Adverse Effect of Other General Anesthetics, SequelaPeSchoNCT02022397
Automated Assessment of Difficult Airway With Facial Recognition Techniques
An observational study of Adverse Effect of Other General Anesthetics, Sequela, run by University of Lausanne Hospitals at 1 site in Switzerland.
Trial summary
General anaesthesia mandates artificial ventilation and tracheal intubation in order to provide patients with artificial breathing. Difficulties related to ventilation and intubation remain the leading cause of morbidity and mortality in general anaesthesia, essentially due to inaccuracies in pre-operative detection… Read more about this study
- Who is it for?
- Ages 16 years and olderHealthy volunteers can take part
- Conditions the study is for
- Adverse Effect of Other General Anesthetics, Sequela
- How many people
- 6,000 participants
- Where
- 1 siteSwitzerland
- Study dates
- Mar 2012to 23 Dec 2024
- How long will I take part?
- 1 Day
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 16 years and older
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Study population: patient undergoing general anesthesia necessitating endotracheal intubation
Who may be able to join
- adult patient (15 years of age)
- patients necessitating endotracheal intubation for general anesthesia
Who cannot join
- -patient refusal
Trial locations and contacts
1 site in 1 country.
Switzerland1 site, 1 recruiting
- Recruiting
Dpt of Anesthesiology, University of Lausanne CHUV
Lausanne, Canton of Vaud, 1011
Patrick Schoettker, Assoc Prof, +41795561043, patrick.schoettker@chuv.ch
Patrick Schoettker, Assoc Prof
About this study
Official title: Automatic Assessment of Difficult Ventilation and Intubation From Automatic Face Analysis and Artificial Intelligence
General anaesthesia mandates artificial ventilation and tracheal intubation in order to provide patients with artificial breathing. Difficulties related to ventilation and intubation remain the leading cause of morbidity and mortality in general anaesthesia, essentially due to inaccuracies in pre-operative detection of anatomical factors predisposing to difficult airways. In this project investigators will develop image and video-processing technologies software solutions to allow automatic recognition of anatomical features playing a key role in identification of difficult ventilation and intubation, leading to modifications in pre-operative anaesthesia management assessment and therefore increase patients' safety.
Read the 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.
- Keywords
- tracheal intubation, predicting difficult intubation, general anesthesia, difficult ventilation
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| difficult intubation | general population necessitating tracheal intubation for general anesthesia |
What is being measured
The primary outcome is the main question the study is designed to answer.
Computerized classification of difficult intubation
automatic classification by artificial intelligence into 3 classes of intubation difficulty
Measured: 1 day
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted27 Dec 2013 (estimated)
- Study startMar 2012
- Primary completion23 Dec 2024 (estimated)
- Study completion23 Dec 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Adverse Effect of Other General Anesthetics, SequelaStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 6,000 participants (estimated)
- Target duration
- 1 Day
Sponsor and investigators
- Lead sponsor
- University of Lausanne Hospitals
- Sponsor type
- University, hospital or other
- Collaborators
- Ecole Polytechnique Fédérale de Lausanne
- Responsible party
- Principal investigator, Patrick Schoettker, University of Lausanne Hospitals
- Investigators
- Patrick Schoettker, Assoc Prof, Principal investigator, University of Lausanne Hospitals
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT02022397
- Sponsor's protocol ID
- 183/09
- Other IDs
- CTI
- Acronym
- PeScho
- Status last verified
- May 2024
- First submitted
- 16 Dec 2013
- Last updated
- 8 May 2024
- Results posted
- No
- Data monitoring committee
- No
Study at a glance
From the registry record, last updated by the sponsor on 8 May 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Automatic Assessment of Difficult Ventilation and Intubation From Automatic Face Analysis and Artificial Intelligence
- ClinicalTrials.gov ID
- NCT02022397
- Acronym / sponsor ID
- PeScho / 183/09
- Keywords
- tracheal intubation, predicting difficult intubation, general anesthesia, difficult ventilation
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 6,000 participants (estimated)
- Lead sponsor
- University of Lausanne HospitalsUniversity, hospital or other
- Collaborators
- Ecole Polytechnique Fédérale de Lausanne
- Study start
- Mar 2012
- Primary completion
- 23 Dec 2024 (estimated)
- Study completion
- 23 Dec 2024 (estimated)
- First posted
- 27 Dec 2013 (estimated)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
General anaesthesia mandates artificial ventilation and tracheal intubation in order to provide patients with artificial breathing. Difficulties related to ventilation and intubation remain the leading cause of morbidity and mortality in general anaesthesia, essentially due to inaccuracies in pre-operative detection of anatomical factors predisposing to difficult airways. In this project investigators will develop image and video-processing technologies software solutions to allow automatic recognition of anatomical features playing a key role in identification of difficult ventilation and intubation, leading to modifications in pre-operative anaesthesia management assessment and therefore increase patients' safety.
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 design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 1 Day
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| difficult intubation | – | –general population necessitating tracheal intubation for general anesthesia |
Who may qualify
- Ages
- 16 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- Study population
- patient undergoing general anesthesia necessitating endotracheal intubation
Key inclusion criteria (2 listed)
- adult patient (15 years of age)
- patients necessitating endotracheal intubation for general anesthesia
Key exclusion criteria (1 listed)
- -patient refusal
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Computerized classification of difficult intubation
Time frame: 1 day
automatic classification by artificial intelligence into 3 classes of intubation difficulty
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 8 May 2024.
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Patrick Schoettker, Assoc Prof principal investigator
University of Lausanne Hospitals
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Adverse Effect of Other General Anesthetics, Sequela, run by University of Lausanne Hospitals at 1 site in Switzerland.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 8 May 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Switzerland. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Patrick Schoettker, Assoc Prof (+41795561043, patrick.schoettker@chuv.ch), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Computerized classification of difficult intubation (time frame: 1 day). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 8 May 2024.