MAEPI and CIR: New Metrics for Robust Evaluation of the Prediction Performance of AI-Based IOL Formulas.

ABSTRACT:

Purpose

To develop a class of new metrics for evaluating the performance of intraocular lens power calculation formulas robust to issues that can arise with AI-based methods.

Methods

The dataset consists of surgical information and biometry measurements of 6893 eyes of 5016 cataract patients who received Alcon SN60WF lenses at University of Michigan's Kellogg Eye Center. We designed two types of new metrics: the MAEPI (Mean Absolute Error in Prediction of Intraocular Lens [IOL]) and the CIR (Correct IOL Rate) and compared the new metrics with traditional metrics including the mean absolute error (MAE), median absolute error, and standard deviation. We evaluated the new metrics with simulation analysis, machine learning (ML) methods, as well as existing IOL formulas (Barrett

SUBMITTER: Li T 

PROVIDER: S-EPMC10064919 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature