CE19 - Technologies pour la santé

Explainable machine-learning approaches of echocardiography data for the prediction of cardiac resynchronization therapy – EXPERT

Submission summary

Cardiac resynchronization therapy (CRT) is an implant-based therapy applied to patients with a specific heart failure (HF) profile. The identification of CRT candidates is a challenging task. The application of current guidelines still induce a non-responder rate of about 30% and death remains high even after CRT implantation. A best selection of patients before implantation is essential to improve the individual quality of care and prevent the risk of non-justified complications. Recently, the assessment of left ventricular mechanics by speckle tracking echocardiography has been shown to provide useful information for patient selection and follow-up. Within the EXPERT project, explainable artificial intelligence (AI) methods, integrating machine-learning (ML) models and physiological in-silico models (patient digital twin), will be proposed to combine physiological knowledge with observed data, using model-based reasoning, to improve the interpretability of the approach while minimizing overfitting and limited robustness.

Concerning methodological developments, novel patient-specific in-silico models of the cardiovsacular system will be proposed and will provide explainable model-based features that have a direct physiological meaning. Data-driven feature extraction will be also performed from clinical, electrocardiographic, and echocardiographic data. A hybrid modeling approach, which combines in-silico and ML models, will be proposed for the prediction of each patient response to a CRT intervention. These novel models should be evaluated clinically for the prediction of each patient response to a CRT intervention and to support the medical decision process for implanting or not a patient. The proposed hybrid classifier will be embedded in a novel decision support system (DSS) and will be used in inference mode to propose a new multivariate score, associated with an estimation of the probability of response. This approach will require the development of a technical architecture integrating all the available patient data and the calculation of a patient-specific probability of response in a timely manner
Another objective of the EXPERT project will be to manage an original and unique pilot platform, for sharing a common secure and well labeled database of echocardiographic data. This scientific ambition bypasses the issues related to manufacturers and federates public partners, including three university hospitals with local servers localized in Rennes, in an open but sovereign initiative. This project could potentially serve the health data hub (HDH) initiative (specifically for echocardiography). The governance will be shared by the consortium members. All parts of the infrastructure and the whole project are purely academic.

A first part of the project will be dedicated to the clinical evaluation of proposed methods on retrospective database of 250 patient through the proposed EXPERT platform. In a second part of the project, the EXPERT methodology will be evaluated in a prospective cohort in its capacity to assist the clinician in the decision making process to indicate or not a CRT implantation to any individual patient. In this observational analysis, the decision, to implant a patient or not, will only be based on guidelines and on clinician decision. The multivariate score will be evaluated, through the DSS, after pre-implantation exam and the score relevance will be evaluated 6 month after device implantation.

The EXPERT project brings together three CHUs and two universities. Five experienced partners with scientific, medical, regulatory and educational skills, are united to develop a tool for exploiting massive data in echocardiography with a high-impact medical application.

Project coordination

Virginie LE ROLLE (LABORATOIRE TRAITEMENT DU SIGNAL ET DE L'IMAGE)

The author of this summary is the project coordinator, who is responsible for the content of this summary. The ANR declines any responsibility as for its contents.

Partner

GREYC Groupe de recherche en Informatique, Image, Automatique et Instrumentation de Caen
Inter-organ cross-talk in cardiometabolic diseases
LTSI LABORATOIRE TRAITEMENT DU SIGNAL ET DE L'IMAGE
DRCI Délégation à la Recherche Clinique et à l'Innovation - CHU de Caen
DRI Direction de la Recherche et de l'Innovation

Help of the ANR 389,236 euros
Beginning and duration of the scientific project: January 2024 - 48 Months

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