VAULT-OCT: vault accuracy using deep learning technology-an artificial intelligence model for predicting implantable collamer lens postoperative vault with AS-OCT.

PURPOSE: To develop an accurate deep learning model, VAULT-OCT, to predict postoperative vault of phakic implantable collamer lenses (ICLs) based on preoperative optical coherence tomography (OCT).

SETTING

Parkhurst NuVision LASIK Eye Surgery, San Antonio, Texas.

DESIGN

Retrospective machine learning study.

METHODS

324 eyes from 162 consecutive patients who underwent ICL implantation were included. VAULT-OCT, the neural network, was trained on preoperative anterior segment OCT (AS-OCT) images paired with postoperative vault measurements for different ICL sizes. Incomplete data were excluded, and the images were consistently resized and normalized. A custom classifier was used in VAULT-OCT, and model performance was evaluated using root mean squared error on the test set, with mean absolute error (MAE) reported as the primary performance metric.

RESULTS

A MAE of 22.3 μm, 21.7 μm, and 98.1 μm and a SD of 13.5 μm, 17.8 μm, and 105.9 μm were achieved with 100%, 100%, and 89.1% of predictions within 200 μm, for the 12.1 mm, 12.6 mm, and 13.2 mm size, respectively.

CONCLUSIONS

This OCT-based deep learning model, VAULT-OCT, achieved a high level of accuracy in predicting postoperative ICL vault, with most predictions falling within a clinically acceptable margin of vault, suggesting the feasibility of basing ICL sizing on preoperative AS-OCT.