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

NCT05140889 | Recruiting

• 1 other identifier: 08073521
• Study Type: observational
• Target: 25
• Locations: 1 country
• Sites: 1

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.

Conditions

Asthma in Children

Keywords

Asthma; Children; Severe Asthma; Artificial Intelligence; Deep Learning; Computed Tomography

Outcome Measures

Primary Outcomes (1)

• Prediction of asthma severity in children

  To build a severity score to prognoses evolution for children with asthma, using a deep-learning model to capture CT scan prognosis information and integrate with clinical and laboratory data obtained from medical records.

  3 years

Study Arms (2)

Group 1

Children with severe asthma

Group 2

Children who undergo chest CT scan for other reasons than asthma

Eligibility Criteria

• Age: 6 Years - 17 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17)
• 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.

You may qualify if:

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

You may not qualify if:

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

Sponsors & Collaborators

• Fondazione IRCCS Policlinico San Matteo di Pavia: lead
• Istituto per la Ricerca e l'Innovazione Biomedica: collaborator
• Università Ca' Foscari Venezia: collaborator

Study Sites (1)

IRCCS Policlinico San Matteo

Pavia, 27100, Italy

RECRUITING

MeSH Terms

Conditions

Asthma

Condition Hierarchy (Ancestors)

Bronchial Diseases; Respiratory Tract Diseases; Lung Diseases, Obstructive; Lung Diseases; Respiratory Hypersensitivity; Hypersensitivity, Immediate; Hypersensitivity; Immune System Diseases

Study Officials

• Amelia Licari, MD

  IRCCS Policlinico San Matteo

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Amelia Licari, MD

CONTACT

+39(0)382502629; a.licari@smatteo.pv.it

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: PROSPECTIVE
• Target Duration: 36 Months
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: MD

Study Record Dates

• First Submitted: October 13, 2021
• First Posted: December 2, 2021
• Study Start: January 20, 2021
• Primary Completion (Estimated): June 30, 2026
• Study Completion (Estimated): June 30, 2026
• Last Updated: July 25, 2024

Record last verified: 2024-07

Data Sharing

• IPD Sharing: Will not share

Locations

IT (1)