Personalized Digital Health and Artificial Intelligence in Childhood Asthma
Personalized Digital Health and Artificial Intelligence in Childhood Asthma
Asthma is a chronic inflammatory disease of the airways that causes recurrent episodes of
 wheezing, breathing difficulties and coughing. The prevalence of asthma is 8% in school-aged
 children and 30% in preschoolers, making asthma the first chronic disease in children.
 Symptoms are due to diffuse but variable airway obstruction, reversible spontaneously or
 after inhalation of beta2 agonists (β-2a) such as salbutamol. Exacerbations of asthma are
 frequent and difficult to assess by parents and the patient himself. It is estimated that
 approximately 2.5% of children with asthma are hospitalized annually. The global burden
 caused by asthma can thus be reduced by improving early detection of bronchial obstruction,
 prescribing immediate treatment with the appropriate background therapy, and reliably and
 objectively assess response to treatment.

 The natural history of asthma symptoms in children shows a great intra and inter-individual
 variability. The difficulty of assessing the severity of an attack by the parents or the
 child himself, when he is old enough to control his chronic disease, is a key element in the
 management of asthma and allows the treatment to be adapted quickly, sometimes avoiding
 hospitalization. Healthcare professionals can assess the severity of the episode using the
 Pediatric Respiratory Assesment Measure (PRAM) score, which has the advantage of being
 adaptable at any age. The Global Alliance against Chronic Respiratory Diseases (GARD)
 integrates in its diagnostic strategy for chronic respiratory diseases, the lung function
 test, which allows the quantification of respiratory function in the context of diagnosis and
 long-term follow-up. Although spirometry are non-invasive tests, they still require a high
 level of patient cooperation, which remains problematic before the age of 7 years.

 The digital stethsocope integrates a capacity for recording auscultations and data
 transmission to high-performance software. This has made it possible to extend auscultation
 beyond what was audible to the human ear alone (over 20-20,000 Hertz).Auscultatory sounds
 analysis, particularly those most often associated with obstructive syndrome could be simple,
 reproducible and a reliable method of assessing the severity and response to treatment in
 children's asthma. Major advances in signal processing and unsupervised learning in
 artificial intelligence research provide the potential for high-performance analysis of
 physiological measures.
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.
Inclusion Criteria:

 -  Patients with clinical diagnosis of acute asthma exacerbations

 -  age > 2 years and < 16 years

 -  information and written consent of a legal representative

 Exclusion Criteria:

 -  Chronic lung diseases other than asthma (cystic fibrosis, bronchopulmonary Dysplasia),

 -  Congenital heart disease

 -  Refusal of consent.