Gradient Boosting Decision Tree Algorithm for the Prediction of Postoperative Intraocular Lens Position in Cataract Surgery.
Gradient Boosting Decision Tree Algorithm for the Prediction of Postoperative Intraocular Lens Position in Cataract Surgery.
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DOI：
10.1167/tvst.9.13.38
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发表时间：
2020-12
影响因子：
3
通讯作者：
Nallasamy N
中科院分区：
文献类型：
--
作者：
Li T;Yang K;Stein JD;Nallasamy N
根据术前生物统计学、人口统计学和人工透镜（IOL）屈光度，开发一种预测白内障手术患者术后前房深度（ACD）的方法。纳入接受白内障手术并进行术前和术后生物统计学测量的患者。从视力结局研究协作（SOURCE）数据库中收集患者人口统计学资料和IOL屈光度。建立了一个梯度提升决策树模型来预测术后ACD。使用平均绝对误差（MAE）和中位绝对误差（MedAE）作为评价指标。所提出的方法的性能进行了比较，与现有的五个公式。总共有847名患者以4：1的比例随机分配到训练/验证集（678名患者）和测试集（169名患者）。使用术前生物统计学和患者性别作为预测因子，所提出的方法在测试集上实现了0.106 ± 0.098（SD）的MAE和0.082的MedAE。MAE显著低于现有的5种方法（P < 0.01）。当排除角膜曲率时，我们的方法获得的MAE为0.123 ± 0.109，MedAE为0.093。当使用IOL屈光度作为额外预测因素时，我们的方法实现了0.105 ± 0.091的MAE和0.080的MedAE。所提出的机器学习方法比以前报道的预测术后ACD的方法实现了更高的准确性。使用所提出的算法提高术后ACD预测的准确性有可能改善白内障手术的屈光结局。
To develop a method for predicting postoperative anterior chamber depth (ACD) in cataract surgery patients based on preoperative biometry, demographics, and intraocular lens (IOL) power. Patients who underwent cataract surgery and had both preoperative and postoperative biometry measurements were included. Patient demographics and IOL power were collected from the Sight Outcomes Research Collaborative (SOURCE) database. A gradient-boosting decision tree model was developed to predict the postoperative ACD. The mean absolute error (MAE) and median absolute error (MedAE) were used as evaluation metrics. The performance of the proposed method was compared with five existing formulas. In total, 847 patients were assigned randomly in a 4:1 ratio to a training/validation set (678 patients) and a testing set (169 patients). Using preoperative biometry and patient sex as predictors, the presented method achieved an MAE of 0.106 ± 0.098 (SD) on the testing set, and a MedAE of 0.082. MAE was significantly lower than that of the five existing methods (P < 0.01). When keratometry was excluded, our method attained an MAE of 0.123 ± 0.109, and a MedAE of 0.093. When IOL power was used as an additional predictor, our method achieved an MAE of 0.105 ± 0.091 and a MedAE of 0.080. The presented machine learning method achieved greater accuracy than previously reported methods for the prediction of postoperative ACD. Increasing accuracy of postoperative ACD prediction with the presented algorithm has the potential to improve refractive outcomes in cataract surgery.
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影响因子：
3.4
作者：
Behndig, Anders;Montan, Per;Kugelberg, Maria
通讯作者：
Kugelberg, Maria
影响因子：
2.8
作者：
Kriechbaum, K;Findl, O;Drexler, W
通讯作者：
Drexler, W
影响因子：
2.8
作者：
OLSEN, T;CORYDON, L;GIMBEL, H
通讯作者：
GIMBEL, H
影响因子：
13.7
作者：
Goto, So;Maeda, Naoyuki;Nishida, Kohji
通讯作者：
Nishida, Kohji
影响因子：
2.8
作者：
Norrby, Sverker
通讯作者：
Norrby, Sverker