Prediction of Endotracheal Tube Depth by Using Deep Convolutional Neural Networks
NCT ID: NCT05085743
Last Updated: 2021-10-20
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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COMPLETED
595 participants
OBSERVATIONAL
2019-11-01
2020-10-31
Brief Summary
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Detailed Description
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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.
Conditions
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Study Design
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CASE_ONLY
RETROSPECTIVE
Study Groups
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Training
Images and related clinical data along with the measured lip to carina length of the training group are fed into and used to fit out deep convolutional neural networks model.
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
Validation
We evaluate the model accuracy and efficacy of predicting the lip to carina length with images and clinical data of those unforeseen cases in the validation group.
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
Interventions
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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
Eligibility Criteria
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Inclusion Criteria
* orotracheal intubated within November 2019 to October 2020
* had taken chest radiographs before and within 24hr after intubation
Exclusion Criteria
* Patient with bronchial insertions found in post-intubation films
* Nasal intubation
18 Years
ALL
No
Sponsors
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Chang Gung Memorial Hospital
OTHER
Responsible Party
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Po Jui Chen
medical doctor
Locations
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Chang Gung Memorial Hospital, Linkou branch
Taoyuan District, Guishan Township, Taiwan
Countries
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References
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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.
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.
Eagle CC. The relationship between a person's height and appropriate endotracheal tube length. Anaesth Intensive Care. 1992 May;20(2):156-60. doi: 10.1177/0310057X9202000206.
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.
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.
Herway ST, Benumof JL. The tracheal accordion and the position of the endotracheal tube. Anaesth Intensive Care. 2017 Mar;45(2):177-188. doi: 10.1177/0310057X1704500207.
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. doi: 10.1093/bja/ael186. Epub 2006 Jul 27.
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. doi: 10.1097/00003246-197601000-00002. No abstract available.
Other Identifiers
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202002007B0
Identifier Type: -
Identifier Source: org_study_id