PrediSuisse: Automatized Assessment of Difficult Airway
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: intubation.
- Who it may be relevant to
- Registry conditions: Anesthesia, Intubation; Difficult or Failed, Airway Complication of Anesthesia. Basic parameters: 18 years — 100 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Switzerland
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
PrediSuisse: Automatized Assessment of Difficult Airway Using Three Videolaryngoscopes With the Help of Facial Recognition Techniques and Neural Network
Overview
In the "PrediSuisse" research project, the investigators aim to create a reliable, reproducible, ultra-portable and radiation-free automatized software, able to identify automatically collected features, facial characteristics, and range of movements, to predict intubation difficulty. The software will generate a difficulty intubation score tailored to three commercially available videolaryngoscopes with different type of blades, corresponding to the predicted endotracheal intubation difficulty while providing the anaesthesiologist a reliable and non-subjective tool to assess individual patient's risks with regards to airway management.
Detailed description
The Swiss multi-institutional research project "PrediSuisse" aims to automatically predict and classify the difficulty of intubation and airway management using three commercially available videolaryngoscopes (VL) by acquiring face/profiles photos and sequences on a training set of 900 patients during the pre-anaesthesia consultation. For each patient, with the help of recently developed Machine Learning (ML), Artificial Intelligence (AI) and Convolutional Neural Network (CNN) techniques, a specially developed software will be trained to provide a predicted airway management difficulty index. This will be performed by correlating those photos/sequences and the real difficulty level of intubation, determined by three experts by reviewing the recordings of the intubations of the training set patients. The software will then be used in routine on a set of 900 other patients to validate the prediction performance.
Interventions
- Other intubation
Tracheal intubation using one of the three existing videolaryngoscopes
Primary outcome measures
- Software creation [Time frame: 18 months]
Secondary outcome measures (1)
- Team Communication [Time frame: 18 months]
Eligibility criteria
Inclusion criteria
- Adult patients (≥ 18 years old) presenting at the pre-anesthesia consult for an elective general anesthesia necessitating a tracheal intubation
- Signed informed consent.
Exclusion criteria
- Patients not speaking French (in Geneva and Lausanne) or Italian (in Lugano).
- Patients previously operated on the airway with anatomical modifications (ENT Flaps, tracheotomies).
- Patients unable to follow procedures or to give consent will also be excluded.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Cohort
Study locations
Switzerland · 1 center
- University Hospital Lausanne CHUV — Lausanne
Identifiers
NCT: NCT06453525 · 04062024