Park Seungbin, Kim Hannah, Kim Laehyun, Kim Jin-Kuk, Lee In Sik, Ryu Ik Hee, Kim Youngjun
Center for Bionics, Korea Institute of Science and Technology, Seoul, Korea.
Division of Bio-Medical Science &Technology, KIST School, Korea University of Science and Technology, Seoul, Korea.
Biomed Eng Online. 2021 Apr 23;20(1):38. doi: 10.1186/s12938-021-00867-7.
Small-incision lenticule extraction (SMILE) is a surgical procedure for the refractive correction of myopia and astigmatism, which has been reported as safe and effective. However, over- and under-correction still occur after SMILE. The necessity of nomograms is emphasized to achieve optimal refractive results. Ophthalmologists diagnose nomograms by analyzing the preoperative refractive data with their individual knowledge which they accumulate over years of experience. Our aim was to predict the nomograms of sphere, cylinder, and astigmatism axis for SMILE accurately by applying machine learning algorithm.
We retrospectively analyzed the data of 3,034 eyes composed of four categorical features and 28 numerical features selected from 46 features. The multiple linear regression, decision tree, AdaBoost, XGBoost, and multi-layer perceptron were employed in developing the nomogram models for sphere, cylinder, and astigmatism axis. The scores of the root-mean-square error (RMSE) and accuracy were evaluated and compared. Subsequently, the feature importance of the best models was calculated.
AdaBoost achieved the highest performance with RMSE of 0.1378, 0.1166, and 5.17 for the sphere, cylinder, and astigmatism axis, respectively. The accuracies of which error below 0.25 D for the sphere and cylinder nomograms and 25° for the astigmatism axis nomograms were 0.969, 0.976, and 0.994, respectively. The feature with the highest importance was preoperative manifest refraction for all the cases of nomograms. For the sphere and cylinder nomograms, the following highly important feature was the surgeon.
Among the diverse machine learning algorithms, AdaBoost exhibited the highest performance in the prediction of the sphere, cylinder, and astigmatism axis nomograms for SMILE. The study proved the feasibility of applying artificial intelligence (AI) to nomograms for SMILE. Also, it may enhance the quality of the surgical result of SMILE by providing assistance in nomograms and preventing the misdiagnosis in nomograms.
小切口透镜切除术（SMILE）是一种用于近视和散光屈光矫正的外科手术，已被报道安全有效。然而，SMILE术后仍会出现欠矫和过矫的情况。人们强调了使用列线图来实现最佳屈光结果的必要性。眼科医生通过运用他们多年积累的个人知识分析术前屈光数据来诊断列线图。我们的目的是通过应用机器学习算法准确预测SMILE的球镜、柱镜和散光轴列线图。
我们回顾性分析了由从46个特征中选取的4个分类特征和28个数值特征组成的3034只眼的数据。使用多元线性回归、决策树、AdaBoost、XGBoost和多层感知器来开发球镜、柱镜和散光轴的列线图模型。评估并比较均方根误差（RMSE）和准确率得分。随后，计算最佳模型的特征重要性。
对于球镜、柱镜和散光轴，AdaBoost分别实现了最高性能，RMSE分别为0.1378、0.1166和5.17。球镜和柱镜列线图误差低于0.25 D以及散光轴列线图误差低于25°时的准确率分别为0.969、0.976和0.994。在所有列线图病例中，重要性最高的特征是术前显验光。对于球镜和柱镜列线图，其次重要的特征是手术医生。
在多种机器学习算法中，AdaBoost在预测SMILE的球镜、柱镜和散光轴列线图方面表现出最高性能。该研究证明了将人工智能（AI）应用于SMILE列线图的可行性。此外，它可能通过在列线图方面提供帮助并防止列线图误诊来提高SMILE手术结果的质量。
