ArticleInternational journal of ophthalmology2023
Vault predicting after implantable collamer lens implantation using random forest network based on different features in ultrasound biomicroscopy images.
Article in International journal of ophthalmology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 3 citations in OpenAlex.
- Prediction of postoperative vault after implantable collamer lens implantation with deep learning.International journal of ophthalmology · 2025Article
- Effect of the iris-ciliary angle on the prediction of the vault for phakic implantable collamer lens in the manufacturer's calculator.BMC ophthalmology · 2024Article
- Clinical prediction of insufficient vaults after implantable collamer lens implantation.International journal of ophthalmology · 2024Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimTo analyze ultrasound biomicroscopy (UBM) images using random forest network to find new features to make predictions about vault after implantable collamer lens (ICL) implantation.
methodsA total of 450 UBM images were collected from the Lixiang Eye Hospital to provide the patient's preoperative parameters as well as the vault of the ICL after implantation. The vault was set as the prediction target, and the input elements were mainly ciliary sulcus shape parameters, which included 6 angular parameters, 2 area parameters, and 2 parameters, distance between ciliary sulci, and anterior chamber height. A random forest regression model was applied to predict the vault, with the number of base estimators (n_estimators) of 2000, the maximum tree depth (max_depth) of 17, the number of tree features (max_features) of Auto, and the random state (random_state) of 40.0.
resultsAmong the parameters selected in this study, the distance between ciliary sulci had a greater importance proportion, reaching 52% before parameter optimization is performed, and other features had less influence, with an importance proportion of about 5%. The importance of the distance between the ciliary sulci increased to 53% after parameter optimization, and the importance of angle 3 and area 1 increased to 5% and 8% respectively, while the importance of the other parameters remained unchanged, and the distance between the ciliary sulci was considered the most important feature. Other features, although they accounted for a relatively small proportion, also had an impact on the vault prediction. After parameter optimization, the best prediction results were obtained, with a predicted mean value of 763.688 µm and an actual mean value of 776.9304 µm. The
conclusionA study based on UBM images using random forest network can be performed for prediction of the vault after ICL implantation and can provide some reference for ICL size selection.
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