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: In the cross-validation, the proposed CNN model achieved an overall balanced accuracy of 90.2% ± 3.6% with 93.5% ± 5.2% sensitivity and 97.8% ± 1.7% area under the receiver operating characteristic curve (AUC) in detecting myopic LASIK and 90.2% ± 5.8% sensitivity and 98.2% ± 1.9% AUC in identifying the hyperopic LASIK. In the out-of-sample test, all eyes were classified correctively.

Conclusions: The lightweight CNN model with corneal OCT maps provides a useful tool for detecting LASIK history.

Translational relevance: Artificial intelligence-assisted OCT may offer better management for patients with LASIK history who need cataract surgeries.