Guo Dongling, He Wenwen, Wei Ling, Song Yunxiao, Qi Jiao, Yao Yunqian, Chen Xu, Huang Jinhai, Lu Yi, Zhu Xiangjia
Eye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University, Shanghai, 200031, China.
NHC Key Laboratory of Myopia, Fudan University, Shanghai, China.
Eye Vis (Lond). 2023 Jun 1;10(1):26. doi: 10.1186/s40662-023-00342-5.
To develop a novel machine learning-based intraocular lens (IOL) power calculation formula for highly myopic eyes.
A total of 1828 eyes (from 1828 highly myopic patients) undergoing cataract surgery in our hospital were used as the internal dataset, and 151 eyes from 151 highly myopic patients from two other hospitals were used as external test dataset. The Zhu-Lu formula was developed based on the eXtreme Gradient Boosting and the support vector regression algorithms. Its accuracy was compared in the internal and external test datasets with the Barrett Universal II (BUII), Emmetropia Verifying Optical (EVO) 2.0, Kane, Pearl-DGS and Radial Basis Function (RBF) 3.0 formulas.
In the internal test dataset, the Zhu-Lu, RBF 3.0 and BUII ranked top three from low to high taking into account standard deviations (SDs) of prediction errors (PEs). The Zhu-Lu and RBF 3.0 showed significantly lower median absolute errors (MedAEs) than the other formulas (all P < 0.05). In the external test dataset, the Zhu-Lu, Kane and EVO 2.0 ranked top three from low to high considering SDs of PEs. The Zhu-Lu formula showed a comparable MedAE with BUII and EVO 2.0 but significantly lower than Kane, Pearl-DGS and RBF 3.0 (all P < 0.05). The Zhu-Lu formula ranked first regarding the percentages of eyes within ± 0.50 D of the PE in both test datasets (internal: 80.61%; external: 72.85%). In the axial length subgroup analysis, the PE of the Zhu-Lu stayed stably close to zero in all subgroups.
The novel IOL power calculation formula for highly myopic eyes demonstrated improved and stable predictive accuracy compared with other artificial intelligence-based formulas.
开发一种基于机器学习的新型高度近视眼人工晶状体（IOL）屈光度计算公式。
将我院1828例（1828只高度近视患者眼睛）接受白内障手术的病例作为内部数据集，另外两家医院151例（151只高度近视患者眼睛）作为外部测试数据集。基于极限梯度提升算法和支持向量回归算法开发了朱-陆公式。将其在内部和外部测试数据集中与巴雷特通用二代（BUII）、正视化验证光学（EVO）2.0、凯恩、珍珠-DGS和径向基函数（RBF）3.0公式的准确性进行比较。
在内部测试数据集中，考虑预测误差（PE）的标准差（SD）时，朱-陆、RBF 3.0和BUII从低到高排名前三。朱-陆和RBF 3.0的中位绝对误差（MedAE）显著低于其他公式（所有P < 0.05）。在外部测试数据集中，考虑PE的SD时，朱-陆、凯恩和EVO 2.0从低到高排名前三。朱-陆公式的MedAE与BUII和EVO 2.0相当，但显著低于凯恩、珍珠-DGS和RBF 3.0（所有P < 0.05）。在两个测试数据集中，朱-陆公式在PE±0.50 D范围内的眼睛百分比方面排名第一（内部：80.61%；外部：72.85%）。在眼轴长度亚组分析中，朱-陆公式在所有亚组中的PE均稳定地接近零。
与其他基于人工智能的公式相比，新型高度近视眼IOL屈光度计算公式显示出更高且稳定的预测准确性。
