ArticleJournal of clinical medicine2021
Use of a Machine Learning Method in Predicting Refraction after Cataract Surgery.
Article in Journal of clinical medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Role of surgeon seniority in predicting surgically induced astigmatism after phacoemulsification surgery: a machine learning study.BMC ophthalmology · 2026Article
- Article
- Neural Network-Based Prediction of Post-Operative Visual Outcomes Following Secondary Pediatric Intraocular Lens Implantation.Children (Basel, Switzerland) · 2025Article
- Artificial intelligence virtual assistants in primary eye care practice.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2025Article
- Review
- Evaluation of the Nallasamy formula: a stacking ensemble machine learning method for refraction prediction in cataract surgery.The British journal of ophthalmology · 2023Article
- Training data size and predication errors in the use of machine-learning assisted intraocular lens power calculation.Scientific reports · 2023Article
- Research on Establishing Corneal Edema after Phacoemulsification Prediction Model Based on Variable Selection with Copula Entropy.Journal of clinical medicine · 2023Article
- A Novel Role for Corneal Pachymetry in Planning Cataract Surgery by Determining Changes in Spherical Equivalent Resulting from a Previous LASIK Treatment.Journal of ophthalmology · 2023Article
- Prediction of Subjective Refraction From Anterior Corneal Surface, Eye Lengths, and Age Using Machine Learning Algorithms.Translational vision science & technology · 2022Article
- Machine learning adaptation of intraocular lens power calculation for a patient group.Eye and vision (London, England) · 2021Article
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Authors and funding
4 authors.
Funding
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Abstract
The present study aims to describe the use of machine learning (ML) in predicting the occurrence of postoperative refraction after cataract surgery and compares the accuracy of this method to conventional intraocular lens (IOL) power calculation formulas. In total, 3331 eyes from 2010 patients were assessed. The objects were divided into training data and test data. The constants for the IOL power calculation formulas and model training for ML were optimized using training data. Then, the occurrence of postoperative refraction was predicted using conventional formulas, or ML models were calculated using the test data. We evaluated the SRK/T formula, Haigis formula, Holladay 1 formula, Hoffer Q formula, and Barrett Universal II formula (BU-II); similar to ML methods, we assessed support vector regression (SVR), random forest regression (RFR), gradient boosting regression (GBR), and neural network (NN). Among the conventional formulas, BU-II had the lowest mean and median absolute error of prediction. Therefore, we compared the accuracy of our method with that of BU-II. The absolute errors of some ML methods were lower than those of BU-II. However, no statistically significant difference was observed. Thus, the accuracy of our method was not inferior to that of BU-II.
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