NCT04183972CompletedNot Applicable
Identification of Interscalene Brachial Plexus Automatically on Ultrasonography Using a Deep Neural Network
Conditions
Trial Snapshot
- Phase
- Not Applicable
- Status
- Completed
- Sponsor
- Huashan Hospital
- Enrollment
- 1,126
- Locations
- 1
- Primary Endpoint
- The distance of the lateral midpoints of the nerve sheath contours
Study Overview
Brief Summary
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
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.
Study Design
- Study Type
- Interventional
- Allocation
- Na
- Intervention Model
- Single Group
- Primary Purpose
- Diagnostic
- Masking
- None
Eligibility Criteria
- Ages
- 18 Years to 80 Years (Adult, Older Adult)
- Sex
- All
- Accepts Healthy Volunteers
- No
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
Outcomes
Primary Outcomes
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
- Accuracy, Sensitivity and specificity(immediately after the procedure)
- The percentage of the intersection over union(immediately after the procedure)
Investigators
Xiao-Yu Yang, MD
Principal Investigator
Huashan Hospital
Study Sites (1)
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