Context - Echocardiography plays a paramount role in day to day clinical practice to highlight structural or functional dysfunction of the heart. The observed abnormalities combined with clinical data of the patient lead to diagnoses, many of which requiring the performance of complementary examinations to diagnose the origin (etiology) of cardiac pathology which will modify the prognosis of patient and the subsequent therapy.
Some of these examinations are useless and do not find any specific abnormality, resulting in constraints for the patients and a cost for the society
Objectives - The objective of this project is to develop rigorous and explainable models for the prediction of the origin of targeted cardiac diseases from echocardiographic images and patient data based on artificial intelligence (AI) paradigm. The strength of this study lies in the development of a strong AI framework based on transformer paradigm to model the complex interactions between echocardiographic image-based information and relevant patient data.
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