Dataset Information

An Advanced Lens Measurement Approach (ALMA) in post refractive surgery IOL power calculation with unknown preoperative parameters.

ABSTRACT:

Purpose

To test a new method to calculate the Intraocular Lens (IOL) power, that combines R Factor and ALxK methods, that we called Advance Lens Measurement Approach (ALMA).

Design

Retrospective, Comparative, Observational study.

Setting

Department of Medicine and Surgery, University of Salerno, Italy.

Methods

Ninety one eyes of 91 patients previously treated with Photorefractive Keratectomy (PRK) or Laser-Assisted in Situ Keratomileusis (LASIK) that underwent phacoemulsification and IOL implantation in the capsular bag were analyzed. For 68 eyes it was possible to zero out the Mean Errors (ME) for each formula and for selected IOL models, in order to eliminate the bias of the lens factor (A-Costant). Main outcome, measured in this study, was the median absolute error (MedAE) of the refraction prediction.

Results

In the sample with ME zeroed (68 eyes) both R Factor and ALxK methods resulted in MedAE of 0.67 D. For R Factor 33 eyes (48.53%) reported a refractive error <0.5D, and 53 eyes (77.94%) reported a refractive error <1D, For ALxK method, 32 eyes (47.06%) reported a refractive error <0.5 D, and 53 eyes (77.94%) reported a refractive error <1 D. ALMA method, reported a MedAE of 0.55 D, and an higher number of patients with a refractive error <0.5 D (35 eyes, 51.47%), and with a refractive error <1 D (54 eyes, 79.41%).

Conclusions

Based on the results obtained from this study, ALMA method can improve R Factor and ALxK methods. This improvement is confirmed both by zeroing the mean error and without zeroing it.

SUBMITTER: Rosa N

PROVIDER: S-EPMC7447029 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

An Advanced Lens Measurement Approach (ALMA) in post refractive surgery IOL power calculation with unknown preoperative parameters.

Rosa Nicola N Cione Ferdinando F Pepe Angela A Musto Salvatore S De Bernardo Maddalena M

PloS one 20200825 8

<h4>Purpose</h4>To test a new method to calculate the Intraocular Lens (IOL) power, that combines R Factor and ALxK methods, that we called Advance Lens Measurement Approach (ALMA).<h4>Design</h4>Retrospective, Comparative, Observational study.<h4>Setting</h4>Department of Medicine and Surgery, University of Salerno, Italy.<h4>Methods</h4>Ninety one eyes of 91 patients previously treated with Photorefractive Keratectomy (PRK) or Laser-Assisted in Situ Keratomileusis (LASIK) that underwent phacoe ...[more]

PMID: 32841281

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Project description: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.

Dataset Information

An Advanced Lens Measurement Approach (ALMA) in post refractive surgery IOL power calculation with unknown preoperative parameters.

Purpose

Design

Setting

Methods

Results

Conclusions

Publications

An Advanced Lens Measurement Approach (ALMA) in post refractive surgery IOL power calculation with unknown preoperative parameters.

Similar Datasets

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