Author(s):
1. Nenad Filipović, Univerzitet u Kragujevcu, Serbia

Abstract:
Heart failure is a multifaceted clinical syndrome that impacts over 15 million individuals throughout Europe. It is closely linked with diminished quality of life for patients and substantial healthcare expenditures. Consequently, pioneering AI tools can expedite the initiation of preventive and therapeutic approaches. The EU-funded STRATIFYHF project will develop, validate and implement an AI-driven decision support system through server and mobile-app. This system will serve the purpose of risk stratification, early detection and evaluation of disease progression, effectively addressing the clinical requirements of both primary and secondary care settings. By amalgamating patient-specific demographic and clinical information through state-of-the-art technologies, the system will employ advanced machine learning and computational modeling techniques to formulate AI-powered instruments for precise risk stratification and accurate prognosis. In this platform fluid-structure coupling for left ventricle was introduced. A nonlinear material model for heart wall using constitutive curves which include the stress-strain relationship was presented. Three important innovations are presented in this project: 1) patient-specific data i.e. demographic, clinical, genetic, lifestyle and socio-economic 2) AI-based digital patient library and algorithms for risk stratification, early diagnosis, and disease progression 3) Multifunctional AI- and computational modelling-based DSS and mobile app for informing a patient-centred, personalised, prevention and treatment strategies Computational platform like STRATIFYHF for sure will open a new avenue for new diagnostic and therapeutic tools for risk stratification and early detection of heart failure in primary and secondary care. Acknowledgments: This paper is supported by the STRATIFYHF project that has received funding from the European Union’s Horizon Europe research and innovation programme under Grant agreement No 101080905. This article reflects only the author's view. The Commission is not responsible for any use that may be made of the information it contains

Key words:
Artificial intelligence-based,decision support system,early detection,heart failure,primary and secondary care

Thematic field:
SYMPOSIUM B - Biomaterials and nanomedicine

Date of abstract submission:
09.07.2024.

Conference:
Contemporary Materials 2024 - Savremeni Materijali

Applied paper from author