QUASAR-AI is a multicentre, multidisciplinary project aimed at exploring the potential of Artificial Intelligence (AI) in identifying specific disease phenotypes and elaborating outcome measures in patients with Chronic Obstructive Pulmonary Disease (COPD) through the extraction of quantitative biomarkers from unenhanced chest Computed Tomography (CT) and their integration with clinical and instrumental data, thus unlocking considerable information incorporated into radiological images but commonly disregarded in routine clinical practice. The AI system, integrating analysis of respiratory muscles from chest CT images, extraction of quantitative parameters, and exploration of the interwoven relationships between clinical, instrumental and CT data with outcome measures, will be developed from a selected cohort of >200 COPD patients from two hospitals. The identification of new biomarkers of muscle status and their integrated evaluation would enable the identification of specific disease subclasses and phenotypes with the long-term goal of targeting medical intervention to specific traits of disease in a personalized way.
Partners: Università degli Studi di Genova (coordinator), Università degli Studi di Palermo.
CNR-IEIIT will develop AI techniques for the analysis of clinical and instrumentation data and for the identification of biomarkers and disease phenotypes.
Funding: Italian Ministry of University and Research (MUR), call PRIN 2022, funded by the European Union - Next Generation EU
Timeline: Nov 2023 - Oct 2025