NCT04760548|Unknown
Segmentation of Structural Abnormalities in Chronic Lung Diseases
NOVAA
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NOVAA
Sponsor study identifier
observational
800
1 country
1
Study Start
January 1, 2008
Actual (reported)Primary Completion
February 17, 2024
Estimated (date passed)This estimate has passed. The registry has not confirmed an actual date for this milestone.
Study Completion
February 17, 2024
Estimated (date passed)This estimate has passed. The registry has not confirmed an actual date for this milestone.
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.
Registry-reported information. Elapsed time and enrollment totals alone do not measure study progress or recruitment pace.
February 17, 2024
Estimated (date passed)This estimate has passed. The registry has not confirmed an actual date for this milestone.
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Some registry-reported names shown below are not yet linked in this graph.
Dates and qualifiers reported by the registry. Estimated dates do not confirm that a milestone has occurred.
January 1, 2008
Actual (reported)February 15, 2021
Actual (reported)February 18, 2021
Actual (reported)February 17, 2024
Estimated (date passed)This estimate has passed. The registry has not confirmed an actual date for this milestone.
February 17, 2024
Estimated (date passed)This estimate has passed. The registry has not confirmed an actual date for this milestone.
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
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
This group is dedicated to developing an automated algorithm
This group is dedicated to testing the semantic performance of an automated algorithm
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
Registry-reported names are shown in full. Names without a link have not yet been matched to a Syfrah profile.
Patients with chronic lung disease and a Clinical examination, pulmonary function test, and CT acquired during an annual routine follow-up
Contact the study team to confirm eligibility.
Hopital Haut Leveque
Pessac, France
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.
Patrick Berger, Pr
Hopital Haut Leveque
First Submitted
February 15, 2021
First Posted
February 18, 2021
Study Start
January 1, 2008
Actual (reported)Primary Completion
February 17, 2024
Estimated (date passed)Blood Collection From Individuals With Lung Disease for Genetic Studies (Qatar)
This estimate has passed. The registry has not confirmed an actual date for this milestone.
Study Completion
February 17, 2024
Estimated (date passed)This estimate has passed. The registry has not confirmed an actual date for this milestone.
Last Updated
January 5, 2024
Record last verified: 2023-09
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Source: ClinicalTrials.gov.
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