Obesity Difficult Airway Airway ManagementAI-AirwayNCT07666074
AI-Based Prediction of Difficult Airway in Bariatric Surgery
An observational study of Obesity Difficult Airway Airway Management, run by Elazıg Fethi Sekin Sehir Hastanesi at 1 site in Turkey (Türkiye).
Trial summary
The aim of this prospective study is to evaluate the accuracy of artificial intelligence (AI) and machine learning algorithms in predicting difficult airways in patients undergoing bariatric surgery. Read more about this study
- Who is it for?
- Ages 18 to 65 yearsHealthy volunteers can take part
- Conditions the study is for
- Obesity Difficult Airway Airway Management
- What is being tested?
- Preoperative Airway Assessment and Direct Laryngoscopy
- How many people
- 340 participants
- Where
- 1 siteTurkey (Türkiye)
- Study dates
- 21 May 2026to 15 Oct 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 to 65 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: The study population consists of adult obese patients (BMI ≥ 35 kg/m²) undergoing elective bariatric surgery under general anesthesia in a tertiary academic medical center. This population represents individuals at a higher baseline risk for difficult airway management and intubation.
Who may be able to join
- Adult patients aged 18 to 65 years.
- Scheduled for elective bariatric surgery under general anesthesia.
- Body Mass Index (BMI) ≥ 35 kg/m².
- Consenting to participate in the study.
Who cannot join
- Patients with known upper airway anatomical deformities, head and neck tumors, or a history of head/neck radiotherapy.
- History of maxillofacial, airway, or cervical spine surgery.
- Emergency surgeries.
- Patients requiring planned awake fiberoptic intubation based on obvious preoperative clinical indicators.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Turkey (Türkiye)1 site, 1 recruiting
- Recruiting
Fethi Sekin City Hospital
Elâzığ, Elâzığ, 23100
Muhammed Başpınar, M.D., +905395831141, bspnr.muhammed@gmail.com
About this study
Official title: Artificial Intelligence-Based Prediction of Difficult Airway in Bariatric Surgery: A Prospective Evaluation of Preoperative Airway Predictors
The aim of this prospective study is to evaluate the accuracy of artificial intelligence (AI) and machine learning algorithms in predicting difficult airways in patients undergoing bariatric surgery. Preoperative airway assessments, including the Upper Lip Bite Test (UBLT), Mallampati score, Body Mass Index (BMI), thyromental distance (TMD), and sternomental distance (SMD), will be recorded. The study investigates whether AI models can provide higher sensitivity and specificity in predicting difficult intubation compared to traditional clinical scoring systems in the obese patient population.
- Being tested
- Preoperative Airway Assessment and Direct Laryngoscopy
- Keywords
- Artificial Intelligence, Airway Management, Obesity, Machine Learning
- Medical subject headings
- Obesity
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Preoperative Airway Assessment and Direct LaryngoscopyDiagnostic test
Also called Upper Lip Bite Test, Modified Mallampati Score, Thyromental Distance, Sternomental Distance
Measurement of preoperative airway parameters including Upper Lip Bite Test (UBLT), Mallampati score, Body Mass Index (BMI), thyromental distance, and sternomental distance. Intraoperative airway view is graded using the Cormack-Lehane classification during standard direct laryngoscopy.
| Study group | Role | What they receive |
|---|---|---|
| Bariatric Surgery Patients | Patients scheduled for elective bariatric surgery under general anesthesia who undergo preoperative airway assessment using clinical and morphometric predictors. Receives: Diagnostic Test: Preoperative Airway Assessment and Direct Laryngoscopy |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic Accuracy of the Artificial Intelligence Model in Predicting Difficult Intubation
The predictive performance of the AI model will be evaluated by comparing its preoperative difficult airway prediction against the actual intraoperative direct laryngoscopy view. The intraoperative view is graded using the Cormack-Lehane classification system. Grades 3 and 4 are clinically defined as difficult intubation, while Grades 1 and 2 are defined as easy intubation. The primary metric of diagnostic accuracy will be the Area Under the Receiver Operating Characteristic (AUC-ROC) curve.
Measured: Intraoperative (assessed during the primary intubation attempt)
Secondary outcomes (2)
Number of Intubation Attempts
Total number of direct laryngoscopy attempts required to achieve successful tracheal intubation.
Measured: Intraoperative
Need for Alternative Airway Management Techniques
The frequency of requiring alternative airway devices or strategies (e.g., video laryngoscope, bougie, or fiberoptic bronchoscope) to secure the airway after a primary direct laryngoscopy.
Measured: Intraoperative
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted24 Jun 2026
- Study start21 May 2026
- Primary completion1 Sep 2026 (estimated)
- Study completion15 Oct 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Obesity Difficult Airway Airway ManagementStudies 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
- 340 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Elazıg Fethi Sekin Sehir Hastanesi
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Muhammed Başpınar, Elazıg Fethi Sekin Sehir Hastanesi
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07666074
- Sponsor's protocol ID
- 2026/30-28
- Acronym
- AI-Airway
- Status last verified
- Jun 2026
- First submitted
- 18 Jun 2026
- Last updated
- 24 Jun 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: De-identified individual participant data (including preoperative clinical/morphometric airway measurements and intraoperative Cormack-Lehane grades) underlying the results reported in the final publication will be shared to promote transparency and reproducibility in machine learning models.
Study at a glance
From the registry record, last updated by the sponsor on 24 Jun 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence-Based Prediction of Difficult Airway in Bariatric Surgery: A Prospective Evaluation of Preoperative Airway Predictors
- ClinicalTrials.gov ID
- NCT07666074
- Acronym / sponsor ID
- AI-Airway / 2026/30-28
- Keywords
- Artificial Intelligence, Airway Management, Obesity, Machine Learning
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 340 participants (estimated)
- Lead sponsor
- Elazıg Fethi Sekin Sehir HastanesiUniversity, hospital or other
- Study start
- 21 May 2026 (actual)
- Primary completion
- 1 Sep 2026 (estimated)
- Study completion
- 15 Oct 2026 (estimated)
- First posted
- 24 Jun 2026 (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.
The aim of this prospective study is to evaluate the accuracy of artificial intelligence (AI) and machine learning algorithms in predicting difficult airways in patients undergoing bariatric surgery. Preoperative airway assessments, including the Upper Lip Bite Test (UBLT), Mallampati score, Body Mass Index (BMI), thyromental distance (TMD), and sternomental distance (SMD), will be recorded. The study investigates whether AI models can provide higher sensitivity and specificity in predicting difficult intubation compared to traditional clinical scoring systems in the obese patient population.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Bariatric Surgery Patients | – | Diagnostic Test: Preoperative Airway Assessment and Direct LaryngoscopyPatients scheduled for elective bariatric surgery under general anesthesia who undergo preoperative airway assessment using clinical and morphometric predictors. |
Who may qualify
- Ages
- 18 to 65 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- The study population consists of adult obese patients (BMI ≥ 35 kg/m²) undergoing elective bariatric surgery under general anesthesia in a tertiary academic medical center. This population represents individuals at a higher baseline risk for difficult airway management and intubation.
Key inclusion criteria (4 listed)
- Adult patients aged 18 to 65 years.
- Scheduled for elective bariatric surgery under general anesthesia.
- Body Mass Index (BMI) ≥ 35 kg/m².
- Consenting to participate in the study.
Key exclusion criteria (4 listed)
- Patients with known upper airway anatomical deformities, head and neck tumors, or a history of head/neck radiotherapy.
- History of maxillofacial, airway, or cervical spine surgery.
- Emergency surgeries.
- Patients requiring planned awake fiberoptic intubation based on obvious preoperative clinical indicators.
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 |
|---|---|---|
| Preoperative Airway Assessment and Direct LaryngoscopyAlso: Upper Lip Bite Test, Modified Mallampati Score, Thyromental Distance, Sternomental DistanceMeasurement of preoperative airway parameters including Upper Lip Bite Test (UBLT), Mallampati score, Body Mass Index (BMI), thyromental distance, and sternomental distance. Intraoperative airway view is graded using the Cormack-Lehane classification during standard direct laryngoscopy. | Diagnostic test | Bariatric Surgery Patients |
Primary outcome
Diagnostic Accuracy of the Artificial Intelligence Model in Predicting Difficult Intubation
Time frame: Intraoperative (assessed during the primary intubation attempt)
The predictive performance of the AI model will be evaluated by comparing its preoperative difficult airway prediction against the actual intraoperative direct laryngoscopy view. The intraoperative view is graded using the Cormack-Lehane classification system. Grades 3 and 4 are clinically defined as difficult intubation, while Grades 1 and 2 are defined as easy intubation. The primary metric of diagnostic accuracy will be the Area Under the Receiver Operating Characteristic (AUC-ROC) curve.
2 secondary outcomes
Number of Intubation Attempts
Time frame: Intraoperative
Need for Alternative Airway Management Techniques
Time frame: Intraoperative
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 24 Jun 2026.
- Individual participant dataSharing planned: Yes. De-identified individual participant data (including preoperative clinical/morphometric airway measurements and intraoperative Cormack-Lehane grades) underlying the results reported in the final publication will be shared to promote transparency and reproducibility in machine learning models.
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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 Obesity Difficult Airway Airway Management, run by Elazıg Fethi Sekin Sehir Hastanesi 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: looking for participants now, as of the sponsor's last update on 24 Jun 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 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?
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 Muhammed Başpınar, M.D. (+905395831141, bspnr.muhammed@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Diagnostic Accuracy of the Artificial Intelligence Model in Predicting Difficult Intubation (time frame: Intraoperative (assessed during the primary intubation attempt)). 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 24 Jun 2026.