Evidence map›Paper›PMID 40625551›Full record

ReviewBiomedical engineering letters2025

Artificial intelligence in ECG-based diagnosis of low left ventricular ejection fraction: a systematic review and meta-analysis.

Gayathiri R R, Arya Bhardwaj, R Pradeep Kumar, Bala Chakravarthy Neelapu, Kunal Pal, J Sivaraman

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Biomedical engineering letters, 2025. 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. 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.

Gayathiri R RDepartment of Biotechnology and Medical Engineering, National Institute of Technology Rourkela, Rourkela, Odisha 769008 India.
Arya BhardwajDepartment of Biotechnology and Medical Engineering, National Institute of Technology Rourkela, Rourkela, Odisha 769008 India.
R Pradeep KumarInstitute of Cardiac Care, MIOT International, Chennai, Tamil Nadu 600089 India.
Bala Chakravarthy NeelapuDepartment of Biotechnology and Medical Engineering, National Institute of Technology Rourkela, Rourkela, Odisha 769008 India.
Kunal PalDepartment of Biotechnology and Medical Engineering, National Institute of Technology Rourkela, Rourkela, Odisha 769008 India.
J SivaramanDepartment of Biotechnology and Medical Engineering, National Institute of Technology Rourkela, Rourkela, Odisha 769008 India.ORCID 0000-0002-8185-2672

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Detecting Left Ventricular Systolic Dysfunction (LVSD) is crucial for counteracting heart failure progression. While Electrocardiograms (ECG) are widely used, their standalone diagnostic accuracy is insufficient. Integrating Artificial Intelligence (AI) with ECG analysis offers a promising approach to enhance precision. A systematic review was conducted to assess AI-enabled ECG for LVSD detection. Of 394 initial studies, 19 qualified for the systematic review, with 17 incorporated into meta-analysis. Study quality was gauged using QUADAS-2. Univariate meta-analysis, Spearman correlation, and bivariate meta-analyses were performed, along with publication bias assessment. The pooled sensitivity and specificity for AI-enabled ECG models were 86.9% and 84.4%, respectively. Studies with an ejection fraction (EF) threshold of 35% had the highest sensitivity, while those with 50% showed lower sensitivity and specificity. A weak positive Spearman correlation was found across all studies (ρ = 0.374,

Indexed as

Artificial intelligenceConfidence intervalEjection fractionElectrocardiogramLeft ventricular systolic dysfunction

Identifiers

PMID40625551
PMCPMC12229370

What OpenQuestion holds

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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.