An AI-driven tool to accurately predict postoperative vault of the EVO Visian ICL™ (Implanted Collamer® Lens).
PATENT PENDING
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 | |
| 12.1 | 99.7% | 99.8% | 100% |
| 12.6 | 97.4% | 99.0% | 99.0% |
| 13.2 | 92.4% | 95.1% | 96.6% |
| Comparison to Current Literature | |||
| Study | Error | Location | Sample Size |
| 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 437 Eyes 221 Patients |
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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