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).
Centro Oculistico Bresciano, Brescia, Italy and I.R.C.C.S. – Bietti Foundation, Rome, Italy.
Retrospective multicenter comparison study.
This study included 561 eyes from 300 consecutive patients who underwent ICL placement surgery. All preoperative and postoperative measurements were obtained by anterior segment optical coherence tomography (AS-OCT; MS-39, C.S.O. SRL, Italy). The actual vault was quantitatively measured and compared with the predicted vault using machine learning of AS-OCT metrics.
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 µm of the intended ICL vault compared to the conventional nomogram (94%, 90%, and 72%, respectively; P < 0.001). ET classifiers achieved an accuracy (percentage of vault in the range of 250–750 µm) of up to 98%.
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.