INnovative risk Stratification of heart faIlure throuGH explainable machine learning and compuTational modeling of Left Ventricle

INSIGHT-LV aims to improve cardiovascular risk prediction by enhancing the evaluation of left ventricle function using multimodal methods. Heart failure (HF) affects over 26 million people globally, with high healthcare costs and hospitalization rates. Current diagnostic tools often fail to accurately predict adverse events, highlighting the need for more effective risk prediction models that integrate diverse clinical data. INSIGHT-LV focuses on two high-risk HF groups: hypertrophic cardiomyopathy (HCM) and COVID-19 patients. HCM, a major cause of sudden cardiac death in young people, lacks updated guidelines and relies on limited predictors. COVID-19 patients show significant cardiovascular risks, creating an urgent need for better risk stratification tools. These two groups represent a substantial part of the HF population requiring improved stratification. The project will provide clinicians with tools to deliver optimized, cost-effective treatment strategies. By characterizing and stratifying HCM and COVID-19 patients, INSIGHT-LV aims to personalize decision-making and advance disease understanding. Using computational modeling, AI, machine learning, and advanced signal and image processing, the project will integrate multimodal datasets, including ECGs, MRIs, genetic tests, and echocardiography, ensuring robust clinical solutions. INSIGHT-LV will test AI models on real-world data to validate improvements in predicting risks such as sudden cardiac death and arrhythmias. This work will set new standards for cardiovascular diagnostics, delivering scientific, societal, and economic benefits.