Development of an Artificial Intelligence-Based Model for Predicting Difficult Intubation Using Video Laryngoscopic Images and Cormack-Lehane Classification
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 132
- 试验地点
- 1
- 主要终点
- Accuracy of Machine Learning Model in Predicting Difficult Intubation Based on Video Laryngoscopy Images
研究概览
简要总结
This prospective observational study aims to develop an artificial intelligence model that can automatically determine the Cormack-Lehane classification from video laryngoscopy images in patients undergoing elective surgery. It also aims to predict the risk of difficult intubation based on this classification. The resulting data will evaluate the applicability of AI-supported decision support systems in clinical airway management.
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 65 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •18-65 years
- •Elective surgery
- •No upper airway pathology
排除标准
- •Known history of difficult intubation
- •Morbid obesity (BMI > 40)
- •History of upper airway surgery
结局指标
主要结局
Accuracy of Machine Learning Model in Predicting Difficult Intubation Based on Video Laryngoscopy Images
时间窗: Immediately after data collection and model training
The primary outcome is the classification accuracy of the machine learning algorithm in identifying difficult intubation cases (Cormack-Lehane grade 3-4) from video laryngoscopy images, compared with expert anesthesiologists' consensus. Accuracy will be reported as a percentage.
次要结局
未报告次要终点
研究者
Gizem Demir Şenoğlu
Principal Investigator, Assistant Professor of Anesthesiology and Reanimation, Düzce University Faculty of Medicine
Duzce University