双侧长眼轴白内障患者中特定公式因素及带眼轴调整的人工智能公式的比较
Comparison of Formula-Specific Factors and Artificial Intelligence Formulas with Axial Length Adjustments in Bilateral Cataract Patients with Long Axial Length.
作者信息
Li Chuang, Wang Mingwei, Feng Rui, Liang Feiyan, Liu Xialin, He Chang, Fan Shuxin
机构信息
State Key Laboratory of Ophthalmology, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Zhongshan Ophthalmic Center, Sun Yat-Sen University, Guangzhou, 510060, People's Republic of China.
出版信息
Ophthalmol Ther. 2022 Oct;11(5):1869-1881. doi: 10.1007/s40123-022-00551-6. Epub 2022 Aug 2.
INTRODUCTION
To evaluate and compare the effectiveness for reducing the prediction error (PE) of the second eye using formula-specific factors, artificial intelligence (AI) formulas (PEARL-DGS and Kane), and the Cooke-modified axial length (CMAL) methods in bilateral cataract patients with long axial length (AL).
METHODS
A total of 98 patients with long AL who underwent sequential bilateral cataract surgeries were retrospectively enrolled. The second-eye IOL power was calculated by the formula-specific factors, AI formulas, and CMAL methods when the first eye suffered from refraction surprise. The correction factors of eight formulas were calculated by regression analysis.
RESULTS
There was a significant correlation between bilateral preoperative biometric parameters (P < 0.05) as well as bilateral PE (P < 0.05). The Kane formula displayed the lowest median absolute error (MedAE) and highest proportion of PE within ± 0.50 and ± 1.00 D compared with other formulas for the first eye. For the second-eye refinement, all three methods could reduce the second-eye MedAE. The formula-specific correction factors were 0.250, 0.331, 0.343, 0.394, 0.409, 0.452, 0.503, and 0.520 for Kane, Barrett Universal II (BUII), PEARL-DGS, Holladay 2, Holladay 1, Haigis, Hoffer Q, and SRK/T, respectively. The new AI-based Kane and PEARL-DGS with or without the CMAL methods could improve the refractive outcomes of the second eye in sequential bilateral cataract patients with long AL. The Kane, BUII, and PEARL-DGS with specific correction factors displayed higher accuracy compared with the other two methods (P < 0.05).
CONCLUSIONS
The new AI-based Kane and PEARL-DGS with or without the CMAL methods could improve the refractive outcomes of the second eye in sequential bilateral cataract patients with long AL. Notably, the Kane, PEARL-DGS, and BUII with specific correction factors displayed higher accuracy.
引言
评估并比较使用公式特定因子、人工智能（AI）公式（PEARL-DGS和Kane）以及库克修正眼轴长度（CMAL）方法，在长眼轴（AL）的双侧白内障患者中减少第二只眼预测误差（PE）的有效性。
方法
回顾性纳入98例接受连续双侧白内障手术的长AL患者。当第一只眼出现屈光意外时，通过公式特定因子、AI公式和CMAL方法计算第二只眼的人工晶状体度数。通过回归分析计算八个公式的校正因子。
结果
双侧术前生物测量参数之间（P < 0.05）以及双侧PE之间（P < 0.05）存在显著相关性。与第一只眼的其他公式相比，Kane公式显示出最低的中位绝对误差（MedAE）以及在±0.50和±1.00 D范围内PE的最高比例。对于第二只眼的优化，所有三种方法都可以降低第二只眼的MedAE。Kane、巴雷特通用II（BUII）、PEARL-DGS、霍拉迪2、霍拉迪1、海吉斯、霍弗Q和SRK/T公式的特定校正因子分别为0.250、0.331、0.343、0.394、0.409、0.452、0.503和0.520。新的基于AI的Kane和PEARL-DGS，无论有无CMAL方法，都可以改善长AL的连续双侧白内障患者第二只眼的屈光结果。与其他两种方法相比，具有特定校正因子的Kane、BUII和PEARL-DGS显示出更高的准确性（P < 0.05）。
结论
新的基于AI且有无CMAL方法的Kane和PEARL-DGS，可以改善长AL的连续双侧白内障患者第二只眼的屈光结果。值得注意的是，具有特定校正因子的Kane、PEARL-DGS和BUII显示出更高的准确性。
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