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| Name | Class |
|---|---|
| Institut National de la Santé Et de la Recherche Médicale, France | OTHER_GOV |
| Collaborative NOVAA study group | UNKNOWN |
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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.
Three chronic lung diseases will constitute the target of the study, by using retrospective data from each lung disease:
Dedicated algorithms will be developped for each disease condition.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| 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 |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Observational study | Other |
|
| Measure | Description | Time Frame |
|---|---|---|
| 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 |
| Measure | Description | Time Frame |
|---|---|---|
| Correlation with pulmonary function test | Correlation of quantitative measurement with pulmonary function | From date of inclusion until the date of final quantification, assessed up to 12 months |
| Longitudinal variation over time |
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Inclusion Criteria:
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Patients with chronic lung disease and a Clinical examination, pulmonary function test, and CT acquired during an annual routine follow-up
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| Name | Affiliation | Role |
|---|---|---|
| Patrick Berger, Pr | Hopital Haut Leveque | Study Chair |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Hopital Haut Leveque | Pessac | France |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 34266943 | Derived | 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. |
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Automated software measurements
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| ID | Term |
|---|---|
| D003550 | Cystic Fibrosis |
| D001249 | Asthma |
| D029424 | Pulmonary Disease, Chronic Obstructive |
| D017563 | Lung Diseases, Interstitial |
| ID | Term |
|---|---|
| D010182 | Pancreatic Diseases |
| D004066 | Digestive System Diseases |
| D008171 | Lung Diseases |
| D012140 | Respiratory Tract Diseases |
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| ID | Term |
|---|---|
| D019370 | Observation |
| ID | Term |
|---|---|
| D008722 | Methods |
| D008919 | Investigative Techniques |
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Comparison of quantitative measurement at two time points
| From date of inclusion until the date of final quantification, assessed up to 12 months |
| Reproducibility | Evaluation of measurements when performed twice | From date of inclusion until the date of final quantification, assessed up to 12 months |
| D030342 |
| Genetic Diseases, Inborn |
| D009358 | Congenital, Hereditary, and Neonatal Diseases and Abnormalities |
| D007232 | Infant, Newborn, Diseases |
| D001982 | Bronchial Diseases |
| D008173 | Lung Diseases, Obstructive |
| D012130 | Respiratory Hypersensitivity |
| D006969 | Hypersensitivity, Immediate |
| D006967 | Hypersensitivity |
| D007154 | Immune System Diseases |
| D002908 | Chronic Disease |
| D020969 | Disease Attributes |
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |