Evidence map›Paper›PMID 41488171›Full record

ArticleJournal of medical ultrasound

Automating Brachial Plexus Scan: Wireless Handheld Ultrasound with Deep Learning over Ten Locations.

Min-Jie Yang, Hao-Kuang Wang, Yi-Qi Zhang

Abstract read
In one paragraph

Article in Journal of medical ultrasound. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Min-Jie YangDepartment of Neurosurgery, E-Da Hospital, Kaohsiung, Taiwan.
Hao-Kuang WangDepartment of Neurosurgery, E-Da Hospital, Kaohsiung, Taiwan.
Yi-Qi ZhangDepartment of Neurosurgery, E-Da Hospital, Kaohsiung, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Topographical ultrasound is gaining traction for brachial plexus visualization due to its value in regional anesthesia. However, existing artificial intelligence models for nerve localization are trained on high-resolution stationary ultrasound images, limiting their applicability to more convenient, low-resolution handheld devices. This study addresses this challenge by proposing a novel image segmentation model suitable for low-resolution images. Methods: Thirty adult patients provided informed consent for participation in this study. A high-frequency, portable ultrasound probe was used to acquire B-mode images and video clips at 20 predefined positions. A training dataset of 60,000 images was constructed with expert annotations for landmark localization. A two-stage convolutional neural network architecture was implemented: Stage 1 for image classification and Stage 2 for segmentation with centroid refinement. Four novice physicians underwent testing for comparison. Model performance was evaluated using classification accuracy and segmentation precision metrics. Results: Our model achieved high accuracy in classifying brachial plexus ultrasound image positions (99.2% sensitivity and 84.5% specificity) compared to novice physicians (81.1% sensitivity and 59.8% specificity). In addition, the model demonstrated significantly superior performance in landmark segmentation, with lower median distance error (0.19 mm vs. 4.9 mm) and superior shape similarity metrics (average symmetric surface distance, Hausdorff distance and intersection over union) compared to novice physicians. Conclusion: We present a novel image segmentation model for brachial plexus ultrasound images. The model achieved high classification accuracy and significantly surpassed novice physicians in landmark segmentation. This performance suggests the potential for the model as an educational tool to aid novice physicians in learning brachial plexus anatomy.

Indexed as

Artificial intelligence-based nerve identificationbrachial plexushandheld ultrasoundportable device deployment

Identifiers

PMID41488171
PMCPMC12758884

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LicenceCC BY-NC-SA
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.