AI4ECG
We are a research collaboration between the Department of Information Technology and the Department of Medical Sciences at Uppsala University. Our focus is the application of machine learning/AI to interpret ECG recordings.
A core ingredient of AI is data. We have a long-standing collaboration with the Telehealth Network of Minas Gerais, Brazil, which has given us access to large amounts of high-quality ECG data used to train high-performing AI models. We also have access to unique datasets from Swedish healthcare that include both medical histories and ECG recordings, enabling us to discover associations between ECG features and health conditions that are not conventionally diagnosed using ECG analysis.
Publications
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A deep learning ECG model for localization of occlusion myocardial infarction. Stefan Gustafsson, Antônio H. Ribeiro, Daniel Gedon, Petrus E. O. G. B. Abreu, Nicolas Pielawski, Gabriela M. M. Paixão, Antonio Luiz P. Ribeiro, Daniel Lindholm, Thomas B. Schön, Johan Sundström. medRxiv (2025). https://doi.org/10.1101/2025.09.11.25335407
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Evaluating Regression and Probabilistic Methods for ECG-based Electrolyte Prediction. Philipp Von Bachmann, Daniel Gedon, Fredrik K. Gustafsson, Antônio H. Ribeiro, Erik Lampa, Stefan Gustafsson, Johan Sundström, Thomas B. Schön. Scientific Reports 14, 15273 (2024). https://doi.org/10.1038/s41598-024-65223-w
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End-to-End Risk Prediction of Atrial Fibrillation from the 12-Lead ECG by Deep Neural Networks. Theogene Habineza, Antônio H. Ribeiro, Daniel Gedon, Joachim A. Behar, Antonio Luiz P. Ribeiro, Thomas B. Schön. Journal of Electrocardiology 81 (2023). https://doi.org/10.1016/j.jelectrocard.2023.09.011
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Screening for Chagas Disease from the Electrocardiogram Using a Deep Neural Network. Carl Jidling, Daniel Gedon, Thomas B. Schön, Claudia Di Lorenzo Oliveira, Clareci Silva Cardoso, Ariela Mota Ferreira, Luana Giatti, Sandhi Maria Barreto, Ester C. Sabino, Antônio L. P. Ribeiro, Antônio H. Ribeiro. PLOS Neglected Tropical Diseases (2023). https://doi.org/10.1371/journal.pntd.0011118
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Development and validation of deep learning ECG-based prediction of myocardial infarction in emergency department patients. Stefan Gustafsson, Daniel Gedon, Erik Lampa, Antônio H. Ribeiro, Martin J. Holzmann, Thomas B. Schön, Johan Sundström. Scientific Reports 12, 19615 (2022). https://doi.org/10.1038/s41598-022-24254-x
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Automatic diagnosis of the 12-lead ECG using a deep neural network. Antônio H. Ribeiro, Manoel Horta Ribeiro, Gabriela M. M. Paixão, Derick M. Oliveira, Paulo R. Gomes, Jéssica A. Canazart, Milton P. S. Ferreira, Carl R. Andersson, Peter W. Macfarlane, Wagner Meira Jr., Thomas B. Schön, Antonio Luiz P. Ribeiro. Nature Communications 11, 1760 (2020). https://doi.org/10.1038/s41467-020-15432-4
Team
New collaborations
In a new project funded by Wallenberg Launch Pad, we have a specific focus on clinical implementation. We are therefore interested in collaborations with clinicians, hospitals, and companies. Please reach out at andreas.lindholm@uu.se or sara.thiringer@it.uu.se to discuss opportunities.
Research funders
