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

NCT ID: NCT04183972

Last Updated: 2021-06-30

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

COMPLETED

Clinical Phase

NA

Total Enrollment

1126 participants

Study Classification

INTERVENTIONAL

Study Start Date

2019-12-01

Study Completion Date

2020-10-31

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.

Conditions

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Ultrasound Therapy; Complications

Study Design

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Allocation Method

NA

Intervention Model

SINGLE_GROUP

Patients who have been scheduled to surgery will be recruited for collecting ultrasound images.
Primary Study Purpose

DIAGNOSTIC

Blinding Strategy

NONE

Study Groups

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Image collecting Group

An computer algorithm will be developed and evaluated by these image data.

Group Type EXPERIMENTAL

ultrasound examination

Intervention Type 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.

Interventions

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ultrasound examination

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.

Intervention Type PROCEDURE

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
Minimum Eligible Age

18 Years

Maximum Eligible Age

80 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Huashan Hospital

OTHER

Sponsor Role lead

Responsible Party

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Xiao-Yu Yang, MD

Principal Investigator

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

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Xiaoyu Yang, MD

Role: PRINCIPAL_INVESTIGATOR

Huashan Hospital

Locations

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Huashan Hospital

Shanghai, Shanghai Municipality, China

Site Status

Countries

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China

References

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Yang XY, Wang LT, Li GD, Yu ZK, Li DL, Guan QL, Zhang QR, Guo T, Wang HL, Wang YW. Artificial intelligence using deep neural network learning for automatic location of the interscalene brachial plexus in ultrasound images. Eur J Anaesthesiol. 2022 Sep 1;39(9):758-765. doi: 10.1097/EJA.0000000000001720. Epub 2022 Jul 20.

Reference Type DERIVED
PMID: 35919026 (View on PubMed)

Other Identifiers

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KY2019-502

Identifier Type: -

Identifier Source: org_study_id

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