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

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.