Artificial Intelligence-Driven Detection of LASIK Using Corneal Optical Coherence Tomography Maps.

ABSTRACT:

Purpose

To train and validate a convolutional neural network (CNN) to detect the history of laser-assisted in situ keratomileusis (LASIK) surgeries using corneal optical coherence tomography (OCT) maps.

Methods

Five corneal OCT maps (pachymetry, epithelial thickness, posterior mean curvature, anterior axial power, and anterior stroma reflectance) were utilized as the input of a lightweight CNN model. OCT scans of healthy volunteers and patients who had undergone myopic or hyperopic LASIK were included. Repeated fivefold cross-validation was used to train and evaluate the proposed CNN. In addition, a separate group of post-LASIK participants, who were not included in the cross-validation, was used for out-of-sample testing to assess the CNN model performance.

Results

I

SUBMITTER: Hong J 

PROVIDER: S-EPMC11925221 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature