Difficult Endotracheal IntubationNCT06904586
Anthropometric and US-Guided Difficult Intubation Prediction With ML Models
An observational study of Difficult Endotracheal Intubation and Artificial Intelligence, run by Duzce University at 1 site in Turkey (Türkiye).
Trial summary
The assessment and management of difficult airway is of critical importance. Unsuccessful airway management leads to serious mortality and morbidity. From the beginning of the pre-anesthesia examination, 3% to 13% of patients who are considered suitable for routine airway management may be difficult to intubate. Read more about this study
- Who is it for?
- Ages 18 to 75 years
- Conditions the study is for
- Difficult Endotracheal Intubation, Artificial Intelligence
- What is being tested?
- Thyromental distance, Neck circumference and Mouth opening distance
- How many people
- 329 participants
- Where
- 1 siteTurkey (Türkiye)
- Study dates
- 1 Mar 2024to 31 Jan 2025
- How long will I take part?
- 6 Months
- Phase
- Observational
- Are there results?
- Yes, posted in the registrySummary in the doctors' view
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 75 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients between the ages of 18 and 65 who were undergoing elective surgery were included in the study.
Who may be able to join
- Patients over 18 years of age
- Patients who will undergo general anesthesia
Who cannot join
- Pregnant women
- Those with congenital and/or acquired facial deformities
- Patients who have previously undergone upper neck airway surgery
- Patients with head and neck tumors
- Patients who will undergo thyroidectomy
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Evaluation of Anthropometric and Ultrasonographic Measurements With Different Machine Learning Methods in Predicting Difficult Intubation: A Prospective Observational Study
The assessment and management of difficult airway is of critical importance. Unsuccessful airway management leads to serious mortality and morbidity. From the beginning of the pre-anesthesia examination, 3% to 13% of patients who are considered suitable for routine airway management may be difficult to intubate. Airway assessment issues include risk assessment and airway examination (bedside and forward) to estimate the risk of difficult airway or aspiration. Airway examination aims to determine the presence of upper airway pathologies or anatomical anomalies. Some physical characteristics are associated with difficult airways and unsuccessful intubation. Examples of these are; limited neck movement, snoring, short sternomental distance, neck circumference thickness, etc. Physical characteristics can be measured with a meter or more detailed upper airway ultrasonographic measurements. In this study, researchers aimed to evaluate the anthropometric and ultrasonographic measurement values of patients who underwent preoperative airway assessment and to see the predictability of difficult intubation with artificial intelligence-supported decision support programs.
Read the detailed description
Difficult intubation, particularly unpredictable difficult intubation, is a challenging scenario for every anesthesiologist. Patients who are initially assessed as suitable for routine airway management may present as difficult to intubate in 5% to 22% of cases. Accurate evaluation and management of difficult airways are crucial, as failure in airway management can lead to serious morbidity and mortality.
Airway assessment helps identify predictable difficult airways, but it does not exclude patients with normal clinical evaluations who may still experience unpredictable difficult intubation. The primary goal of airway examination is to detect upper airway pathologies or anatomical anomalies. Several physical characteristics are associated with difficult airways and failed intubation, including limited neck mobility, snoring, a short sternomental distance, and increased neck circumference.
Common airway assessment tools, such as the Mallampati classification and the upper lip bite test, require patient cooperation, which limits their applicability in sedated, trauma, or unresponsive patients. The Cormack-Lehane classification, used during direct laryngoscopy, is invasive and does not allow for pre-procedural preparation. In this context, non-invasive, bedside, rapid, and accessible ultrasonographic assessments and anthropometric measurements have gained importance in predicting difficult airways.
With technological advancements, decision-support systems and artificial intelligence (AI)-assisted applications are increasingly used to prevent adverse outcomes. Successful airway management is particularly critical in high-risk patients, where rapid decision-making is essential. Easily accessible, bedside, non-invasive ultrasonographic measurements, integrated with AI-based learning programs, have the potential to predict difficult intubation in advance. This enables early preparation, timely interventions, and the reduction of life-threatening risks.
In this study, researchers aimed to predict difficult intubation preoperatively using non-invasive anthropometric and ultrasonographic upper airway measurements, combined with AI-assisted decision-support programs, without requiring any invasive procedures.
Our hypothesis is that preoperative airway assessment through anthropometric and ultrasonographic measurements, supported by AI-based decision-support programs, can accurately predict difficult intubation and facilitate early preparation
- Being tested
- Thyromental distance, Neck circumference, Mouth opening distance, Distance from jawbone to hyoid bone with neck in neutral position, Distance from jawbone to hyoid bone with neck in extension
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Thyromental distanceOther
Distance between the chin and thyroid cartilage with a tape measure when the patient is in a neutral position
Neck circumferenceOther
Measurement of neck circumference with a tape measure when the patient is in a neutral position
Mouth opening distanceOther
Distance between the upper and lower teeth at the point where the mouth opening is maximum when the patient is in a neutral position.
Distance from jawbone to hyoid bone with neck in neutral positionOther
Distance from mentum to hyoid bone with neck in neutral position by ultrasonography
Distance from jawbone to hyoid bone with neck in extensionOther
Ultrasound measurement of distance from mentum to hyoid bone with neck in extension
Distance between skin and tracheaOther
Ultrasound measurement of distance between skin and trachea
Distance between skin and epiglottisOther
Distance between skin and epiglottis measured by ultrasonography
Distance between skin and anterior commissure of vocal cord:Other
Distance between skin and anterior commissure of vocal cord measured by ultrasonography
Distance between skin and hyoid boneOther
Distance between skin and hyoid bone measured by ultrasonography
Maximum Tongue ThicknessOther
Measurement of Maximal Tongue Thickness by Ultrasonography
| Study group | Role | What they receive |
|---|---|---|
| Patients between the ages of 18 and 20 who will receive general anesthesia | ASA I-III patients over the age of 18 who meet the inclusion criteria to undergo general anesthesia Receives: Other: Thyromental distance; Other: Neck circumference; Other: Mouth opening distance; Other: Distance from jawbone to hyoid bone with neck in neutral position; Other: Distance from jawbone to hyoid bone with neck in extension; Other: Distance between skin and trachea; Other: Distance between skin and epiglottis; Other: Distance between skin and anterior commissure of vocal cord:; Other: Distance between skin and hyoid bone; Other: Maximum Tongue Thickness |
What is being measured
The primary outcome is the main question the study is designed to answer.
Support Vector Machine Algorithm Percentage of Accuracy in Predicted Difficult Intubations
The dataset, labeled based on expert assessment of difficult intubation, was classified using eight widely accepted machine learning algorithms: logistic regression (LR) [6], support vector machine (SVM) [7], random forest (RF) [8], K-nearest neighbors (KNN) [9], Gaussian naive Bayes (GNB) [10], CatBoost [11], XGBoost [12], and decision tree (DT) [13]. From the original 30 parameters, the 15 most influential features were selected based on feature extraction methods and literature relevance. Preprocessing steps included handling missing values, with incomplete records excluded. The dataset was split into training (80%) and test (20%) sets. Models were trained on the training set, with hyperparameter tuning performed via 5-fold cross-validation to avoid overfitting. Final model performance was evaluated on the independent test set.
Measured: Taking ultrasonographic and anthropometric measurements of each patient took approximately 20 minutes. Machine learning estimates for each patient are approximately 1 min.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted1 Apr 2025
- Study start1 Mar 2024
- Primary completion3 Dec 2024
- Study completion31 Jan 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Posted resultsParticipant flow, outcome measures and adverse events reported by the sponsor.
- Other trials for Difficult Endotracheal 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
- 329 participants (actual)
- Target duration
- 6 Months
Sponsor and investigators
- Lead sponsor
- Duzce University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Gizem Demir Şenoğlu, Duzce University
- Investigators
- Gizem DEMIR SENOGLU, Principal investigator, Duzce University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06904586
- Sponsor's protocol ID
- 2022/65
- Status last verified
- May 2025
- First submitted
- 9 Dec 2024
- Last updated
- 31 May 2025
- Results posted
- Yes, on ClinicalTrials.gov
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 31 May 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Evaluation of Anthropometric and Ultrasonographic Measurements With Different Machine Learning Methods in Predicting Difficult Intubation: A Prospective Observational Study
- ClinicalTrials.gov ID
- NCT06904586
- Acronym / sponsor ID
- 2022/65
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 329 participants (actual)
- Lead sponsor
- Duzce UniversityUniversity, hospital or other
- Study start
- 1 Mar 2024 (actual)
- Primary completion
- 3 Dec 2024 (actual)
- Study completion
- 31 Jan 2025 (actual)
- First posted
- 1 Apr 2025 (actual)
- Results
- Posted 31 May 2025
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
The assessment and management of difficult airway is of critical importance. Unsuccessful airway management leads to serious mortality and morbidity. From the beginning of the pre-anesthesia examination, 3% to 13% of patients who are considered suitable for routine airway management may be difficult to intubate. Airway assessment issues include risk assessment and airway examination (bedside and forward) to estimate the risk of difficult airway or aspiration. Airway examination aims to determine the presence of upper airway pathologies or anatomical anomalies. Some physical characteristics are associated with difficult airways and unsuccessful intubation. Examples of these are; limited neck movement, snoring, short sternomental distance, neck circumference thickness, etc. Physical characteristics can be measured with a meter or more detailed upper airway ultrasonographic measurements. In this study, researchers aimed to evaluate the anthropometric and ultrasonographic measurement values of patients who underwent preoperative airway assessment and to see the predictability of difficult intubation with artificial intelligence-supported decision support programs.
Detailed description
Difficult intubation, particularly unpredictable difficult intubation, is a challenging scenario for every anesthesiologist. Patients who are initially assessed as suitable for routine airway management may present as difficult to intubate in 5% to 22% of cases. Accurate evaluation and management of difficult airways are crucial, as failure in airway management can lead to serious morbidity and mortality.
Airway assessment helps identify predictable difficult airways, but it does not exclude patients with normal clinical evaluations who may still experience unpredictable difficult intubation. The primary goal of airway examination is to detect upper airway pathologies or anatomical anomalies. Several physical characteristics are associated with difficult airways and failed intubation, including limited neck mobility, snoring, a short sternomental distance, and increased neck circumference.
Common airway assessment tools, such as the Mallampati classification and the upper lip bite test, require patient cooperation, which limits their applicability in sedated, trauma, or unresponsive patients. The Cormack-Lehane classification, used during direct laryngoscopy, is invasive and does not allow for pre-procedural preparation. In this context, non-invasive, bedside, rapid, and accessible ultrasonographic assessments and anthropometric measurements have gained importance in predicting difficult airways.
With technological advancements, decision-support systems and artificial intelligence (AI)-assisted applications are increasingly used to prevent adverse outcomes. Successful airway management is particularly critical in high-risk patients, where rapid decision-making is essential. Easily accessible, bedside, non-invasive ultrasonographic measurements, integrated with AI-based learning programs, have the potential to predict difficult intubation in advance. This enables early preparation, timely interventions, and the reduction of life-threatening risks.
In this study, researchers aimed to predict difficult intubation preoperatively using non-invasive anthropometric and ultrasonographic upper airway measurements, combined with AI-assisted decision-support programs, without requiring any invasive procedures.
Our hypothesis is that preoperative airway assessment through anthropometric and ultrasonographic measurements, supported by AI-based decision-support programs, can accurately predict difficult intubation and facilitate early preparation
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 6 Months
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Patients between the ages of 18 and 20 who will receive general anesthesia | – | Other: Thyromental distance, Other: Neck circumference, Other: Mouth opening distance, Other: Distance from jawbone to hyoid bone with neck in neutral position, Other: Distance from jawbone to hyoid bone with neck in extension, Other: Distance between skin and trachea, Other: Distance between skin and epiglottis, Other: Distance between skin and anterior commissure of vocal cord:, Other: Distance between skin and hyoid bone, Other: Maximum Tongue ThicknessASA I-III patients over the age of 18 who meet the inclusion criteria to undergo general anesthesia |
Who may qualify
- Ages
- 18 to 75 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients between the ages of 18 and 65 who were undergoing elective surgery were included in the study.
Key inclusion criteria (2 listed)
- Patients over 18 years of age
- Patients who will undergo general anesthesia
Key exclusion criteria (5 listed)
- Pregnant women
- Those with congenital and/or acquired facial deformities
- Patients who have previously undergone upper neck airway surgery
- Patients with head and neck tumors
- Patients who will undergo thyroidectomy
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 |
|---|---|---|
| Thyromental distanceDistance between the chin and thyroid cartilage with a tape measure when the patient is in a neutral position | Other | Patients between the ages of 18 and 20 who will receive general anesthesia |
| Neck circumferenceMeasurement of neck circumference with a tape measure when the patient is in a neutral position | Other | Patients between the ages of 18 and 20 who will receive general anesthesia |
| Mouth opening distanceDistance between the upper and lower teeth at the point where the mouth opening is maximum when the patient is in a neutral position. | Other | Patients between the ages of 18 and 20 who will receive general anesthesia |
| Distance from jawbone to hyoid bone with neck in neutral positionDistance from mentum to hyoid bone with neck in neutral position by ultrasonography | Other | Patients between the ages of 18 and 20 who will receive general anesthesia |
| Distance from jawbone to hyoid bone with neck in extensionUltrasound measurement of distance from mentum to hyoid bone with neck in extension | Other | Patients between the ages of 18 and 20 who will receive general anesthesia |
| Distance between skin and tracheaUltrasound measurement of distance between skin and trachea | Other | Patients between the ages of 18 and 20 who will receive general anesthesia |
| Distance between skin and epiglottisDistance between skin and epiglottis measured by ultrasonography | Other | Patients between the ages of 18 and 20 who will receive general anesthesia |
| Distance between skin and anterior commissure of vocal cord:Distance between skin and anterior commissure of vocal cord measured by ultrasonography | Other | Patients between the ages of 18 and 20 who will receive general anesthesia |
| Distance between skin and hyoid boneDistance between skin and hyoid bone measured by ultrasonography | Other | Patients between the ages of 18 and 20 who will receive general anesthesia |
| Maximum Tongue ThicknessMeasurement of Maximal Tongue Thickness by Ultrasonography | Other | Patients between the ages of 18 and 20 who will receive general anesthesia |
Primary outcome
Support Vector Machine Algorithm Percentage of Accuracy in Predicted Difficult Intubations
Time frame: Taking ultrasonographic and anthropometric measurements of each patient took approximately 20 minutes. Machine learning estimates for each patient are approximately 1 min.
The dataset, labeled based on expert assessment of difficult intubation, was classified using eight widely accepted machine learning algorithms: logistic regression (LR) [6], support vector machine (SVM) [7], random forest (RF) [8], K-nearest neighbors (KNN) [9], Gaussian naive Bayes (GNB) [10], CatBoost [11], XGBoost [12], and decision tree (DT) [13]. From the original 30 parameters, the 15 most influential features were selected based on feature extraction methods and literature relevance. Preprocessing steps included handling missing values, with incomplete records excluded. The dataset was split into training (80%) and test (20%) sets. Models were trained on the training set, with hyperparameter tuning performed via 5-fold cross-validation to avoid overfitting. Final model performance was evaluated on the independent test set.
Results and safety
Loading the posted results from ClinicalTrials.gov…
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 31 May 2025.
- Posted resultsParticipant flow, baseline, all outcome measures and adverse events.
- 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.
Gizem DEMIR SENOGLU principal investigator
Duzce 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 Difficult Endotracheal Intubation and Artificial Intelligence, 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 2 inclusion and 5 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 31 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 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?
Yes. Results are posted in the registry (first posted 31 May 2025). A summary is in the For doctors view, and the full results are on ClinicalTrials.gov.
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?
Support Vector Machine Algorithm Percentage of Accuracy in Predicted Difficult Intubations (time frame: Taking ultrasonographic and anthropometric measurements of each patient took approximately 20 minutes. Machine learning estimates for each patient are approximately 1 min.). 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 31 May 2025.