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

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

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.

Study Overview

• Status: Completed
• Conditions: Pneumonia, Bacterial; Ventilator-Associated Pneumonia; Radiography, Thoracic; Artificial Intelligence (AI); Intensive Care Unit (ICU)

• Study Type: Observational
• Enrollment (Actual): 119

Contacts and Locations

Study Locations

• Turkey (Türkiye)
  • Ankara
    • Yenimahalle, Ankara, Turkey (Türkiye), 06370
      • Dr. Abdurrahman Yurtaslan Ankara Oncology Hospital

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

The study population consists of adult patients admitted to a tertiary care anesthesia intensive care unit who required invasive mechanical ventilation and received a clinical diagnosis of ventilator-associated pneumonia (VAP). Patients were identified retrospectively through hospital electronic medical records and infection control committee databases. This cohort represents a critically ill ICU population with high disease severity and diverse comorbid conditions.

Description

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)

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort

VAP Patients; This cohort includes adult patients admitted to the anesthesia intensive care unit who were mechanically ventilated for at least 48 hours and received a clinical diagnosis of ventilator-associated pneumonia (VAP). For each patient, chest radiographs obtained prior to and at the time of VAP diagnosis were analyzed. Images were evaluated by three approaches: clinical assessment, independent radiologist interpretation, and artificial intelligence-based analysis using the Chester AI system. The study is observational, and no intervention was applied.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Change in Chester AI-derived Infiltration and Pneumonia Probability Scores Between Pre-diagnosis and VAP Diagnosis Chest X-rays	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.	From pre-diagnosis chest X-ray to the time of VAP diagnosis (typically within 1-5 days)

Collaborators and Investigators

• Sponsor: Dr Abdurrahman Yurtaslan Ankara Oncology Training and Research Hospital

Study record dates

Study Major Dates

• Study Start (Actual): March 1, 2026
• Primary Completion (Actual): March 20, 2026
• Study Completion (Actual): March 25, 2026

Study Registration Dates

• First Submitted: March 26, 2026
• First Submitted That Met QC Criteria: March 31, 2026
• First Posted (Actual): April 3, 2026

Study Record Updates

• Last Update Posted (Actual): April 3, 2026
• Last Update Submitted That Met QC Criteria: March 31, 2026
• Last Verified: March 1, 2026

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Healthcare-Associated Pneumonia; Pathologic Processes; Disease Attributes; Respiratory Tract Infections; Infections; Respiratory Tract Diseases; Lung Diseases; Pneumonia; Bacterial Infections; Bacterial Infections and Mycoses; Cross Infection; Iatrogenic Disease; Pathological Conditions, Signs and Symptoms; Pneumonia, Ventilator-Associated; Pneumonia, Bacterial
• Other Study ID Numbers: 2026-02/23

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No