CompletedNot Applicable

Prediction of Endotracheal Tube Depth by Using Deep Convolutional Neural Networks

Taiwan595 participantsStarted 2019-11-01

Plain-language 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.

Who can participate

Age range18 Years

SexALL

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AI-rewrites the medical criteria so a patient or caregiver can understand them. Always confirm with the trial site.

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

What they're measuring

The lip to carina length predicted by AI model

Timeframe: 1 minute after DCNNs analysis

Trial details

NCT IDNCT05085743

SponsorChang Gung Memorial Hospital

Sponsor typeOTHER

Study typeOBSERVATIONAL

Primary completion2020-10-31

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