AI-Based Prediction of Difficult Airway in Bariatric Surgery (AI-Airway)
18. juni 2026 opdateret af: Muhammed Başpınar, Elazıg Fethi Sekin Sehir Hastanesi
Artificial Intelligence-Based Prediction of Difficult Airway in Bariatric Surgery: A Prospective Evaluation of Preoperative Airway Predictors
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.
Studieoversigt
Status
Rekruttering
Betingelser
Intervention / Behandling
Undersøgelsestype
Observationel
Tilmelding (Anslået)
340
Kontakter og lokationer
Dette afsnit indeholder kontaktoplysninger for dem, der udfører undersøgelsen, og oplysninger om, hvor denne undersøgelse udføres.
Studiekontakt
- Navn: Muhammed Başpınar, M.D.
- Telefonnummer: +905395831141
- E-mail: bspnr.muhammed@gmail.com
Studiesteder
-
-
Elâzığ
-
Elâzığ, Elâzığ, Tyrkiet (Türkiye), 23100
- Rekruttering
- Fethi Sekin City Hospital
-
Kontakt:
- Muhammed Başpınar, M.D.
- Telefonnummer: +905395831141
- E-mail: bspnr.muhammed@gmail.com
-
-
Deltagelseskriterier
Forskere leder efter personer, der passer til en bestemt beskrivelse, kaldet berettigelseskriterier. Nogle eksempler på disse kriterier er en persons generelle helbredstilstand eller tidligere behandlinger.
Berettigelseskriterier
Aldre berettiget til at studere
- Voksen
- Ældre voksen
Tager imod sunde frivillige
Ja
Prøveudtagningsmetode
Ikke-sandsynlighedsprøve
Studiebefolkning
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.
Beskrivelse
Inclusion Criteria:
- 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.
Exclusion Criteria:
- 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.
Studieplan
Dette afsnit indeholder detaljer om studieplanen, herunder hvordan undersøgelsen er designet, og hvad undersøgelsen måler.
Hvordan er undersøgelsen tilrettelagt?
Design detaljer
Kohorter og interventioner
Gruppe / kohorte |
Intervention / Behandling |
|---|---|
|
Bariatric Surgery Patients
Patients scheduled for elective bariatric surgery under general anesthesia who undergo preoperative airway assessment using clinical and morphometric predictors.
|
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.
Andre navne:
|
Hvad måler undersøgelsen?
Primære resultatmål
Resultatmål |
Foranstaltningsbeskrivelse |
Tidsramme |
|---|---|---|
|
Diagnostic Accuracy of the Artificial Intelligence Model in Predicting Difficult Intubation
Tidsramme: Intraoperative (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.
|
Intraoperative (assessed during the primary intubation attempt)
|
Sekundære resultatmål
Resultatmål |
Foranstaltningsbeskrivelse |
Tidsramme |
|---|---|---|
|
Number of Intubation Attempts
Tidsramme: Intraoperative
|
Total number of direct laryngoscopy attempts required to achieve successful tracheal intubation.
|
Intraoperative
|
|
Need for Alternative Airway Management Techniques
Tidsramme: Intraoperative
|
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.
|
Intraoperative
|
Samarbejdspartnere og efterforskere
Det er her, du vil finde personer og organisationer, der er involveret i denne undersøgelse.
Datoer for undersøgelser
Disse datoer sporer fremskridtene for indsendelser af undersøgelsesrekord og resumeresultater til ClinicalTrials.gov. Studieregistreringer og rapporterede resultater gennemgås af National Library of Medicine (NLM) for at sikre, at de opfylder specifikke kvalitetskontrolstandarder, før de offentliggøres på den offentlige hjemmeside.
Studer store datoer
Studiestart (Faktiske)
21. maj 2026
Primær færdiggørelse (Anslået)
1. september 2026
Studieafslutning (Anslået)
15. oktober 2026
Datoer for studieregistrering
Først indsendt
18. juni 2026
Først indsendt, der opfyldte QC-kriterier
18. juni 2026
Først opslået (Faktiske)
24. juni 2026
Opdateringer af undersøgelsesjournaler
Sidste opdatering sendt (Faktiske)
24. juni 2026
Sidste opdatering indsendt, der opfyldte kvalitetskontrolkriterier
18. juni 2026
Sidst verificeret
1. juni 2026
Mere information
Begreber relateret til denne undersøgelse
Yderligere relevante MeSH-vilkår
Andre undersøgelses-id-numre
- 2026/30-28
Plan for individuelle deltagerdata (IPD)
Planlægger du at dele individuelle deltagerdata (IPD)?
JA
IPD-planbeskrivelse
De-identified individual participant data (including preoperative clinical/morphometric airway measurements and intraoperative Cormack-Lehane grades) underlying the results reported in the final publication will be shared to promote transparency and reproducibility in machine learning models.
IPD-delingsadgangskriterier
Data will be shared upon reasonable request with qualified researchers who provide a methodologically sound proposal.
Proposals should be directed to the corresponding author's email.
To gain access, data requestors will need to sign a data access agreement.
IPD-deling Understøttende informationstype
- STUDY_PROTOCOL
- SAP
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