Evidence map›Paper›PMID 37835786›Full record

ArticleDiagnostics (Basel, Switzerland)2023

Usefulness of T2-Weighted Images with Deep-Learning-Based Reconstruction in Nasal Cartilage.

Yufan Gao, Weiyin Vivian Liu, Liang Li, Changsheng Liu, Yunfei Zha

Open access · goldAbstract read
In one paragraph

Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.6field-weighted citation impact, top 16% 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

4 citing papers in PubMed, 7 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

5 authors at 1 institution in 1 country.

Yufan GaoDepartment of Radiology, Renmin Hospital of Wuhan University, Wuhan 430060, China.ORCID 0009-0003-0246-356X
Weiyin Vivian LiuMR Research, GE Healthcare, Beijing 100176, China.ORCID 0000-0002-7917-7612
Liang LiDepartment of Radiology, Renmin Hospital of Wuhan University, Wuhan 430060, China.
Changsheng LiuDepartment of Radiology, Renmin Hospital of Wuhan University, Wuhan 430060, China.
Yunfei ZhaDepartment of Radiology, Renmin Hospital of Wuhan University, Wuhan 430060, China.
Wuhan University · CN

Funding

the National Natural Science Foundation of China 81871332the National Natural Science Foundation of China 82171895
6 · The paper itself

Abstract

objectiveThis study aims to evaluate the feasibility of visualizing nasal cartilage using deep-learning-based reconstruction (DLR) fast spin-echo (FSE) imaging in comparison to three-dimensional fast spoiled gradient-echo (3D FSPGR) images. MATERIALS AND

methodsThis retrospective study included 190 set images of 38 participants, including axial T1- and T2-weighted FSE images using DLR (T1WI

resultsBoth qualitative and quantitative results confirmed superior FSE

conclusionsTwo-dimensional equivalent-thin-slice T1- and T2-weighted images using DLR showed better image quality and shorter scan time than 3D FSPGR and conventional construction images in nasal cartilages. The anatomical details were preserved without losing clinical performance on diagnosis and prognosis, especially for pre-rhinoplasty planning.

Indexed as

deep learningmagnetic resonance imagingnasal cartilagerhinoplasty

Identifiers

PMID37835786
PMCPMC10572289
OpenAlexW4387058944

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.