4.6 Article

AI-powered effective lens position prediction improves the accuracy of existing lens formulas

Journal

BRITISH JOURNAL OF OPHTHALMOLOGY
Volume 106, Issue 9, Pages 1222-1226

Publisher

BMJ PUBLISHING GROUP
DOI: 10.1136/bjophthalmol-2020-318321

Keywords

lens and zonules; treatment surgery

Categories

Funding

  1. Lighthouse Guild, New York, NY
  2. National Eye Institute, Bethesda, MD [1R01EY026641-01A1]

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This study assessed the use of machine learning to predict postoperative anterior chamber depth (ACD) and integrate the predictions into existing intraocular lens (IOL) calculation formulas. The results showed that using the more accurately predicted postoperative ACD significantly improved the prediction accuracy of the four existing IOL power formulas.
Aims To assess whether incorporating a machine learning (ML) method for accurate prediction of postoperative anterior chamber depth (ACD) improves the refraction prediction performance of existing intraocular lens (IOL) calculation formulas. Methods A dataset of 4806 patients with cataract was gathered at the Kellogg Eye Center, University of Michigan, and split into a training set (80% of patients, 5761 eyes) and a testing set (20% of patients, 961 eyes). A previously developed ML-based method was used to predict the postoperative ACD based on preoperative biometry. This ML-based postoperative ACD was integrated into new effective lens position (ELP) predictions using regression models to rescale the ML output for each of four existing formulas (Haigis, Hoffer Q, Holladay and SRK/T). The performance of the formulas with ML-modified ELP was compared using a testing dataset. Performance was measured by the mean absolute error (MAE) in refraction prediction. Results When the ELP was replaced with a linear combination of the original ELP and the ML-predicted ELP, the MAEs +/- SD (in Diopters) in the testing set were: 0.356 +/- 0.329 for Haigis, 0.352 +/- 0.319 for Hoffer Q, 0.371 +/- 0.336 for Holladay, and 0.361 +/- 0.331 for SRK/T which were significantly lower (p<0.05) than those of the original formulas: 0.373 +/- 0.328 for Haigis, 0.408 +/- 0.337 for Hoffer Q, 0.384 +/- 0.341 for Holladay and 0.394 +/- 0.351 for SRK/T. Conclusion Using a more accurately predicted postoperative ACD significantly improves the prediction accuracy of four existing IOL power formulas.

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