Evidence map›Paper›PMID 36658623›Full record

ArticleEuropean journal of medical research2023

The predictive value of deep learning-based cardiac ultrasound flow imaging for hypertrophic cardiomyopathy complicating arrhythmias.

Haotang Wu, Zhiyong Huang, Juanjuan Liu, Jiancheng Dai, Yong Zhao, Weiquan Luo

Open access · goldAbstract read
In one paragraph

Article in European journal of medical research, 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
0.9field-weighted citation impact, top 25% 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, 4 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

6 authors at 1 institution in 1 country.

Haotang WuDepartment of Ultrasound, Zhongshan Hospital of Traditional Chinese Medicine, No. 3, Kangxin Road, West District, Zhongshan, 528400, China.
Zhiyong HuangDepartment of Ultrasound, Zhongshan Hospital of Traditional Chinese Medicine, No. 3, Kangxin Road, West District, Zhongshan, 528400, China.
Juanjuan LiuDepartment of Ultrasound, Zhongshan Hospital of Traditional Chinese Medicine, No. 3, Kangxin Road, West District, Zhongshan, 528400, China.
Jiancheng DaiDepartment of Ultrasound, Zhongshan Hospital of Traditional Chinese Medicine, No. 3, Kangxin Road, West District, Zhongshan, 528400, China.
Yong ZhaoDepartment of Internal Medicine-Cardiovascular, Zhongshan Hospital of Traditional Chinese Medicine, Zhongshan, 528400, China.
Weiquan LuoDepartment of Ultrasound, Zhongshan Hospital of Traditional Chinese Medicine, No. 3, Kangxin Road, West District, Zhongshan, 528400, China. luoweiquantg@163.com.
Zhongshan Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo investigate the predictive value of deep learning-based cardiac ultrasound flow imaging for hypertrophic cardiomyopathy (HCM) complicated by arrhythmias.

methodsThe clinical data of 158 patients with hypertrophic cardiomyopathy were retrospectively collected from July 2019 to December 2021, and additionally divided into training group 106 cases, validation group 26 cases and test group 26 cases according to the ratio of 4:1:1, and divided into concurrent and non-concurrent groups according to whether they were complicated by arrhythmia or not, respectively. General data of patients (age, gender, BMI, systolic blood pressure, diastolic blood pressure, HR) were collected, a deep learning model for cardiac ultrasound flow imaging was established, and image data, LVEF, LAVI, E/e', vortex area change rate, circulation intensity change rate, mean blood flow velocity, and mean EL value were extracted.

resultsThe differences in general data (age, gender, BMI, systolic blood pressure, diastolic blood pressure, HR) between the three groups were not statistically significant, P > 0.05. The differences in age, gender, BMI, systolic blood pressure, diastolic blood pressure, HR between the patients in the concurrent and non-concurrent groups in the training group were not statistically significant, P > 0.05.

conclusionsDeep learning-based cardiac ultrasound flow imaging can identify cardiac ultrasound images more accurately and has a high predictive value for arrhythmias complicating hypertrophic cardiomyopathy, and vortex area change rate, circulation intensity change rate, mean flow velocity, mean EL, LAVI, and E/e' are all risk factors for arrhythmias complicating hypertrophic cardiomyopathy.

Indexed as

Cardiomyopathy, HypertrophicDeep LearningArrhythmias, CardiacEchocardiography, DopplerHumansRetrospective StudiesArrhythmiasCardiac ultrasound flow imagingDeep learningHypertrophic cardiomyopathyPredictive value

Identifiers

PMID36658623
PMCPMC9850559
OpenAlexW4317425752

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.