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

NCT05411406 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 423

Last updated 2023-09-26

No results posted yet for this study

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 (MASCAN algorithm), together with patient-specific meta-data for the prediction of difficult mask ventilation. A secondary aim of the study is to verify whether voice and breathing scans improve the performance of the algorithm. From the clinical point of view, we believe that an automated assessment would be beneficial, as it preserves time and health-care resources while acting observer-independent, thus providing a rational, reproducible risk estimation.

Conditions

  • Mask Ventilation
  • General Anesthesia

Sponsors & Collaborators

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

    collaborator UNKNOWN

  • Universitätsklinikum Hamburg-Eppendorf

    lead OTHER

Principal Investigators

  • Martin Petzoldt, MD · Universitätsklinikum Hamburg-Eppendorf

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2022-11-07
Primary Completion
2023-05-15
Completion
2023-05-15

Countries

  • Germany

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05411406 on ClinicalTrials.gov