Aim:
Develop an artificial intelligence based algorithm for unsupervised diagnostic and classification of childhood asthma exacerbation.
Methodology: A Longitudinal prospective monocentric observational study will be performed in the Pediatric Emergency Division (PED) and the Pediatric Respiratory Unit (PRU) of the Geneva University Hospitals (HUG) during 24 months. This clinical study will include patients aged from 2 to16 years with acute asthma exacerbations. The intervention consists in recording auscultation of asthmatic patients at rest, during acute exacerbation and after treatment by bronchodilatators (β-2 agonists) inhalation in the PED, with a Digital Stethoscope (DS). Auscultation will be recorded during hospitalization every day, at home 7 days after the acute episode, combining intdoor and outdoor measures, and evaluating the exposome. A last record will be done at 6 to 8 weeks after the acute episode, with a lung function test if the patient is up to 7 years. A validation and training audio database will be constituted for the development of Artificial Intelligence (AI) algorithms, allowing analysis of respiratory rate, inspiratory/expiratory time ratio, PRAM score, wheezing variation of intensity and unsupervised diagnosis.
Expected results:
Creation of a performant AI algorithm for unsupervised acute asthma exacerbation diagnosis, with \> 70 % of Sensitivity and \> 70% of Specificity compared to the expert. Response to treatment will improve patient empowerment and personalized medicine in childhood asthma management.
