Adopting machine learning to predict nomogram for small incision lenticule extraction (SMILE).

PURPOSE

To predict nomogram for small incision lenticule extraction (SMILE) using machine learning technology and preoperative clinical data.

METHODS

A total of 1025 eyes with postoperative spherical equivalent within ± 0.50D after SMILE were included in this study. The XGBoost, gradient boosting regression (GBR), random forest (RF), LightGBM, linear regression (LR) and support vector regression (SVR) were applied to predict the nomogram. The performance of six machine learning methods was assessed by calculating the root mean absolute error (RMSE) and the mean absolute error (MAE). Four junior residents were selected to design the nomogram based on preoperative clinical data in testing set, and were compared with the machine learning models by calculating the accuracy of eyes within three specific thresholds (± 0.05D, ± 0.15D, ± 0.25D).

RESULTS

The actual nomogram was not significantly different from the nomogram predicted machine learning methods (P > 0.05). The RMSE of six models ranged from 0.075 to 0.110, and MAE were 0.055 to 0.085 on nomogram prediction. The XGBoost provided significantly higher accuracy within 0.05 to 0.25 D than the SVR and junior residents (McNemar test, P < 0.001). However, there were no statistically significant differences in accuracy within 0.05 to 0.25 D that the XGBoost, GBR, RF, LightGBM, and LR achieved (P > 0.05).

CONCLUSIONS

Machine learning of the preoperative clinical data could accurately predict nomogram for SMILE. The machine learning methods may assist the refractive surgeons and shorten the learning curve of junior residents while making the nomogram adjustment.