SwissCardIA


Cardiovascular diseases remain the leading cause of death worldwide. New, innovative approaches are needed to drive progress in this field. This has led to the genesis of the SwissCardIA collaboration, focusing on the use of artificial intelligence (AI) to improve the diagnosis, evaluation, and management of cardiovascular disease.

We believe that progress in this area requires a broad expertise to tackle the complexity of the tasks. SwissCardIA brings together a multidisciplinary research team with expertise in three key domains:

  1. Machine learning (ML) and data science (DS) (EPFL)
  2. Numerical modelling (EPFL)
  3. Clinical cardiology (CHUV, Unisante)

Moreover, SwissCardIA benefits from the support of the Center for Intelligent Systems (CIS) at EPFL.

Latest News

    We plan to start soon an AI-assisted cohort in cardiology - more information coming soon

Publications and Reports

  1. CM-UNet: a self-supervised learning-based model for coronary artery segmentation in X-ray angiography.
    Camille Challier, Xiaowu Sun, Thabo Mahendiran, Ortal Senouf, Bernard De Bruyne, Denise Auberson, Olivier Müller, Stephane Fournier, Pascal Frossard, Emmanuel Abbé, Dorina Thanou. In 2025 International Conference of the IEEE Engineering in Medicine and Biology Society (Oral).
  2. AngioPy Segmentation: An open-source, user-guided deep learning tool for coronary artery segmentation.
    Mahendiran, Thabo, Dorina Thanou, Ortal Senouf, Yassine Jamaa, Stephane Fournier, Bernard De Bruyne, Emmanuel Abbé, Olivier Muller, and Edward Andò. International journal of cardiology 418 (2025): 132598.
  3. Graph Neural Network based Future Clinical Events Prediction from Invasive Coronary Angiography.
    Xiaowu Sun, Theofilos Belmpas, Ortal Senouf, Emmanuel Abbé, Pascal Frossard, Bernard De Bruyne, Olivier Muller, Stéphane Fournier, Thabo Mahendiran, Dorina Thanou. In 2024 IEEE International Symposium on Biomedical Imaging (ISBI), pp. 1-5. IEEE, 2024.
  4. Anatomy-informed multimodal learning for myocardial infarction prediction.
    Ivan-Daniel Sievering, Ortal Senouf, Thabo Mahendiran, David Nanchen, Stephane Fournier, Olivier Muller, Pascal Frossard, Emmanuel Abbé, Dorina Thanou. IEEE open journal of engineering in medicine and biology (2024).
  5. A study of ChatGPT in facilitating Heart Team decisions on severe aortic stenosis.
    Adil Salihu, David Meier, Nathalie Noirclerc, Ioannis Skalidis, Sarah Mauler-Wittwer, Frédérique Recordon, Matthias Kirsch, Christan Roguelov, Alexandre Berger, Xiaowu Sun, Emmanuel Abbe, Carlo Marcucci, Valentina Rancati, Lorenzo Rosner, Emmanuelle Scala, David C. Rotzinger, Marc Humbert, Olivier Muller, Henri Lu, Stephane Fournier. EuroIntervention 20, no. 8 (2024): e496-e503.
  6. Deep learning-based prediction of future myocardial infarction using invasive coronary angiography: a feasibility study.
    Thabo Mahendiran, Dorina Thanou, Ortal Senouf, David Meier, Nicolas Dayer, Fahrang Aminfar, Denise Auberson, Omar Raita, Pascal Frossard, Mattia Pagnoni, Stéphane Cook, Bernard De Bruyne, Olivier Muller, Emmanuel Abbé, Stephane Fournier. Open Heart 10, no. 1 (2023).
  7. Towards AI-assisted cardiology: a reflection on the performance and limitations of using large language models in clinical decision-making.
    Adil Salihu, Mehdi Ali Gadiri, Ioannis Skalidis, David Meier, Denise Auberson, Annick Fournier, Romain Fournier, Dorina Thanou, Emmanuel Abbé, Olivier Muller, Stephane Fournier. Eurointervention 19, no. 10 (2023): e798.
  8. Can knowledge transfer techniques compensate for the limited myocardial infarction data by leveraging haemodynamics? An in silico study.
    Riccardo Tenderini, Federico Betti, Ortal Yona Senouf, Olivier Muller, Simone Deparis, Annalisa Buffa, Emmanuel Abbé. In International Conference on Artificial Intelligence in Medicine, pp. 218-228. Cham: Springer Nature Switzerland, 2023.
  9. ChatGPT takes on the European Exam in Core Cardiology: an artificial intelligence success story?
    Ioannis Skalidis, Aurelien Cagnina, Wongsakorn Luangphiphat, Thabo Mahendiran, Olivier Muller, Emmanuel Abbe, Stephane Fournier. European Heart Journal-Digital Health 4, no. 3 (2023): 279-281.
  10. Les dispositifs intelligents et l'IA en cardiologie peuvent-ils améliorer la pratique cliniqu? [Can smart devices and AI in cardiology improve clinical practice?]
    Niccolo Maurizi, Ioannis Skalidis, Denise Auberson, Thabo Mahendiran, Stephane Fournier, Emmanuel Abbe, and Olivier Muller.. Revue medicale suisse 19, no. 828 (2023): 1041-1046.
  11. L'intelligence artificielle et la cardiologie: la machine est lancée. [Artificial Intelligence and Cardiology: The Machine is Launched.]
    Olivier Muller, Emmanuel Abbé, and François Mach. Revue Médicale Suisse 18, no. 783 (2022): 1027-1028.
  12. Attention-based learning of views fusion applied to myocardial infarction diagnosis from x-ray CT.
    Jakub Gwizdala, Ortal Senouf, Denise Auberson, David Meier, David Rotzinger, Stephane Fournier, Salah Qanadli, Olivier Muller, Pascal Frossard, Emmanuel Abbé, Dorina Thanou. In MedNeurIPS. 2022.
  13. Predicting future myocardial infarction from angiographies with deep learning.
    Dorina Thanou, Ortal Senouf, Omar Raita, Emmanuel Abbé, Pascal Frossard, Farhang Aminfar, Denise Auberson, Nicolas Dayer, David Meier, Mattia Pagnoni, Olivier Muller, Stéphane Fournier, Thabo Mahendiran. Advances in Neural Information Processing Systems 34 (NeurIPS 2021).

Members


Professor Emmanuel Abbé (ML and DS)
Dr Denise Auberson (Cardiology)
Dr Edward Andò (Center for Imaging)
Professor Annalise Buffa (NM)
Adjunct Professor Simone Deparis (NM)
Dr Fabio Marcinno (NM)
Dr Stephane Fournier (Cardiology)
Professor Pascal Frossard (ML and signal processing)
Prof Baris Gencer (Preventive Cardiology)
Dr Thabo Mahendiran (Cardiology)
Professor Olivier Muller (Cardiology)
Dr David Nanchen (Cardiology)
Dr Mattia Pagnoni (Cardiology)
Dr Ortal Senouf (ML and DS)
Dr David Rotzinger (Radiology)
Dr Ricardo Tenderini (NM)
Dr Dorina Thanou (ML and signal processing)
Dr Xiaowu Sun (ML and DS)

Current projects

1. Prediction of future myocardial infarction (MI)

A major goal of the collaboration is the application of AI to improve the prediction of MI and other adverse patient events. Given the complexity of the prediction task, our approach involves the integration of multiple data sources:

  • Cardiac imaging with a particular focus on Invasive coronary angiography and CT coronary angiography
  • 3D coronary reconstructionto better incorporate the anatomical drivers of CAD
  • Numerical modelsthat harness 3D models to compute the haemodynamic drivers of coronary artery disease, such as fractional flow reserve (FFR) and wall shear stress (WSS)

2. AI4HealthyCities: improving the cardiovascular health of Lausanne, Switzerland

This collaboration between EPFL, Unisanté-CHUV, Direction générale de la santé (Vaud), and the Novartis Foundation will aim to improve the stratification of cardiovascular disease (CVD) risk in Lausanne. In particular, our approach focuses on health inequalities and integrates the social determinants of health - the economic, social, environmental, and psychosocial factors that influence health. These are important drivers of CVD that are not accounted for in current mainstream risk calculators.