Segmentation of Structural Abnormalities in Chronic Lung Diseases
NOVAA
1 other identifier
observational
800
1 country
1
Brief Summary
Lung structural abnormalities are complex, time-consuming, and may lack reproducibility to evaluate visually on CT scans. The study's aim is to perform automated recognition of structural abnormalities in CT scans of patients with chronic lung diseases by using dedicated software.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for all trials
Started Jan 2008
Longer than P75 for all trials
1 active site
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
Click on a node to explore related trials.
Study Timeline
Key milestones and dates
Study Start
First participant enrolled
January 1, 2008
CompletedFirst Submitted
Initial submission to the registry
February 15, 2021
CompletedFirst Posted
Study publicly available on registry
February 18, 2021
CompletedPrimary Completion
Last participant's last visit for primary outcome
February 17, 2024
CompletedStudy Completion
Last participant's last visit for all outcomes
February 17, 2024
CompletedJanuary 5, 2024
September 1, 2023
16.1 years
February 15, 2021
December 31, 2023
Conditions
Outcome Measures
Primary Outcomes (1)
Validity of automated measurement
Correlations and comparisons with other biomarker of the disease severity
From date of inclusion until the date of final quantification, assessed up to 12 months
Secondary Outcomes (3)
Correlation with pulmonary function test
From date of inclusion until the date of final quantification, assessed up to 12 months
Longitudinal variation over time
From date of inclusion until the date of final quantification, assessed up to 12 months
Reproducibility
From date of inclusion until the date of final quantification, assessed up to 12 months
Study Arms (3)
Train dataset
This group is dedicated to developing an automated algorithm
Test dataset
This group is dedicated to testing the semantic performance of an automated algorithm
Clinical Validations
Patients groups are dedicated to assessing the clinical validity of the measurement in independent validation cohorts, with or without longitudinal evaluations such as monitoring of a treatment effect
Interventions
Eligibility Criteria
Patients with chronic lung disease and a Clinical examination, pulmonary function test, and CT acquired during an annual routine follow-up
You may qualify if:
- Patients with chronic lung disease and clinical examination, pulmonary function test, and CT acquired during a routine follow-up
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Study Sites (1)
Hopital Haut Leveque
Pessac, France
Related Publications (1)
Dournes G, Hall CS, Willmering MM, Brody AS, Macey J, Bui S, Denis de Senneville B, Berger P, Laurent F, Benlala I, Woods JC. Artificial intelligence in computed tomography for quantifying lung changes in the era of CFTR modulators. Eur Respir J. 2022 Mar 3;59(3):2100844. doi: 10.1183/13993003.00844-2021. Print 2022 Mar.
PMID: 34266943DERIVED
MeSH Terms
Conditions
Interventions
Condition Hierarchy (Ancestors)
Intervention Hierarchy (Ancestors)
Study Officials
- STUDY CHAIR
Patrick Berger, Pr
Hopital Haut Leveque
Study Design
- Study Type
- observational
- Observational Model
- OTHER
- Time Perspective
- OTHER
- Sponsor Type
- OTHER
- Responsible Party
- SPONSOR INVESTIGATOR
- PI Title
- Director
Study Record Dates
First Submitted
February 15, 2021
First Posted
February 18, 2021
Study Start
January 1, 2008
Primary Completion
February 17, 2024
Study Completion
February 17, 2024
Last Updated
January 5, 2024
Record last verified: 2023-09