Start/end date: 2 March, 2026 – 2 February, 2029
Funding Agency/Programme: Programma Nazionale Ricerca, Innovazione e Competitività per la transizione verde e digitale 2021-2027. Azione 1.1.4 “Ricerca collaborativa. Ministero delle Imprese e del Made in Italy.
Partnership: XENIA PROGETTI S.R.L. (coordinator); Fondazione Ri.MED; ARTES 4.0 – ADVANCED ROBOTICS AND ENABLING DIGITAL TECHNOLOGIES & SYSTEMS 4.0. (partner)
Abstract: STRIKE aims to develop an automatic decision-support platform to assist clinical cardiologists in the diagnosis and prognosis of patients suffering from Atrial Fibrillation (AF). AF is a pathological condition characterized by an irregular heartbeat, which can cause the formation of thrombi, which in turn can lead to serious complications such as stroke. The computer platform will implement an automated processing pipeline that, starting from the cardiac CT scan of the patient, will provide a thromboembolic risk index. This index will be based on heamodynamic alterations in the region of the left atrium known as the Left Atrial Appendage (LAA), differently from the commonly used risk indices that are based on anamnestic and demographic data. The LAA is characterised by highly individual morphologies, and recent clinical studies have identified it as the most critical anatomical district in the triggering of ischemic risk: over 90% of the thrombi causing complications in patients suffering from atrial fibrillation form in the LAA. In fact, during AF crises, the appendage (as well as the atrium) loses its normal active contraction capacity, promoting the formation of blood stasis zones that can lead to clot formation.
The STRIKE platform intends to offer a new medical support for the definition of a thromboembolic risk index that is more objective, based on an approach that takes into account the hemodynamic alterations in the patient specific LAA. The project involves a retrospective study for clinical validation of the platform on a dataset of cardiac CT scans of selected patients.
The project has the potential to transform the standard of care for patients suffering from AF, helping the identification of high-risk cases and reducing complications due to inadequate treatments. In the process of creating the platform, several intermediate objectives are included which represent a wealth of knowledge not only in the academic field, but especially in the clinical one. In fact, during the development of the project, a large dataset of LAA morphologies derived from clinical CT scans and associated ischemic risk will be created for the purpose of training ischemic risk prediction models.
Ri.MED Scientific Coordinator: Danila Vella
Ri.MED team: Gaetano Burriesci; Alessandra Monteleone; Sofia Di Leonardo.
“Progetto finanziato nell’ambito del Programma Nazionale ‘Ricerca, Innovazione e Competitività per la transizione verde e digitale 2021-2027’, cofinanziato dall’Unione europea – Fondo europeo di sviluppo regionale (FESR)”