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:
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.
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 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%.
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.
