ArticleInternational journal of ophthalmology2025
Prediction of postoperative vault after implantable collamer lens implantation with deep learning.
Article in International journal of ophthalmology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- A comparative study on the influence of age-specific ocular anatomical differences on the prediction of post-ICL surgery vault.BMC ophthalmology · 2026Article
- Retrospective Validation of an AI Based Tool for Vault Prediction After Implantable Collamer Lens Surgery for High Myopia.Clinical ophthalmology (Auckland, N.Z.) · 2026Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimTo predict the post-operative vault and the suitable size of the implantable collamer lens (ICL) by comparing the performance of multiple artificial intelligence (AI) algorithms.
methodsA retrospective analysis of 83 patients with 132 eyes was conducted from 2020 to 2023. All patients underwent implantation of EVO-V4C ICLs. ICLs were selected based on STAAR's recommended formula. Postoperative vault values were measured using anterior segment optical coherence tomography (ASOCT). First, feature selection was performed on patients' preoperative examination parameters to identify those most closely related to postoperative vault and incorporate them into the machine learning model. Subsequently, four regression models, namely MLP, XGBoost, RFR, and KNN, were employed to predict the vault, and their predictive performances were compared. The ICL size was set as the prediction target, with the vault and other input features serving as new inputs for predicting the ICL size.
resultsAmong all preoperative parameters, 16 parameters were most closely related to postoperative vault and were included in the prediction model. In vault prediction, XGBoost performed the best in the regression model (
conclusionAI effectively predicts postoperative vault and determines ICL size. XGBoost outperforms other machine-learning algorithms tested. Its accurate predictions help ophthalmologists choose the right ICL size, ensuring proper vaulting.
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