Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA.
Kellogg Eye Center, Department of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, Michigan, USA.
Br J Ophthalmol. 2023 Apr;107(4):483-487. doi: 10.1136/bjophthalmol-2021-320283. Epub 2021 Dec 2.
To assess whether incorporating a machine learning (ML) method for accurate prediction of postoperative anterior chamber depth (ACD) improves cataract surgery refraction prediction performance of a commonly used ray tracing power calculation suite (OKULIX).
A dataset of 4357 eyes of 4357 patients with cataract was gathered at the Kellogg Eye Center, University of Michigan. A previously developed machine learning (ML)-based method was used to predict the postoperative ACD based on preoperative biometry measured with the Lenstar LS900 optical biometer. Refraction predictions were computed with standard OKULIX postoperative ACD predictions and ML-based predictions of postoperative ACD. The performance of the ray tracing approach with and without ML-based ACD prediction was evaluated using mean absolute error (MAE) and median absolute error (MedAE) in refraction prediction as metrics.
Replacing the standard OKULIX postoperative ACD with the ML-predicted ACD resulted in statistically significant reductions in both MAE (1.7% after zeroing mean error) and MedAE (2.1% after zeroing mean error). ML-predicted ACD substantially improved performance in eyes with short and long axial lengths (p<0.01).
Using an ML-powered postoperative ACD prediction method improves the prediction accuracy of the OKULIX ray tracing suite by a clinically small but statistically significant amount, with the greatest effect seen in long eyes.
评估在常用的光追法（ray tracing）屈光度计算套件（OKULIX）中纳入机器学习（ML）方法进行术后前房深度（ACD）的精确预测是否能提高白内障手术屈光度预测的性能。
在密歇根大学凯洛格眼科中心收集了 4357 例 4357 只白内障眼的数据。使用先前开发的基于 ML 的方法，根据 Lenstar LS900 光学生物测量仪测量的术前生物测量值预测术后 ACD。使用标准的 OKULIX 术后 ACD 预测和基于 ML 的术后 ACD 预测计算屈光度预测。使用平均绝对误差（MAE）和中值绝对误差（MedAE）作为衡量指标，评估带有和不带有基于 ML 的 ACD 预测的光追方法的性能。
用 ML 预测的 ACD 替代标准的 OKULIX 术后 ACD，使 MAE（零平均误差后为 1.7%）和 MedAE（零平均误差后为 2.1%）都有统计学显著降低。ML 预测的 ACD 显著提高了短眼和长眼的预测性能（p<0.01）。
使用基于 ML 的术后 ACD 预测方法可显著提高 OKULIX 光追套件的预测准确性，虽然改善幅度较小，但具有统计学意义，对长眼的影响最大。
