Difficult Airway IntubationNCT07152093
Development of an Artificial Intelligence-Based Model for Predicting Difficult Intubation Using Video Laryngoscopic Images and Cormack-Lehane Classification
An observational study of Difficult Airway Intubation, run by Duzce University at 1 site in Turkey (Türkiye).
Trial summary
This prospective observational study aims to develop an artificial intelligence model that can automatically determine the Cormack-Lehane classification from video laryngoscopy images in patients undergoing elective surgery. It also aims to predict the risk of difficult intubation based on this classification. Read more about this study
- Who is it for?
- Ages 18 to 65 years
- Conditions the study is for
- Difficult Airway Intubation
- How many people
- 132 participants
- Where
- 1 siteTurkey (Türkiye)
- Study dates
- 1 May 2025to 1 Oct 2025
- How long will I take part?
- 1 Day
- Phase
- Observational
- Are there results?
- None posted in the registry
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 to 65 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: The study population will consist of adult patients undergoing elective surgery under general anesthesia at the operating rooms of Düzce University Medical Faculty Hospital. All patients will have their airways assessed using video laryngoscopy as part of routine anesthesia induction. Only patients without known upper airway pathology will be included. Patients will be prospectively and consecutively recruited. Video laryngoscopy images will be captured during intubation and used for machine learning analysis. The Cormack-Lehane grade will be independently confirmed by two experienced anesthesiologists. Patients will be classified into normal and difficult intubation groups.
Who may be able to join
- 18-65 years
- Elective surgery
- ASA I-II
- No upper airway pathology
Who cannot join
- Known history of difficult intubation
- Morbid obesity (BMI > 40)
- Pregnancy
- History of upper airway surgery
Trial locations and contacts
1 site in 1 country.
Turkey (Türkiye)1 site
Duzce University Faculty of Medicine, Department of Anesthesiology and Reanimation
Düzce, Merkez
About this study
This prospective observational study aims to develop an artificial intelligence model that can automatically determine the Cormack-Lehane classification from video laryngoscopy images in patients undergoing elective surgery. It also aims to predict the risk of difficult intubation based on this classification. The resulting data will evaluate the applicability of AI-supported decision support systems in clinical airway management.
- Conditions
- Difficult Airway Intubation
- Keywords
- Difficult Airway, Cormack-Lehane, Video Laryngoscopy, Artificial Intelligence, Machine Learning
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 |
|---|---|---|
| Group 1: Normal Intubation Group | Intubations in patients assessed as Cormack-Lehane (CL) Class 1-2. | |
| Difficult Intubation Group | Intubations in patients evaluated as Cormack-Lehane Class 3-4. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy of Machine Learning Model in Predicting Difficult Intubation Based on Video Laryngoscopy Images
The primary outcome is the classification accuracy of the machine learning algorithm in identifying difficult intubation cases (Cormack-Lehane grade 3-4) from video laryngoscopy images, compared with expert anesthesiologists' consensus. Accuracy will be reported as a percentage.
Measured: Immediately after data collection and model training
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted3 Sep 2025
- Study start1 May 2025
- Primary completion30 Sep 2025
- Study completion1 Oct 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Difficult Airway IntubationStudies 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
- 132 participants (actual)
- Target duration
- 1 Day
Sponsor and investigators
- Lead sponsor
- Duzce University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Gizem Demir Şenoğlu, Duzce University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07152093
- Sponsor's protocol ID
- 2025/183
- Status last verified
- Nov 2025
- First submitted
- 21 Aug 2025
- Last updated
- 18 Nov 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 18 Nov 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development of an Artificial Intelligence-Based Model for Predicting Difficult Intubation Using Video Laryngoscopic Images and Cormack-Lehane Classification
- ClinicalTrials.gov ID
- NCT07152093
- Acronym / sponsor ID
- 2025/183
- Conditions
- Difficult Airway Intubation
- Keywords
- Difficult Airway, Cormack-Lehane, Video Laryngoscopy, Artificial Intelligence, Machine Learning
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 132 participants (actual)
- Lead sponsor
- Duzce UniversityUniversity, hospital or other
- Study start
- 1 May 2025 (actual)
- Primary completion
- 30 Sep 2025 (actual)
- Study completion
- 1 Oct 2025 (actual)
- First posted
- 3 Sep 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.
This prospective observational study aims to develop an artificial intelligence model that can automatically determine the Cormack-Lehane classification from video laryngoscopy images in patients undergoing elective surgery. It also aims to predict the risk of difficult intubation based on this classification. The resulting data will evaluate the applicability of AI-supported decision support systems in clinical airway management.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 1 Day
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Group 1: Normal Intubation Group | – | –Intubations in patients assessed as Cormack-Lehane (CL) Class 1-2. |
| Difficult Intubation Group | – | –Intubations in patients evaluated as Cormack-Lehane Class 3-4. |
Who may qualify
- Ages
- 18 to 65 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- The study population will consist of adult patients undergoing elective surgery under general anesthesia at the operating rooms of Düzce University Medical Faculty Hospital. All patients will have their airways assessed using video laryngoscopy as part of routine anesthesia induction. Only patients without known upper airway pathology will be included. Patients will be prospectively and consecutively recruited. Video laryngoscopy images will be captured during intubation and used for machine learning analysis. The Cormack-Lehane grade will be independently confirmed by two experienced anesthesiologists. Patients will be classified into normal and difficult intubation groups.
Key inclusion criteria (4 listed)
- 18-65 years
- Elective surgery
- ASA I-II
- No upper airway pathology
Key exclusion criteria (4 listed)
- Known history of difficult intubation
- Morbid obesity (BMI > 40)
- Pregnancy
- History of upper airway surgery
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
Accuracy of Machine Learning Model in Predicting Difficult Intubation Based on Video Laryngoscopy Images
Time frame: Immediately after data collection and model training
The primary outcome is the classification accuracy of the machine learning algorithm in identifying difficult intubation cases (Cormack-Lehane grade 3-4) from video laryngoscopy images, compared with expert anesthesiologists' consensus. Accuracy will be reported as a percentage.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 18 Nov 2025.
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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. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
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 Intubation, run by Duzce University at 1 site in Turkey (Türkiye).
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 4 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: finished, as of the sponsor's last update on 18 Nov 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 Turkey (Türkiye). 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy of Machine Learning Model in Predicting Difficult Intubation Based on Video Laryngoscopy Images (time frame: Immediately after data collection and model training). 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 18 Nov 2025.