Endotracheal TubeNCT06842043
AI-Assisted Chest X-Ray for Misplaced Endotracheal and Nasogastric Tubes and Pneumothorax in Emergency and Critical Care Settings
A study testing AI-assisted model in people with Endotracheal Tube and Nasogastric Tube, run by National Taiwan University Hospital at 1 site in Taiwan.
Trial 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)… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Endotracheal Tube, Nasogastric Tube, Pneumothorax
- What is being tested?
- AI-assisted model
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 10,900 participants
- Where
- 1 siteTaiwan
- Study dates
- 1 Apr 2026to 31 Dec 2027
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- 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.
- ● 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
Who cannot join
- 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).
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Taiwan1 site
National Taiwan University Hospital
Taipei, Taiwan, 100225
Chu-Lin Tsai, Medical Doctor, +886-2-2312-3456 ext. 267684, chulintsai2@ntu.edu.tw
About this study
Official title: 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
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.
Read more
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
- Endotracheal Tube, Nasogastric Tube, Pneumothorax
- Being tested
- AI-assisted model
- Keywords
- Computer-aided detection system, Artificial intelligence, Pneumothorax diagnosis, Endotracheal tube, Nasogastric tube, Clinical effectiveness, Cost effectiveness
- Medical subject headings
- Pneumothorax
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-assisted modelOther
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.
| Study group | Role | What they receive |
|---|---|---|
| Intervention | Experimental | Not described Receives: Other: AI-assisted model |
| standard clinical practice | No intervention | Not described |
What is being measured
The primary outcome is the main question the study is designed to answer.
In-hospital Mortality
The patient's survival is monitored after undergoing a chest X-ray until hospital discharge.
Measured: During the hospital stay, an average of 1 week
Secondary outcomes (2)
Length of Hospital Stay
The time a patient spends in the hospital from admission to discharge, usually measured in days.
Measured: During the hospital stay, an average of 1 week
Misplacement Detection Time
Evaluates whether the AI system can reduce the time to detect misplaced catheters or pneumothorax, thereby improving the timeliness of clinical intervention.
Measured: During the hospital stay, an average of 1 week
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted24 Feb 2025
- Study start1 Apr 2026 (estimated)
- Primary completion31 Dec 2027 (estimated)
- Study completion31 Dec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Endotracheal TubeStudies of the same condition, open and under way.
- More trials run by National Taiwan University HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Model description
- Each group requires 5,450 patients.
- Masking
- None (open label)
- Enrolment
- 10,900 participants (estimated)
Sponsor and investigators
- Lead sponsor
- National Taiwan University Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- Fu Jen Catholic University Hospital, Min-Sheng General Hospital, National Taiwan University
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06842043
- Sponsor's protocol ID
- 202412055DINA
- Status last verified
- Mar 2026
- First submitted
- 12 Feb 2025
- Last updated
- 17 Mar 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 17 Mar 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- 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
- ClinicalTrials.gov ID
- NCT06842043
- Acronym / sponsor ID
- 202412055DINA
- Conditions
- Endotracheal Tube, Nasogastric Tube, Pneumothorax
- Keywords
- Computer-aided detection system, Artificial intelligence, Pneumothorax diagnosis, Endotracheal tube, Nasogastric tube, Clinical effectiveness, Cost effectiveness
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 10,900 participants (estimated)
- Lead sponsor
- National Taiwan University HospitalUniversity, hospital or other
- Collaborators
- Fu Jen Catholic University Hospital, Min-Sheng General Hospital, National Taiwan University
- Study start
- 1 Apr 2026 (estimated)
- Primary completion
- 31 Dec 2027 (estimated)
- Study completion
- 31 Dec 2027 (estimated)
- First posted
- 24 Feb 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
- Model description
- Each group requires 5,450 patients.
| Arm | Type | Receives |
|---|---|---|
| Intervention | Experimental | Other: AI-assisted model |
| standard clinical practice | No intervention | – |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (6 listed)
- 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.
- ● 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.
Key exclusion criteria (2 listed)
- 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).
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI-assisted modelphysicians 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. | Other | Intervention |
Primary outcome
In-hospital Mortality
Time frame: During the hospital stay, an average of 1 week
The patient's survival is monitored after undergoing a chest X-ray until hospital discharge.
2 secondary outcomes
Length of Hospital Stay
Time frame: During the hospital stay, an average of 1 week
Misplacement Detection Time
Time frame: During the hospital stay, an average of 1 week
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 17 Mar 2026.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing AI-assisted model in people with Endotracheal Tube and Nasogastric Tube, run by National Taiwan University Hospital at 1 site in Taiwan.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 6 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 17 Mar 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Taiwan. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Each site lists its own contact: choose the nearest one under Trial locations. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
In-hospital Mortality (time frame: During the hospital stay, an average of 1 week). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 17 Mar 2026.