Evidence map›Paper›PMID 37854385›Full record

ArticleInternational journal of ophthalmology2023

Vault predicting after implantable collamer lens implantation using random forest network based on different features in ultrasound biomicroscopy images.

Bin Fang, Qiu-Jian Zhu, Hui Yang, Li-Cheng Fan

Open access · goldAbstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.7field-weighted citation impact, top 28% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 3 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 1 institution in 1 country.

Bin FangSchool of Mechanical and Electrical Engineering, Soochow University, Suzhou 215100, Jiangsu Province, China.
Qiu-Jian ZhuLixiang Eye Hospital of Soochow University, Suzhou 215021 Jiangsu Province, China.
Hui YangSchool of Mechanical and Electrical Engineering, Soochow University, Suzhou 215100, Jiangsu Province, China.
Li-Cheng FanSchool of Mechanical and Electrical Engineering, Soochow University, Suzhou 215100, Jiangsu Province, China.
Soochow University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

implantable collamer lensrandom forest networkultrasound biomicroscopy imagesvault prediction

Identifiers

PMID37854385
PMCPMC10559038
OpenAlexW4386922683

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.