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.
|VAULT Prediction Error Stratified by Size and Magnitude of Prediction Error|
|ICL Size||Percent of Predictions within Error Range|
|≤ 250 µm||≤ 400 µm||≤ 500 µm|
|Comparison to Current Literature|
|Rocamora et al.||132.0 μm (MAE)||Argentina||115 Eyes 59 Patients|
|Kim et al.||104.7 μm (MAE)||Korea||892 Eyes 471 Patients|
|Kang et al.||106.88/143.69 μm (MAE)||Korea||2756 Eyes|
|Shen et al.||159.03 μm (RMSE)||China||6297 Eyes 3536 Patients|
|Chen et al.||129.89 μm (MAE)||China||1941 Eyes 1941 Patients|
|Russo et al.||96.94 µm (MAE)||Italy||561 Eyes 300 Patients|
|VAULT||12.1: 66.3 µm (MAE) |
12.6: 103 μm (MAE)
13.2: 91.8 µm (MAE)
|USA||3059 Images |
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.
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