To predict the nomograms for SMILE, various machine learning algorithms were applied: multiple linear regression, decision tree, Adaboost, XGBoost, and MLP with different number of layers. The best performance was achieved with Adaboost. The RMSE from the results of the multiple linear regression of the sphere and cylinder nomograms was similar to those of Adaboost; however, the accuracy of the former was remarkably lower than Adaboost. For the astigmatism axis, the multiple linear regression yielded an extensively high RMSE and low accuracy compared to those of Adaboost. Comparing the decision tree, AdaBoost, and XGBoost, the decision tree exhibited comparable performance to Adaboost, whereas XGBoost did not. In addition, although deep learning based on MLP has recently exhibited high performance in other studies, it was not the case in this study. Summarizing the results, relatively simpler models, such as decision tree or Adaboost, worked better compared to other complex and deep models to predict nomograms for SMILE with the data cohort that was used in this study.
Although deep learning is excellent in the domains of computer vision or natural language processing, it has been reported that shallow models, such as gradient-boosted decision trees, exhibit good performance in problems with tabular heterogeneous data.17 Although deep learning has garnered significant attention over the last years, gradient boosting, such as XGBoost, is one of the most widely used algorithms in Kaggle competitions for applying machine learning to structured tabular data.18,19 Several studies have reported higher performance with Adaboost compared to XGBoost, despite the popularity of XGBoost. 20,21,22,23,24,25,26
Compared with the previous studies, this study proposes the following contributions. First, it is a novel approach to apply and compare the extensive range of data-driven machine learning algorithms for nomograms subject to SMILE. There has been no particular approach even for nomograms subject to other refractive surgery. Previous studies mainly applied multiple linear regression to nomograms. Although recently there was a research on applying machine learning to nomograms for SMILE16, it only attempted shallow MLP.
Compared to the other studies, the number of the data used in this study was remarkably large: hundreds versus thousands. This approach was possible because the center, i.e., the data provider, has endeavored to establish an enormous database considering the impact of big data and data-driven artificial intelligence technologies in the medical field. Moreover, the features considered to affect the nomograms in this study were extensive compared to in other studies. Without an ideal criterion to determine the relevance of the factors for the refractive outcomes, these factors were selected based on scientific studies, common sense and even a feeling.5 Although a large number of features does not necessarily ensure a better performance of machine learning, we intended not to miss any relevance between the possible features and the nomograms.
Another contribution of this study was the consideration of the “surgeon” feature. In the results of the feature importance of the models, the surgeon feature had the second highest importance followed by the preoperative manifest refraction. Unlike LASIK, a SMILE nomogram mainly depends on the personal experience of the surgeon16, and it is essential that all the surgeons develop a nomogram to refine their results.27 It is certain that the effect of a surgeon on a nomogram is strong. To our knowledge, all the previous studies for nomograms subject to refractive surgery only utilized the data cohort of one sole surgeon, which limits the feasibility of general application. For example, Liang et al.7 stated that the nomogram used in their study is not available for other surgeons. We believe that our novel approach could be referential for further enhanced nomogram development for SMILE considering surgeon effect.
The proposed method may enhance the quality of the surgical result of SMILE by providing assistance in nomograms and preventing the misdiagnosis in nomograms. Considering that less surgical experience in SMILE can cause significant inflammatory response and tissue trauma,28,29 a referential nomogram guide for ophthalmologists with less experience with the proposed method may be beneficial.
The limitation of this retrospective study is the absent of clinical validation. It is necessary to clinically verify that the proposed nomogram enhance the predictability for the postoperative surgical outcomes of SMILE. However, the positive clinical results considering the results of Cui et al.16 is anticipated. They confirmed the comparable safety and predictability in the postoperative results of patients group that underwent SMILE with nomograms from the machine learning model, which had no statistically significant difference compared to the surgeon nomograms.