Predicting troponin biomarker elevation from electrocardiograms using a deep neural network
The model exhibited clinically meaningful performance, notably its high negative predictive accuracy. Therefore, clinical use of comparable neural networks in first-line, quick-response triage of patients with chest pain or dyspnoea appears as a valuable option in future medical practice.
Authors
- Aidin Rawshani (ORCID: https://orcid.org/0000-0002-4509-873X)
- Christian Dworeck (ORCID: https://orcid.org/0000-0002-2012-5818)
- Truls Råmunddal (ORCID: https://orcid.org/0000-0001-9483-5962)
- Charlotta Ljungman (ORCID: https://orcid.org/0000-0002-2785-1919)
- Jan Borén (ORCID: https://orcid.org/0000-0003-0786-8091)
- Peter Lundgren (ORCID: https://orcid.org/0000-0003-0500-4070)
- Kristofer Skoglund (ORCID: https://orcid.org/0000-0002-9095-1218)
- Vibha Gupta (ORCID: https://orcid.org/0000-0001-6955-9134)
- Pétur Pétursson (ORCID: https://orcid.org/0000-0003-0807-9406)
- Zacharias Mandalenakis (ORCID: https://orcid.org/0000-0001-5093-9743)
- Elmir Ömerovic (ORCID: https://orcid.org/0000-0002-3875-8621)
- Erik Andersson (ORCID: https://orcid.org/0000-0003-2716-5502)
- Araz Rawshani (ORCID: https://orcid.org/0000-0003-2066-3533)
- Gustav Smith
- Lukas Hilgendorf (ORCID: https://orcid.org/0009-0000-9518-3644)
Institutions
- Lund University (SE)
- Sahlgrenska University Hospital (SE)
- University of Gothenburg (SE)
Publication Details
- Journal
- Open Heart
- Published
- 2024-10-01
- DOI
- https://doi.org/10.1136/openhrt-2024-002937
- Citations
- 4
- Primary Topic
- Acute Myocardial Infarction Research
- Type
- article
- Field-Weighted Citation Impact
- 1.61
Funders
- Wallenberg Centre for Molecular and Translational Medicine