Identification of Interscalene Brachial Plexus on Ultrasonography Using a Deep Neural Network

Study Overview
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Brief Summary
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The purpose of the study is to develop and validate an algorithm based on deep neural networks (DNNs) to identify interscalene brachial plexus on ultrasonography automatically.

Detailed Description
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The investigators plan to develop a deep learning-based network to automatically identify interscalene brachial nerves on ultrasound images. The trained model will be validated on an independent dataset. The performance of the network will also be compared against practicing anesthesiologists.

Official Title
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Identification of Interscalene Brachial Plexus Automatically on Ultrasonography Using a Deep Neural Network

Conditions
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Ultrasound Therapy; Complications

Intervention / Treatment
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* Procedure: ultrasound examination


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: ASA physical status class I or II scheduled for elective surgery Exclusion Criteria: skin lesion or infection of neck any known peripheral neuropathy brachial nerve plexus injury previous injury or operation on neck pregnancy allergic to ultrasound gel

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 80 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Diagnostic
Intervention Model: Single Group Assignment
Interventional Model Description: Patients who have been scheduled to surgery will be recruited for collecting ultrasound images.
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: Image collecting Group<br>An computer algorithm will be developed and evaluated by these image data. | Procedure: ultrasound examination<br>* the participants will be placed in the supine position, with head turned slightly away from the operating side and arms beside the body. The operator will identify right and left interscalene brachial plexuses by ultrasound equipment (Sonosite EDGE or GE LOGIQ e). Clear images and videos of brachial plexus will be captured and saved.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| The distance of the lateral midpoints of the nerve sheath contours | between model predictions and the ground truth; between nonexpert anesthesiologist predictions and the ground truth | immediately after the procedure | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Accuracy, Sensitivity and specificity | Accuracy, Sensitivity and specificity of the network and nonexpert anesthesiologists | immediately after the procedure | 
| The percentage of the intersection over union | between model predictions and the ground truth; between nonexpert anesthesiologist predictions and the ground truth | immediately after the procedure | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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peripheral nerve block, ultrasound image, deep neural network, brachial plexus