ArticleScientific reports2023
Optimal view detection for ultrasound-guided supraclavicular block using deep learning approaches.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 3 citations in OpenAlex.
- Spatial Validation of General-Purpose Multimodal AI-Generated Target Regions in Peripheral Nerve Block Ultrasound.Journal of imaging informatics in medicine · 2026Article
- Review
- Current evidence on artificial intelligence in regional anesthesia.World journal of clinical cases · 2024Review
- Artificial intelligence for ultrasound scanning in regional anaesthesia: a scoping review of the evidence from multiple disciplines.British journal of anaesthesia · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Successful ultrasound-guided supraclavicular block (SCB) requires the understanding of sonoanatomy and identification of the optimal view. Segmentation using a convolutional neural network (CNN) is limited in clearly determining the optimal view. The present study describes the development of a computer-aided diagnosis (CADx) system using a CNN that can determine the optimal view for complete SCB in real time. The aim of this study was the development of computer-aided diagnosis system that aid non-expert to determine the optimal view for complete supraclavicular block in real time. Ultrasound videos were retrospectively collected from 881 patients to develop the CADx system (600 to the training and validation set and 281 to the test set). The CADx system included classification and segmentation approaches, with Residual neural network (ResNet) and U-Net, respectively, applied as backbone networks. In the classification approach, an ablation study was performed to determine the optimal architecture and improve the performance of the model. In the segmentation approach, a cascade structure, in which U-Net is connected to ResNet, was implemented. The performance of the two approaches was evaluated based on a confusion matrix. Using the classification approach, ResNet34 and gated recurrent units with augmentation showed the highest performance, with average accuracy 0.901, precision 0.613, recall 0.757, f1-score 0.677 and AUROC 0.936. Using the segmentation approach, U-Net combined with ResNet34 and augmentation showed poorer performance than the classification approach. The CADx system described in this study showed high performance in determining the optimal view for SCB. This system could be expanded to include many anatomical regions and may have potential to aid clinicians in real-time settings.Trial registration The protocol was registered with the Clinical Trial Registry of Korea (KCT0005822, https://cris.nih.go.kr ).
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