Evidence map›Paper›PMID 36923825›Full record

ArticleHeliyon2023

3D VOSNet: Segmentation of endoscopic images of the larynx with subsequent generation of indicators.

I-Miao Chen, Pin-Yu Yeh, Ya-Chu Hsieh, Ting-Chi Chang, Samantha Shih, Wen-Fang Shen, Chiun-Li Chin

Open access · goldAbstract read
In one paragraph

Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
2.3field-weighted citation impact, top 12% 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

1 citing paper in PubMed, 13 citations in OpenAlex.

  1. Review
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

7 authors at 1 institution in 1 country.

I-Miao ChenDepartment of Medical Informatics, Chung Shan Medical University, Taichung, Taiwan.
Pin-Yu YehDepartment of Medical Informatics, Chung Shan Medical University, Taichung, Taiwan.
Ya-Chu HsiehDepartment of Medical Informatics, Chung Shan Medical University, Taichung, Taiwan.
Ting-Chi ChangDepartment of Medical Informatics, Chung Shan Medical University, Taichung, Taiwan.
Samantha ShihMorrison Academy Taichung, Taichung, Taiwan.
Wen-Fang ShenDepartment of Medical Informatics, Chung Shan Medical University, Taichung, Taiwan.
Chiun-Li ChinDepartment of Medical Informatics, Chung Shan Medical University, Taichung, Taiwan.
Chung Shan Medical University · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Video laryngoscope is available for visualizing the motion of vocal cords and aid in the assessment of analyzing the larynx-related lesion preliminarily. Laryngeal Electromyography (EMG) needs to be performed to diagnose the factors of vocal cord paralysis, which may cause patient feeling unwell. Thus, the problem is the lack of credible larynx indicators to evaluate larynx-related diseases in the department of otolaryngology. Therefore, this paper aims to propose a 3D VOSNet model, which has the characteristics of sequence segmentation to extract the time-series features in the video laryngoscope. The 3D VOSNet model can keep the time-series features of three images before and after of the specific image to achieve translation and occlusion invariance, which explicitly signifies that our model can segment and classify each item in the video of laryngoscopy not affected by extrinsic causes such as shaking or occlusion during laryngoscope. Numerical results revealed that the testing accuracy rates of the glottal, right vocal cord, and the left vocal cord are 89.91%, 94.63%, and 93.48%, respectively. Our proposed model can segment glottal and vocal cords from the sequence of laryngoscopy. Finally, using the proposed algorithm computes six larynx indicators, which are the area of the glottal, area of vocal cords, length of vocal cords, deviation of length of vocal cords, and symmetry of the vocal cords. In order to assist otolaryngologists in staying credible and objective when making decisions

Indexed as

Laryngeal electromyographyTime-series featuresVideo laryngoscopeVocal cordX-indicators

Identifiers

PMID36923825
PMCPMC10009724
OpenAlexW4323038085

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.