Difficult IntubationAI-AIRWAYNCT07700485
Preoperative Airway Images for Difficult Airway Prediction
An observational study of Difficult Intubation and Difficult Laryngoscopy, run by Memorial Atasehir Hospital at 1 site in Turkey (Türkiye).
Trial summary
This prospective observational study will evaluate whether commonly available multimodal artificial intelligence models can predict difficult laryngoscopy and difficult intubation using standardized preoperative airway photographs. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Difficult Intubation, Difficult Laryngoscopy
- How many people
- 319 participants
- Where
- 1 siteTurkey (Türkiye)
- Study dates
- 25 Jun 2026to 1 Sep 2026
- How long will I take part?
- 1 Day
- 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
- No healthy volunteers
- Adult, Older adult
Study population: Adult patients scheduled for elective surgical procedures requiring endotracheal intubation at Dr. Siyami Ersek Thoracic and Cardiovascular Surgery Training and Research Hospital.
Who may be able to join
- Age 18 years or older
- Scheduled for elective surgery requiring endotracheal intubation
- Able to cooperate with the standardized preoperative airway photography protocol
- Able to provide written informed consent
Who cannot join
- Age younger than 18 years
- Emergency surgery
- Refusal or inability to provide informed consent
- Inability to cooperate with the standardized photographic protocol
- Known craniofacial or cervical deformity
- History of major head and neck surgery or radiotherapy
Show all 12Show fewer
- Obstruction of key anatomical landmarks by facial hair, dressings, cervical collars, or other external devices
- Incomplete or poor-quality image sets despite repeated acquisition
- Missing clinical airway assessment data
- No endotracheal intubation performed
- Airway difficulty could not be reliably evaluated
- Planned awake fiberoptic intubation or other preplanned advanced airway technique because of known difficult airway
Trial locations and contacts
1 site in 1 country.
Turkey (Türkiye)1 site
Dr. Siyami Ersek Thoracic and Cardiovascular Surgery Training and Research Hospital
Istanbul, Kadıköy, 34734
About this study
Official title: Multimodal Artificial Intelligence for Image-Based Prediction of Difficult Airway: A Prospective Observational Study
This prospective observational study will evaluate whether commonly available multimodal artificial intelligence models can predict difficult laryngoscopy and difficult intubation using standardized preoperative airway photographs. Adult patients scheduled for elective surgery requiring endotracheal intubation will undergo an eight-view preoperative airway photography protocol. The anonymized image sets will be assessed by ChatGPT, Gemini, and Grok using the same structured prompt. Their predictions will be compared with expert anesthesiologist image-based assessments, conventional airway evaluation findings, and prospectively recorded intraoperative airway outcomes. The primary aim is to determine the diagnostic performance of AI models for predicting difficult intubation. A key secondary aim is to evaluate their performance for predicting difficult laryngoscopy. The study is intended to explore whether image-based AI assessment may support preoperative airway risk stratification as a clinician-supervised screening tool.
Read the detailed description
Preoperative airway assessment is important for identifying patients at risk for difficult laryngoscopy or difficult intubation. However, conventional bedside airway predictors have limited accuracy when used alone. Multimodal artificial intelligence models may provide additional image-based information by evaluating visible anatomical features from standardized preoperative airway photographs.
In this prospective observational study, adult patients undergoing elective surgery requiring endotracheal intubation will be enrolled between June and September 2026. Each participant will undergo standardized eight-view airway photography during the pre-anesthetic evaluation. The image set will include frontal facial, lateral profile, maximal mouth opening, modified Mallampati, neck extension, and anterior neck views. Images will be anonymized before assessment.
The same image sets will be independently evaluated by multimodal AI models, including ChatGPT, Gemini, and Grok, using an identical structured prompt. The AI models will provide categorical and binary predictions for difficult laryngoscopy and difficult intubation based only on visible image-based anatomical features. No intraoperative outcome data, expert predictions, or conventional airway assessment results will be provided to the AI models.
AI-generated predictions will be compared with expert anesthesiologist image-based assessments, conventional airway evaluation parameters, and prospectively recorded intraoperative reference outcomes. Difficult laryngoscopy will be defined as Cormack-Lehane grade III or IV. Difficult intubation will be defined using objective intraoperative criteria, including more than one intubation attempt, need for bougie or stylet assistance, rescue use of video laryngoscopy or supraglottic airway device, intubation time exceeding 60 seconds, or Intubation Difficulty Scale score greater than 5.
The study will assess the sensitivity, specificity, positive predictive value, negative predictive value, accuracy, receiver operating characteristic performance, and agreement between AI models and expert anesthesiologist assessments. The findings may help clarify whether multimodal AI can serve as a clinician-supervised adjunct for preoperative difficult airway risk stratification.
- Conditions
- Difficult Intubation, Difficult Laryngoscopy
- Keywords
- Difficult airway, Difficult intubation, Difficult laryngoscopy, Artificial intelligence, Multimodal artificial intelligence, Preoperative airway assessment, Airway photographs, Image-based prediction
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 |
|---|---|---|
| Elective Surgery Patients Requiring Endotracheal Intubation | Adult patients scheduled for elective surgery requiring endotracheal intubation who will undergo standardized preoperative airway photography and prospective intraoperative airway outcome recording. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic Performance of Multimodal AI Models for Predicting Difficult Intubation
The primary outcome is the diagnostic performance of multimodal artificial intelligence models for predicting true difficult intubation based on standardized preoperative airway photographs. Difficult intubation will be determined using prospectively recorded intraoperative reference criteria, including more than one intubation attempt, need for bougie or stylet assistance, rescue use of video laryngoscopy or supraglottic airway device, intubation time exceeding 60 seconds, or Intubation Difficulty Scale score greater than 5. Diagnostic performance will be assessed using sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and receiver operating characteristic analysis.
Measured: From preoperative airway photography to completion of intraoperative endotracheal intubation, up to 1 day
Secondary outcomes (1)
Diagnostic Performance of Multimodal AI Models for Predicting Difficult Laryngoscopy
The key secondary outcome is the diagnostic performance of multimodal artificial intelligence models for predicting true difficult laryngoscopy based on standardized preoperative airway photographs. Difficult laryngoscopy will be defined as Cormack-Lehane grade III or IV recorded during intraoperative airway management. Diagnostic performance will be assessed using sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and receiver operating characteristic analysis.
Measured: From preoperative airway photography to completion of intraoperative laryngoscopy, up to 1 day
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted14 Jul 2026
- Study start25 Jun 2026
- Primary completion1 Sep 2026 (estimated)
- Study completion1 Sep 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (3)Papers the sponsor has linked to this study.
- Other trials for Difficult 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
- 319 participants (estimated)
- Target duration
- 1 Day
Sponsor and investigators
- Lead sponsor
- Memorial Atasehir Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Murat Ferhat Ferhatoğlu, Memorial Atasehir Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07700485
- Sponsor's protocol ID
- E-28001928-604.01-318945497
- Acronym
- AI-AIRWAY
- Status last verified
- Jul 2026
- First submitted
- 7 Jul 2026
- Last updated
- 14 Jul 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Individual participant data will not be shared due to privacy and confidentiality considerations, particularly because the study involves preoperative airway images. De-identified aggregate data will be presented in the final analysis and publication.
Publications
Wang Z, Jin Y, Zheng Y, Chen H, Feng J, Sun J. Evaluation of preoperative difficult airway prediction methods for adult patients without obvious airway abnormalities: a systematic review and meta-analysis. BMC Anesthesiol. 2024 Jul 17;24(1):242. doi: 10.1186/s12871-024-02627-1.
PubMed 39020308Garcia-Garcia F, Lee DJ, Mendoza-Garces FJ, Garcia-Gutierrez S. Reliable prediction of difficult airway for tracheal intubation from patient preoperative photographs by machine learning methods. Comput Methods Programs Biomed. 2024 May;248:108118. doi: 10.1016/j.cmpb.2024.108118. Epub 2024 Mar 12.
PubMed 38489935Tavolara TE, Gurcan MN, Segal S, Niazi MKK. Identification of difficult to intubate patients from frontal face images using an ensemble of deep learning models. Comput Biol Med. 2021 Sep;136:104737. doi: 10.1016/j.compbiomed.2021.104737. Epub 2021 Aug 4.
PubMed 34391000
Study at a glance
From the registry record, last updated by the sponsor on 14 Jul 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Multimodal Artificial Intelligence for Image-Based Prediction of Difficult Airway: A Prospective Observational Study
- ClinicalTrials.gov ID
- NCT07700485
- Acronym / sponsor ID
- AI-AIRWAY / E-28001928-604.01-318945497
- Conditions
- Difficult Intubation, Difficult Laryngoscopy
- Keywords
- Difficult airway, Difficult intubation, Difficult laryngoscopy, Artificial intelligence, Multimodal artificial intelligence, Preoperative airway assessment, Airway photographs, Image-based prediction
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 319 participants (estimated)
- Lead sponsor
- Memorial Atasehir HospitalUniversity, hospital or other
- Study start
- 25 Jun 2026 (actual)
- Primary completion
- 1 Sep 2026 (estimated)
- Study completion
- 1 Sep 2026 (estimated)
- First posted
- 14 Jul 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.
This prospective observational study will evaluate whether commonly available multimodal artificial intelligence models can predict difficult laryngoscopy and difficult intubation using standardized preoperative airway photographs. Adult patients scheduled for elective surgery requiring endotracheal intubation will undergo an eight-view preoperative airway photography protocol. The anonymized image sets will be assessed by ChatGPT, Gemini, and Grok using the same structured prompt. Their predictions will be compared with expert anesthesiologist image-based assessments, conventional airway evaluation findings, and prospectively recorded intraoperative airway outcomes. The primary aim is to determine the diagnostic performance of AI models for predicting difficult intubation. A key secondary aim is to evaluate their performance for predicting difficult laryngoscopy. The study is intended to explore whether image-based AI assessment may support preoperative airway risk stratification as a clinician-supervised screening tool.
Detailed description
Preoperative airway assessment is important for identifying patients at risk for difficult laryngoscopy or difficult intubation. However, conventional bedside airway predictors have limited accuracy when used alone. Multimodal artificial intelligence models may provide additional image-based information by evaluating visible anatomical features from standardized preoperative airway photographs.
In this prospective observational study, adult patients undergoing elective surgery requiring endotracheal intubation will be enrolled between June and September 2026. Each participant will undergo standardized eight-view airway photography during the pre-anesthetic evaluation. The image set will include frontal facial, lateral profile, maximal mouth opening, modified Mallampati, neck extension, and anterior neck views. Images will be anonymized before assessment.
The same image sets will be independently evaluated by multimodal AI models, including ChatGPT, Gemini, and Grok, using an identical structured prompt. The AI models will provide categorical and binary predictions for difficult laryngoscopy and difficult intubation based only on visible image-based anatomical features. No intraoperative outcome data, expert predictions, or conventional airway assessment results will be provided to the AI models.
AI-generated predictions will be compared with expert anesthesiologist image-based assessments, conventional airway evaluation parameters, and prospectively recorded intraoperative reference outcomes. Difficult laryngoscopy will be defined as Cormack-Lehane grade III or IV. Difficult intubation will be defined using objective intraoperative criteria, including more than one intubation attempt, need for bougie or stylet assistance, rescue use of video laryngoscopy or supraglottic airway device, intubation time exceeding 60 seconds, or Intubation Difficulty Scale score greater than 5.
The study will assess the sensitivity, specificity, positive predictive value, negative predictive value, accuracy, receiver operating characteristic performance, and agreement between AI models and expert anesthesiologist assessments. The findings may help clarify whether multimodal AI can serve as a clinician-supervised adjunct for preoperative difficult airway risk stratification.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 1 Day
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Elective Surgery Patients Requiring Endotracheal Intubation | – | –Adult patients scheduled for elective surgery requiring endotracheal intubation who will undergo standardized preoperative airway photography and prospective intraoperative airway outcome recording. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Adult patients scheduled for elective surgical procedures requiring endotracheal intubation at Dr. Siyami Ersek Thoracic and Cardiovascular Surgery Training and Research Hospital.
Key inclusion criteria (4 listed)
- Age 18 years or older
- Scheduled for elective surgery requiring endotracheal intubation
- Able to cooperate with the standardized preoperative airway photography protocol
- Able to provide written informed consent
Key exclusion criteria (12 listed)
- Age younger than 18 years
- Emergency surgery
- Refusal or inability to provide informed consent
- Inability to cooperate with the standardized photographic protocol
- Known craniofacial or cervical deformity
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
Diagnostic Performance of Multimodal AI Models for Predicting Difficult Intubation
Time frame: From preoperative airway photography to completion of intraoperative endotracheal intubation, up to 1 day
The primary outcome is the diagnostic performance of multimodal artificial intelligence models for predicting true difficult intubation based on standardized preoperative airway photographs. Difficult intubation will be determined using prospectively recorded intraoperative reference criteria, including more than one intubation attempt, need for bougie or stylet assistance, rescue use of video laryngoscopy or supraglottic airway device, intubation time exceeding 60 seconds, or Intubation Difficulty Scale score greater than 5. Diagnostic performance will be assessed using sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and receiver operating characteristic analysis.
1 secondary outcome
Diagnostic Performance of Multimodal AI Models for Predicting Difficult Laryngoscopy
Time frame: From preoperative airway photography to completion of intraoperative laryngoscopy, up to 1 day
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 14 Jul 2026.
- Background: PubMed 39020308Wang Z, Jin Y, Zheng Y, Chen H, Feng J, Sun J. Evaluation of preoperative difficult airway prediction methods for adult patients without obvious airway abnormalities: a systematic review and meta-analysis. BMC Anesthesiol. 2024 Jul 17;24(1):242. doi: 10.1186/s12871-024-02627-1.
- Background: PubMed 38489935Garcia-Garcia F, Lee DJ, Mendoza-Garces FJ, Garcia-Gutierrez S. Reliable prediction of difficult airway for tracheal intubation from patient preoperative photographs by machine learning methods. Comput Methods Programs Biomed. 2024 May;248:108118. doi: 10.1016/j.cmpb.2024.108118. Epub 2024 Mar 12.
- Background: PubMed 34391000Tavolara TE, Gurcan MN, Segal S, Niazi MKK. Identification of difficult to intubate patients from frontal face images using an ensemble of deep learning models. Comput Biol Med. 2021 Sep;136:104737. doi: 10.1016/j.compbiomed.2021.104737. Epub 2021 Aug 4.
- Individual participant dataSharing planned: No. Individual participant data will not be shared due to privacy and confidentiality considerations, particularly because the study involves preoperative airway images. De-identified aggregate data will be presented in the final analysis and publication.
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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 Intubation and Difficult Laryngoscopy, run by Memorial Atasehir Hospital 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 12 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: under way, not looking for participants, as of the sponsor's last update on 14 Jul 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?
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?
Diagnostic Performance of Multimodal AI Models for Predicting Difficult Intubation (time frame: From preoperative airway photography to completion of intraoperative endotracheal intubation, up to 1 day). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07700485, which also lists 3 linked publications.
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 14 Jul 2026; AzTrials last refreshed its copy on 7 Nov 2026.