ArticleTranslational vision science & technology2021
Development of a Web-Based Ensemble Machine Learning Application to Select the Optimal Size of Posterior Chamber Phakic Intraocular Lens.
Article in Translational vision science & technology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- Predicting vault after implantable collamer lens implantation using intraoperative OCT: establishment and application of models.Journal of cataract and refractive surgery · 2026Article
- EVO-ICL Vault Prediction: A Data Wrangling Framework Integrating Multicenter Big Data and Machine Learning.Ophthalmology and therapy · 2026Article
- Validation of a new implantable collamer lens sizing algorithm based on SS-OCT images.Journal of cataract and refractive surgery · 2026Article
- Artificial intelligence in refractive surgery: progress, challenges, and future directions.Frontiers in cell and developmental biology · 2026Review
- Integrating reproductive and metabolic factors for uterine fibroid risk assessment: a two-center machine learning study with SHAP interpretability.Frontiers in oncology · 2026Article
- Clinical Applications of Anterior Segment Optical Coherence Tomography in Managing Phakic and Secondary Intraocular Lens Implants: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2025Review
- Using Artificial Intelligence to Predict Implantable Collamer Lens Vault: A Low Parameter-Dependent Model for Better Surgical Outcomes.Translational vision science & technology · 2025Article
- Exploring Machine Learning Models for Vault Safety in ICL Implantation: A Comparative Analysis of Regression and Classification Models.Ophthalmology and therapy · 2025Article
- Evaluating the Predictive Accuracy of an AI-Based Tool for Postoperative Vault Estimation in Phakic Intraocular Lens Implantation.Clinical ophthalmology (Auckland, N.Z.) · 2025Article
- Prediction of postoperative vault after implantable collamer lens implantation with deep learning.International journal of ophthalmology · 2025Article
- Paired-eye comparison of endothelial cell density and vault height after implantable collamer lens implantation.Scientific reports · 2024Article
- Code-Free Machine Learning Approach for EVO-ICL Vault Prediction: A Retrospective Two-Center Study.Translational vision science & technology · 2024Article
- Bibliometric and visualized analysis of posterior chamber phakic intraocular lens research between 2003 and 2023.Frontiers in medicine · 2024Review
- Development and Validation of Data-Level Innovation Data-Balancing Machine Learning Models for Predicting Optimal Implantable Collamer Lens Size and Postoperative Vault.Ophthalmology and therapy · 2024Article
- Comparison of the Accuracy of Seven Vault Prediction Formulae for Implantable Collamer Lens Implantation.Ophthalmology and therapy · 2024Article
- Implantable Collamer Lens Procedure Planning: A Review of Global Approaches.Clinical ophthalmology (Auckland, N.Z.) · 2024Review
- Vault Height Is a Key Predictive Factor for Anterior Segment Measurement Error by IOLMaster 700 in Eyes With Phakic Intraocular Lens.Translational vision science & technology · 2023Article
- Predicting post-operative vault and optimal implantable collamer lens size using machine learning based on various ophthalmic device combinations.Biomedical engineering online · 2023Article
- Does multidimensional daily information predict the onset of myopia? A 1-year prospective cohort study.Biomedical engineering online · 2023Article
- Development of an implantable collamer lens sizing model: a retrospective study using ANTERION swept-source optical coherence tomography and a literature review.BMC ophthalmology · 2023Review
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: Selecting the optimal lens size by predicting the postoperative vault can reduce complications after implantation of an implantable collamer lens with a central-hole (ICL with KS-aquaport). We built a web-based machine learning application that incorporated clinical measurements to predict the postoperative ICL vault and select the optimal ICL size. Methods: We applied the stacking ensemble technique based on eXtreme Gradient Boosting (XGBoost) and a light gradient boosting machine to pre-operative ocular data from two eye centers to predict the postoperative vault. We assigned the Korean patient data to a training (N = 2756 eyes) and internal validation (N = 693 eyes) datasets (prospective validation). Japanese patient data (N = 290 eyes) were used as an independent external dataset from different centers to validate the model. Results: We developed an ensemble model that showed statistically better performance with a lower mean absolute error for ICL vault prediction (106.88 µm and 143.69 µm in the internal and external validation, respectively) than the other machine learning techniques and the classic ICL sizing methods did when applied to both validation datasets. Considering the lens size selection accuracy, our proposed method showed the best performance for both reference datasets (75.9% and 67.4% in the internal and external validation, respectively). Conclusions: Applying the ensemble approach to a large dataset of patients who underwent ICL implantation resulted in a more accurate prediction of vault size and selection of the optimal ICL size. Translational Relevance: We developed a web-based application for ICL sizing to facilitate the use of machine learning calculators for clinicians.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.