AI-Assisted Chest X-Ray for Misplaced Endotracheal and Nasogastric Tubes and Pneumothorax in Emergency and Critical Care Settings

NCT06842043 | Not Yet Recruiting DMC

• 1 other identifier: 202412055DINA
• Study Type: interventional
• Target: 10,900
• Locations: 1 country
• Sites: 1

Brief Summary

Background Advancements in artificial intelligence (AI) have driven significant breakthroughs in computer-aided detection (CAD) for chest X-ray imaging. National Taiwan University Hospital (NTUH) research team previously developed an AI-based emergency Capstone CXR system (MOST 111-2634-F-002-015-, Capstone project), which led to the creation of a chest X-ray module. This chest X-ray module has an established model supported by extensive research and is ready for direct application in clinical trials without requiring additional model training. This study will utilize three submodules of the system: detection of misplaced endotracheal tubes, detection of misplaced nasogastric tubes, and identification of pneumothorax. Objective This study aims to apply a real-time chest X-ray CAD system in emergency and critical care settings to evaluate its clinical and economic benefits without requiring additional chest X-ray examinations or altering standard care and procedures. The study will evaluate the CAD system's impact on mortality reduction, post-intubation complications, hospital stay duration, workload, and interpretation time, alongside a cost-effectiveness comparison with standard care. Methods This study adopts a pilot trial and cluster randomized controlled trial design, with random assignment conducted at the ward level. In the intervention group, units are granted access to AI diagnostic results, while the control group continues standard care practices. Consent will be obtained from attending physicians, residents, and advanced practice nurses in each participating ward. Once consent is secured, these healthcare providers in the intervention group will be authorized to use the CAD system. Intervention units will have access to AI-generated interpretations, whereas control units will maintain routine medical procedures without access to the AI diagnostic outputs. Results The study was funded in September 2024. Data collection is expected to last from January 2025 to December 2027. Conclusions This study anticipates that the real-time chest X-ray CAD system will automate the identification and detection of misplaced endotracheal and nasogastric tubes on chest X-rays, as well as assist clinicians in diagnosing pneumothorax. By reducing the workload of physicians, the system is expected to shorten the time required to detect tube misplacement and pneumothorax, decrease patient mortality and hospital stays, and ultimately lower healthcare costs.

Conditions

Nasogastric Tube; Pneumothorax; Endotracheal Tube

Keywords

Computer-aided detection system; Artificial intelligence; Pneumothorax diagnosis; Endotracheal tube; Nasogastric tube; Clinical effectiveness; Cost effectiveness

Outcome Measures

Primary Outcomes (1)

• In-hospital Mortality

  The patient's survival is monitored after undergoing a chest X-ray until hospital discharge.

  During the hospital stay, an average of 1 week

Secondary Outcomes (2)

• Length of Hospital Stay

  During the hospital stay, an average of 1 week

• Misplacement Detection Time

  During the hospital stay, an average of 1 week

Study Arms (2)

Intervention

EXPERIMENTAL

Other: AI-assisted model

standard clinical practice

NO INTERVENTION

Interventions

AI-assisted model OTHER

physicians will be authorized to access the AI model's predictions during patient care as an additional decision-making reference. These predictions will be generated in seconds and can help identify issues such as tube misplacement (e.g., nasogastric tube, endotracheal tube) and pneumothorax through AI analysis of CXRs, which will alert the physician to review the images.

Intervention

Eligibility Criteria

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

You may qualify if:

• Emergency critical care or intensive care units.
• The units included the patients requiring chest X-rays due to endotracheal intubation, nasogastric tube insertion, or ventilator use with a risk of pneumothorax.

You may not qualify if:

• The unit supervisor doesn't agree to participate in the trial.
• The unit is unable to implement the AI-assisted system (e.g., no data connection or system support).
• ● Patients who are adults and require chest X-ray due to one of the following conditions: endotracheal intubation, nasogastric intubation, or the use of a ventilator with the potential to cause pneumothorax.
• Patients in isolation wards.
• Patients in Infant Intensive Care Unit

Sponsors & Collaborators

• National Taiwan University Hospital: lead
• Min-Sheng General Hospital: collaborator
• Fu Jen Catholic University Hospital: collaborator
• National Taiwan University: collaborator

Study Sites (1)

National Taiwan University Hospital

Taipei, Taiwan, 100225, Taiwan

Location

MeSH Terms

Conditions

Pneumothorax

Condition Hierarchy (Ancestors)

Pleural Diseases; Respiratory Tract Diseases

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Model Details: Each group requires 5,450 patients.

Study Record Dates

• First Submitted: February 12, 2025
• First Posted: February 24, 2025
• Study Start: April 1, 2026
• Primary Completion (Estimated): December 31, 2027
• Study Completion (Estimated): December 31, 2027
• Last Updated: March 17, 2026

Record last verified: 2026-03

Locations

TW (1)