Literature DB >> 35379599

Evaluation of the Nallasamy formula: a stacking ensemble machine learning method for refraction prediction in cataract surgery.

Tingyang Li1, Joshua Stein2,3,4, Nambi Nallasamy5,2.   

Abstract

AIMS: To develop a new intraocular lens power selection method with improved accuracy for general cataract patients receiving Alcon SN60WF lenses. METHODS AND ANALYSIS: A total of 5016 patients (6893 eyes) who underwent cataract surgery at University of Michigan's Kellogg Eye Center and received the Alcon SN60WF lens were included in the study. A machine learning-based method was developed using a training dataset of 4013 patients (5890 eyes), and evaluated on a testing dataset of 1003 patients (1003 eyes). The performance of our method was compared with that of Barrett Universal II, Emmetropia Verifying Optical (EVO), Haigis, Hoffer Q, Holladay 1, PearlDGS and SRK/T.
RESULTS: Mean absolute error (MAE) of the Nallasamy formula in the testing dataset was 0.312 Dioptres and the median absolute error (MedAE) was 0.242 D. Performance of existing methods were as follows: Barrett Universal II MAE=0.328 D, MedAE=0.256 D; EVO MAE=0.322 D, MedAE=0.251 D; Haigis MAE=0.363 D, MedAE=0.289 D; Hoffer Q MAE=0.404 D, MedAE=0.331 D; Holladay 1 MAE=0.371 D, MedAE=0.298 D; PearlDGS MAE=0.329 D, MedAE=0.258 D; SRK/T MAE=0.376 D, MedAE=0.300 D. The Nallasamy formula performed significantly better than seven existing methods based on the paired Wilcoxon test with Bonferroni correction (p<0.05).
CONCLUSIONS: The Nallasamy formula (available at https://lenscalc.com/) outperformed the seven other formulas studied on overall MAE, MedAE, and percentage of eyes within 0.5 D of prediction. Clinical significance may be primarily at the population level. © Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Entities:  

Keywords:  Lens and zonules; Optics and Refraction

Year:  2022        PMID: 35379599      PMCID: PMC9530066          DOI: 10.1136/bjophthalmol-2021-320599

Source DB:  PubMed          Journal:  Br J Ophthalmol        ISSN: 0007-1161            Impact factor:   5.908


  25 in total

1.  Accuracy of Intraocular Lens Calculation Formulas.

Authors:  Ronald B Melles; Jack T Holladay; William J Chang
Journal:  Ophthalmology       Date:  2017-09-23       Impact factor: 12.079

2.  The effect of testing distance on intraocular lens power calculation.

Authors:  Michael J Simpson; W Neil Charman
Journal:  J Refract Surg       Date:  2014-11       Impact factor: 3.573

3.  The Hoffer Q formula: a comparison of theoretic and regression formulas.

Authors:  K J Hoffer
Journal:  J Cataract Refract Surg       Date:  1993-11       Impact factor: 3.351

4.  Correction of radial keratotomy hyperopia.

Authors:  R E Damiano; S L Forstot
Journal:  J Cataract Refract Surg       Date:  1994-05       Impact factor: 3.351

5.  Intraocular lens power formula accuracy: Comparison of 7 formulas.

Authors:  Jack X Kane; Anton Van Heerden; Alp Atik; Constantinos Petsoglou
Journal:  J Cataract Refract Surg       Date:  2016-10       Impact factor: 3.351

6.  Evaluation of an Algorithm for Identifying Ocular Conditions in Electronic Health Record Data.

Authors:  Joshua D Stein; Moshiur Rahman; Chris Andrews; Joshua R Ehrlich; Shivani Kamat; Manjool Shah; Erin A Boese; Maria A Woodward; Jeff Cowall; Edward H Trager; Prabha Narayanaswamy; David A Hanauer
Journal:  JAMA Ophthalmol       Date:  2019-05-01       Impact factor: 7.389

7.  Accuracy of a new intraocular lens power calculation method based on artificial intelligence.

Authors:  David Carmona González; Carlos Palomino Bautista
Journal:  Eye (Lond)       Date:  2020-04-28       Impact factor: 3.775

8.  Gradient Boosting Decision Tree Algorithm for the Prediction of Postoperative Intraocular Lens Position in Cataract Surgery.

Authors:  Tingyang Li; Kevin Yang; Joshua D Stein; Nambi Nallasamy
Journal:  Transl Vis Sci Technol       Date:  2020-12-21       Impact factor: 3.283

9.  Gender differences in refraction prediction error of five formulas for cataract surgery.

Authors:  Yibing Zhang; Tingyang Li; Aparna Reddy; Nambi Nallasamy
Journal:  BMC Ophthalmol       Date:  2021-04-21       Impact factor: 2.209

10.  The Bayesian Additive Regression Trees Formula for Safe Machine Learning-Based Intraocular Lens Predictions.

Authors:  Gerald P Clarke; Adam Kapelner
Journal:  Front Big Data       Date:  2020-12-18
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.