Artificial intelligence driven intraocular lens power calculation in extreme axial myopia

人工智能驱动的极度轴性近视眼内晶状体度数计算

阅读:1

Abstract

Accurate intraocular lens (IOL) power calculation is critical in cataract surgery, especially in patients with extreme axial myopia where traditional formulas often yield inaccurate results. This study retrospectively evaluated the accuracy of two AI-driven IOL formulas (Hill-RBF, Kane), the Barrett Universal II formula, and the traditional SRK/T formula in patients with axial lengths ≥ 30.0 mm. Data from 80 eyes of 51 patients treated at the Institute of Science Tokyo were analyzed. Postoperative refractive errors were recalculated, and accuracy was assessed using mean error (ME), mean absolute error (MAE), and median absolute error (MedAE). Statistical analyses included the Wilcoxon signed-rank test and chi-square test. The Kane and Hill-RBF formulas demonstrated significantly lower MAE (0.51 D and 0.52 D, respectively) compared to SRK/T (P < 0.05). MAE of the Barrett Universal II formula was 0.66D, which was not significantly different from SRK/T. In eyes with axial lengths ≥ 32.0 mm, Kane achieved the lowest MAE and MedAE (0.44 D and 0.40 D). Both Kane and Hill-RBF showed lower refractive errors > ± 1.0 D (7.5%) compared to SRK/T (42.5%). AI-driven formulas, particularly Kane and Hill-RBF, significantly improve refractive accuracy in extreme axial myopia. Their clinical adoption may enhance postoperative visual outcomes and reduce the need for corrective interventions.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。