BronchiectasisNCT05751538
Artificial Intelligence in Computed Tomography for Quantifying Lung Changes of Bronchiectasis Patients
An observational study of Bronchiectasis, run by Ruijin Hospital at 1 site in China.
Trial summary
Bronchiectasis is a chronic respiratory disease characterized by permanent bronchiectasis.The incidence and prevalence of bronchiectasis have assumed continuously grows in global. Chest computed tomography (CT) remains the imaging standard for demonstrating cystic fibrosis (CF) airway structural disease in vivo. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Bronchiectasis
- How many people
- 730 participants
- Where
- 1 siteChina
- Study dates
- 1 Mar 2023to 30 Dec 2023
- 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 Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: Patients with bronchiectasis and clinical examination, pulmonary function test, and CT data
Who may be able to join
- Patients diagnosed with bronchiectasis (according to the Chinese consensus, patient's previous chest CT examination must show bronchiectasis)
Who cannot join
- Patients with CT data and medical records missing
Trial locations and contacts
1 site in 1 country.
China1 site
Ruijin Hospital, Medical School of Shanghai Jiaotong University
Shanghai, Shanghai Municipality, 200025
About this study
Official title: Artificial Intelligence Based on Machine Learning in Computed Tomography for Quantifying Lung Changes of Bronchiectasis Patients
Bronchiectasis is a chronic respiratory disease characterized by permanent bronchiectasis.The incidence and prevalence of bronchiectasis have assumed continuously grows in global. Chest computed tomography (CT) remains the imaging standard for demonstrating cystic fibrosis (CF) airway structural disease in vivo. However, visual scoring systems as an outcome measure are time consuming, require training and lack high reproducibility. Our objective was to validate a fully automated artificial intelligence (AI)-driven scoring system of CF lung disease severity.
- Conditions
- Bronchiectasis
- Being tested
- No intervention
- Keywords
- Bronchiectasis, Machine Learning, Artificial Intelligence
- Medical subject headings
- Bronchiectasis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
No interventionOther
No intervention
| Study group | Role | What they receive |
|---|---|---|
| Train dataset | This group is dedicated to developing an automated algorithm. Receives: Other: No intervention | |
| Test dataset | This group is dedicated to testing the performance of an automated algorithm. Receives: Other: No intervention | |
| Clinical Validation | This group is dedicated to assessing the clinical validity of the measurement in an independent validation cohort. Receives: Other: No intervention |
What is being measured
The primary outcome is the main question the study is designed to answer.
Correlations of the artificial intelligence-driven scores with manual scores
Correlations and comparisons of the artificial intelligence-driven scores with manual scores by thoracic radiologists on CT scans of bronchiectasis patients.
Measured: From date of inclusion until the date of final quantification, assessed up to 12 months
Secondary outcomes (1)
Correlation of the artificial intelligence-driven scores with pulmonary function test
Correlation of quantitative measurement with pulmonary function
Measured: From date of inclusion until the date of final quantification, assessed up to 12 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted2 Mar 2023
- Study start1 Mar 2023 (estimated)
- Primary completion30 Jun 2023 (estimated)
- Study completion30 Dec 2023 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for BronchiectasisStudies of the same condition, open and under way.
- More trials run by Ruijin HospitalThe sponsor's other registered studies.
- 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
- Cohort
- Time perspective
- Prospective
- Enrolment
- 730 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Ruijin Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05751538
- Sponsor's protocol ID
- AI-BE
- Status last verified
- Nov 2022
- First submitted
- 8 Feb 2023
- Last updated
- 2 Mar 2023
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 2 Mar 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence Based on Machine Learning in Computed Tomography for Quantifying Lung Changes of Bronchiectasis Patients
- ClinicalTrials.gov ID
- NCT05751538
- Acronym / sponsor ID
- AI-BE
- Conditions
- Bronchiectasis
- Keywords
- Bronchiectasis, Machine Learning, Artificial Intelligence
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 730 participants (estimated)
- Lead sponsor
- Ruijin HospitalUniversity, hospital or other
- Study start
- 1 Mar 2023 (estimated)
- Primary completion
- 30 Jun 2023 (estimated)
- Study completion
- 30 Dec 2023 (estimated)
- First posted
- 2 Mar 2023 (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.
Bronchiectasis is a chronic respiratory disease characterized by permanent bronchiectasis.The incidence and prevalence of bronchiectasis have assumed continuously grows in global. Chest computed tomography (CT) remains the imaging standard for demonstrating cystic fibrosis (CF) airway structural disease in vivo. However, visual scoring systems as an outcome measure are time consuming, require training and lack high reproducibility. Our objective was to validate a fully automated artificial intelligence (AI)-driven scoring system of CF lung disease severity.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Train dataset | – | Other: No interventionThis group is dedicated to developing an automated algorithm. |
| Test dataset | – | Other: No interventionThis group is dedicated to testing the performance of an automated algorithm. |
| Clinical Validation | – | Other: No interventionThis group is dedicated to assessing the clinical validity of the measurement in an independent validation cohort. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Patients with bronchiectasis and clinical examination, pulmonary function test, and CT data
Key inclusion criteria (1 listed)
- Patients diagnosed with bronchiectasis (according to the Chinese consensus, patient's previous chest CT examination must show bronchiectasis)
Key exclusion criteria (1 listed)
- Patients with CT data and medical records missing
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| No interventionNo intervention | Other | Clinical Validation, Test dataset, Train dataset |
Primary outcome
Correlations of the artificial intelligence-driven scores with manual scores
Time frame: From date of inclusion until the date of final quantification, assessed up to 12 months
Correlations and comparisons of the artificial intelligence-driven scores with manual scores by thoracic radiologists on CT scans of bronchiectasis patients.
1 secondary outcome
Correlation of the artificial intelligence-driven scores with pulmonary function test
Time frame: From date of inclusion until the date of final quantification, assessed up to 12 months
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 2 Mar 2023.
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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.
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 Bronchiectasis, run by Ruijin Hospital at 1 site in China.
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 1 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 2 Mar 2023. 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 China. 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 Jieming Qu (86-021-64370045, jmqu0906@163.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Correlations of the artificial intelligence-driven scores with manual scores (time frame: From date of inclusion until the date of final quantification, assessed up to 12 months). 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 2 Mar 2023.