QTcNet: a deep learning model for direct heart rate corrected QT interval estimation
QTcNet, trained on large-scale algorithmically labelled data, consistently outperformed conventional algorithms across three independent, external validation datasets. Fine-tuning of QTcNet may adapt the model to the characteristics of specific cohorts but reduces external validity in other cohorts. We openly release the full model and code, along with a ready-to-use online implementation at https://qtcnet.uni-muenster.de, facilitating further research and community-driven improvement.
Authors
- Lars Eckardt (ORCID: https://orcid.org/0000-0002-3735-8503)
- Julian Varghese (ORCID: https://orcid.org/0000-0002-7206-3719)
- Maxim Zotov (ORCID: https://orcid.org/0009-0002-8167-2863)
- Dominik Heider (ORCID: https://orcid.org/0000-0002-3108-8311)
- Lucas Plagwitz (ORCID: https://orcid.org/0000-0001-7626-8853)
- Antonius Büscher (ORCID: https://orcid.org/0000-0003-1774-4252)
- Florian Doldi (ORCID: https://orcid.org/0000-0002-5237-6283)
- Lucas Bickmann (ORCID: https://orcid.org/0000-0001-9043-7844)
- Jannes Magerfleisch
Institutions
- University of Münster (DE)
- University Hospital Münster (DE)
- Otto-von-Guericke-Universität Magdeburg (DE)
Publication Details
- Journal
- EP Europace
- Published
- 2025-10-25
- DOI
- https://doi.org/10.1093/europace/euaf274
- Citations
- 2
- Primary Topic
- Heart Rate Variability and Autonomic Control
- Type
- article
- Field-Weighted Citation Impact
- 2.78