AI-Powered Effective Lens Position Prediction Improves the Accuracy of Existing Lens Formulas.

ABSTRACT:

Aims

To assess whether incorporating a machine learning (ML) method for accurate prediction of postoperative anterior chamber depth (ACD) improves the refraction prediction performance of existing intraocular lens (IOL) calculation formulas.

Methods

A dataset of 4806 cataract patients were gathered at the Kellogg Eye Center, University of Michigan, and split into a training set (80% of patients, 5761 eyes) and a testing set (20% of patients, 961 eyes). A previously developed ML-based method was used to predict the postoperative ACD based on preoperative biometry. This ML-based postoperative ACD was integrated into new effective lens position (ELP) predictions using regression models to rescale the ML output for each of four existing formulas (Haigis, Hoffer Q, Holladay, and

SUBMITTER: Li T 

PROVIDER: S-EPMC7654911 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature