Using Artificial Intelligence for the Detection of Respiratory Diseases Associated With Pollution

NCT06512142 | Enrolling By Invitation

• 1 other identifier: PostdocTrusculescu/11.2023
• Study Type: observational
• Target: 30,000
• Locations: 1 country
• Sites: 1

Brief Summary

Generating a picture at the city level, through geospatial and temporal grouping a lung diseases associated or exacerbated by pollution (COPD, AB and neoplasm pulmonary or pleural) and correlating this data with pollution data. Development of an accurate and prognostic HRCT imaging diagnostic tool, computer assisted in the mentioned pathology, by generating an algorithm capable of to detect early the follow-up tomographic imaging lesions, as well as to evaluate objective their speed of evolution. Validation of the proposed algorithm by comparison with medical diagnosis.

Conditions

COPD Asthma; Lung Neoplasm; Emphysema

Keywords

complex networks; lung imaging; spectral analysis; geolocation

Outcome Measures

Primary Outcomes (2)

• Correlation between lung diseases and pollution

  Establishing the relationship between the incidence and exacerbation of lung diseases (COPD, acute bronchitis, and pulmonary or pleural neoplasms) and pollution levels through geospatial and temporal analysis at the city /county level

  December 2023 - December 2024

• Development of a diagnostic algorithm

  Creating an accurate, computer-assisted HRCT imaging diagnostic tool designed to detect early tomographic lesions and evaluate the progression speed of these lesions in patients with the mentioned pathologies.

  September 2024 - March 2024

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The primary specialized Victor Babes Respiratory Hospital patients

You may qualify if:

• Patients must be diagnosed with COPD and/or asthma and/or pulmonary neoplasm and/or secondary pulmonary, pleural or mediastinal determinations, suspected or confirmed, according to ICD-10.
• Patients will be included regardless of the type of hospitalization, continuous or day.
• The existence of freely expressed consent, carried out according to Ethics standards professional (from the observation sheet).
• For stage 2
• The use of HRCT images, in DICOM format, with a maximum cup thickness of 1.5 mm and with an average of 250 cups; no image acquisition errors;
• Imaging monitoring at a time interval;.
• Respiratory function evaluation data available: spirometry +/- the factor of gaseous diffusion (DLco);

You may not qualify if:

• For both stages:
• \- Patients who do not have a stable real domicile (eg social cases) or are not from the Timis county
• For stage 2
• Patients who do not have HRCT images available or cannot be followed.
• Patients who have insufficient quality HRCT images.
• Patients who have a history of lung surgery.
• Patients who have a history of allergies to contrast agents used in imaging HRCT.

Sponsors & Collaborators

• University of Medicine and Pharmacy "Victor Babes" Timisoara: lead

Study Sites (1)

"Victor Babes" University Hospital

Timișoara, Timiș County, Romania

Location

MeSH Terms

Conditions

Lung Neoplasms; Emphysema

Condition Hierarchy (Ancestors)

Respiratory Tract Neoplasms; Thoracic Neoplasms; Neoplasms by Site; Neoplasms; Lung Diseases; Respiratory Tract Diseases; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Dr.

Study Record Dates

• First Submitted: July 15, 2024
• First Posted: July 22, 2024
• Study Start: November 1, 2023
• Primary Completion: December 30, 2024
• Study Completion: March 31, 2025
• Last Updated: July 22, 2024

Record last verified: 2024-07

Data Sharing

• IPD Sharing: Will not share

Locations

RO (1)