Preoperative Airway Images for Difficult Airway Prediction

NCT07700485 | Active Not Recruiting

• 1 other identifier: E-28001928-604.01-318945497
• Study Type: observational
• Target: 319
• Locations: 1 country
• Sites: 1

Brief 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. 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.

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

Outcome Measures

Primary Outcomes (1)

• 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.

  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

  From preoperative airway photography to completion of intraoperative laryngoscopy, up to 1 day

Study Arms (1)

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.

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Adult patients scheduled for elective surgical procedures requiring endotracheal intubation at Dr. Siyami Ersek Thoracic and Cardiovascular Surgery Training and Research Hospital.

You may qualify if:

• 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

You may not qualify if:

• 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
• 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

Sponsors & Collaborators

• Memorial Atasehir Hospital: lead

Study Sites (1)

Dr. Siyami Ersek Thoracic and Cardiovascular Surgery Training and Research Hospital

Istanbul, Kadıköy, 34734, Turkey (Türkiye)

Location

Related Publications (3)

• 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.

  PMID: 39020308 BACKGROUND

• Garcia-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.

  PMID: 38489935 BACKGROUND

• 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

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 1 Day
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Medical Doctor

Study Record Dates

• First Submitted: July 7, 2026
• First Posted: July 14, 2026
• Study Start: June 25, 2026
• Primary Completion (Estimated): September 1, 2026
• Study Completion (Estimated): September 1, 2026
• Last Updated: July 14, 2026

Record last verified: 2026-07

Data Sharing

• IPD Sharing: Will not share

Locations

TU (1)