ArticleThe British journal of ophthalmology2023
Evaluation of the Nallasamy formula: a stacking ensemble machine learning method for refraction prediction in cataract surgery.
Article in The British journal of ophthalmology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 24 citations in OpenAlex.
- Accuracy of Kane, Hill-RBF, and PEARL-DGS versus traditional IOL formulas in highly myopic eyes: a systematic review and meta-analysis.BMC ophthalmology · 2026Pooled it
- Improved IOL Power Calculation With Femtosecond Laser Enhanced Refractive Outcome Prediction.Translational vision science & technology · 2025Article
- Innovations in Intraocular Lens Power Calculation-A Review.Journal of clinical medicine · 2025Review
- Comparing IOL refraction prediction accuracy and A-constant optimization for cataract surgery patients across South Indian and Midwestern United States populations.BMC ophthalmology · 2025Article
- Highly efficient stacking ensemble learning model for automated keratoconus screening.Eye and vision (London, England) · 2025Article
- Advanced Artificial-Intelligence-Based Jiang Formula for Intraocular Lens Power in Congenital Ectopia Lentis.Translational vision science & technology · 2025Article
- Prediction of Seven Artificial Intelligence-Based Intraocular Lens Power Calculation Formulas in Medium-Long Caucasian Eyes.Life (Basel, Switzerland) · 2025Article
- Evaluation of an artificial intelligence-based intraocular lens calculator: AI-based IOL-optimized formula.Journal of cataract and refractive surgery · 2024Article
- Accuracy assessment of artificial intelligence IOL calculation formulae: utilizing the heteroscedastic statistics and the Eyetemis Analysis Tool.Eye (London, England) · 2024Article
- Comparison of the formula accuracy for calculating multifocal intraocular lens power: a single center retrospective study in Korean patients.Scientific reports · 2024Article
- A Review of Intraocular Lens Power Calculation Formulas Based on Artificial Intelligence.Journal of clinical medicine · 2024Review
- Article
- Comparison of Machine and Human Expert Evaluation of Capsulorrhexis Creation Performance Through Analysis of Surgical Video Recordings.Clinical ophthalmology (Auckland, N.Z.) · 2024Article
- Artificial Intelligence for Anterior Segment Diseases: A Review of Potential Developments and Clinical Applications.Ophthalmology and therapy · 2023Review
- MAEPI and CIR: New Metrics for Robust Evaluation of the Prediction Performance of AI-Based IOL Formulas.Translational vision science & technology · 2023Article
- Novel Prediction Method Applied to Wound Age Estimation: Developing a Stacking Ensemble Model to Improve Predictive Performance Based on Multi-mRNA.Diagnostics (Basel, Switzerland) · 2023Article
- CatSkill: Artificial Intelligence-Based Metrics for the Assessment of Surgical Skill Level from Intraoperative Cataract Surgery Video Recordings.Ophthalmology scienceArticle
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
aimsTo develop a new intraocular lens power selection method with improved accuracy for general cataract patients receiving Alcon SN60WF lenses. METHODS AND ANALYSIS: A total of 5016 patients (6893 eyes) who underwent cataract surgery at University of Michigan's Kellogg Eye Center and received the Alcon SN60WF lens were included in the study. A machine learning-based method was developed using a training dataset of 4013 patients (5890 eyes), and evaluated on a testing dataset of 1003 patients (1003 eyes). The performance of our method was compared with that of Barrett Universal II, Emmetropia Verifying Optical (EVO), Haigis, Hoffer Q, Holladay 1, PearlDGS and SRK/T.
resultsMean absolute error (MAE) of the Nallasamy formula in the testing dataset was 0.312 Dioptres and the median absolute error (MedAE) was 0.242 D. Performance of existing methods were as follows: Barrett Universal II MAE=0.328 D, MedAE=0.256 D; EVO MAE=0.322 D, MedAE=0.251 D; Haigis MAE=0.363 D, MedAE=0.289 D; Hoffer Q MAE=0.404 D, MedAE=0.331 D; Holladay 1 MAE=0.371 D, MedAE=0.298 D; PearlDGS MAE=0.329 D, MedAE=0.258 D; SRK/T MAE=0.376 D, MedAE=0.300 D. The Nallasamy formula performed significantly better than seven existing methods based on the paired Wilcoxon test with Bonferroni correction (p<0.05).
conclusionsThe Nallasamy formula (available at https://lenscalc.com/) outperformed the seven other formulas studied on overall MAE, MedAE, and percentage of eyes within 0.5 D of prediction. Clinical significance may be primarily at the population level.
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