Rescue Devices and Difficult Intubation. What next?

Authors

  •   C. G. S. Prasad ESIC Medical College - PGIMSR, Rajajinagar - 560010, Bangalore, Karnataka
  •   Pradeep A. Dongare ESIC Medical College - PGIMSR, Rajajinagar - 560010, Bangalore, Karnataka

DOI:

https://doi.org/10.4103/kaj/2018/v16i3-4/158142

Abstract

No Abstract.

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Published

2018-12-31

How to Cite

Prasad, C. G. S., & Dongare, P. A. (2018). Rescue Devices and Difficult Intubation. What next?. Karnataka Anaesthesia Journal, 16(3-4), 41–42. https://doi.org/10.4103/kaj/2018/v16i3-4/158142

Issue

Volume 16, Issue 3-4, July-December 2018

Section

Editorial
Crossref
Scopus
Google Scholar
Europe PMC

References

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Article to be published (Chethana GM et al. Correlation between airway parameters and intubation success…in this issue).

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Cuendet GL, Schoettker P, Yüce A, Sorci M, Gao H, Perruchoud C et al. Facial image analysis for fully automatic prediction of difficult endotracheal intubation, IEEE Trans Biomed Eng. 2016; 63:328- 329. https://doi.org/10.1109/TBME.2015.2457032. PMid:26186767.

Moustafa MA, El-Metainy S, Mahar K, Mahmoud Abdel-magied E. Defining difficult laryngoscopy findings by using multiple parameters: A machine learning approach, Egypt J Anaesth [Internet]. 2017; 33:153-158. Available from: http://dx.doi.org/10.1016/j. egja.2017.02.002.

Oud M. Internal-state analysis in a layered artificial neural network trained to categorize lung sounds, IEEE T Syst Man Cyb A. 2002; 32(6):757-760. https://doi. org/10.1109/TSMCA.2002.807032.