Clinical trial

AI-Based Prediction of Difficult Airway in Bariatric Surgery

Recruiting now · Not applicable · 1 countries · Registry ID NCT07666074

Recruiting nowNot applicableObservational

What this study is about

The aim of this prospective study is to evaluate the accuracy of artificial intelligence (AI) and machine learning algorithms in predicting difficult airways in patients undergoing bariatric surgery. Preoperative airway assessments, including the Upper Lip Bite Test (UBLT), Mallampati score, Body Mass Index (BMI), thyromental distance (TMD), and sternomental distance (SMD), will be recorded. The study investigates whether AI models can provide higher sensitivity and specificity in predicting difficult intubation compared to traditional clinical scoring systems in the obese patient population.

A promising-looking record is not the same as confirmed eligibility. The study team must review the full criteria and current recruitment status.

Basic eligibility

Age18 Years to 65 Years

SexAll

Healthy volunteersAccepted

ConditionObesity Difficult Airway Airway Management

Full registry criteria

Inclusion Criteria: 1. Adult patients aged 18 to 65 years. 2. Scheduled for elective bariatric surgery under general anesthesia. 3. Body Mass Index (BMI) ≥ 35 kg/m². 4. Consenting to participate in the study. Exclusion Criteria: 1. Patients with known upper airway anatomical deformities, head and neck tumors, or a history of head/neck radiotherapy. 2. History of maxillofacial, airway, or cervical spine surgery. 3. Emergency surgeries. 4. Patients requiring planned awake fiberoptic intubation based on obvious preoperative clinical indicators.

Treatments and study arms

Preoperative Airway Assessment and Direct Laryngoscopy

Diagnostic Test

Measurement of preoperative airway parameters including Upper Lip Bite Test (UBLT), Mallampati score, Body Mass Index (BMI), thyromental distance, and sternomental distance. Intraoperative airway view is graded using the Cormack-Lehane classification during standard direct laryngoscopy.

Primary outcomes

Diagnostic Accuracy of the Artificial Intelligence Model in Predicting Difficult IntubationIntraoperative (assessed during the primary intubation attempt)

The predictive performance of the AI model will be evaluated by comparing its preoperative difficult airway prediction against the actual intraoperative direct laryngoscopy view. The intraoperative view is graded using the Cormack-Lehane classification system. Grades 3 and 4 are clinically defined as difficult intubation, while Grades 1 and 2 are defined as easy intubation. The primary metric of diagnostic accuracy will be the Area Under the Receiver Operating Characteristic (AUC-ROC) curve.

Study locations

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Fethi Sekin City Hospital🌐 Elâzığ, Elâzığ, Turkey (Türkiye)

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Muhammed Başpınar, M.D.Contact

Email · Call +905395831141