Vault Accuracy Using deep Learning Technology

An AI-driven tool to accurately predict postoperative vault of the EVO Visian ICL™ (Implanted Collamer® Lens).


Taj Nasser, MD

Greg Parkhurst, MD

Matt Hirabayashi, MD

Gurpal Virdi, MD


To create an accurate, repeatable, and continuously improving machine-learning based tool for the prediction of post-operative ICL™ Vault using various imaging modalities (e.g., Ultrasound Biomicroscopy and Anterior Segment OCT). This approach is novel and image-based, training the model on the unique anatomy of each eye. The models are trained with data from refractive surgery cases in the United States at a high volume surgery center.


3,059 images from 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. A neural network was chosen and extensively adapted for the purposes of this project.


12.1 MAE: 66.3 µm

12.6 MAE: 103 µm

13.2 MAE: 66.3 µm

VAULT Prediction Error Stratified by Size and Magnitude of Prediction Error
ICL SizePercent of Predictions within Error Range
 ≤ 250 µm≤ 400 µm≤ 500 µm
Comparison to Current Literature
StudyErrorLocationSample Size
Rocamora et al.132.0 μm  (MAE)Argentina115 Eyes 59 Patients
Kim et al.104.7 μm (MAE)Korea892 Eyes 471 Patients
Kang et al.106.88/143.69 μm  (MAE)Korea2756 Eyes
Shen et al.159.03 μm (RMSE)China6297 Eyes 3536 Patients
Chen et al.129.89 μm (MAE) China1941 Eyes 1941 Patients
Russo et al.96.94 µm (MAE)Italy561 Eyes 300 Patients
VAULT12.1: 66.3 µm (MAE)
12.6: 103 μm (MAE)
13.2: 91.8 µm (MAE)
USA3059 Images
437 Eyes
221 Patients

Next Steps

We are actively training the model on anterior segment OCTs.


An user friendly interface will allow surgeons to upload their images and patient data for the model to process and return a clean output with predicted vault by ICL size. Eventually, the model can be integrated into UBM or OCT machines.

User Interface

(input page)

(results page)



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