ArticleEuropean heart journal. Digital health2024
Automatic triage of twelve-lead electrocardiograms using deep convolutional neural networks: a first implementation study.
Article in European heart journal. Digital health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- AI-Based QRS Onset Detection in the Early Ventricular Activation Site ECGs.IEEE journal of biomedical and health informatics · 2026Article
- Objective Assessment of Pathological ECG Waveforms Using Gender-specific Normal Waveform Distributions.Arrhythmia & electrophysiology review · 2026Article
- Explainable deep learning-based classification of Wolff-Parkinson-White electrocardiographic signals.Frontiers in physiology · 2026Article
- Generative artificial intelligence for general practice; new potential ahead, but are we ready?The European journal of general practice · 2025Article
- Article
- Towards prehospital risk stratification using deep learning for ECG interpretation in suspected acute coronary syndrome.BMJ health & care informatics · 2025Article
- Deep learning for electrocardiogram interpretation: Bench to bedside.European journal of clinical investigation · 2025Review
- Advancements in artificial intelligence-driven techniques for interventional cardiology.Cardiology journal · 2024Review
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Authors and funding
7 authors.
Funding
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Abstract
Aims: Expert knowledge to correctly interpret electrocardiograms (ECGs) is not always readily available. An artificial intelligence (AI)-based triage algorithm (DELTAnet), able to support physicians in ECG prioritization, could help reduce current logistic burden of overreading ECGs and improve time to treatment for acute and life-threatening disorders. However, the effect of clinical implementation of such AI algorithms is rarely investigated. Methods and results: Adult patients at non-cardiology departments who underwent ECG testing as a part of routine clinical care were included in this prospective cohort study. DELTAnet was used to classify 12-lead ECGs into one of the following triage classes: normal, abnormal not acute, subacute, and acute. Performance was compared with triage classes based on the final clinical diagnosis. Moreover, the associations between predicted classes and clinical outcomes were investigated. A total of 1061 patients and ECGs were included. Performance was good with a mean concordance statistic of 0.96 (95% confidence interval 0.95-0.97) when comparing DELTAnet with the clinical triage classes. Moreover, zero ECGs that required a change in policy or referral to the cardiologist were missed and there was a limited number of cases predicted as acute that did not require follow-up (2.6%). Conclusion: This study is the first to prospectively investigate the impact of clinical implementation of an ECG-based AI triage algorithm. It shows that DELTAnet is efficacious and safe to be used in clinical practice for triage of 12-lead ECGs in non-cardiology hospital departments.
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