Multi-modal deep-learning troponin prediction from electrocardiograms and demographic data
This study presents new opportunities for enhancing triage processes, enabling more rapid and accurate alerts to physicians regarding acute myocardial infarctions. The primary benefit of predicting Tn elevation lies in the objectivity of the label compared with compounded clinical outcomes and diagnoses.
Authors
- Aidin Rawshani (ORCID: https://orcid.org/0000-0002-4509-873X)
- Deepak L. Bhatt (ORCID: https://orcid.org/0000-0002-1278-6245)
- Truls Råmunddal (ORCID: https://orcid.org/0000-0001-9483-5962)
- Peter Lundgren (ORCID: https://orcid.org/0000-0003-0500-4070)
- Kristofer Skoglund (ORCID: https://orcid.org/0000-0002-9095-1218)
- Pétur Pétursson (ORCID: https://orcid.org/0000-0003-0807-9406)
- Erik Andersson (ORCID: https://orcid.org/0000-0003-2716-5502)
- Araz Rawshani (ORCID: https://orcid.org/0000-0003-2066-3533)
- Vibha Gupta (ORCID: https://orcid.org/0000-0002-3785-8380)
- Lukas Hilgendorf (ORCID: https://orcid.org/0009-0000-9518-3644)
Institutions
- National Academy of Medicine (US)
- Sahlgrenska University Hospital (SE)
- Forskning.no (Norway) (NO)
- Mount Sinai Hospital (US)
- University of Gothenburg (SE)
- Icahn School of Medicine at Mount Sinai (US)
Publication Details
- Journal
- European Heart Journal - Digital Health
- Published
- 2025-10-24
- DOI
- https://doi.org/10.1093/ehjdh/ztaf126
- Primary Topic
- Acute Myocardial Infarction Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Västra Götalandsregionen
- Svenska Läkaresällskapet
- Wallenberg Centre for Molecular and Translational Medicine