Li Tingyang, Yang Kevin, Stein Joshua D, Nallasamy Nambi
Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
Kellogg Eye Center, Department of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, MI, USA.
Transl Vis Sci Technol. 2020 Dec 21;9(13):38. doi: 10.1167/tvst.9.13.38. eCollection 2020 Dec.
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 ( < 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.
基于术前生物测量、人口统计学和人工晶状体（IOL）度数，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且术前和术后均进行生物测量的患者。从视力结果研究协作组（SOURCE）数据库收集患者的人口统计学数据和IOL度数。开发一种梯度提升决策树模型来预测术后ACD。使用平均绝对误差（MAE）和中位数绝对误差（MedAE）作为评估指标。将所提出方法的性能与五个现有公式进行比较。
总共847例患者以4:1的比例随机分配到训练/验证集（678例患者）和测试集（169例患者）。使用术前生物测量和患者性别作为预测因子，所提出的方法在测试集上的MAE为0.106±0.098（标准差），MedAE为0.082。MAE显著低于五个现有方法（<0.01）。排除角膜曲率测量值后，我们的方法MAE为0.123±0.109，MedAE为0.093。当将IOL度数用作额外的预测因子时，我们的方法MAE为0.105±0.091，MedAE为0.080。
所提出的机器学习方法在预测术后ACD方面比先前报道的方法具有更高的准确性。
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效果。
