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

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

UNKNOWN

Total Enrollment

367 participants

Study Classification

OBSERVATIONAL

Study Start Date

2021-12-01

Study Completion Date

2022-11-25

Brief Summary

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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.

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Detailed Description

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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.

Conditions

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Thyroid Surgery Intubation; Difficult or Failed

Study Design

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Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Interventions

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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.

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

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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

Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No