ArticleJournal of veterinary internal medicine
Use of Artificial Intelligence to Detect Cardiac Rhythm Disturbances in Athletes: A Scoping Review.
Article in Journal of veterinary internal medicine. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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.
Who cites it
2 citing papers in PubMed.
- Causal AI in Cardiac Arrhythmia: From Pattern Recognition to Mechanistic Insight.Clinical cardiology · 2026Review
- Use of Artificial Intelligence to Detect Cardiac Rhythm Disturbances in Athletes: A Scoping Review.Journal of veterinary internal medicineArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial 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.
objectivesEvaluate the application of AI to ECG interpretation for arrhythmia detection, with emphasis on current and potential use in athletic species, particularly horses. ANIMALS: About 17 studies were included: 13 involving humans, 3 in horses, and 1 in dogs.
methodsA 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.
resultsDeep 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. CONCLUSION AND CLINICAL IMPORTANCE: 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.