Acoustic Monitoring of Double-Lumen Ventilated Lungs for the Detection of Selective Unilateral Lung Ventilation

Tejman‐Yarden, Shai; Lederman, Dror; Eilig, Israel; Zlotnik, Alexander; Weksler, Natan; Cohen, Arnon D; Gurman, Gabriel M.

doi:10.1213/01.ane.0000240909.48774.49

Cited by 20 publications

(14 citation statements)

References 19 publications

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“…Although our case report originates from an operating room (OR) setting where considerable airway expertise exists, our observations suggest potential applications for airway management outside the OR, where inadvertent esophageal intubation may occur, as in the hands of paramedics or where capnography, the accepted standard for objective confirmation of endotracheal tube position, 9 is not available. While the ability to use an acoustic signal as an aid in verification of endotracheal intubation has been demonstrated previously, 2,13 unlike other methods, VRI also creates a visual image of distribution of vibrations within the lung. 10,12 One such report limits the inferences which can be drawn, but may serve as a catalyst to trigger further bench studies using this technology.…”

Section: Discussionmentioning

confidence: 99%

Case report: Vibration response imaging findings following inadvertent esophageal intubation

Cinel

Smith

Tay

et al. 2008

Can J Anesth/J Can Anesth

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Purpose:We describe the effect that inadvertent esophageal intubation has on the images and on the vibration distribution of vibration response imaging (VRI). Clinical features:Vibration response imaging (VRI) is a novel, non-invasive, computer-based technology that measures vibration energy of lung sounds during respiration and displays regional intensity, in both visual and graphic format. Vibration response images, obtained prior to tracheal intubation (spontaneous breathing) and during endotracheal ventilation using a controlled mode, resulted in evenly distributed vibrations throughout the patient's lungs. During inadvertent esophageal ventilation, however, the majority of vibrations were detected in the upper regions of the image, compared to those of the lower (60% vs 8%, respectively). During spontaneous breathing and endotracheal ventilation, the midclavicular column of sensors, located over the centre of each lung, detected more vibrations compared to either the medial or the axillary column of sensors. During inadvertent esophageal ventilation, more vibrations were detected by the medial column of sensors (nearest to the midline/esophagus); and fewer were detected by the sensors that were positioned more laterally. Conclusion: This report illustrates the potential for a visual image of distribution of lung vibration energy to differentiate endotracheal intubation from inadvertent esophageal intubation. Éléments cliniques : L'imagerie par réponse vibratoire (VRI) est une nouvelle technologie informatique non invasive qui mesure l'énergie de vibration des sons pulmonaires pendant la respiration et affiche l'intensité régionale en format visuel et graphique. Les images de réponse vibratoire obtenues avant l'intubation trachéale (respiration spontanée) et pendant la ventilation endotrachéale à l'aide d'un mode contrôlé ont eu pour résultat des vibrations uni-

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Section: Discussionmentioning

confidence: 99%

Case report: Vibration response imaging findings following inadvertent esophageal intubation

Cinel

Smith

Tay

et al. 2008

Can J Anesth/J Can Anesth

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“…In addition, the method can not be used to detect OLI incidents as in such cases the capnogram is generally typical in shape and shows normal ETCO2 values [22,25]. Other techniques have been proposed (e.g., [26][27][28][29][30]), but none of them has been proven effective. Therefore, attempts to find the ultimate technique for correct tube position confirmation have been continued.…”

Section: Introductionmentioning

confidence: 99%

Classification of Upper Airways Images for Endotracheal Intubation Verification

Lederman¹

2017

Computer Science &Amp; Information Technology (CS &Amp; IT)

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This paper addresses the problem of classification of upper airways images for endotracheal intubation verification in order to improve the safety of patients undergoing general anaesthesia. The proposed method is based on textural features utilized in a continuous probabilistic framework using parallel Gaussian mixture models (GMMs). The classification decision is made based on a maximum likelihood approach, which is insensitive to the angle at which the image was taken. Evaluation of the proposed approach is done using a dataset of 200 images that includes three classes of anatomical structures of the upper airways. The results show that the approach can be used to efficiently and reliably represent and classify medical images acquired during various procedures.

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“…1,6,18,20,34 In addition, ETCO2 cannot be used to detect OLI incidents as in such cases the capnogram is generally typical in shape and shows normal ETCO2 values. 6,34 Other techniques have been proposed, 17,21,28,29,35 but none of them has been proven effective. Therefore, attempts to find the ultimate technique for correct tube position confirmation have been continued.…”

Section: Introductionmentioning

confidence: 99%

Endotracheal Intubation Confirmation Based on Video Image Classification Using a Parallel GMMs Framework: A Preliminary Evaluation

Lederman

2010

Ann Biomed Eng

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In this paper, the problem of endotracheal intubation confirmation is addressed. Endotracheal intubation is a complex procedure which requires high skills and the use of secondary confirmation devices to ensure correct positioning of the tube. A novel confirmation approach, based on video images classification, is introduced. The approach is based on identification of specific anatomical landmarks, including esophagus, upper trachea and main bifurcation of the trachea into the two primary bronchi ("carina"), as indicators of correct or incorrect tube insertion and positioning. Classification of the images is performed using a parallel Gaussian mixture models (GMMs) framework, which is composed of several GMMs, schematically connected in parallel, where each GMM represents a different imaging angle. The performance of the proposed approach was evaluated using a dataset of cow-intubation videos and a dataset of human-intubation videos. Each one of the video images was manually (visually) classified by a medical expert into one of three categories: upper-tracheal intubation, correct (carina) intubation, and esophageal intubation. The image classification algorithm was applied off-line using a leave-one-case-out method. The results show that the system correctly classified 1517 out of 1600 (94.8%) of the cow-intubation images, and 340 out of the 358 human images (95.0%). The classification results compared favorably with a "standard" GMM approach utilizing textural based features, as well as with a state-of-the-art classification method, tested on the cow-intubation dataset.

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Acoustic Monitoring of Double-Lumen Ventilated Lungs for the Detection of Selective Unilateral Lung Ventilation

Cited by 20 publications

References 19 publications

Case report: Vibration response imaging findings following inadvertent esophageal intubation

Case report: Vibration response imaging findings following inadvertent esophageal intubation

Classification of Upper Airways Images for Endotracheal Intubation Verification

Endotracheal Intubation Confirmation Based on Video Image Classification Using a Parallel GMMs Framework: A Preliminary Evaluation

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