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Raman analysis of saliva from COPD patients as biomarker: AI-based point-of-care for the disease monitoring and management (CORSAI)

Project/agreement No.
ES RTD/2022/17
Project funding
785 636.00 EUR, from which 154 237.00 EUR RSU part.
Project manager
Project realization
01.02.2022. - 31.01.2025.

Aim

This project investigates the application of Raman spectroscopy, when combined with Deep Learning analysis, to analyze the saliva of COPD (Chronic obstructive pulmonary disease) patients. By combining the obtained results with other clinical data, a system is created that would have the capacity to provide rapid and sensitive information about COPD phenotypes, exacerbation risks and characteristics. Such data will help and support clinicians to personalize the management and treatment of patients with COPD, as well as to assess the long-term effectiveness of therapy.

Description

Chronic obstructive pulmonary disease (COPD) is a chronic lung disease. COPD is a common, preventable and treatable disease, characterized by persistent, permanent respiratory symptoms and a decrease in lung function due to changes in the airways and alveoli caused by inhaled harmful particles and gases. Some COPD patients are characterized by frequent exacerbations of the disease. By optimizing patient therapy, better symptom control can be achieved, mortality can be decreased and the impact of the disease on the patient's quality of life can be reduced. Poorly controlled COPD has high associated costs, both due to patient's inability to work and due to the expenses and burden towards the medical system. Both in the treatment of stable COPD and in the treatment of COPD exacerbations, it is essential to know additional information about the patient's disease phenotype in order to choose and adapt management and treatment optimally. Knowledge about the phenotype of the disease and its related characteristics is very useful in the selection and adjustment of the patient's therapy in the long term, including evaluating the effectiveness of therapy. This data helps doctors personalize patient therapy and effective disease management.