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

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.

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Key information

Conditions

Ventilator-associated Pneumonia Artificial Intelligence (AI) Bacterial Infections Bacterial Infections and Mycoses Cross Infection Disease Attributes Healthcare-Associated Pneumonia Iatrogenic Disease Infections Intensive Care Unit (ICU) Lung Diseases Pathologic Processes Pathological Conditions, Signs and Symptoms Pneumonia Pneumonia, Bacterial Pneumonia, Ventilator-Associated Radiography, Thoracic Respiratory Tract Diseases Respiratory Tract Infections

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Dr. Abdurrahman Yurtaslan Ankara Oncology Hospital

Yenimahalle, Ankara, 06370, Turkey (Türkiye)

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

Adult patients (≥18 years)
Admission to the anesthesia intensive care unit
Requirement of invasive mechanical ventilation for at least 48 hours
Clinical diagnosis of ventilator-associated pneumonia (VAP) based on institutional criteria
Availability of at least one chest X-ray prior to VAP diagnosis and one chest X-ray at the time of diagnosis
Availability of digital chest radiographs in the PACS system suitable for analysis

Exclusion criteria

Age <18 years
Absence of accessible digital chest radiographs
Chest radiographs with severe technical limitations preventing evaluation
Chest radiographs that could not be processed by the AI system (no score generated)
Presence of extensive pre-existing infiltrative lung disease preventing reliable assessment of new infiltrates
Missing key clinical data (e.g., VAP diagnosis date, mechanical ventilation duration)

Treatment and study plan

Primary outcomes

Change in Chester AI-derived Infiltration and Pneumonia Probability Scores Between Pre-diagnosis and VAP Diagnosis Chest X-rays

Time frame: From pre-diagnosis chest X-ray to the time of VAP diagnosis (typically within 1-5 days)

The primary outcome is the change in probability scores for "infiltration" and "pneumonia" generated by the Chester AI Radiology Assistant between chest X-rays obtained prior to VAP diagnosis and those obtained at the time of diagnosis. These scores range from 0 to 1 and represent the likelihood of the presence of each finding.

Sponsors and collaborators

Lead sponsor

Dr Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital

Other

Registry information

Official study title

Artificial Intelligence-Based Evaluation of Chest X-Rays in the Diagnosis of Ventilator-Associated Pneumonia in the Intensive Care Unit: A Retrospective Comparative Study

Important dates

Study start: 2026
Primary completion: 2026
Study completion: 2026
First posted: Apr 3, 2026
Registry last updated: Apr 3, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Published trials that share one or more normalized conditions with this study.

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