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Updated: Jun 28, 2025
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
Published on: November 30, 2022
VAULT: vault accuracy using deep learning technology: new image-based artificial intelligence model for predicting
Taj Nasser1, Matthew Hirabayashi, Gurpal Virdi
1From the Parkhurst NuVision, San Antonio, Texas (Nasser, Parkhurst); University of Missouri Columbia School of Medicine, Columbia, Missouri (Hirabayashi, Virdi); Mason Eye Institute, Columbia, Missouri (Hirabayashi, Virdi); Texas State University, San Marcos, Texas (Abramson).
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.
Design:
Retrospective machine learning study.
Methods:
437 eyes of 221 consecutive patients who underwent ICL implantation were included. A neural network was trained on preoperative very high-frequency digital ultrasound images, patient demographics, and postoperative vault.
Results:
3059 images from 437 eyes of 221 patients were used to train the algorithm on individual ICL sizes. The 13.7 mm size was excluded because of insufficient data. A mean absolute error 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 sizes, 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.
