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.
Eligibility
Sex
ALL
Min age
18 Years
Max age
—
Healthy volunteers
No
Inclusion Criteria:
* 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
Exclusion Criteria:
* 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
Primary outcome measure(s)
Diagnostic Performance of Multimodal AI Models for Predicting Difficult Intubation — From preoperative airway photography to completion of intraoperative endotracheal intubation, up to 1 day 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.
Trial sites (1)
Facility
City
Region
Status
Dr. Siyami Ersek Thoracic and Cardiovascular Surgery Training and Research Hospital
This page summarises publicly available registry data for informational purposes — not medical advice. Eligibility is determined by each study team; patients should discuss participation with their clinician.
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