Integrating Deep Learning CT-scan Model, Biological and Clinical Variables to Predict Severity of Asthma in Children

Artificial intelligence (AI) offers substantial opportunities for healthcare, supporting better diagnosis, treatment, prevention and personalized care. Analysis of health images is one of the most promising fields for applying AI in healthcare, contributing to better prediction, diagnosis and treatment of diseases. Deep learning (DL) is currently one of the most powerful machine learning techniques. DL algorithms are able to learn from raw (or with little pre-processing) input data and build by themselves sophisticated abstract feature representations (useful patterns) that enable very accurate task decision making. Recently, DL has shown promising results in assisting lung disease analysis using computed tomography (CT) images. Current severe asthma guidelines recommend high-resolution and multidetector CT as a tool for disease evaluation. CT scans contain prognostic information, as the presence of bronchial wall thickening, air trapping, bronchial luminal narrowing, and bronchiectasis are associated with longer disease duration and disease severity in adults. Only a small number of studies have reported chest CT findings in children with severe asthma, and their relationship to clinical and pathobiological parameters yielded inconsistent results. Thus, to which extent CT scans add prognostic information beyond what can be inferred from clinical and biological data is still unresolved in children. The project is expected to build an DL-severity score to prognoses severe evolution for children with asthma, using a DL model to capture CT scan prognosis information.

NCT Number	NCT05140889
Lead Sponsor	Fondazione IRCCS Policlinico San Matteo di Pavia Collaborators: Istituto per la Ricerca e l'Innovazione Biomedica, Università Ca' Foscari Venezia
Conditions	Asthma in Children
Enrollment	25 participants
Start Date	2021-01-20
Primary Completion	2026-06-30 (estimated)
Study Completion	2026-06-30 (estimated)
Updated on ClinicalTrials.gov	2024-07-25