期刊论文详细信息
Eye and Vision
Artificial intelligence-based refractive error prediction and EVO-implantable collamer lens power calculation for myopia correction
Research
Yang Shen1  Yilin Xu1  Xiaoying Wang1  Mingrui Cheng1  Boliang Li1  Xun Chen1  Yinjie Jiang1  Lingling Niu1  Xingtao Zhou1  Yadi Lei1  Chongyang Wang2 
[1] Eye Ear Nose and Throat Hospital, Fudan University, No. 19 BaoQing Road, XuHui District, 200031, Shanghai, China;National Health Commission Key Lab of Myopia, Fudan University, Shanghai, China;Shanghai Research Center of Ophthalmology and Optometry, Shanghai, China;Research and Development Department, Shanghai MediWorks Precision Instruments Company Limited, Shanghai, China;
关键词: Artificial intelligence;    Machine learning;    Refractive error;    Myopia;    Implantable collamer lens;    Toric implantable collamer lens;    Lens power calculation;   
DOI  :  10.1186/s40662-023-00338-1
 received in 2022-09-13, accepted in 2023-03-16,  发布年份 2023
来源: Springer
PDF
【 摘 要 】

BackgroundImplantable 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.MethodsWe 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.ResultsAfter 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.ConclusionWith 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.

【 授权许可】

CC BY   
© The Author(s) 2023

【 预 览 】
附件列表
Files Size Format View
RO202308154465124ZK.pdf 3358KB PDF download
MediaObjects/12888_2023_4756_MOESM3_ESM.docx 223KB Other download
41116_2023_36_Article_IEq701.gif 1KB Image download
Fig. 2 427KB Image download
40517_2023_258_Article_IEq135.gif 1KB Image download
40517_2023_256_Article_IEq78.gif 1KB Image download
40517_2023_256_Article_IEq86.gif 1KB Image download
Fig. 1 664KB Image download
Fig. 4 1103KB Image download
41116_2023_36_Article_IEq108.gif 1KB Image download
41116_2023_36_Article_IEq232.gif 1KB Image download
Fig. 1 41KB Image download
【 图 表 】

Fig. 1

41116_2023_36_Article_IEq232.gif

41116_2023_36_Article_IEq108.gif

Fig. 4

Fig. 1

40517_2023_256_Article_IEq86.gif

40517_2023_256_Article_IEq78.gif

40517_2023_258_Article_IEq135.gif

Fig. 2

41116_2023_36_Article_IEq701.gif

【 参考文献 】
  • [1]
  • [2]
  • [3]
  • [4]
  • [5]
  • [6]
  • [7]
  • [8]
  • [9]
  • [10]
  • [11]
  • [12]
  • [13]
  • [14]
  • [15]
  • [16]
  • [17]
  • [18]
  • [19]
  • [20]
  • [21]
  • [22]
  • [23]
  • [24]
  • [25]
  • [26]
  • [27]
  • [28]
  • [29]
  • [30]
  • [31]
  • [32]
  • [33]
  • [34]
  • [35]
  • [36]
  • [37]
  • [38]
  • [39]
  • [40]
  • [41]
  文献评价指标  
  下载次数:2次 浏览次数:0次