Artificial Intelligence (AI)NCT06961240
THE ROLE OF ARTIFICIAL INTELLIGENCE TRAINED WITH PRE-MEASURED NUMERICAL DATA IN PREDICTION OF DIFFICULT INTUBATION
An observational study of Artificial Intelligence (AI) and Difficult Intubation, run by Kutahya Health Sciences University at 1 site in Turkey (Türkiye).
Trial summary
This study aims to develop an artificial intelligence (AI)-based model to predict difficult intubation in patients undergoing general anesthesia. Since patients are apneic during intubation without spontaneous breathing efforts, minimizing apnea duration is critical. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Artificial Intelligence (AI), Difficult Intubation
- What is being tested?
- AI-Based Difficult Airway Prediction Model
- How many people
- 250 participants
- Where
- 1 siteTurkey (Türkiye)
- Study dates
- 2 May 2025to 11 Nov 2025
- 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 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Adult patients aged 18 years and older who are scheduled for elective surgery under general anesthesia with endotracheal intubation. Participants will be recruited from the anesthesia outpatient clinic. Only patients who provide written informed consent and are able to undergo standardized airway assessment and portrait imaging will be included. Patients with emergency conditions, pregnancy, or a history of facial or head and neck surgery will be excluded.
Who may be able to join
- Patients aged 18 years or older
- Patients scheduled to undergo general anesthesia with endotracheal intubation
- Patients who provide written informed consent
- Patients able to undergo preoperative clinical evaluation and imaging without contraindications
Who cannot join
- Patients younger than 18 years of age
- Pregnant patients
- Emergency surgery cases
- Patients with a history of facial surgery altering facial anatomy
- Patients with a history of head and neck surgery
- Patients not receiving general anesthesia
Trial locations and contacts
1 site in 1 country.
Turkey (Türkiye)1 site
Kütahya Health Sciences University
Kütahya, 43100
About this study
This study aims to develop an artificial intelligence (AI)-based model to predict difficult intubation in patients undergoing general anesthesia. Since patients are apneic during intubation without spontaneous breathing efforts, minimizing apnea duration is critical. Traditional methods for predicting difficult intubation rely on physical markers such as sternomental distance, thyromental distance, mouth opening, neck extension, Mallampati score, neck circumference, and upper lip bite test. However, performing these assessments quickly and objectively in every patient is challenging. Therefore, utilizing computer-assisted imaging systems and AI techniques may facilitate clinical practice.
In this study, 250 patients presenting to the anesthesia outpatient clinic, who provide informed consent, will be evaluated. Demographic data (age, gender, height, weight, body mass index) will be recorded. Measurements including mouth opening, thyromental distance, sternomental distance, and neck circumference will be performed. Additionally, Mallampati score, neck extension ability, and upper lip bite test results will be noted. Portrait photographs capturing shoulder and upper body anatomy from multiple angles will be taken. During the operation, the Cormack-Lehane score observed by anesthesiologists with at least three years of experience during intubation will also be recorded.
Read more
The collected data will consist of both tabular (structured) data and visual data. Data preprocessing will involve cleaning missing and outlier values, encoding categorical variables, and normalizing continuous variables. Key anatomical points (e.g., chin tip, thyroid notch, sternum) will be identified using landmark detection algorithms on the images.
Of the dataset, 200 patients will be used for model training and 50 patients for testing. Machine learning methods (Random Forest, Support Vector Machines, Gradient Boosting) and deep learning methods (Artificial Neural Networks, Convolutional Neural Networks) will be employed. Tabular and image data will first be modeled separately and then combined using ensemble methods. Model performance will be evaluated with metrics including accuracy, sensitivity, specificity, F1 score, and AUC-ROC.
The models will be developed using Python programming language with libraries such as TensorFlow, Scikit-learn, and NumPy, supported by GPU-based computing.
This study is unique in its aim to compare classical physical examination-based predictions with AI-based predictions, enhancing the accuracy of difficult intubation forecasts. Strengthening clinical decision-making processes and improving patient safety are among the primary goals.
Inclusion Criteria:
Patients aged 18 years and older Patients undergoing general anesthesia with endotracheal intubation Patients providing informed consent
Exclusion Criteria:
Patients under 18 years of age Pregnant patients Emergency surgery cases Patients with a history of facial surgeries that alter appearance Patients with prior head and neck surgeries Patients not receiving general anesthesia The results of this study aim to contribute to the development of a reliable, generalizable AI model for early prediction of difficult airways in clinical settings.
- Conditions
- Artificial Intelligence (AI), Difficult Intubation
- Being tested
- AI-Based Difficult Airway Prediction Model
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI-Based Difficult Airway Prediction ModelDiagnostic test
The "AI-Based Difficult Airway Prediction Model" is an artificial intelligence system designed to predict difficult intubation in patients undergoing general anesthesia. It combines clinical data (age, BMI, Mallampati score, mouth opening, thyromental distance, sternomental distance, neck circumference) and anatomical image data. Machine learning (Random Forests, SVM, Gradient Boosting) and deep learning (ANN, CNN) algorithms are used to classify airway difficulty. The model's predictions are compared with clinical assessments by anesthesiologists using Cormack-Lehane grading. The goal is to improve prediction accuracy, enhance airway management, and support clinical decision-making.
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy of AI-Based Prediction for Difficult Intubation
The primary outcome is the accuracy of the AI-Based Difficult Airway Prediction Model in identifying patients with difficult intubation, compared to the Cormack-Lehane grading obtained during direct laryngoscopy by experienced anesthesiologists. The model's predictive performance will be evaluated using metrics such as sensitivity, specificity, and AUC-ROC.
Measured: Within 24 hours preoperatively and intraoperatively
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted7 May 2025
- Study start2 May 2025
- Primary completion30 Oct 2025
- Study completion11 Nov 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (4)Papers the sponsor has linked to this study.
- Other trials for Artificial Intelligence (AI)Studies of the same condition, open and under way.
- More trials run by Kutahya Health Sciences UniversityThe 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
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 250 participants (actual)
Sponsor and investigators
- Lead sponsor
- Kutahya Health Sciences University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Serkan TELLİ, Kutahya Health Sciences University
- Investigators
- suleyman camgoz, Study chair, Kutahya Health Sciences University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06961240
- Sponsor's protocol ID
- 2025/05-35
- Status last verified
- Nov 2025
- First submitted
- 29 Apr 2025
- Last updated
- 19 Nov 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
Kim JH, Jung HS, Lee SE, Hou JU, Kwon YS. Improving difficult direct laryngoscopy prediction using deep learning and minimal image analysis: a single-center prospective study. Sci Rep. 2024 Jun 20;14(1):14209. doi: 10.1038/s41598-024-65060-x.
PubMed 38902319Xia M, Jin C, Zheng Y, Wang J, Zhao M, Cao S, Xu T, Pei B, Irwin MG, Lin Z, Jiang H. Deep learning-based facial analysis for predicting difficult videolaryngoscopy: a feasibility study. Anaesthesia. 2024 Apr;79(4):399-409. doi: 10.1111/anae.16194. Epub 2023 Dec 13.
PubMed 38093485Hayasaka T, Kawano K, Kurihara K, Suzuki H, Nakane M, Kawamae K. Creation of an artificial intelligence model for intubation difficulty classification by deep learning (convolutional neural network) using face images: an observational study. J Intensive Care. 2021 May 6;9(1):38. doi: 10.1186/s40560-021-00551-x.
PubMed 33952341Telli S, Camgoz S, Arslan NN, Ozdemir D. Pre-operative prediction of difficult tracheal intubation using an explainable multi-pose shared encoder vision transformer: a prospective observational study. BMC Anesthesiol. 2026 Aug 11;26(1):551. doi: 10.1186/s12871-026-04156-5.
PubMed 42698099
Study at a glance
From the registry record, last updated by the sponsor on 19 Nov 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- THE ROLE OF ARTIFICIAL INTELLIGENCE TRAINED WITH PRE-MEASURED NUMERICAL DATA IN PREDICTION OF DIFFICULT INTUBATION
- ClinicalTrials.gov ID
- NCT06961240
- Acronym / sponsor ID
- 2025/05-35
- Conditions
- Artificial Intelligence (AI), Difficult Intubation
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 250 participants (actual)
- Lead sponsor
- Kutahya Health Sciences UniversityUniversity, hospital or other
- Study start
- 2 May 2025 (actual)
- Primary completion
- 30 Oct 2025 (actual)
- Study completion
- 11 Nov 2025 (actual)
- First posted
- 7 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.
This study aims to develop an artificial intelligence (AI)-based model to predict difficult intubation in patients undergoing general anesthesia. Since patients are apneic during intubation without spontaneous breathing efforts, minimizing apnea duration is critical. Traditional methods for predicting difficult intubation rely on physical markers such as sternomental distance, thyromental distance, mouth opening, neck extension, Mallampati score, neck circumference, and upper lip bite test. However, performing these assessments quickly and objectively in every patient is challenging. Therefore, utilizing computer-assisted imaging systems and AI techniques may facilitate clinical practice.
In this study, 250 patients presenting to the anesthesia outpatient clinic, who provide informed consent, will be evaluated. Demographic data (age, gender, height, weight, body mass index) will be recorded. Measurements including mouth opening, thyromental distance, sternomental distance, and neck circumference will be performed. Additionally, Mallampati score, neck extension ability, and upper lip bite test results will be noted. Portrait photographs capturing shoulder and upper body anatomy from multiple angles will be taken. During the operation, the Cormack-Lehane score observed by anesthesiologists with at least three years of experience during intubation will also be recorded.
The collected data will consist of both tabular (structured) data and visual data. Data preprocessing will involve cleaning missing and outlier values, encoding categorical variables, and normalizing continuous variables. Key anatomical points (e.g., chin tip, thyroid notch, sternum) will be identified using landmark detection algorithms on the images.
Of the dataset, 200 patients will be used for model training and 50 patients for testing. Machine learning methods (Random Forest, Support Vector Machines, Gradient Boosting) and deep learning methods (Artificial Neural Networks, Convolutional Neural Networks) will be employed. Tabular and image data will first be modeled separately and then combined using ensemble methods. Model performance will be evaluated with metrics including accuracy, sensitivity, specificity, F1 score, and AUC-ROC.
The models will be developed using Python programming language with libraries such as TensorFlow, Scikit-learn, and NumPy, supported by GPU-based computing.
This study is unique in its aim to compare classical physical examination-based predictions with AI-based predictions, enhancing the accuracy of difficult intubation forecasts. Strengthening clinical decision-making processes and improving patient safety are among the primary goals.
Inclusion Criteria:
Patients aged 18 years and older Patients undergoing general anesthesia with endotracheal intubation Patients providing informed consent
Exclusion Criteria:
Patients under 18 years of age Pregnant patients Emergency surgery cases Patients with a history of facial surgeries that alter appearance Patients with prior head and neck surgeries Patients not receiving general anesthesia The results of this study aim to contribute to the development of a reliable, generalizable AI model for early prediction of difficult airways in clinical settings.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Adult patients aged 18 years and older who are scheduled for elective surgery under general anesthesia with endotracheal intubation. Participants will be recruited from the anesthesia outpatient clinic. Only patients who provide written informed consent and are able to undergo standardized airway assessment and portrait imaging will be included. Patients with emergency conditions, pregnancy, or a history of facial or head and neck surgery will be excluded.
Key inclusion criteria (4 listed)
- Patients aged 18 years or older
- Patients scheduled to undergo general anesthesia with endotracheal intubation
- Patients who provide written informed consent
- Patients able to undergo preoperative clinical evaluation and imaging without contraindications
Key exclusion criteria (6 listed)
- Patients younger than 18 years of age
- Pregnant patients
- Emergency surgery cases
- Patients with a history of facial surgery altering facial anatomy
- Patients with a history of head and neck surgery
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-Based Difficult Airway Prediction ModelThe "AI-Based Difficult Airway Prediction Model" is an artificial intelligence system designed to predict difficult intubation in patients undergoing general anesthesia. It combines clinical data (age, BMI, Mallampati score, mouth opening, thyromental distance, sternomental distance, neck circumference) and anatomical image data. Machine learning (Random Forests, SVM, Gradient Boosting) and deep learning (ANN, CNN) algorithms are used to classify airway difficulty. The model's predictions are compared with clinical assessments by anesthesiologists using Cormack-Lehane grading. The goal is to improve prediction accuracy, enhance airway management, and support clinical decision-making. | Diagnostic test | – |
Primary outcome
Accuracy of AI-Based Prediction for Difficult Intubation
Time frame: Within 24 hours preoperatively and intraoperatively
The primary outcome is the accuracy of the AI-Based Difficult Airway Prediction Model in identifying patients with difficult intubation, compared to the Cormack-Lehane grading obtained during direct laryngoscopy by experienced anesthesiologists. The model's predictive performance will be evaluated using metrics such as sensitivity, specificity, and AUC-ROC.
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 19 Nov 2025.
- Background: PubMed 38902319Kim JH, Jung HS, Lee SE, Hou JU, Kwon YS. Improving difficult direct laryngoscopy prediction using deep learning and minimal image analysis: a single-center prospective study. Sci Rep. 2024 Jun 20;14(1):14209. doi: 10.1038/s41598-024-65060-x.
- Background: PubMed 38093485Xia M, Jin C, Zheng Y, Wang J, Zhao M, Cao S, Xu T, Pei B, Irwin MG, Lin Z, Jiang H. Deep learning-based facial analysis for predicting difficult videolaryngoscopy: a feasibility study. Anaesthesia. 2024 Apr;79(4):399-409. doi: 10.1111/anae.16194. Epub 2023 Dec 13.
- Background: PubMed 33952341Hayasaka T, Kawano K, Kurihara K, Suzuki H, Nakane M, Kawamae K. Creation of an artificial intelligence model for intubation difficulty classification by deep learning (convolutional neural network) using face images: an observational study. J Intensive Care. 2021 May 6;9(1):38. doi: 10.1186/s40560-021-00551-x.
- Derived from this study: PubMed 42698099Telli S, Camgoz S, Arslan NN, Ozdemir D. Pre-operative prediction of difficult tracheal intubation using an explainable multi-pose shared encoder vision transformer: a prospective observational study. BMC Anesthesiol. 2026 Aug 11;26(1):551. doi: 10.1186/s12871-026-04156-5.
- Individual participant dataSharing planned: Undecided
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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.
suleyman camgoz study chair
Kutahya Health Sciences University
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 Artificial Intelligence (AI) and Difficult Intubation, run by Kutahya Health Sciences 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 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: finished, as of the sponsor's last update on 19 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 AI-Based Prediction for Difficult Intubation (time frame: Within 24 hours preoperatively and intraoperatively). 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 NCT06961240, which also lists 4 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 19 Nov 2025.