Ling Wei1,2,3,4, Yunxiao Song5, Wenwen He1,2,3,4, Xu Chen6, Bo Ma7, Yi Lu1,2,3,4, Xiangjia Zhu1,2,3,4. 1. Department of Ophthalmology and Eye Institute, Eye & ENT Hospital, Fudan University, Shanghai, China. 2. National Health Commission Key Laboratory of Myopia, Fudan University, Shanghai, China. 3. Key Laboratory of Myopia, Chinese Academy of Medical Science, Shanghai, China. 4. Shanghai Key Laboratory of Visual Impairment and Restoration, Shanghai, China. 5. New York University Shanghai, Shanghai, China. 6. Shanghai Aier Eye Hospital, Shanghai, China. 7. Department of Ophthalmology, Ninth People's Hospital of Shanghai Jiaotong University, Shanghai, China.
Abstract
Purpose: To develop a machine learning-based calculator to improve the accuracy of IOL power predictions for highly myopic eyes. Methods: Data of 1,450 highly myopic eyes from 1,450 patients who had cataract surgeries at our hospital were used as internal dataset (train and validate). Another 114 highly myopic eyes from other hospitals were used as external test dataset. A new calculator was developed using XGBoost regression model based on features including demographics, biometrics, IOL powers, A constants, and the predicted refractions by Barrett Universal II (BUII) formula. The accuracies were compared between our calculator and BUII formula, and axial length (AL) subgroup analysis (26.0-28.0, 28.0-30.0, or ≥30.0 mm) was further conducted. Results: The median absolute errors (MedAEs) and median squared errors (MedSEs) were lower with the XGBoost calculator (internal: 0.25 D and 0.06 D2; external: 0.29 D and 0.09 D2) vs. the BUII formula (all P ≤ 0.001). The mean absolute errors and were 0.33 ± 0.28 vs. 0.45 ± 0.31 (internal), and 0.35 ± 0.24 vs. 0.43 ± 0.29 D (external). The mean squared errors were 0.19 ± 0.32 vs. 0.30 ± 0.36 (internal), and 0.18 ± 0.21 vs. 0.27 ± 0.29 D2 (external). The percentages of eyes within ±0.25 D of the prediction errors were significantly greater with the XGBoost calculator (internal: 49.66 vs. 29.66%; external: 78.28 vs. 60.34%; both P < 0.05). The same trend was in MedAEs and MedSEs in all subgroups (internal) and in AL ≥30.0 mm subgroup (external) (all P < 0.001). Conclusions: The new XGBoost calculator showed promising accuracy for highly or extremely myopic eyes.
Purpose: To develop a machine learning-based calculator to improve the accuracy of IOL power predictions for highly myopic eyes. Methods: Data of 1,450 highly myopic eyes from 1,450 patients who had cataract surgeries at our hospital were used as internal dataset (train and validate). Another 114 highly myopic eyes from other hospitals were used as external test dataset. A new calculator was developed using XGBoost regression model based on features including demographics, biometrics, IOL powers, A constants, and the predicted refractions by Barrett Universal II (BUII) formula. The accuracies were compared between our calculator and BUII formula, and axial length (AL) subgroup analysis (26.0-28.0, 28.0-30.0, or ≥30.0 mm) was further conducted. Results: The median absolute errors (MedAEs) and median squared errors (MedSEs) were lower with the XGBoost calculator (internal: 0.25 D and 0.06 D2; external: 0.29 D and 0.09 D2) vs. the BUII formula (all P ≤ 0.001). The mean absolute errors and were 0.33 ± 0.28 vs. 0.45 ± 0.31 (internal), and 0.35 ± 0.24 vs. 0.43 ± 0.29 D (external). The mean squared errors were 0.19 ± 0.32 vs. 0.30 ± 0.36 (internal), and 0.18 ± 0.21 vs. 0.27 ± 0.29 D2 (external). The percentages of eyes within ±0.25 D of the prediction errors were significantly greater with the XGBoost calculator (internal: 49.66 vs. 29.66%; external: 78.28 vs. 60.34%; both P < 0.05). The same trend was in MedAEs and MedSEs in all subgroups (internal) and in AL ≥30.0 mm subgroup (external) (all P < 0.001). Conclusions: The new XGBoost calculator showed promising accuracy for highly or extremely myopic eyes.