Inquadramento diagnostico di asma attraverso modelli di IA applicati a dati FeNO e FOT.

Artificial intelligence algorithms are increasingly finding their way into the diagnosis, early treatment, prevention and monitoring of chronic respiratory diseases. Diagnosis of asthma often requires the use of various instrumental and laboratory investigations for better framing and phenotyping, including analysis of the fraction of exhaled nitric oxide (FeNO) for profiling eosinophilic inflammation and the forced oscillation technique (FOT), which plays a role not codified by guidelines. This project aims to develop a new predictive model for the early diagnosis of asthma by modelling data from the analysis of FeNO and FOT combined with clinical, laboratory and spirometric data using machine learning algorithms [ABSTRACT FROM AUTHOR]