Big-data and artificial-intelligence-assisted vault prediction and EVO-ICL size selection for myopia correction

Abstract

AimsTo predict the vault and the EVO-implantable collamer lens (ICL) size by artificial intelligence (AI) and big data analytics.MethodsSix thousand two hundred and ninety-seven eyes implanted with an ICL from 3536 patients were included. The vault values were measured by the anterior segment analyzer (Pentacam HR). Permutation importance and Impurity-based feature importance are used to investigate the importance between the vault and input parameters. Regression models and classification models are applied to predict the vault. The ICL size is set as the target of the prediction, and the vault and the other input features are set as the new inputs for the ICL size prediction. Data were collected from 2015 to 2020. Random Forest, Gradient Boosting and XGBoost were demonstrated satisfying accuracy and mean area under the curve (AUC) scores in vault predicting and ICL sizing.ResultsIn the prediction of the vault, the Random Forest has the best results in the regression model (R2=0.315), then follows the Gradient Boosting (R2=0.291) and XGBoost (R2=0.285). The maximum classification accuracy is 0.828 in Random Forest, and the mean AUC is 0.765. The Random Forest predicts the ICL size with an accuracy of 82.2% and the Gradient Boosting and XGBoost, which are also compatible with 81.5% and 81.8% accuracy, respectively.ConclusionsRandom Forest, Gradient Boosting and XGBoost models are applicable for vault predicting and ICL sizing. AI may assist ophthalmologists in improving ICL surgery safety, designing surgical strategies, and predicting clinical outcomes.

Validation of the vault prediction model based on the sulcus-to-sulcus diameter and lens thickness: a 925-eye prospective study

“…In addition, artificial intelligence has been used to try to perform ICL size selection in recent years. Shen et al [ 27 ] trained and validated several machine learning models by a 3536-patient database and found that the random forest model had the highest fitting degree ( R 2 = 0.315) and the best prediction accuracy (82.8%); obviously, this result was not entirely satisfactory. A similar situation also appeared in the research by Kamiya et al [ 28 ].…”

Artificial intelligence technology for myopia challenges: A review

“…AI technology also have applications in refractive surgery. Methods like random forest, gradient boosting, XGBoost, and SVM regression (SVR) can assist in implant size selection and arch height prediction in ICL surgery ( Kamiya et al, 2021 ; Kang et al, 2021 ; Shen et al, 2021 ). Proper sizing ensures a safe postoperative ICL dome and reduces complications such as angle-closure glaucoma and anterior subcapsular cataract.…”

Introductory Chapter: Refractive Surgery

“…Visian ICL comes in spherical powers from −18.0 to +10.0 diopters, and there is a toric variant from +0.5 to +6.0 diopters of the cylinder. The ICL is produced in four sizes, 12.1 mm, 12.6 mm, 13.2 mm, and 13.7 mm to fit the size of the sulcus of the patient as best as possible [92,97]. STAAR Surgical Visian ICL is a great option for patients that are not suitable candidates for corneal refractive surgery and are not ready for refractive lens exchange due to their age.…”