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

NCT05176184 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 367

Last updated 2022-01-04

No results posted yet for this study

Summary

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.

Conditions

  • Thyroid
  • Surgery
  • Intubation; Difficult or Failed

Interventions

DIAGNOSTIC_TEST

A deep learning model for predicting a difficult laryngoscopy based on a cervical spine lateral X-ray image

The deep learning model uses the input of preprocessed C-spine lateral X-ray images and outputs the level of difficulty of a laryngoscopy. The easy laryngoscopy is defined as a combination of the Cormack-Lehane grades 1-2 and the difficult laryngoscopy is defined as a combination of grades 3-4. In addition, before general anesthesia, airway evaluations related to the difficulty of laryngoscopy are performed and the results are compared with the actual level of difficulty.

Sponsors & Collaborators

  • Seoul National University Hospital

    lead OTHER

Principal Investigators

  • Hyung-Chul Lee · Seoul National University Hospital

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2021-12-01
Primary Completion
2022-11-25
Completion
2022-11-25

Countries

  • South Korea

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 NCT05176184 on ClinicalTrials.gov