Evidence map›Paper›PMID 40741026›Full record

ArticleWorld journal of cardiology2025

Artificial intelligence-enabled single-lead electrocardiogram in early detection of ischemic heart disease.

Wen-Hua Song, Gary Tse, Kang-Yin Chen, Tong Liu

Abstract readEditorial
PubMed Full text at PMC Publisher
In one paragraph

Article in World journal of cardiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Wen-Hua SongTianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, The Second Hospital of Tianjin Medical University, Tianjin 300211, China.
Gary TseTianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, The Second Hospital of Tianjin Medical University, Tianjin 300211, China.
Kang-Yin ChenTianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, The Second Hospital of Tianjin Medical University, Tianjin 300211, China.
Tong LiuTianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, The Second Hospital of Tianjin Medical University, Tianjin 300211, China. liutongdoc@126.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the rapid advancement and widespread adoption of new artificial intelligence (AI) technologies, personalized medicine and more accurate diagnosis using medical imaging are now possible. Among its many applications, AI has shown remarkable potential in the analysis of electrocardiograms (ECGs), which provide essential insights into the electrical activity of the heart and allowing early detection of ischemic heart disease (IHD). Notably, single-lead ECG (SLECG) analysis has emerged as a key focus in recent research due to its potential for widespread and efficient screening. This editorial focuses on the latest research progress of AI-enabled SLECG utilized in the diagnosis of IHD.

Indexed as

Artificial intelligenceDiagnosisElectrocardiogramIschemic heart diseaseMachine learning

Identifiers

PMID40741026
PMCPMC12304860

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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 OpenQuestion graph.