Prediction of difficult intubation with advanced face recognition techniques: a preliminary study

Détails

ID Serval
serval:BIB_949B55DB8723
Type
Actes de conférence (partie): contribution originale à la littérature scientifique, publiée à l'occasion de conférences scientifiques, dans un ouvrage de compte-rendu (proceedings), ou dans l'édition spéciale d'un journal reconnu (conference proceedings).
Sous-type
Abstract (résumé de présentation): article court qui reprend les éléments essentiels présentés à l'occasion d'une conférence scientifique dans un poster ou lors d'une intervention orale.
Collection
Publications
Titre
Prediction of difficult intubation with advanced face recognition techniques: a preliminary study
Titre de la conférence
Annual meeting of the Swiss Society of Anaesthesiology and Resuscitation
Auteur(s)
Perruchoud C., Thiran J.P., Yuce A., Schoettker P.
Adresse
Lausanne, Switzerland, November 4-6, 2010
ISBN
1424-7860
Statut éditorial
Publié
Date de publication
2010
Peer-reviewed
Oui
Volume
140
Série
Swiss Medical Weekly
Pages
15
Langue
anglais
Notes
Meeting Abstract
Résumé
Introduction: Difficult tracheal intubation remains a constant and significant source of morbidity and mortality in anaesthetic practice. Insufficient airway assessment in the preoperative period continues to be a major cause of unanticipated difficult intubation. Although many risk factors have already been identified, preoperative airway evaluation is not always regarded as a standard procedure and the respective weight of each risk factor remains unclear. Moreover the predictive scores available are not sensitive, moderately specific and often operator-dependant. In order to improve the preoperative detection of patients at risk for difficult intubation, we developed a system for automated and objective evaluation of morphologic criteria of the face and neck using video recordings and advanced techniques borrowed from face recognition.
Method and results: Frontal video sequences were recorded in 5 healthy volunteers. During the video recording, subjects were requested to perform maximal flexion-extension of the neck and to open wide the mouth with tongue pulled out. A robust and real-time face tracking system was then applied, allowing to automatically identify and map a grid of 55 control points on the face, which were tracked during head motion. These points located important features of the face, such as the eyebrows, the nose, the contours of the eyes and mouth, and the external contours, including the chin. Moreover, based on this face tracking, the orientation of the head could also be estimated at each frame of the video sequence. Thus, we could infer for each frame the pitch angle of the head pose (related to the vertical rotation of the head) and obtain the degree of head extension. Morphological criteria used in the most frequent cited predictive scores were also extracted, such as mouth opening, degree of visibility of the uvula or thyreo-mental distance.
Discussion and conclusion: Preliminary results suggest the high feasibility of the technique. The next step will be the application of the same automated and objective evaluation to patients who will undergo tracheal intubation. The difficulties related to intubation will be then correlated to the biometric characteristics of the patients. The objective in mind is to analyze the biometrics data with artificial intelligence algorithms to build a highly sensitive and specific predictive test.
Web of science
Création de la notice
17/01/2011 18:20
Dernière modification de la notice
03/03/2018 19:38
Données d'usage