Heart failure (HF) affects millions globally, posing a major healthcare challenge, and accurate diagnosis is difficult due to non-specific symptoms, often leading to delays in treatment. Artificial Intelligence (AI) offers transformative tools to enhance early diagnosis, risk stratification, and treatment of HF. This presentation, given at ESC Heart Failure 2025, reviews numerical and data-driven methods for cardiac and muscle modeling - including finite element analysis, artificial neural networks, and physics-informed neural networks - and their integration into surrogate and hybrid FEM-PINN modeling frameworks. It also introduces the architecture of the STRATIFYHF platform, an AI-based decision support system for early detection, risk stratification, and progression monitoring of heart failure, coordinated by the Bioengineering Research and Development Center (BioIRC), Kragujevac, Serbia. This work was developed within the framework of the STRATIFYHF project, funded by the European Union's Horizon Europe programme (Grant Agreement No. 101080905).
Human activity recognition (HAR) using sensor data allows the automatic detection of human behavior and actions in everyday environments. The development of scalable and privacy-preserving HAR systems is supported by the nonintrusive collection of time-series data using wearable…
Human activity recognition (HAR) using sensor data allows the automatic detection of human behavior and actions in everyday environments. The development of scalable and privacy-preserving HAR systems is supported by the nonintrusive collection of time-series data using wearable…
These are the data and results reported in the paper "A probabilistic digital twin framework for corrosion-fatigue prognosis of floating offshore wind turbines".
These are the data and results reported in the paper "A probabilistic digital twin framework for corrosion-fatigue prognosis of floating offshore wind turbines".