Automated Assessment of Difficult Airway With Facial Recognition Techniques
NCT ID: NCT02022397
Last Updated: 2024-05-08
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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RECRUITING
6000 participants
OBSERVATIONAL
2012-03-31
2024-12-23
Brief Summary
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Detailed Description
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In the first step of the pre-operative assessment procedure, the patient will be analyzed by the software. The patient will be automatically guided through a 10 minutes series of tests and the software will analyze in real-time his/her morphological and dynamic features in order to classify the patient into one of 5 categories described in the next Section. Details relevant to difficult ventilation and intubation (static and dynamic), such as quantifying the exact inter-incisors distance (mouth opening), visibility and detection of anatomical landmarks in the open mouth (uvulae, pillars, tonsils, tongue, posterior pharynx), thyro-mental distance, neck circumference, neck mobility with maximal anterior and posterior movement. The analysis will be performed by:
* automatically computing these relevant measures using robust computer vision algorithms capable to detect, describe and track the face and the neck with high level of accuracy and robustness to extreme poses (left and right rotation and up and down movement of the face)
* developing powerful image processing techniques to describe and compute intra-oral structures. The two sets of measures will be then combined into a machine learning approach capable to classify the patient. The results of the analysis as well as all the recorded videos of every single test will be stored on a central database and accessed in real-time by the doctor to continue the pre-operative consultancy.
The patient will then undergo his planned surgery at the initially planned time and be intubated for that purpose. Proper recording of the grade of intubation in the operating room will be documented and introduced in the assessment database. By this mean, the database will evolve with the assessment and the final post-operative intubation score so that to improve the automatic predictability of the machine learning algorithm.
Conditions
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Study Design
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COHORT
PROSPECTIVE
Study Groups
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difficult intubation
general population necessitating tracheal intubation for general anesthesia
No interventions assigned to this group
Eligibility Criteria
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Inclusion Criteria
* patients necessitating endotracheal intubation for general anesthesia
Exclusion Criteria
16 Years
ALL
Yes
Sponsors
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Ecole Polytechnique Fédérale de Lausanne
OTHER
University of Lausanne Hospitals
OTHER
Responsible Party
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Patrick Schoettker
Associate Professor
Principal Investigators
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Patrick Schoettker, Assoc Prof
Role: PRINCIPAL_INVESTIGATOR
University of Lausanne Hospitals
Locations
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Dpt of Anesthesiology, University of Lausanne CHUV
Lausanne, Canton of Vaud, Switzerland
Countries
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Central Contacts
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Facility Contacts
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Other Identifiers
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CTI
Identifier Type: OTHER_GRANT
Identifier Source: secondary_id
183/09
Identifier Type: -
Identifier Source: org_study_id
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