A Deep Learning Method to Predict Difficult Laryngoscopy Using Cervical Spine X-ray Image

January 3, 2022 updated by: Seoul National University Hospital

A Deep Learning Method to Predict Difficult Laryngoscopy Using Cervical Spine X-ray Image: Prospective Validation Study

An unanticipated difficult laryngoscopy is associated with serious airway-related complications. The investigators developed a deep learning-based model that predicts a difficult laryngoscopy (Cormack-Lehane grade 3-4) from a cervical spine lateral X-ray using data from 14,135 patients undergoing thyroid surgery. This model showed excellent predictive performance, which was higher than that of other deep learning architectures. In this study, the investigators prospectively validate the model for predicting a difficult laryngoscopy and compare predictive power with clinical airway evaluation.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Detailed Description

Predicting a difficulty of a laryngoscopy is important for patient safety, as an unanticipated difficult laryngoscopy is associated with serious airway-related complications, such as brain damage, cardiopulmonary arrest, or death. Although clinical predictors, such as the modified Mallampati classification, thyromental distance, inter-incisor gap, and the upper lip bite test, are used for airway evaluation in clinical practice, these indicators have low sensitivity and large inter-assessor variability and require patient cooperation.

The investigators developed a deep learning-based model that predicts a difficult laryngoscopy from a cervical spine lateral X-ray using data from 14,135 patients undergoing thyroid surgery. And this study is under submission.

This deep learning model showed the highest performance in predicting difficult laryngoscopy compared to other deep learning models (VGG-Net, ResNet, Xception, ResNext, DenseNet, and SENet) with a sensitivity of 95.6%, a specificity of 91.2%, and an area under ROC curve (AUROC) of 0.972.

However, as the model was a retrospective design using existing medical records, the presence or absence of cricoid pressure to obtain the optimal laryngoscopy was not evaluated, and not compared with airway evaluations.

In this study, the investigators prospectively validate the model for predicting a difficult laryngoscopy and compare predictive power with clinical airway evaluation. If this study prospective confirm our results, this approach can be helpful in improving patient safety and preventing airway-related complications through objective and accurate airway evaluation.

Study Type

Observational

Enrollment (Anticipated)

367

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Contact Backup

Study Locations

  • Korea, Republic of
    • Select A State Or Province
      • Seoul, Select A State Or Province, Korea, Republic of, 03080
        • Recruiting
        • Seoul National University Hospital
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years and older (Adult, Older Adult)

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

Sampling Method

Probability Sample

Study Population

Patients who undergoing thyroid surgery under general anesthesia

Description

Inclusion Criteria:

  • elective thyroid surgery under general anesthesia

Exclusion Criteria:

  • age < 18 years
  • no C-spine lateral X-ray image obtained within 3 months before surgery
  • Patient who safety is not guaranteed when using a direct laryngoscope. (poor dental condition, risk of neck extension)
  • Patients who not cooperate with the physical examination for airway evaluation

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Observational Models: Cohort
  • Time Perspectives: Prospective

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
The area under the receiver operating characteristic curve of deep learning model and airway evaluations for predicting a difficult laryngoscopy.
Time Frame: during induction of anesthesia
Difficult laryngoscopy definition: Cormack-Lehane grade 3 or 4 . Airway evaluations: Inter-incisor gap (millimeter), thyromental distance (millimeter), thyromental height (millimeter), sternomental distance (millimeter), and modified Mallampati class
during induction of anesthesia

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
The area under the receiver operating characteristic curve of deep learning model and airway evaluations for predicting a difficult intubation.
Time Frame: during induction of anesthesia
Difficult intubation: Intubation Difficulty Scale (score)
during induction of anesthesia
Other Performances for predicting a difficult laryngoscopy of deep learning model.
Time Frame: during induction of anesthesia
sensitivity (percent), specificity(percent), Positive predictive value(percent), Negative predictive value (percent), F1-score, and balanced accuracy.
during induction of anesthesia

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Seoul National University Hospital

Investigators

  • Study Chair: Hyung-Chul Lee, Seoul National University Hospital

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Helpful Links

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

December 1, 2021

Primary Completion (Anticipated)

November 25, 2022

Study Completion (Anticipated)

November 25, 2022

Study Registration Dates

First Submitted

December 12, 2021

First Submitted That Met QC Criteria

January 3, 2022

First Posted (Actual)

January 4, 2022

Study Record Updates

Last Update Posted (Actual)

January 4, 2022

Last Update Submitted That Met QC Criteria

January 3, 2022

Last Verified

December 1, 2021

More Information

Terms related to this study

Keywords

Other Study ID Numbers

  • 2111-111-1272

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

No

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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