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Journal of veterinary internal medicine2025; 39(6); e70257; doi: 10.1111/jvim.70257

Use of Artificial Intelligence to Detect Cardiac Rhythm Disturbances in Athletes: A Scoping Review.

Abstract: Artificial intelligence (AI) is increasingly used to enhance electrocardiogram (ECG) interpretation in human medicine. In equine athletes, exercise-associated arrhythmias are common and linked to sudden cardiac death at rates higher than in humans. However, ECG interpretation in horses remains time-consuming and subjective, with the clinical relevance of mild rhythm disturbances often unclear. Objective: Evaluate the application of AI to ECG interpretation for arrhythmia detection, with emphasis on current and potential use in athletic species, particularly horses. Methods: About 17 studies were included: 13 involving humans, 3 in horses, and 1 in dogs. Methods: A scoping review of relevant, peer-reviewed studies published between 2000 and 2024 was conducted to identify research applying AI to ECG interpretation for arrhythmia detection. Studies were assessed for species, AI model type, diagnostic accuracy, and relevance to ECGs recorded during exercise. Primary outcomes included arrhythmia detection performance and applicability to veterinary medicine. Results: Deep learning models, including convolutional neural networks, achieved accuracies ranging from 79.4% to 98.6% in studies of humans. Research in horses showed encouraging results using restitution analysis and transfer learning approaches. However, small sample sizes and species-specific ECG morphology remain major limitations to broader application in veterinary medicine. Conclusions: Artificial intelligence holds promise for enhancing the accuracy and efficiency of arrhythmia detection in ECGs of equine athletes. Development of species-specific algorithms may facilitate real-time monitoring of cardiac function during exercise, supporting improved cardiovascular assessment in athletic horses.
© 2025 The Author(s). Journal of Veterinary Internal Medicine published by Wiley Periodicals LLC on behalf of American College of Veterinary Internal Medicine.
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Publication Date: 2025-09-29 PubMed ID: 41017277PubMed Central: PMC12477403DOI: 10.1111/jvim.70257Google Scholar: Lookup
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  • Journal Article
  • Scoping Review

Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

Overview

  • This research article reviews the use of artificial intelligence (AI) for detecting cardiac rhythm disturbances, specifically arrhythmias, in athletes by interpreting electrocardiograms (ECGs).
  • It focuses on comparing AI applications in human medicine with potential uses in veterinary medicine, especially in athletic horses, where arrhythmias are common and linked to sudden cardiac death.

Introduction and Background

  • Artificial intelligence is increasingly integrated into medical diagnostics to improve interpretation accuracy and efficiency, especially for ECGs, which are critical for identifying heart rhythm disturbances.
  • In human athletes, AI has already been applied with promising results for automated arrhythmia detection, aiding early diagnosis and treatment.
  • In equine athletes (horses), arrhythmias during exercise are quite common and pose a higher risk of sudden cardiac death compared to humans.
  • Despite this, interpreting equine ECGs remains a challenge due to:
    • The subjective and time-consuming nature of manual ECG interpretation
    • The uncertain clinical significance of mild rhythm disturbances
    • Species-specific differences in ECG morphology compared to humans

Objective

  • The main goal was to evaluate how AI has been applied to improve ECG interpretation and arrhythmia detection, with a focus on its current and potential use in athletic species like horses.

Methods

  • A scoping review was conducted of peer-reviewed studies published from 2000 to 2024.
  • Relevant studies involving AI algorithms for ECG arrhythmia detection were identified.
  • A total of 17 studies were included in the review:
    • 13 on humans
    • 3 on horses
    • 1 on dogs
  • Each study was evaluated based on several criteria:
    • Species studied (human, equine, canine)
    • Type of AI model used
    • Diagnostic accuracy achieved
    • Relevance and applicability to ECGs recorded during exercise
  • Primary outcomes focused on arrhythmia detection performance and potential veterinary applications.

Results

  • AI models, especially deep learning techniques like convolutional neural networks (CNNs), demonstrated high accuracy in human studies covering a wide range of arrhythmias:
    • Accuracy ranged from 79.4% up to 98.6%
    • These models facilitated efficient and objective ECG interpretation in humans
  • In horses, preliminary research showed promising results using two main AI approaches:
    • Restitution analysis – studying changes in heartbeat properties over time
    • Transfer learning – leveraging AI models pre-trained on human data adapted to equine ECGs
  • Despite these advances, there were significant limitations for veterinary application:
    • Small sample sizes in equine studies limited generalizability
    • Distinct ECG morphology of horses differed markedly from humans, complicating direct application of human AI models

Conclusions and Implications

  • Artificial intelligence shows significant promise to enhance detection of cardiac rhythm disturbances in equine athletes by providing rapid, objective, and accurate ECG analysis.
  • Developing species-specific AI algorithms tailored to equine ECG characteristics is necessary to overcome current limitations and improve diagnostic accuracy.
  • Such advances could enable real-time monitoring of cardiac function during exercise, supporting better cardiovascular health assessment and management in athletic horses.
  • In summary, adapting AI technology from human medicine has the potential to revolutionize cardiac care in veterinary sports medicine, enhancing both diagnosis and prevention of sudden cardiac events in horses.

Cite This Article

APA
Kapusniak A, Lara NM, Hitchens PL, Bailey S, Nath L, Franklin S. (2025). Use of Artificial Intelligence to Detect Cardiac Rhythm Disturbances in Athletes: A Scoping Review. J Vet Intern Med, 39(6), e70257. https://doi.org/10.1111/jvim.70257

Publication

Journal of veterinary internal medicine
ISSN: 1939-1676
NlmUniqueID: 8708660
Country: United States
Language: English
Volume: 39
Issue: 6
Pages: e70257
PII: e70257

Researcher Affiliations

Kapusniak, Amie
  • School of Animal and Veterinary Science, University of Adelaide, Roseworthy, SA, Australia.
Lara, Natalia Medrano
  • Equine Centre, Melbourne Veterinary School, Werribee, VIC, Australia.
Hitchens, Peta L
  • Equine Centre, Melbourne Veterinary School, Werribee, VIC, Australia.
Bailey, Simon
  • Equine Centre, Melbourne Veterinary School, Werribee, VIC, Australia.
Nath, Laura
  • School of Animal and Veterinary Science, University of Adelaide, Roseworthy, SA, Australia.
Franklin, Samantha
  • School of Animal and Veterinary Science, University of Adelaide, Roseworthy, SA, Australia.

MeSH Terms

  • Animals
  • Dogs
  • Humans
  • Arrhythmias, Cardiac / veterinary
  • Arrhythmias, Cardiac / diagnosis
  • Artificial Intelligence
  • Athletes
  • Electrocardiography / veterinary
  • Horse Diseases / diagnosis
  • Horses
  • Physical Conditioning, Animal

Conflict of Interest Statement

Authors declare no off‐label use of antimicrobials. The authors declare no conflicts of interest.

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