VAULT: Vault Accuracy Using deep Learning Technology – A Novel Image-Based AI Model for Predicting Implantable Collamer Lens Postoperative Vault

Abstract

Purpose: To develop an accurate deep learning model to predict postoperative vault of phakic Implantable Collamer Lenses (ICLs). Setting: Parkhurst NuVision LASIK Eye Surgery, San Antonio, Texas, USA. Design: Retrospective Machine Learning Study. Methods: 437 eyes of 221 consecutive patients who underwent ICL implantation were included. A neural network was trained on pre-operative Very High Frequency (VHF) digital ultrasound images, patient demographics, and postoperative vault. Results: 3,059 images from 437 eyes of 221 patients were used to train the algorithm on individual ICL sizes. The 13.7 mm size was excluded due to insufficient data. A mean absolute error (MAE) of 66.3 µm, 103 µm, and 91.8 µm were achieved with 100%, 99.0%, and 96.6% of predictions within 500 µm for the 12.1 mm, 12.6 mm, and 13.2 mm size respectively. Conclusions: This deep learning model achieved a high level of accuracy of predicting postoperative ICL vault with the overwhelming majority of predictions successfully within a clinically acceptable margin of vault.