Prediction of Endotracheal Tube Depth by Using Deep Convolutional Neural Networks

Intubation Clinical Trial

Official title:

The Prediction of Proper Depth of Endotracheal Tube Fixation Before Intubation by Using Deep Convolutional Neural Networks and Chest Radiographs

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT05085743
• Other study ID #: 202002007B0
• Status: Completed
• Start date: November 1, 2019
• Est. completion date: October 31, 2020

Study information

• Verified date: October 2021
• Source: Chang Gung Memorial Hospital
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

Malposition of an endotracheal tube (ETT) may lead to a great disaster. Developing a handy way to predict the proper depth of ETT fixation is in need. Deep convolutional neural networks (DCNNs) are proven to perform well on chest radiographs analysis. The investigators hypothesize that DCNNs can also evaluate pre-intubation chest radiographs to predict suitable ETT depth and no related studies are found. The authors evaluated the ability of DCNNs to analyze pre-intubation chest radiographs along with patients' data to predict the proper depth of ETT fixation before intubation.

Description:

This was a retrospective, IRB-approved study using chest radiographs images obtained from Picture Archive and Communication System (PACS) at Chang Gung Memorial Hospital, Linkou branch, Taiwan.

A total of 595 de-identified patients' chest radiographs was obtained for this study. The inclusion criteria for this study are patients 18 years or older who were orotracheal intubated within November 2019 to October 2020 and had taken chest radiographs before and immediately after the intubation (<24 hours). Both pre-intubation and post-intubation chest radiographs of a same patient were obtained. Clinical data including age, sex, body height, body weight, depth of ETT fixation were also recorded. All ETT tip to carina distance was manually measured by a same anesthesiologist from post-intubation films and documented. Lip to carina length of each patient can be calculated by adding ETT fixation depth and ETT tip to carina distance.

Pre-intubation chest radiographs (n=595) along with clinical data including age, sex, body height, body weight, and measured lip to carina length are collected for model building. For this study, 476/595 (80%) of those were used for training and 119/595 (20%) for validation randomly selected by AI model. In training process, images and related clinical data along with the measured lip to carina length are fed into and used to fit out AI model. Then, in validation process, the investigators evaluate the model accuracy and efficacy of predicting the lip to carina length with images and clinical data of those unforeseen cases.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 595
• Est. completion date: October 31, 2020
• Est. primary completion date: October 31, 2020
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

• 18 years or older

• orotracheal intubated within November 2019 to October 2020

• had taken chest radiographs before and within 24hr after intubation

Exclusion Criteria:

• Bad chest radiographs quality that patients' carina can not be recognized

• Patient with bronchial insertions found in post-intubation films

• Nasal intubation

Related Conditions & MeSH terms

• Intubation
• Machine Learning

Intervention

Diagnostic Test:
• Deep convolutional neural networks analysis
using Deep convolutional neural networks to analyze pre-intubation chest radiographs along with patients' data to predict the proper depth of ETT fixation

Locations

Country	Name	City	State
Taiwan	Chang Gung Memorial Hospital, Linkou branch	Taoyuan	Guishan Township

Sponsors (1)

Lead Sponsor	Collaborator
Chang Gung Memorial Hospital

Country where clinical trial is conducted

Taiwan, 

References & Publications (8)

Chong DY, Greenland KB, Tan ST, Irwin MG, Hung CT. The clinical implication of the vocal cords-carina distance in anaesthetized Chinese adults during orotracheal intubation. Br J Anaesth. 2006 Oct;97(4):489-95. Epub 2006 Jul 27. — View Citation

Conrardy PA, Goodman LR, Lainge F, Singer MM. Alteration of endotracheal tube position. Flexion and extension of the neck. Crit Care Med. 1976 Jan-Feb;4(1):8-12. — View Citation

Eagle CC. The relationship between a person's height and appropriate endotracheal tube length. Anaesth Intensive Care. 1992 May;20(2):156-60. — View Citation

Herway ST, Benumof JL. The tracheal accordion and the position of the endotracheal tube. Anaesth Intensive Care. 2017 Mar;45(2):177-188. Review. — View Citation

Lakhani P, Flanders A, Gorniak R. Endotracheal Tube Position Assessment on Chest Radiographs Using Deep Learning. Radiol Artif Intell. 2020 Nov 18;3(1):e200026. doi: 10.1148/ryai.2020200026. eCollection 2021 Jan. — View Citation

Lakhani P. Deep Convolutional Neural Networks for Endotracheal Tube Position and X-ray Image Classification: Challenges and Opportunities. J Digit Imaging. 2017 Aug;30(4):460-468. doi: 10.1007/s10278-017-9980-7. — View Citation

Techanivate A, Rodanant O, Charoenraj P, Kumwilaisak K. Depth of endotracheal tubes in Thai adult patients. J Med Assoc Thai. 2005 Jun;88(6):775-81. — View Citation

Varshney M, Sharma K, Kumar R, Varshney PG. Appropriate depth of placement of oral endotracheal tube and its possible determinants in Indian adult patients. Indian J Anaesth. 2011 Sep;55(5):488-93. doi: 10.4103/0019-5049.89880. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	The lip to carina length predicted by AI model	The mean absolute error of AI-predicted length in comparison with measured length is used to evaluate AI performance	1 minute after DCNNs analysis
Secondary	Rate of endotracheal tube malpositioning according to AI model recommendation	Endotracheal tube malpositioning is used to elevate the safty of AI recommendation.	1 minute after DCNNs analysis