Automated Assessment of Difficult Airway With Facial Recognition Techniques (PeScho)
Automatic Assessment of Difficult Ventilation and Intubation From Automatic Face Analysis and Artificial Intelligence
Study Overview
Status
Status
Conditions
Conditions
Detailed Description
Any tracheal intubation requires a pre-operative screening and assessment in order to obtain the essential medical history of the patient, optimize patients' condition in case of any co-existing disease before the operation and select the best method of anesthesia for the day of surgery. The aim of this assessment is to identify potential anesthetic difficulties, such as predictors of difficult airways, which still nowadays represent the first cause of litigation in anesthesia related closed claim studies.
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.
Study Type
Study Type
Enrollment (Estimated)
Enrollment
Contacts and Locations
Study Contact
Study Contact
- Name: Patrick Schoettker, Assoc Prof
- Phone Number: +41795561043
- Email: patrick.schoettker@chuv.ch
Study Locations
-
-
VD
-
Lausanne, VD, Switzerland, 1011
- Recruiting
- Dpt of Anesthesiology, University of Lausanne CHUV
-
Contact:
- Patrick Schoettker, Assoc Prof
- Phone Number: +41795561043
- Email: patrick.schoettker@chuv.ch
-
Principal Investigator:
- Patrick Schoettker, Assoc Prof
-
-
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
Inclusion Criteria:
- adult patient (15 years of age)
- patients necessitating endotracheal intubation for general anesthesia
Exclusion Criteria:
-patient refusal
Study Plan
How is the study designed?
Design Details
Number of groups / cohorts
Cohorts and Interventions
Group / CohortGroup / Cohort |
|---|
|
difficult intubation
general population necessitating tracheal intubation for general anesthesia
|
What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Computerized classification of difficult intubation
Time Frame: 1 day
|
automatic classification by artificial intelligence into 3 classes of intubation difficulty
|
1 day
|
Collaborators and Investigators
Sponsor
Sponsor
Collaborators
Collaborators
Investigators
Investigators
- Principal Investigator: Patrick Schoettker, Assoc Prof, University of Lausanne Hospitals
Study record dates
Study Major Dates
Study Start
Study Start
Primary Completion (Estimated)
Primary Completion
Study Completion (Estimated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Estimated)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Keywords
Other Study ID Numbers
Other Study ID Numbers
- 183/09
- CTI (Other Grant/Funding Number: Swiss Commission Technology and Innovation 12636.1)
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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