RT Journal Article
SR Electronic
T1 Big-data and artificial-intelligence-assisted vault prediction and EVO-ICL size selection for myopia correction
JF British Journal of Ophthalmology
JO Br J Ophthalmol
FD BMJ Publishing Group Ltd.
SP bjophthalmol-2021-319618
DO 10.1136/bjophthalmol-2021-319618
A1 Shen, Yang
A1 Wang, Lin
A1 Jian, Weijun
A1 Shang, Jianmin
A1 Wang, Xin
A1 Ju, Lie
A1 Li, Meiyan
A1 Zhao, Jing
A1 Chen, Xun
A1 Ge, Zongyuan
A1 Wang, Xiaoying
A1 Zhou, Xingtao
YR 2021
UL http://bjo.bmj.com/content/early/2021/09/06/bjophthalmol-2021-319618.abstract
AB Aims To predict the vault and the EVO-implantable collamer lens (ICL) size by artificial intelligence (AI) and big data analytics.Methods Six 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.Results In 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.Conclusions Random 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.Data are available on reasonable request.