Clinical Effectiveness and Cost-Effectiveness of Real-Time Chest X-Ray Computer-Aided Detection System for Misplaced Endotracheal and Nasogastric Tubes and Pneumothorax in Emergency and Critical Care Settings
Trial ID: NCT06842043
Status: NOT_YET_RECRUITING
Study type: INTERVENTIONAL
Phase: NA
Start date: 2026-04-01
Completion date: 2027-12-31
Sponsor: National Taiwan University Hospital
Collaborators: Fu Jen Catholic University Hospital; Min-Sheng General Hospital; National Taiwan University
Conditions: Endotracheal Tube; Nasogastric Tube; Pneumothorax
Interventions: AI-assisted model
Locations: National Taiwan University Hospital
Countries: Taiwan
Enrollment: 10900
Age range: 18 Years - None
Sex: ALL

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.

Eligibility criteria:
Inclusion Criteria for units:

* 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.

Exclusion Criteria for units:

* 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).

Inclusion Criteria for Patients:

● 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.

Exclusion Criteria for Patients: Patients in isolation wards or pediatric

* Patients in isolation wards.
* Patients in Infant Intensive Care Unit

Outcomes:
In-hospital Mortality