Literature DB >> 33992611

The PEARL-DGS Formula: The Development of an Open-source Machine Learning-based Thick IOL Calculation Formula.

Guillaume Debellemanière1, Mathieu Dubois1, Mathieu Gauvin2, Avi Wallerstein2, Luis F Brenner3, Radhika Rampat1, Alain Saad1, Damien Gatinel4.   

Abstract

PURPOSE: To describe an open-source, reproducible, step-by-step method to design sum-of-segments thick intraocular lens (IOL) calculation formulas, and to evaluate a formula built using this methodology.
DESIGN: Retrospective, multicenter case series
METHODS: A set of 4242 eyes implanted with Finevision IOLs (PhysIOL, Liège, Belgium) was used to devise the formula design process and build the formula. A different set of 677 eyes from the same center was kept separate to serve as a test set. The resulting formula was evaluated on the test set as well as another independent data set of 262 eyes.
RESULTS: The lowest standard deviation (SD) of prediction errors on Set 1 were obtained with the PEARL-DGS formula (±0.382 D), followed by K6 and Olsen (±0.394 D), EVO 2.0 (±0.398 D), RBF 3.0, and BUII (±0.402 D). The formula yielding the lowest SD on Set 2 was the PEARL-DGS (±0.269 D), followed by Olsen (±0.272 D), K6 (±0.276 D), EVO 2.0 (±0.277 D), and BUII (±0.301 D).
CONCLUSION: Our methodology achieved an accuracy comparable to other state-of-the-art IOL formulas. The open-source tools provided in this article could allow other researchers to reproduce our results using their own data sets, with other IOL models, population settings, biometric devices, and measured, rather than calculated, posterior corneal radius of curvature or sum-of-segments axial lengths.
Copyright © 2021. Published by Elsevier Inc.

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Year:  2021        PMID: 33992611     DOI: 10.1016/j.ajo.2021.05.004

Source DB:  PubMed          Journal:  Am J Ophthalmol        ISSN: 0002-9394            Impact factor:   5.258


  5 in total

1.  Accuracy of new-generation intraocular lens calculation formulas in eyes with variations in predicted refraction.

Authors:  Pingjun Chang; Shuyi Qian; Yalan Wang; Siyan Li; Fuman Yang; Yiwen Hu; Zhuohan Liu; Yun-E Zhao
Journal:  Graefes Arch Clin Exp Ophthalmol       Date:  2022-07-08       Impact factor: 3.117

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

Authors:  Tingyang Li; Joshua Stein; Nambi Nallasamy
Journal:  Br J Ophthalmol       Date:  2022-04-04       Impact factor: 5.908

Review 3.  Application of artificial intelligence in cataract management: current and future directions.

Authors:  Laura Gutierrez; Jane Sujuan Lim; Li Lian Foo; Wei Yan Ng; Michelle Yip; Gilbert Yong San Lim; Melissa Hsing Yi Wong; Allan Fong; Mohamad Rosman; Jodhbir Singth Mehta; Haotian Lin; Darren Shu Jeng Ting; Daniel Shu Wei Ting
Journal:  Eye Vis (Lond)       Date:  2022-01-07

4.  Comparison of Formula-Specific Factors and Artificial Intelligence Formulas with Axial Length Adjustments in Bilateral Cataract Patients with Long Axial Length.

Authors:  Chuang Li; Mingwei Wang; Rui Feng; Feiyan Liang; Xialin Liu; Chang He; Shuxin Fan
Journal:  Ophthalmol Ther       Date:  2022-08-02

5.  Accuracy of newer intraocular lens power formulas in short and long eyes using sum-of-segments biometry.

Authors:  H John Shammas; Leonardo Taroni; Marco Pellegrini; Maya C Shammas; Renu V Jivrajka
Journal:  J Cataract Refract Surg       Date:  2022-04-27       Impact factor: 3.528

  5 in total

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