Predictability of the vault after implantable collamer lens implantation using OCT and artificial intelligence in White patient eyes

Purpose:To compare the predicted vault using machine learning with the achieved vault using the online manufacturer's nomogram in patients undergoing posterior chamber implantation with an implantable collamer lens (ICL).Setting:Centro Oculistico Bresciano, Brescia, Italy, and I.R.C.C.S. - Bietti Foundation, Rome, Italy.Design:Retrospective multicenter comparison study.Methods:561 eyes from 300 consecutive patients who underwent ICL placement surgery were included in this study. All preoperative and postoperative measurements were obtained by anterior segment optical coherence tomography (AS-OCT; MS-39). The actual vault was quantitatively measured and compared with the predicted vault using machine learning of AS-OCT metrics.Results:A strong correlation between model predictions and achieved vaulting was detected by random forest regression (RF; R2= 0.36), extra tree regression (ET; R2= 0.50), and extreme gradient boosting regression (R2= 0.39). Conversely, a high residual difference was observed between achieved vaulting values and those predicted by the multilinear regression (R2= 0.33) and ridge regression (R2= 0.33). ET and RF regressions showed significantly lower mean absolute errors and higher percentages of eyes within ±250 m of the intended ICL vault compared with the conventional nomogram (94%, 90%, and 72%, respectively; P <.001). ET classifiers achieved an accuracy (percentage of vault in the range of 250 to 750 m) of up to 98%.Conclusions:Machine learning of preoperative AS-OCT metrics achieved excellent predictability of ICL vault and size, which was significantly higher than the accuracy of the online manufacturer's nomogram, providing the surgeon with a valuable aid for predicting the ICL vault.