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Related Concept Videos

Spinal Nerves: Plexus I01:22

Spinal Nerves: Plexus I

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Nerve plexuses are networks of interlacing nerves that serve as communication hubs to distribute and organize nerve action across various body regions. The nerve plexuses are organized into the cervical plexus located in the neck region, brachial plexus in the shoulder area, lumbar plexus found in the lower back, sacral plexus situated in the pelvis, and coccygeal plexus located in the coccygeal region.
The Cervical Plexus
The cervical plexus, formed by the anterior rami of the first four...
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Spinal Nerves: Plexus II01:21

Spinal Nerves: Plexus II

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The plexuses of the lower body include the lumbar, sacral, and coccygeal plexuses, which innervate the abdomen, pelvis, legs, and coccygeal region. These plexuses control the transmission of sensory information and coordinate motor functions of the lower body.
The Lumbar Plexus
The lumbar plexus is situated within the lumbar region of the back and is primarily formed by the first four lumbar spinal nerves (L1 to L4). This plexus extends its branches into several nerves, including the...
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Convolutional neural network for brachial plexus segmentation at the interscalene level.

Yang Xi1, Hao Chong1, Yan Zhou1

  • 1Department of Pain Managemengt, Beijing Jishuitan Hospital, Capital Medical University, Beijing, 100035, China.

BMC Anesthesiology
|January 8, 2024
PubMed
Summary

A convolutional neural network (CNN) accurately segmented the brachial plexus for regional anesthesia, improving safety in shoulder surgery. This AI model shows promise for real-time guidance during brachial plexus blocks.

Keywords:
Anesthesia regionalBrachial plexus blockNeural network modelsUltrasound imagingValidation study

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Anesthesiology

Background:

  • Regional anesthesia using ultrasound-guided brachial plexus block is crucial for shoulder and upper limb surgeries.
  • Needle misplacement during these procedures can lead to serious complications.
  • Accurate visualization of the brachial plexus is essential for safe and effective anesthesia.

Purpose of the Study:

  • To develop and validate a convolutional neural network (CNN) model.
  • To achieve accurate segmentation of the brachial plexus at the interscalene level using ultrasound images.
  • To enhance the precision and safety of ultrasound-guided brachial plexus blocks.

Main Methods:

  • A prospective study involving 127 patients undergoing ultrasound-guided brachial plexus block.
  • Development of a Unet semantic segmentation model to train the CNN.
  • Evaluation of model performance using Dice and Jaccard indices against clinician-drawn ground truth.

Main Results:

  • The CNN achieved a mean Dice index of 0.748, closely approximating the threshold for good overlap (0.75).
  • The Jaccard index was 0.630, exceeding the threshold for good overlap (0.5).
  • The model demonstrated strong performance in segregating brachial plexus structures.

Conclusions:

  • The developed CNN model effectively segments the brachial plexus at the interscalene level.
  • Further advancements could enable real-time AI guidance for interscalene block administration.
  • This technology has the potential to significantly improve procedural safety and efficacy.