Comparison of intraocular lens computations using a neural network versus the Holladay formula

Purpose: To compare the accuracy of intraocular lens (IOL) calculations using Holladay personalized calculations and a new method of trained neural networks. Setting: A private ophthalmic practice. Methods: We developed and trained a neural network to predict IOL powers using a personalized Holladay program and clinical data from 200 consecutive cases of one surgeon's results with one IOL. Clinical data included preoperative axial length, both keratometry values, anterior chamber depth, and human lens thickness. The neural network was trained to produce the actual postoperative refractive error, and the Holladay surgeon factor was continuously refined using the same results. After the network was successfully trained against the clinical data, it was used to compute IOL power in a double-masked study. Ninety-five patients were randomized between the Holladay personalized calculation and the neural network computation. There were no significant differences in age or preoperative refractive errors between the two groups. Manifest refractions were obtained during the masked period at least 6 weeks after surgery. Results: Mean postoperative error from predicted refraction was +0.271 diopters (D) for the neural network group and −0.217 D for the Holladay personal group. Mean absolute error from predicted refraction was +0.63 D for the neural network group and +0.93 D for the Holladay personal group. There was a significant difference in postoperative refractive errors and mean absolute error between the two groups (P < .022; nonparametric Mann-Whitney test). An error of less than ±0.75 D was obtained by 72.5% of the neural network group and 50.0% of the Holladay group. Conclusions: The neural network prediction formula can improve IOL implantation calculations by tightening the variance of errors.