Difficult IntubationNCT01612949
Facial Analysis to Classify Difficult Intubation
An observational study of Difficult Intubation, run by Wake Forest University Health Sciences at 1 site in United States.
Trial summary
The aim of this project is to develop a computer algorithm that can accurately predict how easy or difficult it is to intubate a patient based upon digital photographs from three different perspectives. Read more about this study
- Who is it for?
- Ages 18 to 99 yearsHealthy volunteers can take part
- Conditions the study is for
- Difficult Intubation
- What is being tested?
- photographing head and neck
- How many people
- 3,500 participants
- Where
- 1 siteUnited States
- Study dates
- May 2012to Dec 2027
- 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 99 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Patients undergoing surgical procedures requiring general anesthesia with endotracheal intubation; patients from all ethnic groups
Who may be able to join
- Patients requiring endotracheal intubation
- Patients consenting to acquisition of photographic images of the head and neck
Who cannot join
- Patients who had undergone head or neck surgery
- Patients in whom central venous catheters or other interventions that prevent full view of the features of the face in frontal and profile views
- Patients who were neither easy nor difficult to intubate by our criteria
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
United States1 site, 1 recruiting
- Recruiting
Wake Forest Baptist Medical Center
Winston-Salem, North Carolina, 27157
Scott Segal, MD, 336-716-4497
Scott Segal, MD, MHCM
About this study
Official title: Comparison of a Computerized Image Analysis to Conventional Airway Examination Techniques to Predict Difficult Endotracheal Intubation
The aim of this project is to develop a computer algorithm that can accurately predict how easy or difficult it is to intubate a patient based upon digital photographs from three different perspectives. Such an application can provide a consistent, quantitative measure of intubation difficulty by analyzing facial features in captured photographs - features which have previously been shown to correlate with how easy or how hard it would be to perform the intubation procedure. This is in contrast to established subjective protocols that also serve to predict intubation difficulty, albeit with lower accuracy. A digital application has the potential to decrease potential complications related to intubation difficulty and increase patient safety.
Read the detailed description
Both control and experimental cohorts will be recruited in this study. In order to drive clinical acceptance of this technique, the investigators will need to study and then demonstrate applicability to all patients regardless of race or gender. This will require the recruitment of a control population of patients who have been demonstrated at surgery to be easy to intubate. Such patients are in relative abundance. The experimental group will consist of patients who are found at surgery to be difficult to intubate. In addition, a prospective cohort will be recruited without prior knowledge of ease or difficulty of intubation, and subsequent ground truth determined at surgery. Patients are defined as easy to intubate if their anesthetic record described a single attempt with a Macintosh 3 blade resulting in a grade 1 laryngoscopic view (full exposure of the vocal cords). Difficult intubation was defined by at least one of: more than one attempt by an operator with at least one year of anesthesia experience, grade 3 or 4 laryngoscopic view on a 4 point scale, 5 need for a second operator, or non-elective use of an alternative airway device such as a bougie, fiberoptic bronchoscope or intubating laryngeal mask airway.
The primary purpose of the study is to develop algorithms capable of discriminating patients who are likely to be difficult to intubate from those who are likely to be easy to intubate based on facial appearance. The primary analysis is the demonstration of statistical significance in the ability of the derived algorithms to determine successfully whether a subject was easy or difficult to intubate. A secondary analysis is the demonstration of a statistical difference in performance between the derived algorithms versus conventional airway assessment tests.
An ongoing modification of the protocol will allow anesthesia personnel to attempt intubation with a Miller (straight) laryngoscope blade, instead of a Macintosh blade. This secondary outcome was chosen because of the strong preference, and in some cases, better skill, with such a blade. It is possible that different facial features will predict difficulty with this blade than has been predicted to date with a Macintosh blade.
- Conditions
- Difficult Intubation
- Being tested
- photographing head and neck
- Keywords
- endotracheal intubation, facial structure, Mallampati score, computerized facial analysis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
photographing head and neckOther
Taking three photographs of head and neck-one photograph from front, one from left and one fron right. The photographs are analyzed by facial structure software to create face model.
| Study group | Role | What they receive |
|---|---|---|
| easy to intubate, model derivation | easy to intubate, model derivation. photographing head and neck Receives: Other: photographing head and neck | |
| difficult to intubate, model derivation | difficult to intubate, model derivation.photographing head and neck Receives: Other: photographing head and neck | |
| easy to intubate, model validation | easy to intubate, model validation. photographing head and neck Receives: Other: photographing head and neck | |
| difficult to intubate, model validation | difficult to intubate, model validation. photographing head and neck Receives: Other: photographing head and neck | |
| Test | A group of unlabeled subjects (mix of easy and difficult intubations) to test the reproducibility of the derived and validated model(s) Receives: Other: photographing head and neck |
What is being measured
The primary outcome is the main question the study is designed to answer.
Computer algorithm to predict difficulty of endotracheal intubation
The outcome will be a computer algorithm that can accurately predict how easy or difficult it is to intubate a patient based upon digital photographs from three different perspectives. Such an application can provide a consistent, quantitative measure of intubation difficulty by analyzing facial features in captured photographs-features which have previously been shown to correlate with how easy or how hard it would be to perform the intubation procedure. A digital application has the potential to decrease complications related to intubation difficulty and increase patient safety.
Measured: Approximately 2 years, based on current enrollment pattern
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted6 Jun 2012 (estimated)
- Study startMay 2012
- Primary completionDec 2027 (estimated)
- Study completionDec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (2)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
- 3,500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Wake Forest University Health Sciences
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Scott Segal, MD, MHCM, Principal investigator, Wake Forest University Health Sciences
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT01612949
- Sponsor's protocol ID
- IRB00036442
- Status last verified
- Mar 2026
- First submitted
- 4 Jun 2012
- Last updated
- 1 Apr 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
Tavolara 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 34391000Connor CW, Segal S. Accurate classification of difficult intubation by computerized facial analysis. Anesth Analg. 2011 Jan;112(1):84-93. doi: 10.1213/ANE.0b013e31820098d6. Epub 2010 Nov 16.
PubMed 21081769
Study at a glance
From the registry record, last updated by the sponsor on 1 Apr 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Comparison of a Computerized Image Analysis to Conventional Airway Examination Techniques to Predict Difficult Endotracheal Intubation
- ClinicalTrials.gov ID
- NCT01612949
- Acronym / sponsor ID
- IRB00036442
- Conditions
- Difficult Intubation
- Keywords
- endotracheal intubation, facial structure, Mallampati score, computerized facial analysis
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 3,500 participants (estimated)
- Lead sponsor
- Wake Forest University Health SciencesUniversity, hospital or other
- Study start
- May 2012 (actual)
- Primary completion
- Dec 2027 (estimated)
- Study completion
- Dec 2027 (estimated)
- First posted
- 6 Jun 2012 (estimated)
- 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 project is to develop a computer algorithm that can accurately predict how easy or difficult it is to intubate a patient based upon digital photographs from three different perspectives. Such an application can provide a consistent, quantitative measure of intubation difficulty by analyzing facial features in captured photographs - features which have previously been shown to correlate with how easy or how hard it would be to perform the intubation procedure. This is in contrast to established subjective protocols that also serve to predict intubation difficulty, albeit with lower accuracy. A digital application has the potential to decrease potential complications related to intubation difficulty and increase patient safety.
Detailed description
Both control and experimental cohorts will be recruited in this study. In order to drive clinical acceptance of this technique, the investigators will need to study and then demonstrate applicability to all patients regardless of race or gender. This will require the recruitment of a control population of patients who have been demonstrated at surgery to be easy to intubate. Such patients are in relative abundance. The experimental group will consist of patients who are found at surgery to be difficult to intubate. In addition, a prospective cohort will be recruited without prior knowledge of ease or difficulty of intubation, and subsequent ground truth determined at surgery. Patients are defined as easy to intubate if their anesthetic record described a single attempt with a Macintosh 3 blade resulting in a grade 1 laryngoscopic view (full exposure of the vocal cords). Difficult intubation was defined by at least one of: more than one attempt by an operator with at least one year of anesthesia experience, grade 3 or 4 laryngoscopic view on a 4 point scale, 5 need for a second operator, or non-elective use of an alternative airway device such as a bougie, fiberoptic bronchoscope or intubating laryngeal mask airway.
The primary purpose of the study is to develop algorithms capable of discriminating patients who are likely to be difficult to intubate from those who are likely to be easy to intubate based on facial appearance. The primary analysis is the demonstration of statistical significance in the ability of the derived algorithms to determine successfully whether a subject was easy or difficult to intubate. A secondary analysis is the demonstration of a statistical difference in performance between the derived algorithms versus conventional airway assessment tests.
An ongoing modification of the protocol will allow anesthesia personnel to attempt intubation with a Miller (straight) laryngoscope blade, instead of a Macintosh blade. This secondary outcome was chosen because of the strong preference, and in some cases, better skill, with such a blade. It is possible that different facial features will predict difficulty with this blade than has been predicted to date with a Macintosh blade.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 5
| Arm | Type | Receives |
|---|---|---|
| easy to intubate, model derivation | – | Other: photographing head and neckeasy to intubate, model derivation. photographing head and neck |
| difficult to intubate, model derivation | – | Other: photographing head and neckdifficult to intubate, model derivation.photographing head and neck |
| easy to intubate, model validation | – | Other: photographing head and neckeasy to intubate, model validation. photographing head and neck |
| difficult to intubate, model validation | – | Other: photographing head and neckdifficult to intubate, model validation. photographing head and neck |
| Test | – | Other: photographing head and neckA group of unlabeled subjects (mix of easy and difficult intubations) to test the reproducibility of the derived and validated model(s) |
Who may qualify
- Ages
- 18 to 99 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Patients undergoing surgical procedures requiring general anesthesia with endotracheal intubation; patients from all ethnic groups
Key inclusion criteria (2 listed)
- Patients requiring endotracheal intubation
- Patients consenting to acquisition of photographic images of the head and neck
Key exclusion criteria (3 listed)
- Patients who had undergone head or neck surgery
- Patients in whom central venous catheters or other interventions that prevent full view of the features of the face in frontal and profile views
- Patients who were neither easy nor difficult to intubate by our criteria
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 |
|---|---|---|
| photographing head and neckTaking three photographs of head and neck-one photograph from front, one from left and one fron right. The photographs are analyzed by facial structure software to create face model. | Other | Test, difficult to intubate, model derivation, difficult to intubate, model validation, easy to intubate, model derivation, easy to intubate, model validation |
Primary outcome
Computer algorithm to predict difficulty of endotracheal intubation
Time frame: Approximately 2 years, based on current enrollment pattern
The outcome will be a computer algorithm that can accurately predict how easy or difficult it is to intubate a patient based upon digital photographs from three different perspectives. Such an application can provide a consistent, quantitative measure of intubation difficulty by analyzing facial features in captured photographs-features which have previously been shown to correlate with how easy or how hard it would be to perform the intubation procedure. A digital application has the potential to decrease complications related to intubation difficulty and increase patient safety.
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 1 Apr 2026.
- 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.
- Results publication: PubMed 21081769Connor CW, Segal S. Accurate classification of difficult intubation by computerized facial analysis. Anesth Analg. 2011 Jan;112(1):84-93. doi: 10.1213/ANE.0b013e31820098d6. Epub 2010 Nov 16.
- 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.
Scott Segal, MD, MHCM principal investigator
Wake Forest University Health Sciences
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, run by Wake Forest University Health Sciences at 1 site in United States.
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 3 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 1 Apr 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 United States. 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 Scott Segal, MD, MHCM ((336) 716-7084, B.Segal@wfusm.edu), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Computer algorithm to predict difficulty of endotracheal intubation (time frame: Approximately 2 years, based on current enrollment pattern). 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 NCT01612949, which also lists 2 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 1 Apr 2026.