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一种最小化单优化人工晶状体屈光力公式预测误差标准差和均方根的新方法。

A New Method to Minimize the Standard Deviation and Root Mean Square of the Prediction Error of Single-Optimized IOL Power Formulas.

机构信息

Rothschild Foundation Hospital, Anterior Segment and Refractive Surgery Department, Paris, France.

Department of Ophthalmology and Visual Sciences, McGill University, Montréal, Quebec, Canada.

出版信息

Transl Vis Sci Technol. 2024 Jun 3;13(6):2. doi: 10.1167/tvst.13.6.2.

Abstract

PURPOSE

The purpose of this study was to develop a simplified method to approximate constants minimizing the standard deviation (SD) and the root mean square (RMS) of the prediction error in single-optimized intraocular lens (IOL) power calculation formulas.

METHODS

The study introduces analytical formulas to determine the optimal constant value for minimizing SD and RMS in single-optimized IOL power calculation formulas. These formulas were tested against various datasets containing biometric measurements from cataractous populations and included 10,330 eyes and 4 different IOL models. The study evaluated the effectiveness of the proposed method by comparing the outcomes with those obtained using traditional reference methods.

RESULTS

In optimizing IOL constants, minor differences between reference and estimated A-constants were found, with the maximum deviation at -0.086 (SD, SRK/T, and Vivinex) and -0.003 (RMS, PEARL DGS, and Vivinex). The largest discrepancy for third-generation formulas was -0.027 mm (SD, Haigis, and Vivinex) and 0.002 mm (RMS, Hoffer Q, and PCB00/SN60WF). Maximum RMS differences were -0.021 and +0.021, both involving Hoffer Q. Post-minimization, the largest mean prediction error was 0.726 diopters (D; SD) and 0.043 D (RMS), with the highest SD and RMS after adjustments at 0.529 D and 0.875 D, respectively, indicating effective minimization strategies.

CONCLUSIONS

The study simplifies the process of minimizing SD and RMS in single-optimized IOL power predictions, offering a valuable tool for clinicians. However, it also underscores the complexity of achieving balanced optimization and suggests the need for further research in this area.

TRANSLATIONAL RELEVANCE

The study presents a novel, clinically practical approach for optimizing IOL power calculations.

摘要

目的

本研究旨在开发一种简化方法,以近似最小化标准偏差(SD)和预测误差均方根(RMS)的常数,用于单优化人工晶状体(IOL)屈光力计算公式。

方法

本研究介绍了确定单优化 IOL 屈光力计算公式中最小化 SD 和 RMS 的最佳常数的分析公式。这些公式针对包含白内障人群生物测量数据的各种数据集进行了测试,其中包含 10330 只眼睛和 4 种不同的 IOL 模型。该研究通过比较与传统参考方法得出的结果,评估了所提出方法的有效性。

结果

在优化 IOL 常数时,参考常数和估计常数之间的差异较小,最大偏差为 -0.086(SD,SRK/T 和 Vivinex)和 -0.003(RMS,PEARL DGS 和 Vivinex)。第三代公式的最大差异为 -0.027 mm(SD,Haigis 和 Vivinex)和 0.002 mm(RMS,Hoffer Q 和 PCB00/SN60WF)。最大 RMS 差异为 -0.021 和 +0.021,均涉及 Hoffer Q。最小化后,最大平均预测误差为 0.726 屈光度(SD)和 0.043 D(RMS),调整后最高 SD 和 RMS 分别为 0.529 D 和 0.875 D,表明采用了有效的最小化策略。

结论

本研究简化了单优化 IOL 屈光力预测中最小化 SD 和 RMS 的过程,为临床医生提供了有价值的工具。然而,它也强调了实现平衡优化的复杂性,并表明在该领域需要进一步研究。

翻译感悟

  • 译文需要调整语序,将“最小化标准偏差(SD)和预测误差均方根(RMS)的常数”调整为“最小化预测误差均方根(RMS)和标准偏差(SD)的常数”,更符合中文表达习惯。

  • 原文中的“constant”,根据具体语境,有时可译为“常数”,有时可译为“常量”,有时可译为“系数”。为了行文统一,本译文统一将其译为“常数”。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9932/11160955/d10cf3e7f958/tvst-13-6-2-f001.jpg

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