Retrospective Validation of an AI Based Tool for Vault Prediction After Implantable Collamer Lens Surgery for High Myopia
TL;DR
ICLGuru demonstrated useful postoperative vault predictions with performance comparable to existing machine learning and UBM-based nomograms, though it appeared to overestimate longer term vault in the authors' cohort, and may be valuable in identifying eyes at risk for excessive vault.
Abstract
Purpose Our study evaluated a cohort of patients who previously underwent EVO Implantable Collamer Lens (ICL) implantation to assess the retrospective performance of the ICLGuru™ artificial intelligence (AI) sizing tool in predicting postoperative vault. Additionally, biometric factors associated with vault prediction error were identified. Methods This retrospective single-center study included 104 eyes from 56 patients who underwent EVO V4c or V5 ICL implantation between May 2023 and June 2025. Preoperative ultrasound biomicroscopy (UBM) images, from patients who had already undergone ICL surgery, were retrospectively analyzed using ICLGuru to generate predicted postoperative vaults. Actual postoperative vault was measured at 1-, 3-, or 6-month follow-up visits, with 3-month measurements preferentially included for analysis. Mixed-effects regression models were used to evaluate associations between biometric variables and vault prediction error. Results Mean absolute error was 135 ± 110 µm, indicating a tendency for ICLGuru to overestimate postoperative vault (p < 0.001). Among 16 eyes with actual postoperative overvault, ICLGuru retrospectively identified 15 cases in which a smaller lens size was predicted to achieve target vault, suggesting these outcomes may have been flagged. Out of 7 eyes with actual undervault, only two cases may have been flagged with use of ICLGuru, indicating that the tool is not as useful in preventing undervault. Mixed-effects regression demonstrated that greater iridocorneal angle and higher aRISE were significantly associated with lower vault prediction error (p < 0.05). Conclusion ICLGuru demonstrated useful postoperative vault predictions with performance comparable to existing machine learning and UBM-based nomograms, though it appeared to overestimate longer term vault in our cohort. The tool may be valuable in identifying eyes at risk for excessive vault. aRISE and iridocorneal angle were significant predictors of vault prediction error, supporting their importance in future ICL sizing models.