Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : Epione research team, Inria Sophia Antipolis – Mé
Durée : 36 mois
Contact : maxime.sermesant@inria.fr
Date limite de publication : 2023-12-15

Contexte :
Clinical Context

Cardiac Arrhythmias are a major healthcare issue. For instance, atrial fibrillation (AF) is the most common cardiac arrhythmia, characterized by chaotic electrical activation of the atria, preventing synchronized contraction. More than 6 million Europeans suffer from it and age is the most powerful predictor of risk. Life-threatening complications and fast progression to persistent or permanent forms call for as early as possible diagnosis and effective treatment. Arrhythmias are often treated with anti-arrhythmic drugs, with limited efficacy and safety. Catheter ablation, an invasive procedure, is more effective. This procedure is by no means optimized, however, and arrhythmias may reoccur. The efficacy of first-time ablation may range from 30%-75% depending on the individual patient and disease, such that multiple ablation procedures may be recommended.

Deep learning context

AI and more precisely machine learning have obtained impressive results in several domains like vision, natural language processing, bioinformatics. However, this data intensive paradigm leads to model that often lack interpretability and robustness. Also, it does not allow an easy integration of prior knowledge available in many scientific fields. This can explain its difficult adoption in domains like healthcare. On the other hand, biophysical modelling of the human body is a well-posed mathematical framework to introduce physiology into predictive analysis of clinical data. Moreover, it provides a natural mechanistic framework to interpret results. However, there is often a large computational cost, even more when the quantification of uncertainty has to be performed. And it is sometimes difficult to circumvent model approximations. A major scientific challenge today consists in combining the versatility of data intensive approaches with the physically grounded modelling approaches developed in scientific fields like biophysics.

Sujet :

The scientific objective of this project is to combine the advantages of biophysics and machine learning, more specifically deep learning methods, and to develop hybrid models exploiting the complementarity of the two approaches. We propose to introduce physiological priors in learning systems through biophysical modelling by learning spatiotemporal dynamics from simulations and by introducing physically motivated constraints relative to these dynamics. The objective is to exploit optimally the large amounts of data available in this field together with well-known properties of biophysical cardiac dynamics. Besides, this would also enable us to propose a data-driven correction of biophysical model error. Finally, we will seek a principled integration of uncertainty quantification within this framework. This will encompass both uncertainty on the training data and in the prediction. The vast amount of knowledge in mathematical analysis and data assimilation will be leveraged to optimise the machine learning formulation and understanding.
This project will be done in collaboration with cardiologists and radiologists to access clinical databases in order to evaluate the proposed methods on diagnosis, therapy planning and prognosis for cardiac pathologies.

Profil du candidat :
Master in computer science or applied mathematics, Engineering school. Background and experience in machine learning. Good technical skills in programming. Eager to work in the medical field.

Formation et compétences requises :
Master in computer science or applied mathematics, Engineering school. Background and experience in machine learning. Good technical skills in programming. Eager to work in the medical field.

Adresse d’emploi :
Epione Research Project

Inria, Sophia Antipolis

2004 route des Lucioles BP 93

06 902 SOPHIA ANTIPOLIS Cedex

FRANCE

Document attaché : 202301120851_2023 – PhD_DeepNum-Cardiac-Electrophysiology.pdf