Evidence map›Paper›PMID 38151930›Full record

ArticleSheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi2023

[Artificial intelligence in wearable electrocardiogram monitoring].

Xingyao Wang, Qian Li, Caiyun Ma, Shuo Zhang, Yujie Lin, Jianqing Li, Chengyu Liu

Abstract readEnglish Abstract
PubMed Full text at PMC Publisher
In one paragraph

Article in Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi, 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
–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

1 citing paper in PubMed.

  1. 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

7 authors.

Xingyao WangSchool of Instrument Science and Engineering, Southeast University, Nanjing 210096, P. R. China.
Qian LiSchool of Instrument Science and Engineering, Southeast University, Nanjing 210096, P. R. China.
Caiyun MaSchool of Instrument Science and Engineering, Southeast University, Nanjing 210096, P. R. China.
Shuo ZhangSchool of Instrument Science and Engineering, Southeast University, Nanjing 210096, P. R. China.
Yujie LinSchool of Instrument Science and Engineering, Southeast University, Nanjing 210096, P. R. China.
Jianqing LiSchool of Instrument Science and Engineering, Southeast University, Nanjing 210096, P. R. China.
Chengyu LiuSchool of Instrument Science and Engineering, Southeast University, Nanjing 210096, P. R. China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Electrocardiogram (ECG) monitoring owns important clinical value in diagnosis, prevention and rehabilitation of cardiovascular disease (CVD). With the rapid development of Internet of Things (IoT), big data, cloud computing, artificial intelligence (AI) and other advanced technologies, wearable ECG is playing an increasingly important role. With the aging process of the population, it is more and more urgent to upgrade the diagnostic mode of CVD. Using AI technology to assist the clinical analysis of long-term ECGs, and thus to improve the ability of early detection and prediction of CVD has become an important direction. Intelligent wearable ECG monitoring needs the collaboration between edge and cloud computing. Meanwhile, the clarity of medical scene is conducive for the precise implementation of wearable ECG monitoring. This paper first summarized the progress of AI-related ECG studies and the current technical orientation. Then three cases were depicted to illustrate how the AI in wearable ECG cooperate with the clinic. Finally, we demonstrated the two core issues-the reliability and worth of AI-related ECG technology and prospected the future opportunities and challenges.

Indexed as

Cardiovascular DiseasesWearable Electronic DevicesArtificial IntelligenceElectrocardiographyHumansReproducibility of ResultsArtificial intelligenceElectrocardiogram monitoringSmart medicalWearable electrocardiogram

Identifiers

PMID38151930
PMCPMC10753313

What OpenQuestion holds

Textmetadata
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.