Med Biol Eng Comput - An endotracheal intubation confirmation system based on carina image detection: a proof of concept.

Tópicos

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Resumo

In this paper, a novel system for automatic confirmation of endotracheal intubation is proposed. The system comprises a miniature CMOS sensor and electric wires attached to a rigid stylet. Video signals are continuously acquired and processed by the algorithm implemented on a PC/DSP. The system is based on detection of the carina image as an anatomical landmark of correct tube positioning and it thus utilizes direct visual cues. Detection of the carina is performed based on unsupervised clustering, using a greedy-Gaussian mixture framework. The performance of the proposed system was initially evaluated using a mannequin model. A scientific prototype was assembled and used to perform repeated intubations on the model and collect a database of video signals which were processed off-line. The videos were categoried by a medical professional into carina, upper-trachea, and esophagus. An accuracy of 100% was achieved in discriminating between the carina and other anatomical structures including esophagus and upper-trachea. As an additional validation, the system was tested using a dataset of 231 video images recorded from five human subjects during intubation. The system correctly classified 120 out of 125 non-carina images (i.e. a sensitivity of 96.0%), and 100 out of 106 carina images (i.e. a specificity 94.3%). Using a 10th-order median filter, applied on the frame-based classification results, a 100% accuracy rate was obtained.

Resumo Limpo

paper novel system automat confirm endotrach intub propos system compris miniatur cmos sensor electr wire attach rigid stylet video signal continu acquir process algorithm implement pcdsp system base detect carina imag anatom landmark correct tube posit thus util direct visual cue detect carina perform base unsupervis cluster use greedygaussian mixtur framework perform propos system initi evalu use mannequin model scientif prototyp assembl use perform repeat intub model collect databas video signal process offlin video categori medic profession carina uppertrachea esophagus accuraci achiev discrimin carina anatom structur includ esophagus uppertrachea addit valid system test use dataset video imag record five human subject intub system correct classifi noncarina imag ie sensit carina imag ie specif use thorder median filter appli framebas classif result accuraci rate obtain

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