Difficult AirwayNCT06982144
AI-based Prediction Model of Difficult Tracheal Intubation Using Medical Image Parameters
An observational study of Difficult Airway, run by Mu Dong Liang at 1 site in China.
Trial summary
Difficult airway is a life-threatening event during anesthesia. Prediction model is helpful to detect high-risk patients and decrease the risk of un-anticipated difficult airway. Present models are usually based on Mallampati grade and the width of mouth open. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Difficult Airway
- How many people
- 228 participants
- Where
- 1 siteChina
- Study dates
- 20 May 2025to 30 May 2026
- Phase
- Observational
- 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
- Adult, Older adult
Study population: Patients with head and neck CT data undergoing surgery under general anesthesia with endotracheal intubation
Who may be able to join
- age ≥18 years old;
- surgical patients undergoing general anesthesia with endotracheal intubation;
- with head and neck CT examination results
- Consent to participate in the study.
Who cannot join
- The presence of laryngeal edema;
- The presence of airway stenosis, including internal airway stenosis (such as foreign body or tumor) or stenosis caused by external tracheal mass compression;
- tracheo-esophageal fistula;
- severe gastroesophageal reflux;
- previous upper airway surgery, such as laryngeal cancer radical surgery, snoring surgery, etc.
- 6）participating in other research projects
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site
Peking University First Hospital
Beijing, Beijing Municipality, 100034
Dong-Liang Mu, +8601083575138, mudongliang@bjnu.edu.cn
About this study
Difficult airway is a life-threatening event during anesthesia. Prediction model is helpful to detect high-risk patients and decrease the risk of un-anticipated difficult airway. Present models are usually based on Mallampati grade and the width of mouth open. However, the prediction accuracy is only about 0.7-0.8 in different populations. Present study is designed to investigate if AI-based prediction model using medical imaging parameters (such as CT and MRI) can increase the accuracy of prediction model.
- Conditions
- Difficult Airway
- Keywords
- difficult airway, AI-based method, medical imaging, prediction model
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Adult patients scheduled for selective surgery | Not described |
What is being measured
The primary outcome is the main question the study is designed to answer.
The accuracy of prediction model based on AI analysis of medical imaging parameters
To establish a prediction model for difficult tracheal intubation based on medical imaging parameters (such as CT and MRI) using AI algorithms and verify its predictive accuracy.
Measured: day 1 (From enrollment to the end of anesthesia induction)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted21 May 2025
- Study start20 May 2025 (estimated)
- Primary completion1 Mar 2026 (estimated)
- Study completion30 May 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Difficult AirwayStudies of the same condition, open and under way.
- 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
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 228 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Mu Dong Liang
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Mu Dong Liang, Peking University First Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06982144
- Sponsor's protocol ID
- 2025R-0037
- Status last verified
- May 2025
- First submitted
- 13 May 2025
- Last updated
- 21 May 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 21 May 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- AI-based Prediction Model of Difficult Tracheal Intubation Using Medical Image Parameters
- ClinicalTrials.gov ID
- NCT06982144
- Acronym / sponsor ID
- 2025R-0037
- Conditions
- Difficult Airway
- Keywords
- difficult airway, AI-based method, medical imaging, prediction model
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 228 participants (estimated)
- Lead sponsor
- Mu Dong LiangUniversity, hospital or other
- Study start
- 20 May 2025 (estimated)
- Primary completion
- 1 Mar 2026 (estimated)
- Study completion
- 30 May 2026 (estimated)
- First posted
- 21 May 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.
Difficult airway is a life-threatening event during anesthesia. Prediction model is helpful to detect high-risk patients and decrease the risk of un-anticipated difficult airway. Present models are usually based on Mallampati grade and the width of mouth open. However, the prediction accuracy is only about 0.7-0.8 in different populations. Present study is designed to investigate if AI-based prediction model using medical imaging parameters (such as CT and MRI) can increase the accuracy of prediction model.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Adult patients scheduled for selective surgery | – | – |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Age groups
- Adult, Older adult
- Study population
- Patients with head and neck CT data undergoing surgery under general anesthesia with endotracheal intubation
Key inclusion criteria (4 listed)
- age ≥18 years old;
- surgical patients undergoing general anesthesia with endotracheal intubation;
- with head and neck CT examination results
- Consent to participate in the study.
Key exclusion criteria (6 listed)
- The presence of laryngeal edema;
- The presence of airway stenosis, including internal airway stenosis (such as foreign body or tumor) or stenosis caused by external tracheal mass compression;
- tracheo-esophageal fistula;
- severe gastroesophageal reflux;
- previous upper airway surgery, such as laryngeal cancer radical surgery, snoring surgery, etc.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
The accuracy of prediction model based on AI analysis of medical imaging parameters
Time frame: day 1 (From enrollment to the end of anesthesia induction)
To establish a prediction model for difficult tracheal intubation based on medical imaging parameters (such as CT and MRI) using AI algorithms and verify its predictive accuracy.
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 21 May 2025.
- Individual participant dataSharing planned: No
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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?
An observational study of Difficult Airway, run by Mu Dong Liang at 1 site in China.
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 4 inclusion and 6 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 21 May 2025. 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 China. 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?
Contact Dongliang Mu Associate professor (+86 13810702725, mudongliang@bjmu.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The accuracy of prediction model based on AI analysis of medical imaging parameters (time frame: day 1 (From enrollment to the end of anesthesia induction)). 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 21 May 2025.