Clinical trial NCT06961240: THE ROLE OF ARTIFICIAL INTELLIGENCE TRAINED WITH PRE-MEASURED NUMERICAL DATA IN PREDICTION OF DIFFICULT INTUBATION
Official title: THE ROLE OF ARTIFICIAL INTELLIGENCE TRAINED WITH PRE-MEASURED NUMERICAL DATA IN PREDICTION OF DIFFICULT INTUBATION
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Artificial Intelligence (AI)
Difficult Intubation
Lead sponsor: Kutahya Health Sciences University
Sponsor class: OTHER
First posted: 2025-05-07
Enrollment: 250
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Adult patients aged 18 years and older who are scheduled for elective surgery under general anesthesia with endotracheal intubation. Participants will be recruited from the anesthesia outpatient clinic. Only patients who provide written informed consent and are able to undergo standardized airway assessment and portrait imaging will be included. Patients with emergency conditions, pregnancy, or a history of facial or head and neck surgery will be excluded.

Brief summary: This study aims to develop an artificial intelligence (AI)-based model to predict difficult intubation in patients undergoing general anesthesia. Since patients are apneic during intubation without spontaneous breathing efforts, minimizing apnea duration is critical. Traditional methods for predicting difficult intubation rely on physical markers such as sternomental distance, thyromental distance, mouth opening, neck extension, Mallampati score, neck circumference, and upper lip bite test. However, performing these assessments quickly and objectively in every patient is challenging. Therefore, utilizing computer-assisted imaging systems and AI techniques may facilitate clinical practice.

In this study, 250 patients presenting to the anesthesia outpatient clinic, who provide informed consent, will be evaluated. Demographic data (age, gender, height, weight, body mass index) will be recorded. Measurements including mouth opening, thyromental distance, sternomental distance, and neck circumference will be performed. Additionally, Mallampati score, neck extension ability, and upper lip bite test results will be noted. Portrait photographs capturing shoulder and upper body anatomy from multiple angles will be taken. During the operation, the Cormack-Lehane score observed by anesthesiologists with at least three years of experience during intubation will also be recorded.

The collected data will consist of both tabular (structured) data and visual data. Data preprocessing will involve cleaning missing and outlier values, encoding categorical variables, and normalizing continuous variables. Key anatomical points (e.g., chin tip, thyroid notch, sternum) will be identified using landmark detection algorithms on the images.

Of the dataset, 200 patients will be used for model training and 50 patients for testing. Machine learning methods (Random Forest, Support Vector Machines, Gradient Boosting) and deep learning methods (Artificial Neural Networks, Convolutional Neural Networks) will be employed. Tabular and image data will first be modeled separately and then combined using ensemble methods. Model performance will be evaluated with metrics including accuracy, sensitivity, specificity, F1 score, and AUC-ROC.

The models will be developed using Python programming language with libraries such as TensorFlow, Scikit-learn, and NumPy, supported by GPU-based computing.

This study is unique in its aim to compare classical physical examination-based predictions with AI-based predictions, enhancing the accuracy of difficult intubation forecasts. Strengthening clinical decision-making processes and improving patient safety are among the primary goals.

Inclusion Criteria:

Patients aged 18 years and older Patients undergoing general anesthesia with endotracheal intubation Patients providing informed consent

Exclusion Criteria:

Patients under 18 years of age Pregnant patients Emergency surgery cases Patients with a history of facial surgeries that alter appearance Patients with prior head and neck surgeries Patients not receiving general anesthesia The results of this study aim to contribute to the development of a reliable, generalizable AI model for early prediction of difficult airways in clinical settings.


Eligibility criteria: Inclusion Criteria:

* Patients aged 18 years or older
* Patients scheduled to undergo general anesthesia with endotracheal intubation
* Patients who provide written informed consent
* Patients able to undergo preoperative clinical evaluation and imaging without contraindications

Exclusion Criteria:

* Patients younger than 18 years of age
* Pregnant patients
* Emergency surgery cases
* Patients with a history of facial surgery altering facial anatomy
* Patients with a history of head and neck surgery
* Patients not receiving general anesthesia

Primary outcomes:
measure: Accuracy of AI-Based Prediction for Difficult Intubation
timeFrame: Within 24 hours preoperatively and intraoperatively
Secondary outcomes:
Other outcomes:
Locations:
Study officials:
affiliation: Kutahya Health Sciences University
name: suleyman camgoz
role: STUDY_CHAIR
References:
citation: Kim JH, Jung HS, Lee SE, Hou JU, Kwon YS. Improving difficult direct laryngoscopy prediction using deep learning and minimal image analysis: a single-center prospective study. Sci Rep. 2024 Jun 20;14(1):14209. doi: 10.1038/s41598-024-65060-x.
citation: Xia M, Jin C, Zheng Y, Wang J, Zhao M, Cao S, Xu T, Pei B, Irwin MG, Lin Z, Jiang H. Deep learning-based facial analysis for predicting difficult videolaryngoscopy: a feasibility study. Anaesthesia. 2024 Apr;79(4):399-409. doi: 10.1111/anae.16194. Epub 2023 Dec 13.
citation: Hayasaka T, Kawano K, Kurihara K, Suzuki H, Nakane M, Kawamae K. Creation of an artificial intelligence model for intubation difficulty classification by deep learning (convolutional neural network) using face images: an observational study. J Intensive Care. 2021 May 6;9(1):38. doi: 10.1186/s40560-021-00551-x.