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.