THE ROLE OF ARTIFICIAL INTELLIGENCE TRAINED WITH PRE-MEASURED NUMERICAL DATA IN PREDICTION OF DIFFICULT INTUBATION

NCT ID: NCT06961240

Last Updated: 2025-11-19

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 250 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2025-05-02
• Study Completion Date: 2025-11-11

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.

Conditions

Artificial Intelligence (AI); Difficult Intubation

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Interventions

AI-Based Difficult Airway Prediction Model

The "AI-Based Difficult Airway Prediction Model" is an artificial intelligence system designed to predict difficult intubation in patients undergoing general anesthesia. It combines clinical data (age, BMI, Mallampati score, mouth opening, thyromental distance, sternomental distance, neck circumference) and anatomical image data. Machine learning (Random Forests, SVM, Gradient Boosting) and deep learning (ANN, CNN) algorithms are used to classify airway difficulty. The model's predictions are compared with clinical assessments by anesthesiologists using Cormack-Lehane grading. The goal is to improve prediction accuracy, enhance airway management, and support clinical decision-making.

Intervention Type: DIAGNOSTIC_TEST

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

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Kutahya Health Sciences University

OTHER

Sponsor Role: lead

Responsible Party

Serkan TELLİ

Asistant. Prof. Dr.

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

suleyman camgoz

Role: STUDY_CHAIR

Kutahya Health Sciences University

Locations

Kütahya Health Sciences University

Kütahya, , Turkey (Türkiye)

Countries

Turkey (Türkiye)

References

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.

Reference Type: BACKGROUND

PMID: 38902319 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 38093485 (View on PubMed)

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.

Reference Type: BACKGROUND

PMID: 33952341 (View on PubMed)

Other Identifiers

2025/05-35

Identifier Type: -

Identifier Source: org_study_id