Anthropometric and US-Guided Difficult Intubation Prediction With ML Models

Completed

Conditions: Difficult Endotracheal Intubation
Artificial Intelligence

Registration Number: NCT06904586

Lead Sponsor: Duzce University

Brief Summary: The assessment and management of difficult airway is of critical importance. Unsuccessful airway management leads to serious mortality and morbidity. From the beginning of the pre-anesthesia examination, 3% to 13% of patients who are considered suitable for routine airway management may be difficult to intubate. Airway assessment issues include risk assessment and airway examination (bedside and forward) to estimate the risk of difficult airway or aspiration. Airway examination aims to determine the presence of upper airway pathologies or anatomical anomalies. Some physical characteristics are associated with difficult airways and unsuccessful intubation. Examples of these are; limited neck movement, snoring, short sternomental distance, neck circumference thickness, etc. Physical characteristics can be measured with a meter or more detailed upper airway ultrasonographic measurements. In this study, researchers aimed to evaluate the anthropometric and ultrasonographic measurement values of patients who underwent preoperative airway assessment and to see the predictability of difficult intubation with artificial intelligence-supported decision support programs.

Detailed Description: Difficult intubation, particularly unpredictable difficult intubation, is a challenging scenario for every anesthesiologist. Patients who are initially assessed as suitable for routine airway management may present as difficult to intubate in 5% to 22% of cases. Accurate evaluation and management of difficult airways are crucial, as failure in airway management can lead to serious morbidity and mortality.

Airway assessment helps identify predictable difficult airways, but it does not exclude patients with normal clinical evaluations who may still experience unpredictable difficult intubation. The primary goal of airway examination is to detect upper airway pathologies or anatomical anomalies. Several physical characteristics are associated with difficult airways and failed intubation, including limited neck mobility, snoring, a short sternomental distance, and increased neck circumference.

Common airway assessment tools, such as the Mallampati classification and the upper lip bite test, require patient cooperation, which limits their applicability in sedated, trauma, or unresponsive patients. The Cormack-Lehane classification, used during direct laryngoscopy, is invasive and does not allow for pre-procedural preparation. In this context, non-invasive, bedside, rapid, and accessible ultrasonographic assessments and anthropometric measurements have gained importance in predicting difficult airways.

With technological advancements, decision-support systems and artificial intelligence (AI)-assisted applications are increasingly used to prevent adverse outcomes. Successful airway management is particularly critical in high-risk patients, where rapid decision-making is essential. Easily accessible, bedside, non-invasive ultrasonographic measurements, integrated with AI-based learning programs, have the potential to predict difficult intubation in advance. This enables early preparation, timely interventions, and the reduction of life-threatening risks.

In this study, researchers aimed to predict difficult intubation preoperatively using non-invasive anthropometric and ultrasonographic upper airway measurements, combined with AI-assisted decision-support programs, without requiring any invasive procedures.

Our hypothesis is that preoperative airway assessment through anthropometric and ultrasonographic measurements, supported by AI-based decision-support programs, can accurately predict difficult intubation and facilitate early preparation

Recruitment & Eligibility

Status: COMPLETED

Sex: All

Target Recruitment: 329

Inclusion Criteria

Patients over 18 years of age
Patients who will undergo general anesthesia

Exclusion Criteria

Pregnant women
Those with congenital and/or acquired facial deformities
Patients who have previously undergone upper neck airway surgery
Patients with head and neck tumors
Patients who will undergo thyroidectomy

Study & Design

Study Type: OBSERVATIONAL

Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Support Vector Machine Algorithm Percentage of Accuracy in Predicted Difficult Intubations	Taking ultrasonographic and anthropometric measurements of each patient took approximately 20 minutes. Machine learning estimates for each patient are approximately 1 min.	The dataset, labeled based on expert assessment of difficult intubation, was classified using eight widely accepted machine learning algorithms: logistic regression (LR) \[6\], support vector machine (SVM) \[7\], random forest (RF) \[8\], K-nearest neighbors (KNN) \[9\], Gaussian naive Bayes (GNB) \[10\], CatBoost \[11\], XGBoost \[12\], and decision tree (DT) \[13\]. From the original 30 parameters, the 15 most influential features were selected based on feature extraction methods and literature relevance. Preprocessing steps included handling missing values, with incomplete records excluded. The dataset was split into training (80%) and test (20%) sets. Models were trained on the training set, with hyperparameter tuning performed via 5-fold cross-validation to avoid overfitting. Final model performance was evaluated on the independent test set.

Secondary Outcome Measures