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Artificial Intelligence-Based Evaluation of Chest X-Rays in Ventilator-Associated Pneumonia

NCT07509697 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 119

Last updated 2026-04-03

No results posted yet for this study

Summary

Ventilator-associated pneumonia (VAP) is a common and serious infection in critically ill patients receiving mechanical ventilation in intensive care units (ICUs). One of the key diagnostic criteria for VAP is the presence of a new or progressive infiltrate on chest X-ray; however, interpretation of bedside chest radiographs is often challenging and subject to inter-observer variability.

This retrospective observational study aims to evaluate the role of artificial intelligence (AI) in the assessment of chest X-rays in patients with VAP. Chest radiographs obtained before and at the time of VAP diagnosis will be analyzed using a deep learning-based AI tool (Chester the AI Radiology Assistant), and changes in "infiltration" and "pneumonia" probability scores will be assessed.

AI-based findings will be compared with clinical decisions and independent radiologist evaluations regarding the presence of new infiltrates. The study aims to determine the level of agreement between these approaches and to explore whether AI-based analysis can support a more objective and standardized interpretation of chest radiographs in the diagnosis of VAP.

Conditions

  • Ventilator-Associated Pneumonia
  • Intensive Care Unit (ICU)
  • Pneumonia, Bacterial
  • Artificial Intelligence (AI)
  • Radiography, Thoracic

Sponsors & Collaborators

  • Dr Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2026-03-01
Primary Completion
2026-03-20
Completion
2026-03-25

Countries

  • Turkey (Türkiye)

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07509697 on ClinicalTrials.gov