Use of a Machine Learning Method in Predicting Refraction after Cataract Surgery.
Use of a Machine Learning Method in Predicting Refraction after Cataract Surgery.
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DOI：
10.3390/jcm10051103
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发表时间：
2021-03-06
影响因子：
3.9
通讯作者：
Masumoto H
中科院分区：
文献类型：
--
作者：
Yamauchi T;Tabuchi H;Takase K;Masumoto H
本研究旨在描述机器学习（ML）在预测白内障术后屈光发生率方面的应用，并将该方法的准确性与传统人工透镜（IOL）屈光度计算公式进行比较。总共评估了2010例患者的3331只眼。对象分为训练数据和测试数据。使用训练数据优化了ML的IOL屈光度计算公式和模型训练的常数。然后，使用常规公式预测术后屈光的发生，或使用测试数据计算ML模型。我们评估了SRK/T公式、Haigis公式、Holladay 1公式、Hoffer Q公式和Barrett Universal II公式（BU-II）;与ML方法类似，我们评估了支持向量回归（SVR）、随机森林回归（RFR）、梯度提升回归（GBR）和神经网络（NN）。在传统的公式中，BU-II的平均和中位数的预测绝对误差最低。因此，我们比较了我们的方法与BU-II的准确性。某些ML方法的绝对误差低于BU-II方法。然而，未观察到统计学显著差异。因此，我们的方法的准确性不低于BU-II。
The present study aims to describe the use of machine learning (ML) in predicting the occurrence of postoperative refraction after cataract surgery and compares the accuracy of this method to conventional intraocular lens (IOL) power calculation formulas. In total, 3331 eyes from 2010 patients were assessed. The objects were divided into training data and test data. The constants for the IOL power calculation formulas and model training for ML were optimized using training data. Then, the occurrence of postoperative refraction was predicted using conventional formulas, or ML models were calculated using the test data. We evaluated the SRK/T formula, Haigis formula, Holladay 1 formula, Hoffer Q formula, and Barrett Universal II formula (BU-II); similar to ML methods, we assessed support vector regression (SVR), random forest regression (RFR), gradient boosting regression (GBR), and neural network (NN). Among the conventional formulas, BU-II had the lowest mean and median absolute error of prediction. Therefore, we compared the accuracy of our method with that of BU-II. The absolute errors of some ML methods were lower than those of BU-II. However, no statistically significant difference was observed. Thus, the accuracy of our method was not inferior to that of BU-II.
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影响因子：
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通讯作者：
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