Multimodal Artificial Intelligence for Image-Based Prediction of Difficult Airway: A Prospective Observational Study
试验速览
- 阶段
- 不适用
- 状态
- 进行中（未招募）
- 发起方
- 入组人数
- 319
- 试验地点
- 1
- 主要终点
- Diagnostic Performance of Multimodal AI Models for Predicting Difficult Intubation
研究概览
简要总结
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.
详细描述
Preoperative airway assessment is important for identifying patients at risk for difficult laryngoscopy or difficult intubation. However, conventional bedside airway predictors have limited accuracy when used alone. Multimodal artificial intelligence models may provide additional image-based information by evaluating visible anatomical features from standardized preoperative airway photographs.
In this prospective observational study, adult patients undergoing elective surgery requiring endotracheal intubation will be enrolled between June and September 2026. Each participant will undergo standardized eight-view airway photography during the pre-anesthetic evaluation. The image set will include frontal facial, lateral profile, maximal mouth opening, modified Mallampati, neck extension, and anterior neck views. Images will be anonymized before assessment.
The same image sets will be independently evaluated by multimodal AI models, including ChatGPT, Gemini, and Grok, using an identical structured prompt. The AI models will provide categorical and binary predictions for difficult laryngoscopy and difficult intubation based only on visible image-based anatomical features. No intraoperative outcome data, expert predictions, or conventional airway assessment results will be provided to the AI models.
AI-generated predictions will be compared with expert anesthesiologist image-based assessments, conventional airway evaluation parameters, and prospectively recorded intraoperative reference outcomes. Difficult laryngoscopy will be defined as Cormack-Lehane grade III or IV. Difficult intubation will be defined using objective intraoperative 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.
The study will assess the sensitivity, specificity, positive predictive value, negative predictive value, accuracy, receiver operating characteristic performance, and agreement between AI models and expert anesthesiologist assessments. The findings may help clarify whether multimodal AI can serve as a clinician-supervised adjunct for preoperative difficult airway risk stratification.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •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
排除标准
- •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
研究组 & 干预措施
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.
结局指标
主要结局
Diagnostic Performance of Multimodal AI Models for Predicting Difficult Intubation
时间窗: 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.
次要结局
- Diagnostic Performance of Multimodal AI Models for Predicting Difficult Laryngoscopy(From preoperative airway photography to completion of intraoperative laryngoscopy, up to 1 day)
研究者
Murat Ferhat Ferhatoğlu
Medical Doctor
Memorial Atasehir Hospital