Condition: Obesity Difficult Airway Airway Management · Sponsor: Elazıg Fethi Sekin Sehir Hastanesi
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.
This description comes directly from the study's public registry record.
Muhammed Başpınar, M.D. · +905395831141 · bspnr.muhammed@gmail.com
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| Fethi Sekin City Hospital | Elâzığ, Elâzığ, Turkey (Türkiye) | Recruiting |
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Source record: clinicaltrials.gov/study/NCT07666074