes, achieved significantly better prediction accuracy than five traditional IOL power formulas including Barrett Universal II.