比较印度南部和美国中西部白内障手术患者的人工晶状体屈光预测准确性和A常数优化。

Comparing IOL refraction prediction accuracy and A-constant optimization for cataract surgery patients across South Indian and Midwestern United States populations.

作者信息

Siddiqui Omer, Warner Elisa, Greenwald Miles, Li Tingyang, Srinivasan Karthik, Haripriya Aravind, Nallasamy Nambi

机构信息

Department of Ophthalmology and Visual Sciences, Kellogg Eye Center, University of Michigan, 1000 Wall St, Ann Arbor, MI, 48105, USA.

Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.

出版信息

BMC Ophthalmol. 2025 Jul 2;25(1):349. doi: 10.1186/s12886-025-04217-2.

DOI:10.1186/s12886-025-04217-2

PMID:40597931

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12219035/

Abstract

BACKGROUND

IOL power selection is a key determinant of refractive outcomes after cataract surgery. Numerous formulas exist to aid in this process; some are derived from geometric-optical principles (e.g., SRK/T, Barrett) while others are based on data-driven and machine learning approaches (e.g., Nallasamy, Pearl-DGS). Given differences in ocular biometry and environmental stimuli, population-specific factors may impact the generalizability of certain formulas. This study compares clinical and biometric characteristics and evaluates the prediction accuracy of seven IOL power formulas, including machine learning–based approaches, in two distinct cataract surgery populations from South India and the Midwestern United States.

METHODS

In this retrospective cross-sectional comparative study, data were collected from two tertiary care eye centers: University of Michigan’s Kellogg Eye Center (Ann Arbor, MI, USA) and Aravind Eye Hospital (Chennai, Tamil Nadu, India). The dataset included demographics, biometry power of the surgically implanted intraocular lens (IOL), and 1-month postoperative refraction. Seven IOL formulas were applied to predict postoperative refraction, and performance was assessed by comparing mean absolute errors both before and after population-specific A-constant optimization.

RESULTS

A total of 985 eyes from Aravind (mean age 60.5 ± 9.5 years) and 1003 from UMich (mean age 70.7 ± 9.5) were analyzed. Aravind patients had significantly lower age, axial length, lens thickness, and central corneal thickness, while UMich patients had lower K measurements, IOL power, and postoperative refraction. Overall, formulas performed better in Aravind for the SN60WF lens. Before A-constant optimization on the Aravind dataset, one formula (Nallasamy) achieved mean absolute error under 0.25 diopters compared to four formulas (Nallasamy, Pearl-DGS, SRK/T, Barrett) afterwards.

CONCLUSIONS

Substantial clinical and biometric differences exist between South Indian and Midwestern US cataract populations. Machine learning-based IOL refraction prediction formulas performed the best on the South Indian dataset both before and after population-specific parameter optimization. Understanding population level differences and creating methods to integrate these factors into IOL formulas may help improve refractive outcomes in cataract surgery.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1186/s12886-025-04217-2.

摘要

背景

人工晶状体（IOL）屈光度的选择是白内障手术后屈光结果的关键决定因素。有许多公式可辅助这一过程；一些公式源自几何光学原理（如SRK/T公式、巴雷特公式），而其他公式则基于数据驱动和机器学习方法（如纳拉萨米公式、Pearl-DGS公式）。鉴于眼部生物测量和环境刺激的差异，特定人群因素可能会影响某些公式的通用性。本研究比较了来自印度南部和美国中西部两个不同白内障手术人群的临床和生物测量特征，并评估了七种IOL屈光度公式（包括基于机器学习的方法）的预测准确性。

方法

在这项回顾性横断面比较研究中，数据收集自两个三级眼科护理中心：美国密歇根大学凯洛格眼科中心（美国密歇根州安阿伯）和阿拉文德眼科医院（印度泰米尔纳德邦金奈）。数据集包括人口统计学信息、手术植入的人工晶状体（IOL）的生物测量屈光度以及术后1个月的屈光情况。应用七种IOL公式预测术后屈光情况，并通过比较特定人群A常数优化前后的平均绝对误差来评估性能。

结果

共分析了来自阿拉文德的985只眼（平均年龄60.5±9.5岁）和来自密歇根大学的1003只眼（平均年龄70.7±9.5岁）。阿拉文德的患者年龄、眼轴长度、晶状体厚度和中央角膜厚度显著较低，而密歇根大学的患者角膜曲率测量值、IOL屈光度和术后屈光较低。总体而言，对于SN60WF晶状体，公式在阿拉文德的表现更好。在对阿拉文德数据集进行A常数优化之前，一个公式（纳拉萨米公式）的平均绝对误差低于0.25屈光度，而在优化后有四个公式（纳拉萨米公式、Pearl-DGS公式、SRK/T公式、巴雷特公式）达到这一水平。