Chronic Obstructive Pulmonary Disease (COPD) is a debilitating and chronic lung syndrome that causes accelerated lung function decline and death in the 20% of cases. Mostly, the non-adherence to therapy contributes to symptoms increase, mortality, inability and therapies failure, highly influencing the management costs associated to COPD. The existing procedure of diagnosing COPD is effective and fast. The acute treatment and the subsequent disease management, instead, strictly depend on the currently long and complex process of identification of three factors: COPD phenotype, adherence to chosen therapy and probability of exacerbation events. The knowledge of these factors is needed by clinicians to stratify patients and personalise the therapies and rehabilitation procedures, to initiate an effective disease management. The application of Raman spectroscopy on saliva, representing an easy collectable and highly informative biofluid, has been already proposed for different infective, neurological and cancer diseases, with promising results in the diagnostic and monitoring fields. In this project, we propose the use of Deep Learning analysis of Raman spectra collected from COPD patient’s saliva to be combined with other clinical data for the development of a system able to provide fast and sensitive information regarding COPD phenotypes, adherence and exacerbation risks. This will support clinicians to personalise COPD therapies and treatments, and to monitor their effectiveness.
|Effective start/end date||1/02/22 → 31/01/25|
- Raman Spectroscopy
- Deep Learning
- Artificial Intelligence
Field of Science
- 3.3 Health sciences
Smart Specialization Area
- Biomedicine, medical technologies and biotechnology
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