ArticleTranslational vision science & technology2019
Prediction of Effective Lens Position Using Multiobjective Evolutionary Algorithm.
Article in Translational vision science & technology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Ray tracing intraocular lens calculation performance improved by AI-powered postoperative lens position prediction.The British journal of ophthalmology · 2023Article
- AI-powered effective lens position prediction improves the accuracy of existing lens formulas.The British journal of ophthalmology · 2022Article
- Three-dimensional topographic changes of anterior chamber depth following phacoemulsification with intraocular lens implantation in cataract patients.International ophthalmology · 2022Article
- Determining the Theoretical Effective Lens Position of Thick Intraocular Lenses for Machine Learning-Based IOL Power Calculation and Simulation.Translational vision science & technology · 2021Article
- Gradient Boosting Decision Tree Algorithm for the Prediction of Postoperative Intraocular Lens Position in Cataract Surgery.Translational vision science & technology · 2020Article
- AI-Powered Effective Lens Position Prediction Improves the Accuracy of Existing Lens Formulas.medRxiv : the preprint server for health sciences · 2020Article
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Authors and funding
7 authors.
Funding
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Abstract
purposeThe purpose of this study was to evaluate the prediction accuracy of effective lens position (ELP) after cataract surgery using a multiobjective evolutionary algorithm (MOEA).
methodsNinety-six eyes of 96 consecutive patients (aged 73.9 ± 8.6 years) who underwent cataract surgery were retrospectively studied; the eyes were randomly distributed to a prediction group (55 eyes) and a verification group (41 eyes). The procedure was repeated randomly 30 times to create 30 data sets for both groups. In the prediction group, based on the parameters of preoperative optical coherence tomography (OCT), biometry, and anterior segment (AS)-OCT, the prediction equation of ELP was created using MOEA and stepwise multiple regression analysis (SMR). Subsequently, the prediction accuracy of ELPs was evaluated and compared with conventional formulas, including SRK/T and the Haigis formula.
resultsThe rate of mean absolute prediction error of 0.3 mm or higher was significantly lower in MOEA (mean 4.9% ± 3.2%, maximum 9.8%) than SMR (mean 7.3% ± 4.8%, maximum 24.4%) (
conclusionsThe study demonstrated that ELP prediction by MOEA was more accurate and was a method of less fluctuation than that of SMR and conventional formulas. TRANSLATIONAL RELEVANCE: MOEA is a promising method for solving clinical problems such as prediction of ocular biometry values by simultaneously optimizing several conditions for subjects affected by various complex factors.
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