Asthma in ChildrenBREATHENCT05140889
Integrating Deep Learning CT-scan Model, Biological and Clinical Variables to Predict Severity of Asthma in Children
An observational study of Asthma in Children, run by Fondazione IRCCS Policlinico San Matteo di Pavia at 1 site in Italy.
Trial 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. Read more about this study
- Who is it for?
- Ages 6 to 17 years
- Conditions the study is for
- Asthma in Children
- How many people
- 25 participants
- Where
- 1 siteItaly
- Study dates
- 20 Jan 2021to 30 Jun 2026
- How long will I take part?
- 36 Months
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 6 to 17 years
- All sexes
- No healthy volunteers
- Child
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.
Who may be able to join
- age 6-17 years
- confirmed diagnosis of severe asthma according to ERS/ATS guidelines
Who cannot join
- other diseases that may mimic asthma according to ERS/ATS guidelines (i.e., cystic fibrosis, primary ciliary dyskinesia, tracheobronchomalacia, etc)
Trial locations and contacts
1 site in 1 country.
Italy1 site, 1 recruiting
- Recruiting
IRCCS Policlinico San Matteo
Pavia, 27100
Amelia Licari, MD, a.licari@smatteo.pv.it
About this study
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.
Read more
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.
Read the 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.
- Conditions
- Asthma in Children
- Keywords
- Asthma, Children, Severe Asthma, Artificial Intelligence, Deep Learning, Computed Tomography
- Medical subject headings
- Asthma
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Group 1 | Children with severe asthma | |
| Group 2 | Children who undergo chest CT scan for other reasons than asthma |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 3 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted2 Dec 2021
- Study start20 Jan 2021
- Primary completion30 Jun 2026 (estimated)
- Study completion30 Jun 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Asthma in ChildrenStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case-control
- Time perspective
- Prospective
- Enrolment
- 25 participants (estimated)
- Target duration
- 36 Months
Sponsor and investigators
- Lead sponsor
- Fondazione IRCCS Policlinico San Matteo di Pavia
- Sponsor type
- University, hospital or other
- Collaborators
- Istituto per la Ricerca e l'Innovazione Biomedica, Università Ca' Foscari Venezia
- Responsible party
- Principal investigator, Amelia Licari, Fondazione IRCCS Policlinico San Matteo di Pavia
- Investigators
- Amelia Licari, MD, Principal investigator, IRCCS Policlinico San Matteo
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05140889
- Sponsor's protocol ID
- 08073521
- Acronym
- BREATHE
- Status last verified
- Jul 2024
- First submitted
- 13 Oct 2021
- Last updated
- 25 Jul 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 25 Jul 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Integrating Deep Learning CT-scan Model, Biological and Clinical Variables to Predict Severity of Asthma in Children
- ClinicalTrials.gov ID
- NCT05140889
- Acronym / sponsor ID
- BREATHE / 08073521
- Conditions
- Asthma in Children
- Keywords
- Asthma, Children, Severe Asthma, Artificial Intelligence, Deep Learning, Computed Tomography
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 25 participants (estimated)
- Lead sponsor
- Fondazione IRCCS Policlinico San Matteo di PaviaUniversity, hospital or other
- Collaborators
- Istituto per la Ricerca e l'Innovazione Biomedica, Università Ca' Foscari Venezia
- Study start
- 20 Jan 2021 (actual)
- Primary completion
- 30 Jun 2026 (estimated)
- Study completion
- 30 Jun 2026 (estimated)
- First posted
- 2 Dec 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Case-control
- Time perspective
- Prospective
- Target follow-up
- 36 Months
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Group 1 | – | –Children with severe asthma |
| Group 2 | – | –Children who undergo chest CT scan for other reasons than asthma |
Who may qualify
- Ages
- 6 to 17 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child
- 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.
Key inclusion criteria (2 listed)
- age 6-17 years
- confirmed diagnosis of severe asthma according to ERS/ATS guidelines
Key exclusion criteria (1 listed)
- other diseases that may mimic asthma according to ERS/ATS guidelines (i.e., cystic fibrosis, primary ciliary dyskinesia, tracheobronchomalacia, etc)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
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.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 25 Jul 2024.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Amelia Licari, MD principal investigator
IRCCS Policlinico San Matteo
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Asthma in Children, run by Fondazione IRCCS Policlinico San Matteo di Pavia at 1 site in Italy.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 25 Jul 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Italy. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Amelia Licari, MD (+39(0)382502629, a.licari@smatteo.pv.it), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Prediction of asthma severity in children (time frame: 3 years). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 25 Jul 2024.