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, 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.