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Completed

NCT Number: NCT04183972

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

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.

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Key information

Conditions

Ultrasound Therapy; Complications

Age range

18 year–80 year

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

Huashan Hospital

Shanghai, Shanghai Municipality, 200040, China

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

ultrasound examination

Procedure

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.

Primary outcomes

  1. The distance of the lateral midpoints of the nerve sheath contours

    Time frame: immediately after the procedure

    between model predictions and the ground truth; between nonexpert anesthesiologist predictions and the ground truth

Secondary outcomes

  1. Accuracy, Sensitivity and specificity

    Time frame: immediately after the procedure

    Accuracy, Sensitivity and specificity of the network and nonexpert anesthesiologists

  2. The percentage of the intersection over union

    Time frame: immediately after the procedure

    between model predictions and the ground truth; between nonexpert anesthesiologist predictions and the ground truth

Sponsors and collaborators

Lead sponsor

Huashan Hospital

Other

Registry information

Official study title

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

Acronym: IBRUNNET

Important dates

Study start
2019
Primary completion
2020
Study completion
2020
First posted
Dec 3, 2019
Registry last updated
Jun 30, 2021

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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