Development of machine learning-based models for vault prediction in implantable collamer lens surgery according to implant orientation.

Purpose: The main objective was to develop a prediction model based on machine learning to calculate the postoperative vault as well as the ideal implantable collamer lens (ICL) size, considering for the first time the implantation orientation in a Caucasian population.

Setting: Arruzafa Ophthalmological Hospital (Cordoba, Spain) and Barraquer Ophthalmology Center (Barcelona, Spain).

Design: Multicenter, randomized, retrospective study.

Methods: Anterior segment biometric data from 235 eyes of patients who underwent ICL lens implantation surgery were collected using the anterior segment optical coherence tomography (AS-OCT) CASIA II, to train and validate five types of multiple regression models based on advanced machine learning techniques. To perform an external validation a dataset of 45 observations was used.

Results: The Pearson correlation coefficient between observed and predicted values was similar in the five models in the external validation, with least absolute shrinkage and selection operator (LASSO) regression being the highest (r = 0.62, p < 0.001), followed by random forest regression model (r = 0.60, p < 0.001) and backward stepwise regression (r = 0.58, ρ < 0.001). In addition, the predictions generated by the different models showed closer agreement with the actual vault compared with the Nakamura formulas. Using the new methods, about 70% of the observations had a prediction error below 150 µm.

Conclusions: Advanced forms of regressions models based on machine learning allow satisfactory calculation of the ideal lens size, offering greater precision to surgeons customizing surgery according to implant orientation.