Evidence map›Paper›PMID 41198945›Full record

ArticleNPJ digital medicine2025

Interpretable arrhythmia detection in ECG scans using deep learning ensembles: a genetic programming approach.

Arkadiusz Czerwinski, Damian Kucharski, Agata M Wijata, Hanadi Aldosari, Frans Coenen, Dhiraj Gupta, Lu Fu, Weidong Lin, Yumei Xue, Jacek Kawa and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

13 authors.

Arkadiusz Czerwinski *Department of Algorithmics and Software, Silesian University of Technology, Gliwice, Poland.
Damian Kucharski *Department of Algorithmics and Software, Silesian University of Technology, Gliwice, Poland.
Agata M WijataFaculty of Biomedical Engineering, Silesian University of Technology, Zabrze, Poland. Agata.Wijata@polsl.pl.
Hanadi AldosariDepartment of Computer Science, University of Liverpool, Liverpool, UK.
Frans CoenenDepartment of Computer Science, University of Liverpool, Liverpool, UK.
Dhiraj GuptaLiverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, Liverpool, UK.
Lu FuDepartment of Cardiology, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
Weidong LinDepartment of Cardiology, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
Yumei XueDepartment of Cardiology, Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
Jacek KawaFaculty of Biomedical Engineering, Silesian University of Technology, Zabrze, Poland.
Yalin ZhengLiverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, Liverpool, UK.
Gregory Y H LipLiverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, Liverpool, UK.
Jakub NalepaDepartment of Algorithmics and Software, Silesian University of Technology, Gliwice, Poland. Jakub.Nalepa@polsl.pl.

Funding

Academic Computer Centre Cyfronet, AGH University of Science and Technology PLG/2024/017393European Funds for Silesia 2021-2027 Program FESL.10.25-IZ.01-07G5/23Silesian University of Technology Grant for maintaining and developing research potential
6 · The paper itself

Abstract

Cardiovascular diseases remain the leading cause of death in developed countries. This study introduces deep learning ensembles for arrhythmia detection and atrial fibrillation (AF) recurrence prediction from electrocardiogram scans, supported by explainable artificial intelligence (XAI) methods. Validation used two datasets: Guangdong Provincial People's Hospital, China (Dataset G, 1172 patients, 71.4 ± 6.3 years, 66% women, 20.5% with arrhythmia) and Liverpool Heart and Chest Hospital, UK (L, 909 patients, 60.5 ± 10.71 years, 33% women, 29.7% with arrhythmia). Our ensembles outperformed individual and voting models with the area under the receiver operating characteristic curve (ROC-AUC): 0.980 (95%CI: 0.956-0.998, p = 0.03) for Dataset G, 0.799 (95%CI: 0.737-0.856, p = 0.07) for Dataset L. The models trained on combined training sets achieved ROC-AUC: 0.980 (95%CI: 0.952-1.0) and 0.800 (95% CI: 0.739-0.861) for the G and L test sets. Precision-recall AUC for AF recurrence was 0.765 (95%CI: 0.669-0.849) for ensembles vs. 0.737 (95%CI: 0.648-0.821) for individual models. XAI enhanced interpretability for clinical applications.

Identifiers

PMID41198945
PMCPMC12592347

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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.