IntubationNCT05085743
Prediction of Endotracheal Tube Depth by Using Deep Convolutional Neural Networks
An observational study of Intubation and Machine Learning, run by Chang Gung Memorial Hospital at 1 site in Taiwan.
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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Intubation, Machine Learning
- What is being tested?
- Deep convolutional neural networks analysis
- How many people
- 595 participants
- Where
- 1 siteTaiwan
- Study dates
- 1 Nov 2019to 31 Oct 2020
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients 18 years or older who were orotracheal intubated at Chang Gung Memorial Hospital, Linkou branch, Taiwan.
Who may be able to join
- 18 years or older
- orotracheal intubated within November 2019 to October 2020
- had taken chest radiographs before and within 24hr after intubation
Who cannot join
- Bad chest radiographs quality that patients' carina can not be recognized
- Patient with bronchial insertions found in post-intubation films
- Nasal intubation
Trial locations and contacts
1 site in 1 country.
Taiwan1 site
Chang Gung Memorial Hospital, Linkou branch
Taoyuan, Guishan Township, 333
About this study
Official title: The Prediction of Proper Depth of Endotracheal Tube Fixation Before Intubation by Using Deep Convolutional Neural Networks and Chest Radiographs
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.
Read the detailed 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.
- Conditions
- Intubation, Machine Learning
- Being tested
- Deep convolutional neural networks analysis
- Keywords
- Depth of ETT fixation, Deep convolutional neural networks, Chest radiographs
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Deep convolutional neural networks analysisDiagnostic test
using Deep convolutional neural networks to analyze pre-intubation chest radiographs along with patients' data to predict the proper depth of ETT fixation
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Diagnostic Test: Deep convolutional neural networks analysis | |
| 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. Receives: Diagnostic Test: Deep convolutional neural networks analysis |
What is being measured
The primary outcome is the main question the study is designed to answer.
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
Measured: 1 minute after DCNNs analysis
Secondary outcomes (1)
Rate of endotracheal tube malpositioning according to AI model recommendation
Endotracheal tube malpositioning is used to elevate the safty of AI recommendation.
Measured: 1 minute after DCNNs analysis
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted20 Oct 2021
- Study start1 Nov 2019
- Primary completion31 Oct 2020
- Study completion31 Oct 2020
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (8)Papers the sponsor has linked to this study.
- Other trials for IntubationStudies of the same condition, open and under way.
- More trials run by Chang Gung Memorial HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case only
- Time perspective
- Retrospective
- Enrolment
- 595 participants (actual)
Sponsor and investigators
- Lead sponsor
- Chang Gung Memorial Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Po Jui Chen, Chang Gung Memorial Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05085743
- Sponsor's protocol ID
- 202002007B0
- Status last verified
- Oct 2021
- First submitted
- 24 Sep 2021
- Last updated
- 20 Oct 2021
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
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.
PubMed 28600640Lakhani 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.
PubMed 33937852Eagle 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.
PubMed 1595848Varshney 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.
PubMed 22174466Techanivate 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.
PubMed 16083218Herway 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.
PubMed 28267939Chong 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.
PubMed 16873383Conrardy 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.
PubMed 1253616
Study at a glance
From the registry record, last updated by the sponsor on 20 Oct 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- The Prediction of Proper Depth of Endotracheal Tube Fixation Before Intubation by Using Deep Convolutional Neural Networks and Chest Radiographs
- ClinicalTrials.gov ID
- NCT05085743
- Acronym / sponsor ID
- 202002007B0
- Conditions
- Intubation, Machine Learning
- Keywords
- Depth of ETT fixation, Deep convolutional neural networks, Chest radiographs
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 595 participants (actual)
- Lead sponsor
- Chang Gung Memorial HospitalUniversity, hospital or other
- Study start
- 1 Nov 2019 (actual)
- Primary completion
- 31 Oct 2020 (actual)
- Study completion
- 31 Oct 2020 (actual)
- First posted
- 20 Oct 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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
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.
Study design
- Observational model
- Case only
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Training | – | Diagnostic Test: Deep convolutional neural networks analysisImages 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. |
| Validation | – | Diagnostic Test: Deep convolutional neural networks analysisWe 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients 18 years or older who were orotracheal intubated at Chang Gung Memorial Hospital, Linkou branch, Taiwan.
Key inclusion criteria (3 listed)
- 18 years or older
- orotracheal intubated within November 2019 to October 2020
- had taken chest radiographs before and within 24hr after intubation
Key exclusion criteria (3 listed)
- Bad chest radiographs quality that patients' carina can not be recognized
- Patient with bronchial insertions found in post-intubation films
- Nasal intubation
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Deep convolutional neural networks analysisusing Deep convolutional neural networks to analyze pre-intubation chest radiographs along with patients' data to predict the proper depth of ETT fixation | Diagnostic test | Training, Validation |
Primary outcome
The lip to carina length predicted by AI model
Time frame: 1 minute after DCNNs analysis
The mean absolute error of AI-predicted length in comparison with measured length is used to evaluate AI performance
1 secondary outcome
Rate of endotracheal tube malpositioning according to AI model recommendation
Time frame: 1 minute after DCNNs analysis
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 20 Oct 2021.
- Background: PubMed 28600640Lakhani 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.
- Background: PubMed 33937852Lakhani 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.
- Background: PubMed 1595848Eagle 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.
- Background: PubMed 22174466Varshney 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.
- Background: PubMed 16083218Techanivate 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.
- Background: PubMed 28267939Herway 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.
- Background: PubMed 16873383Chong 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.
- Background: PubMed 1253616Conrardy 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.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Intubation and Machine Learning, run by Chang Gung Memorial Hospital at 1 site in Taiwan.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 20 Oct 2021. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Taiwan. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The lip to carina length predicted by AI model (time frame: 1 minute after DCNNs analysis). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05085743, which also lists 8 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 20 Oct 2021.