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

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, Liege, 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. ((C) 2021 Published by Elsevier Inc.)

Automated summary

This study describes an open-source, reproducible method for designing IOL calculation formulas and evaluates the formula built using this methodology. The results show that the methodology achieved accuracy comparable to other advanced IOL formulas in terms of consistency and precision.