Facial Analysis to Classify Difficult Intubation

NCT01612949 | Recruiting

• 1 other identifier: IRB00036442
• Study Type: observational
• Target: 3,500
• Locations: 1 country
• Sites: 1

Brief 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. 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.

Conditions

Difficult Intubation

Keywords

endotracheal intubation; facial structure; Mallampati score; computerized facial analysis

Outcome Measures

Primary Outcomes (1)

• 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.

  Approximately 2 years, based on current enrollment pattern

Study Arms (5)

easy to intubate, model derivation

easy to intubate, model derivation. photographing head and neck

Other: photographing head and neck

difficult to intubate, model derivation

difficult to intubate, model derivation.photographing head and neck

Other: photographing head and neck

easy to intubate, model validation

easy to intubate, model validation. photographing head and neck

Other: photographing head and neck

difficult to intubate, model validation

difficult to intubate, model validation. photographing head and neck

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)

Other: photographing head and neck

Interventions

photographing head and neck OTHER

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.

Test; difficult to intubate, model derivation; difficult to intubate, model validation; easy to intubate, model derivation; easy to intubate, model validation

Eligibility Criteria

• Age: 18 Years - 99 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients undergoing surgical procedures requiring general anesthesia with endotracheal intubation; patients from all ethnic groups

You may qualify if:

• Patients requiring endotracheal intubation
• Patients consenting to acquisition of photographic images of the head and neck

You may not qualify if:

• 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

Sponsors & Collaborators

• Wake Forest University Health Sciences: lead

Study Sites (1)

Wake Forest Baptist Medical Center

Winston-Salem, North Carolina, 27157, United States

RECRUITING

Related Publications (2)

• 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.

  PMID: 34391000 BACKGROUND

• Connor 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.

  PMID: 21081769 RESULT

Study Officials

• Scott Segal, MD, MHCM

  Wake Forest University Health Sciences

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Scott Segal, MD, MHCM

CONTACT

(336) 716-7084; B.Segal@wfusm.edu

Angela Goodson

CONTACT

336-716-4497; agoodson@wakehealth.edu

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: June 4, 2012
• First Posted: June 6, 2012
• Study Start: May 1, 2012
• Primary Completion (Estimated): December 1, 2027
• Study Completion (Estimated): December 1, 2027
• Last Updated: April 1, 2026

Record last verified: 2026-03

Locations

US (1)