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Purpose <p>To systematically assess and compare the accuracy of artificial intelligence (AI) -based intraocular lens (IOL) power calculation formulas with traditional IOL formulas in patients with short eye length.</p> Design <p>A systematic review and network meta-analysis.</p> Methods <p>We performed an exhaustive search of the PubMed, Embase, Web of Science, and Cochrane Library databases to identify relevant studies published until February 2024. The extracted data comprised the mean absolute error (MAE) and the percentage of eyes with refractive prediction errors (PE) within ± 0.50 and ± 1.00 diopters (D). Network meta-analysis was performed using Review Manager 5.3 and StataSE 16.0.</p> Results <p>A network meta-analysis of 21 formulas was carried out in 10 studies, including 756 eyes with axial length (AL) &lt; 22&#xa0;mm. The results showed that the top AI-based formula was Pearl-DGS. For the percentage of eyes with PE within ± 0.50 D, the Pearl-DGS formula demonstrated the highest accuracy. In terms of the percentage of eyes with PE within ± 1.00 D, the FullMonte IOL formula performed poorly, and no significant differences were observed among the other formulas.</p> Conclusions <p>The Pearl-DGS formula emerged as the leading AI-based method for determining IOL power in patients with short eye lengths, demonstrating superior accuracy compared to conventional vergence formulas.</p>

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Network meta-analysis of intraocular lens power calculation formulas based on artificial intelligence in short eyes

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月18日

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  • Xin Zheng,
  • Meng Li,
  • Zhao-Xing Guo,
  • Zhi-Yong Tian,
  • Jing-Shang Zhang,
  • Ying-Yan Mao,
  • Peng Zhao,
  • Zhong-Yan Li,
  • Xiu-Hua Wan

摘要

Purpose

To systematically assess and compare the accuracy of artificial intelligence (AI) -based intraocular lens (IOL) power calculation formulas with traditional IOL formulas in patients with short eye length.

Design

A systematic review and network meta-analysis.

Methods

We performed an exhaustive search of the PubMed, Embase, Web of Science, and Cochrane Library databases to identify relevant studies published until February 2024. The extracted data comprised the mean absolute error (MAE) and the percentage of eyes with refractive prediction errors (PE) within ± 0.50 and ± 1.00 diopters (D). Network meta-analysis was performed using Review Manager 5.3 and StataSE 16.0.

Results

A network meta-analysis of 21 formulas was carried out in 10 studies, including 756 eyes with axial length (AL) < 22 mm. The results showed that the top AI-based formula was Pearl-DGS. For the percentage of eyes with PE within ± 0.50 D, the Pearl-DGS formula demonstrated the highest accuracy. In terms of the percentage of eyes with PE within ± 1.00 D, the FullMonte IOL formula performed poorly, and no significant differences were observed among the other formulas.

Conclusions

The Pearl-DGS formula emerged as the leading AI-based method for determining IOL power in patients with short eye lengths, demonstrating superior accuracy compared to conventional vergence formulas.