A Novel Artificial Intelligence System for Endotracheal Intubation.
Objective:Adequate visualization of the glottic opening is a key factor to successful endotracheal intubation (ETI); however, few objective tools exist to help guide providers’ ETI attempts toward the glottic opening in real-time. Machine learning/artificial intelligence has helped to automate the d...
| Publicado en: | Prehospital Emergency Care Vol. 20; no. 5; pp. 667 - 672 |
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| Autores principales: | , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
Sep/Oct2016
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=117877361&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 117877361 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10903127 FZB jtl: Prehospital Emergency Care issn: 10903127 maglogo: Y pubinfo: dt: Sep/Oct2016 vid: 20 iid: 5 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 117877361 117877361 117877361 10.3109/10903127.2016.1139220 117877361 ppf: 667 ppct: 5 formats: tig: atl: A Novel Artificial Intelligence System for Endotracheal Intubation. aug: au: Carlson, Jestin N. Das, Samarjit De la Torre, Fernando Frisch, Adam Guyette, Francis X. Hodgins, Jessica K. Yealy, Donald M. sug: subj: Diffusion of Innovation Intubation, Intratracheal Methods Technology, Medical Guided Imagery Glottis Anatomy and Histology Automation Research Subject Recruitment Physicians, Emergency Emergency Medical Technicians Videorecording Laryngoscopy Computer Systems Signal Processing, Computer Assisted Methods Cross Sectional Studies ab: Objective:Adequate visualization of the glottic opening is a key factor to successful endotracheal intubation (ETI); however, few objective tools exist to help guide providers’ ETI attempts toward the glottic opening in real-time. Machine learning/artificial intelligence has helped to automate the detection of other visual structures but its utility with ETI is unknown. We sought to test the accuracy of various computer algorithms in identifying the glottic opening, creating a tool that could aid successful intubation. Methods:We collected a convenience sample of providers who each performed ETI 10 times on a mannequin using a video laryngoscope (C-MAC, Karl Storz Corp, Tuttlingen, Germany). We recorded each attempt and reviewed one-second time intervals for the presence or absence of the glottic opening. Four different machine learning/artificial intelligence algorithms analyzed each attempt and time point: k-nearest neighbor (KNN), support vector machine (SVM), decision trees, and neural networks (NN). We used half of the videos to train the algorithms and the second half to test the accuracy, sensitivity, and specificity of each algorithm.Results:We enrolled seven providers, three Emergency Medicine attendings, and four paramedic students. From the 70 total recorded laryngoscopic video attempts, we created 2,465 time intervals. The algorithms had the following sensitivity and specificity for detecting the glottic opening: KNN (70%, 90%), SVM (70%, 90%), decision trees (68%, 80%), and NN (72%, 78%).Conclusions:Initial efforts at computer algorithms using artificial intelligence are able to identify the glottic opening with over 80% accuracy. With further refinements, video laryngoscopy has the potential to provide real-time, direction feedback to the provider to help guide successful ETI. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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