Evidence mapPaperPMID 38822240Full record

ArticleBMC medical imaging2024

Evaluation of deep learning-based reconstruction late gadolinium enhancement images for identifying patients with clinically unrecognized myocardial infarction.

Xuefang Lu, Weiyin Vivian Liu, Yuchen Yan, Wenbing Yang, Changsheng Liu, Wei Gong, Guangnan Quan, Jiawei Jiang, Lei Yuan, Yunfei Zha

Erratum issuedAbstract read
In one paragraph

Article in BMC medical imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
field-weighted citation impact
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

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Xuefang Lu *Department of Radiology, Renmin Hospital of Wuhan University, No. 238 Jiefang Road, Wuchang District, Wuhan, 430060, China.
Weiyin Vivian Liu *MR Research, GE Healthcare, Beijing, China.
Yuchen YanDepartment of Radiology, Renmin Hospital of Wuhan University, No. 238 Jiefang Road, Wuchang District, Wuhan, 430060, China.
Wenbing YangDepartment of Radiology, Renmin Hospital of Wuhan University, No. 238 Jiefang Road, Wuchang District, Wuhan, 430060, China.
Changsheng LiuDepartment of Radiology, Renmin Hospital of Wuhan University, No. 238 Jiefang Road, Wuchang District, Wuhan, 430060, China.
Wei GongDepartment of Radiology, Renmin Hospital of Wuhan University, No. 238 Jiefang Road, Wuchang District, Wuhan, 430060, China.
Guangnan QuanGE Healthcare, Beijing, China.
Jiawei JiangComputer School, Wuhan University, Wuhan, China.
Lei YuanInformation Center, Renmin Hospital of Wuhan University, Wuhan, China.
Yunfei ZhaDepartment of Radiology, Renmin Hospital of Wuhan University, No. 238 Jiefang Road, Wuchang District, Wuhan, 430060, China. zhayunfei999@126.com.

Funding

Interdisciplinary Innovative Talents Foundation from Renmin Hospital of Wuhan University JCRCZN-2022-013National Natural Science Foundation of China Grant Numbers 82171895
6 · The paper itself

Abstract

backgroundThe presence of infarction in patients with unrecognized myocardial infarction (UMI) is a critical feature in predicting adverse cardiac events. This study aimed to compare the detection rate of UMI using conventional and deep learning reconstruction (DLR)-based late gadolinium enhancement (LGE

methodsThis prospective study included 98 patients (68 men; mean age: 55.8 ± 8.1 years) with suspected UMI treated at our hospital from April 2022 to August 2023. LGE

resultsThe SNR

conclusionsSTRM selection for LGE

Indexed as

Contrast MediaDeep LearningMyocardial InfarctionAgedFemaleGadoliniumHumansMagnetic Resonance ImagingMaleMiddle AgedProspective StudiesSignal-To-Noise RatioContrast MediaGadoliniumDeep learning reconstructionDiagnostic efficacyLate gadolinium enhancementMagnetic resonance imagingUnrecognized myocardial infarction

Identifiers

PMID38822240
PMCPMC11141010

What Socratic holds

Textmetadata
LicenceCC BY
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Registered trials

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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 Socratic graph.