Clinical trial NCT05140889: Integrating Deep Learning CT-scan Model, Biological and Clinical Variables to Predict Severity of Asthma in Children
Official title: Integrating Deep Learning CT-scan Model, Biological and Clinical Variables to Predict Severity of Asthma in Children
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Asthma in Children
Keywords: Asthma
Children
Severe Asthma
Artificial Intelligence
Deep Learning
Computed Tomography
Lead sponsor: Fondazione IRCCS Policlinico San Matteo di Pavia
Sponsor class: OTHER
Collaborators: name: Istituto per la Ricerca e l'Innovazione Biomedica
name: Università Ca' Foscari Venezia
First posted: 2021-12-02
Enrollment: 25
Sex: ALL
Minimum age (years): 6
Maximum age (years): 17
Standard ages: CHILD
Accepts healthy volunteers: no
Observational model: CASE_CONTROL
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: Eligible participants will be identified among children referred to our Pediatric Clinic for severe asthma by their general practitioner or by their primary care pediatrician.

Children who undergo chest CT scan for other reasons than asthma will be selected as controls.

Brief summary: 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.

Detailed description: The aims of this project are:

* to build a large database of clinical, biological and radiological data collected from pediatric patients with severe asthma;
* to design and train a forecaster model based on DL techniques to predict asthma severity in children;
* to estimate transition probabilities between asthma severity levels using a multi-state Markov model taking into account qualitative and quantitative information obtained from CT imaging.

Our evaluation of DL-severity and existing clinical scores in childhood asthma is expected to reveal that emerging methodologies assisted by DL techniques can provide accurate severity predictions, when compared with existing clinical scores. Such an accurate prediction model would allow pediatricians to identify features that are the most indicative of severity and progression of asthma and would be employed to formulate intervention strategies and early medical attention for children.

Eligibility criteria: Inclusion Criteria:

* age 6-17 years
* confirmed diagnosis of severe asthma according to ERS/ATS guidelines

Exclusion Criteria:

* other diseases that may mimic asthma according to ERS/ATS guidelines (i.e., cystic fibrosis, primary ciliary dyskinesia, tracheobronchomalacia, etc)

Primary outcomes:
measure: Prediction of asthma severity in children
timeFrame: 3 years
Secondary outcomes:
Other outcomes:
Locations:
city: Pavia
country: Italy
facility: IRCCS Policlinico San Matteo
Study officials:
affiliation: IRCCS Policlinico San Matteo
name: Amelia Licari, MD
role: PRINCIPAL_INVESTIGATOR
References: