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

  • Three new publications from the collaboration:
    • Tenderini, R. et al. (2023). Can knowledge transfer techniques compensate for the limited myocardial infarction data by leveraging haemodynamics? An in silico study. Lecture Notes in Artificial Intelligence (LNAI), conference proceedings of AIME 2023 (in press)
    • Skalidis, I., Cagnina, A., Luangphiphat, W., Mahendiran, T., Muller, O., Abbe, E., and Fournier, S. (2023). ChatGPT takes on the European Exam in Core Cardiology: an artificial intelligence success story? European Heart Journal - Digital Health, 10.1093/ehjdh/ztad029
    • Maurizi, N., Skalidis, I., Auberson, D., Mahendiran, T., Fournier, S., Abbe, E., and Muller, O. (2023). Les dispositifs intelligents et l'IA en cardiologie peuvent-ils améliorer la pratique cliniqu? Rev Med Suisse 828, 104-1046.

Publications and Reports

  1. A study of ChatGPT in facilitating Heart Team decisions on severe aortic stenosis, Adil Salihu1, MD; David Meier1, MD; Nathalie Noirclerc1, MD; Ioannis Skalidis1, MD; Sarah Mauler-Wittwer1, MD; Frédérique Recordon1, BSc; Matthias Kirsch2, MD, PhD; Christan Roguelov1, MD; Alexandre Berger1, MD; Xiaowu Sun3, MSc, PhD; Emmanuel Abbe3, MS, PhD; Carlo Marcucci4, MD; Valentina Rancati4, MD; Lorenzo Rosner4, MD; Emmanuelle Scala4, MD; David C. Rotzinger5, MD, PhD; Marc Humbert6, MD; Olivier Muller1, MD, PhD; Henri Lu1,7, MD; Stephane Fournier1*, MD, PhD
  2. Xiaowu Sun, Theofilos Belmpas, Ortal Senouf, Emmanuel Abbé, Pascal Frossard, Bernard De Bruyne, Olivier Muller, Stéphane Fournier, Thabo Mahendiran, Dorina Thanou. GRAPH NEURAL NETWORK BASED FUTURE CLINICAL EVENTS PREDICTION FROM INVASIVE CORONARY ANGIOGRAPHY
  3. Adil Salihu, Mehdi Ali Gadiri, Ioannis Skalidis, David Meier, Denise Auberson, Annick Fournier, Romain Fournier, Dorina Thanou, Emmanuel Abbé, Olivier Muller, Stephane Fournier. Towards AI-assisted cardiology: a reflection on the performance and limitations of using large language models in clinical decision-making.
  4. Tenderini, R. et al. (2023). Can knowledge transfer techniques compensate for the limited myocardial infarction data by leveraging haemodynamics? An in silico study. Lecture Notes in Artificial Intelligence (LNAI), conference proceedings of AIME 2023 (in press)
  5. Skalidis, I., Cagnina, A., Luangphiphat, W., Mahendiran, T., Muller, O., Abbe, E., and Fournier, S. (2023). ChatGPT takes on the European Exam in Core Cardiology: an artificial intelligence success story? European Heart Journal - Digital Health, 10.1093/ehjdh/ztad029
  6. Maurizi, N., Skalidis, I., Auberson, D., Mahendiran, T., Fournier, S., Abbe, E., and Muller, O. (2023). Les dispositifs intelligents et l'IA en cardiologie peuvent-ils améliorer la pratique cliniqu? Rev Med Suisse 828, 1041-1046.
  7. Muller O, Abbe E, Mach F. L'intelligence artificielle et la cardiologie: la machine est lancée. Rev Med Suisse 2022;783:102-8.
  8. Mahendiran T, Thanou D, Senouf O et al. Deep learning-based prediction of future myocardial infarction using invasive coronary angiography: a feasibility study. Open Heart 2023;10:e002237. doi: 10.1136/openhrt-2022-002237
  9. I.-D. Sievering, O. Senouf, T. Mahendiran, D. Nanchen, S. Fournier, O. Muller, P. Frossard, E. Abbé, and D. Thanou. Anatomy-informed multimodal learning for myocardial infarction prediction, NeurIPS workshop for Medical Images, Dec., 2022.
  10. J. Gwizdala, O. Senouf, D. Auberson, D. Meier, D. Rotzinger, S. Fournier, S. Qanadli, O. Muller, P. Frossard, E. Abbé, and D. Thanou. Attention-based learning of views fusion applied to myocardial infarction diagnosis from x-ray CT, NeurIPS workshop for Medical Images, Dec., 2022.
  11. D. Thanou, O. Senouf, O. Raita, E. Abbé, P. Frossard, F. Aminfar, D. Auberson, N. Dayer, D. Meier, M. Pagnoni, O. Muller, S. Fournier, and T. Mahendiran. Predicting future myocardial infarction from angiographies with deep learning, NeurIPS workshop for Medical Images, Sept., 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)
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)

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.