We're building a better ClinicalTrials.gov. Check it out and tell us what you think!
Working…
ClinicalTrials.gov
ClinicalTrials.gov Menu

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

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Know the risks and potential benefits of clinical studies and talk to your health care provider before participating. Read our disclaimer for details.
 
ClinicalTrials.gov Identifier: NCT05140889
Recruitment Status : Recruiting
First Posted : December 2, 2021
Last Update Posted : February 3, 2022
Sponsor:
Collaborators:
Istituto per la Ricerca e l'Innovazione Biomedica
Università Ca' Foscari Venezia
Information provided by (Responsible Party):
Amelia Licari, IRCCS Policlinico S. Matteo

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.


Condition or disease
Asthma in Children

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.

Layout table for study information
Study Type : Observational [Patient Registry]
Estimated Enrollment : 25 participants
Observational Model: Case-Control
Time Perspective: Prospective
Target Follow-Up Duration: 36 Months
Official Title: Integrating Deep Learning CT-scan Model, Biological and Clinical Variables to Predict Severity of Asthma in Children
Actual Study Start Date : January 20, 2021
Estimated Primary Completion Date : June 30, 2024
Estimated Study Completion Date : June 30, 2024

Resource links provided by the National Library of Medicine


Group/Cohort
Group 1
Children with severe asthma
Group 2
Children who undergo chest CT scan for other reasons than asthma



Primary Outcome Measures :
  1. Prediction of asthma severity in children [ Time Frame: 3 years ]
    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.



Information from the National Library of Medicine

Choosing to participate in a study is an important personal decision. Talk with your doctor and family members or friends about deciding to join a study. To learn more about this study, you or your doctor may contact the study research staff using the contacts provided below. For general information, Learn About Clinical Studies.


Layout table for eligibility information
Ages Eligible for Study:   6 Years to 17 Years   (Child)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
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.

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)

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT05140889


Contacts
Layout table for location contacts
Contact: Amelia Licari, MD +39(0)382502629 a.licari@smatteo.pv.it

Locations
Layout table for location information
Italy
IRCCS Policlinico San Matteo Recruiting
Pavia, Italy, 27100
Contact: Amelia Licari, MD       a.licari@smatteo.pv.it   
Sponsors and Collaborators
IRCCS Policlinico S. Matteo
Istituto per la Ricerca e l'Innovazione Biomedica
Università Ca' Foscari Venezia
Investigators
Layout table for investigator information
Principal Investigator: Amelia Licari, MD IRCCS Policlinico San Matteo
Layout table for additonal information
Responsible Party: Amelia Licari, MD, IRCCS Policlinico S. Matteo
ClinicalTrials.gov Identifier: NCT05140889    
Other Study ID Numbers: 08073521
First Posted: December 2, 2021    Key Record Dates
Last Update Posted: February 3, 2022
Last Verified: January 2022
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: No

Layout table for additional information
Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by Amelia Licari, IRCCS Policlinico S. Matteo:
Asthma
Children
Severe Asthma
Artificial Intelligence
Deep Learning
Computed Tomography
Additional relevant MeSH terms:
Layout table for MeSH terms
Asthma
Bronchial Diseases
Respiratory Tract Diseases
Lung Diseases, Obstructive
Lung Diseases
Respiratory Hypersensitivity
Hypersensitivity, Immediate
Hypersensitivity
Immune System Diseases