Status:

COMPLETED

Prediction of Difficult Mask Ventilation Using 3D-Facescan and Machine Learning

Lead Sponsor:

Universitätsklinikum Hamburg-Eppendorf

Collaborating Sponsors:

Institute of Medical Technology and Intelligent Systems at Hamburg University of Technology

Conditions:

Mask Ventilation

General Anesthesia

Eligibility:

All Genders

18+ years

Brief Summary

The aim of this study is to prove feasibility and assess the diagnostic performance of a machine learning algorithm that relies on data from 3D-face scans with predefined motion-sequences and scenes (...

Eligibility Criteria

Inclusion

  • Patients scheduling for ENT or OMS surgery in general anaesthesia, who require facemask ventilation and tracheal intubation after induction of anesthesia
  • Patients aged at least 18 years
  • Ability to understand the patient information and to personally sign and date the informed consent to participate in the study
  • The patient is co-operative and available for the entire study
  • Provided informed consent/patient representative

Exclusion

  • Pregnant or breastfeeding woman
  • Rapid sequence induction or other contraindications for facemask ventilation
  • Planned awake tracheal intubation

Key Trial Info

Start Date :

November 7 2022

Trial Type :

OBSERVATIONAL

Allocation :

ACTUAL

End Date :

May 15 2023

Estimated Enrollment :

423 Patients enrolled

Trial Details

Trial ID

NCT05411406

Start Date

November 7 2022

End Date

May 15 2023

Last Update

September 26 2023

Active Locations (1)

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Page 1 of 1 (1 locations)

1

University Medical Center Hamburg-Eppendorf

Hamburg, Germany, 20246

Prediction of Difficult Mask Ventilation Using 3D-Facescan and Machine Learning | DecenTrialz