Health TrialsbyHealthPanel

AI-Based Prediction of Difficult Airway in Bariatric Surgery

ConditionObesity Difficult Airway Airway Management
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18-65
SponsorElazıg Fethi Sekin Sehir Hastanesi

About this trial

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.

Eligibility criteria

This trial accepts healthy volunteers

Qualifiers

Adult patients aged 18 to 65 years.

Scheduled for elective bariatric surgery under general anesthesia.

Body Mass Index (BMI) ≥ 35 kg/m².

Consenting to participate in the study.

Disqualifiers

Patients with known upper airway anatomical deformities, head and neck tumors, or a history of head/neck radiotherapy.

History of maxillofacial, airway, or cervical spine surgery.

Emergency surgeries.

Patients requiring planned awake fiberoptic intubation based on obvious preoperative clinical indicators.

Trial population

The study population consists of adult obese patients (BMI ≥ 35 kg/m²) undergoing elective bariatric surgery under general anesthesia in a tertiary academic medical center. This population represents individuals at a higher baseline risk for difficult airway management and intubation.

Trial design

Design model

Cohort

Time perspective

Prospective

Treatments tested in this trial

  • 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.

Treatment groups

340 Participants
are divided into 1 treatment group
Group A: Bariatric Surgery Patients1 intervention

Trial outcomes

Primary outcomes

1

Diagnostic Accuracy of the Artificial Intelligence Model in Predicting Difficult Intubation

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.

Time frame
Intraoperative (assessed during the primary intubation attempt)

Secondary outcomes

1

Number of Intubation Attempts

Total number of direct laryngoscopy attempts required to achieve successful tracheal intubation.

Time frame
Intraoperative
2

Need for Alternative Airway Management Techniques

The frequency of requiring alternative airway devices or strategies (e.g., video laryngoscope, bougie, or fiberoptic bronchoscope) to secure the airway after a primary direct laryngoscopy.

Time frame
Intraoperative

Other outcomes

Sponsors and contacts

Click on the lead sponsor to view all of their trials.

Elazıg Fethi Sekin Sehir Hastanesi

Lead sponsor

Source ClinicalTrials.gov
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