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

Not Applicable

Not yet recruiting

• Conditions: Endotracheal Tube; Nasogastric Tube; Pneumothorax

• Registration Number: NCT06842043
• Lead Sponsor: National Taiwan University Hospital

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.

Detailed Description

Not available

Study Timeline

• Status Verified: 2025-02
• Study First Submitted: 2025-02-12
• Study First Submitted QC: 2025-02-18
• Last Update Submitted: 2025-02-18
• Study First Posted: 2025-02-24
• Last Update Posted: 2025-02-24
• Study Start: 2025-03-01
• Primary Completion: 2027-12-31
• Study Completion: 2027-12-31

Recruitment & Eligibility

• Status: NOT_YET_RECRUITING
• Sex: All
• Target Recruitment: 10900

Inclusion Criteria

Not provided

Exclusion Criteria

Not provided

Study & Design

• Study Type: INTERVENTIONAL
• Study Design: PARALLEL

Primary Outcome Measures

Name	Time	Method
In-hospital Mortality	During the hospital stay, an average of 1 week	The patient's survival is monitored after undergoing a chest X-ray until hospital discharge.

Secondary Outcome Measures

Name	Time	Method
Length of Hospital Stay	During the hospital stay, an average of 1 week	The time a patient spends in the hospital from admission to discharge, usually measured in days.
Misplacement Detection Time	During the hospital stay, an average of 1 week	Evaluates whether the AI system can reduce the time to detect misplaced catheters or pneumothorax, thereby improving the timeliness of clinical intervention.

Trial Locations

Locations (1)

National Taiwan University Hospital; 🇨🇳 Taipei, Taiwan