Evidence map›Paper›PMID 31378779›Full record

Observational studyMedical science monitor : international medical journal of experimental and clinical research2019

Evaluation of Early Gadolinium Enhancement (EGE) and Cardiac Functional Parameters in Cine-Magnetic Resonance Imaging (MRI) on Artificial Intelligence in Patients with Acute Myocarditis: A Case-Controlled Observational Study.

Wei-Feng Yuan, Xin-Xiang Zhao, Fu-Bi Hu, Chen Bai, Fang Tang

Open access · hybridAbstract readObservational Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in Medical science monitor : international medical journal of experimental and clinical research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
0.5field-weighted citation impact, top 32% 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

6 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Review
  4. Artificial intelligence: Applications in cardio-oncology and potential impact on racial disparities.American heart journal plus : cardiology research and practice · 2024
    Article
  5. Imaging Methods: Magnetic Resonance Imaging.Circulation. Cardiovascular imaging · 2023
    Review
  6. Artificial intelligence and imaging: Opportunities in cardio-oncology.American heart journal plus : cardiology research and practice · 2022
    Article
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 2 institutions in 1 country.

Wei-Feng YuanDepartment of Radiology, The First Affiliated Hospital of Chengdu Medical College, Chengdu, Sichuan, China (mainland).
Xin-Xiang ZhaoDepartment of Radiology, The Second Affiliated Hospital of Kunming Medical University, Kunming, Yunnan, China (mainland).
Fu-Bi HuDepartment of Radiology, The First Affiliated Hospital of Chengdu Medical College, Chengdu, Sichuan, China (mainland).
Chen BaiDepartment of Radiology, The First Affiliated Hospital of Chengdu Medical College, Chengdu, Sichuan, China (mainland).
Fang TangDepartment of Radiology, The First Affiliated Hospital of Chengdu Medical College, Chengdu, Sichuan, China (mainland).
First Affiliated Hospital of Chengdu Medical College · CNKunming Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

<strong>BACKGROUND</strong> The diagnosis of myocarditis is challenging, and the treatment is generally delayed due to misdiagnosis or missed diagnosis. Endomyocardial biopsy (EMB) is not a specific or sensitive method. A case-controlled observational study was conducted to evaluate early gadolinium enhancement (EGE) and left ventricular functional parameters on Artificial Intelligence in cine-MRI in patients with acute myocarditis. <strong>MATERIAL AND METHODS</strong> We selected 21 patients with pathologically proven acute myocarditis. We analyzed the EGE findings (total/serial number and location of positive-segments using the 17-segment model according to the American Heart Association) and clinical characteristics (symptoms, arrhythmias in ECG, coronary angiography, and EMB). All patients were divided into positive EGE and negative EGE groups to analyze left ventricular functional parameters (LVEF, FS, LVEDD, LVEDV, LVESV, LVMM, LVSV, CO, and CI) on Artificial Intelligence. <strong>RESULTS</strong> We enrolled 21 patients (11 males) with a mean age of 32.6±9.8 years (range, 16 to 51 years). Abnormalities on EGE were found in 2/3 of patients, involving 41 segments among multiple locations on the myocardium. The differences in LVEF (40.2±10.2%

Indexed as

AdolescentAdultArtificial IntelligenceCase-Control StudiesContrast MediaFemaleGadoliniumGadolinium DTPAHeartHumansMagnetic Resonance Imaging, CineMaleMicrocirculationMiddle AgedMyocarditisMyocardiumContrast MediaGadoliniumGadolinium DTPA

Identifiers

PMID31378779
PMCPMC6671557
OpenAlexW2962877807

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.