Difficult Airway Intubation
Conditions
Keywords
Difficult Airway, Cormack-Lehane, Video Laryngoscopy, Artificial Intelligence, Machine Learning
Brief summary
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.
Interventions
None listed
Sponsors
Duzce University
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 65 Years
Healthy volunteers
No
Inclusion criteria
* 18-65 years Elective surgery ASA I-II No upper airway pathology
Exclusion criteria
* Known history of difficult intubation Morbid obesity (BMI \> 40) Pregnancy History of upper airway surgery
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Accuracy of Machine Learning Model in Predicting Difficult Intubation Based on Video Laryngoscopy Images | 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. |
Countries
Turkey (Türkiye)
Outcome results
None listed