AI-Based Prediction of Difficult Airway in Bariatric Surgery

Recruiting now

Conditions studied: Obesity Difficult Airway Airway Management

In brief

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.

Key facts

Study ID

NCT07666074

Run by

Elazıg Fethi Sekin Sehir Hastanesi

People needed

340

Starts

2026-05-21

Expected to finish

2026-10-15

Last updated by the study team

2026-06-24

Who can join

Age: 18 and older, up to 65. Sex: any. Healthy volunteers: accepted.

You may qualify if…

• 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.

You may not qualify if…

• 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.

Where it is running

• Fethi Sekin City Hospital — Elâzığ, Elâzığ, Turkey (Türkiye) (enrolling)

Full record on ClinicalTrials.gov

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