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 μm 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 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.