ArticleJournal of medical ultrasound
Automating Brachial Plexus Scan: Wireless Handheld Ultrasound with Deep Learning over Ten Locations.
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.
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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.
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.
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Who cites it
3 citing papers in PubMed.
- Artificial Intelligence in Point-of-Care Ultrasound: Domains, Barriers and a Framework for Future Development.Journal of the American College of Emergency Physicians open · 2026Article
- Data annotation and its evaluation in artificial intelligence-based anatomy recognition for ultrasound-guided regional anesthesia: a clinical perspective.Frontiers in medicine · 2026Article
- Neuromuscular ultrasound of the brachial plexus: know-how, know-when.Clinical neurophysiology practice · 2026Review
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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