ArticleBMC ophthalmology2024
Evaluation of prediction errors in nine intraocular lens calculation formulas using an explainable machine learning model.
Article in BMC ophthalmology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Role of surgeon seniority in predicting surgically induced astigmatism after phacoemulsification surgery: a machine learning study.BMC ophthalmology · 2026Article
- Accuracy of new-generation intraocular lens formulas in IOL power calculation for cataract extraction combined with vitrectomy.International ophthalmology · 2026Article
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Authors and funding
5 authors.
Funding
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Abstract
backgroundThe purpose of the study was to evaluate the relationship between prediction errors (PEs) and ocular biometric variables in cataract surgery using nine intraocular lens (IOL) formulas with an explainable machine learning model.
methodsWe retrospectively analyzed the medical records of consecutive patients who underwent standard cataract surgery with a Tecnis 1-piece IOL (ZCB00) at a single center. We calculated predicted refraction using the following IOL formulas: Barrett Universal II (BUII), Cooke K6, EVO V2.0, Haigis, Hoffer QST, Holladay 1, Kane, SRK/T, and PEARL-DGS. We used a LightGBM-based machine learning model to evaluate the explanatory power of ocular biometric variables for PEs and assessed the relationship between PEs and ocular biometric variables using Shapley additive explanation (SHAP) values.
resultsWe included 1,430 eyes of 1,430 patients in the analysis. The SRK/T formula exhibited the highest R
conclusionThe explanatory power of currently used ocular biometric variables for PEs in new-generation formulas such as BUII, Cooke K6, EVO V2.0 and Kane is low, implying that these formulas are already optimized. Therefore, the introduction of new ocular biometric variables into IOL calculation formulas could potentially reduce PEs, enhancing the accuracy of surgical outcomes.
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