Prediction of Endotracheal Tube Depth by Using Deep Convolutional Neural Networks

Study Overview
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Brief Summary
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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.

Detailed Description
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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.

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

Conditions
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Intubation, Machine Learning

Intervention / Treatment
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* Diagnostic Test: Deep convolutional neural networks analysis


Participation Criteria
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Eligibility Criteria
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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

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Training<br>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. | Diagnostic Test: Deep convolutional neural networks analysis<br>* using Deep convolutional neural networks to analyze pre-intubation chest radiographs along with patients' data to predict the proper depth of ETT fixation<br>|
| Validation<br>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. | Diagnostic Test: Deep convolutional neural networks analysis<br>* using Deep convolutional neural networks to analyze pre-intubation chest radiographs along with patients' data to predict the proper depth of ETT fixation<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Depth of ETT fixation, Deep convolutional neural networks, Chest radiographs