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

PURPOSE: To develop a prediction model based on machine learning to calculate the postoperative vault and the ideal implantable collamer lens (ICL) size, considering for the first time the implantation orientation in a White 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 CASIA II to train and validate 5 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 5 models in the external validation, with least absolute shrinkage and selection operator regression being the highest ( r = 0.62, P < .001), followed by random forest regression model ( r = 0.60, P < .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.