ArticleBMJ open2024
Clinical decision support system based on deep learning for evaluating implantable collamer lens size and vault after implantable collamer lens surgery: a retrospective study.
Article in BMJ open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 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
- From ciliary sulcus asymmetry to vault prediction: an hSTS-based approach to individualized sizing of implantable collamer lenses in myopic eyes.Frontiers in medicine · 2026Article
- Artificial intelligence in refractive surgery: progress, challenges, and future directions.Frontiers in cell and developmental biology · 2026Review
- Quantitative analysis of iris-ciliary angle area to optimize ICL selection: a retrospective study.BMC ophthalmology · 2025Article
- Exploring Machine Learning Models for Vault Safety in ICL Implantation: A Comparative Analysis of Regression and Classification Models.Ophthalmology and therapy · 2025Article
- Prediction of postoperative vault after implantable collamer lens implantation with deep learning.International journal of ophthalmology · 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
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo aid doctors in selecting the optimal preoperative implantable collamer lens (ICL) size and to enhance the safety and surgical outcomes of ICL procedures, a clinical decision support system (CDSS) is proposed in our study.
designA retrospective study of patients after ICL surgery.
settingChina Tertiary Myopia Prevention and Control Center.
participants2772 eyes belonging to 1512 patients after ICL surgery. Data were collected between 2018 and 2022. OUTCOME MEASURES: A CDSS is constructed and used to predict vault at 1 month postoperatively and preoperative ICL dimensions using various artificial intelligence methods. Accuracy metrics as well as area under curve (AUC) parameters are used to determine the CDSS prediction methods.
resultsAmong the ICL size prediction models, conventional neural networks (CNNs) achieve the best prediction accuracy at 91.37% and exhibit the highest AUC of 0.842. Regarding the prediction model for vault values 1 month after surgery, CNN surpasses the other methods with an accuracy of 85.27%, which has the uppermost AUC of 0.815. Thus, we select CNN as the prediction algorithm for the CDSS.
conclusionsThis study introduces a CDSS to assist doctors in selecting the optimal ICL size for patients while improving the safety and postoperative outcomes of ICL surgery.
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