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Table 3 Performance of machine learning-based formulas

From: Machine learning adaptation of intraocular lens power calculation for a patient group

Study Carmona González D et al.
(N = 260) [17]
Zhao J et al.
(N = 53) [21]
Wei L et al.
(N = 1450) [20]
Current
Calculation method Hill-RBF* SVR + MAR Hill-RBF* Kane XGBoost SRK/T + SVR
IOL 10 models 10 models SBL-3 SBL-3 8 models SN60WF
Country Spain Spain China China China Japan
Mean prediction error (SD), D  − 0.17 (0.40) 0.04 (0.30)  − 0.51 (0.61)  − 0.50 (0.60) N.A. 0.01 (0.38)
MedAE, D 0.28 0.18 0.55 0.45 0.29 0.21
Within ± 0.25 D 48.1% 65.4% 24.5% 28.3% 43.9% 54.4%
Within ± 0.50 D 80.8% 90.4% 47.2% 52.8% 72.8% 83.5%
Within ± 1.00 D 100.0% 100.0% 81.1% 83.0% 99.1% 98.5%
  1. SVR support vector regression; MAR multivariate adaptive regression spline; XGBoost extreme gradient boosting machine learning; SD standard deviation; D diopter; MedAE median absolute error; N.A. not available
  2. * version 2