multI-discipliNary, multi-Sectoral and multi-national trainIng network on Digital biomarkErs for supraventricular arrHythmia charactErizAtion and Risk assessmenT

INSIDE-HEART brings together universities, companies and hospitals from countries (Italy, Finland, France, Israel, Netherlands, Spain, and Sweden) with the main scope of establishing a multi-disciplinary network to tackle the design and the early-phase validation of digital biomarkers, specifically targeting the diagnosis of supraventricular arrhythmias (SVAs) and their associated potential for adverse risk assessment, via the joint combination of signal processing, artificial intelligence and non-clinical devices.

This will be achieved by performing excellent research through a unique doctoral training without walls among field-expert academic, industrial and clinical entities.

The composite nature of the INSIDE-HEART network ensures a highly qualified training and research infrastructure for the specific goal, which aims to generate a new profile of researcher with multi-sectoral expertise able to fill the existing gap, i.e., the absence of digital biomarkers for SVAs reliably estimated with non-clinical devices, taking into account basic research, clinical needs and business interests.

Research and training are designed to consider relevant aspects such as public concern of private data management, gender and ethics related to SVAs, all according to Responsible Research & Innovation principles and Open Science practices.All activities in INSIDE-HEART are designed to pursue innovation in three domains: i) Educational domain by implementing a new multi-sectoral paradigm of PhD training to shape modern professional researchers with cross-competencies in the field, and able to accelerate the translation from basic science to market and clinics; ii) Basic science domain - by producing new knowledge about digital biomarkers by means of a multi-sectoral approach to explore the complex aspects related to SVAs; and iii) Technological domain by developing new data-driven and model-based methodologies to compute digital biomarkers and support the clinical decision.