Artificial intelligence-based refractive error prediction and EVO-implantable collamer lens power calculation for myopia correction

• Published in: Eye and vision (Novato, Calif.) Vol. 10; no. 1; p. 22
• Main Authors: Jiang, Yinjie, Shen, Yang, Chen, Xun, Niu, Lingling, Li, Boliang, Cheng, Mingrui, Lei, Yadi, Xu, Yilin, Wang, Chongyang, Zhou, Xingtao, Wang, Xiaoying
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 01.05.2023; BioMed Central; BMC
• Subjects: Analysis; Artificial intelligence; Astigmatism; Contact lenses; Cornea; Eye surgery; Implantable collamer lens; Implants, Artificial; Intraocular lenses; Machine learning; Myopia; Nomograms; Prognosis; Prosthesis; Refractive error; Toric implantable collamer lens; Visual acuity; Canada; Japan; Germany; Toric implantable collamer lens; Implantable collamer lens; Machine learning; Myopia; Refractive error; Artificial intelligence; Lens power calculation
• ISSN: 2326-0254; 2326-0246; 2326-0254
• DOI: 10.1186/s40662-023-00338-1

Implantable collamer lens (ICL) has been widely accepted for its excellent visual outcomes for myopia correction. It is a new challenge in phakic IOL power calculation, especially for those with low and moderate myopia. This study aimed to establish a novel stacking machine learning (ML) model for predicting postoperative refraction errors and calculating EVO-ICL lens power. We enrolled 2767 eyes of 1678 patients (age: 27.5 ± 6.33 years, 18-54 years) who underwent non-toric (NT)-ICL or toric-ICL (TICL) implantation during 2014 to 2021. The postoperative spherical equivalent (SE) and sphere were predicted using stacking ML models [support vector regression (SVR), LASSO, random forest, and XGBoost] and training based on ocular dimensional parameters from NT-ICL and TICL cases, respectively. The accuracy of the stacking ML models was compared with that of the modified vergence formula (MVF) based on the mean absolute error (MAE), median absolute error (MedAE), and percentages of eyes within ± 0.25, ± 0.50, and ± 0.75 diopters (D) and Bland-Altman analyses. In addition, the recommended spheric lens power was calculated with 0.25 D intervals and targeting emmetropia. After NT-ICL implantation, the random forest model demonstrated the lowest MAE (0.339 D) for predicting SE. Contrarily, the SVR model showed the lowest MAE (0.386 D) for predicting the sphere. After TICL implantation, the XGBoost model showed the lowest MAE for predicting both SE (0.325 D) and sphere (0.308 D). Compared with MVF, ML models had numerically lower values of standard deviation, MAE, and MedAE and comparable percentages of eyes within ± 0.25 D, ± 0.50 D, and ± 0.75 D prediction errors. The difference between MVF and ML models was larger in eyes with low-to-moderate myopia (preoperative SE >  - 6.00 D). Our final optimal stacking ML models showed strong agreement between the predictive values of MVF by Bland-Altman plots. With various ocular dimensional parameters, ML models demonstrate comparable accuracy than existing MVF models and potential advantages in low-to-moderate myopia, and thus provide a novel nomogram for postoperative refractive error prediction and lens power calculation.