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Automatic triage of twelve-lead electrocardiograms using deep convolutional neural networks: a first implementation study.
van de Leur, Rutger R; van Sleuwen, Meike T G M; Zwetsloot, Peter-Paul M; van der Harst, Pim; Doevendans, Pieter A; Hassink, Rutger J; van Es, René.
Afiliação
  • van de Leur RR; Department of Cardiology, University Medical Center Utrecht, Heidelberglaan 100, Utrecht 3584 CX, The Netherlands.
  • van Sleuwen MTGM; Department of Cardiology, University Medical Center Utrecht, Heidelberglaan 100, Utrecht 3584 CX, The Netherlands.
  • Zwetsloot PM; Department of Cardiology, University Medical Center Utrecht, Heidelberglaan 100, Utrecht 3584 CX, The Netherlands.
  • van der Harst P; Department of Cardiology, University Medical Center Utrecht, Heidelberglaan 100, Utrecht 3584 CX, The Netherlands.
  • Doevendans PA; Department of Cardiology, University Medical Center Utrecht, Heidelberglaan 100, Utrecht 3584 CX, The Netherlands.
  • Hassink RJ; Netherlands Heart Institute, Utrecht, The Netherlands.
  • van Es R; Central Military Hospital, Utrecht, The Netherlands.
Eur Heart J Digit Health ; 5(1): 89-96, 2024 Jan.
Article em En | MEDLINE | ID: mdl-38264701
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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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Observational_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Eur Heart J Digit Health Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Holanda País de publicação: Reino Unido
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Observational_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Eur Heart J Digit Health Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Holanda País de publicação: Reino Unido