Optimizing IOL Calculators with Deep Learning Prediction of Total Corneal Astigmatism.

ABSTRACT: Background/Objectives: This study aims to identify the most accurate regression model for predicting total corneal astigmatism (TCA) from anterior corneal astigmatism (ACA) and to fine-tune the best model's architecture to further optimize predictive accuracy. Methods: A retrospective review of 19,468 eyes screened for refractive surgery was conducted using electronic medical records. Corneal topography data were acquired using the Pentacam HR. Various types (7) and subtypes (21) of regression learners were tested, with a deep neural network (DNN) emerging as the most suitable. The DNN was further refined, experimenting with 23 different architectures. Model performance was evaluated using root mean squared error (RMSE), R², average residual error, and circular err

SUBMITTER: Wallerstein A 

PROVIDER: S-EPMC11432570 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature