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Prediction of Endotracheal Tube Depth by Using Deep Convolutional Neural Networks

NCT05085743 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 595

Last updated 2021-10-20

No results posted yet for this study

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.

Conditions

  • Intubation
  • Machine Learning

Interventions

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

Sponsors & Collaborators

  • Chang Gung Memorial Hospital

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2019-11-01
Primary Completion
2020-10-31
Completion
2020-10-31

Countries

  • Taiwan

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05085743 on ClinicalTrials.gov