Key result
Deep learning accurately detects the optimal view for supraclavicular block with ~0.94 AUROC.
Why the study?
Successful ultrasound-guided supraclavicular block requires identification of the optimal view, but segmentation with CNNs is limited in clearly determining this view.
Can a deep learning-based computer-aided diagnosis system accurately determine the optimal view for ultrasound-guided supraclavicular block?
Observational (n=881)
No
Can a deep learning-based computer-aided diagnosis system accurately determine the optimal view for ultrasound-guided supraclavicular block?
Answers are grounded in this paper and cite their sources.
A deep learning-based computer-aided diagnosis system demonstrates high performance in identifying the optimal ultrasound view for supraclavicular blocks, potentially assisting non-experts in real-time clinical settings.
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 ).
No takes yet. Share an insight, caveat, or question.
Jo et al. (2023) conducted an observational in Ultrasound-guided supraclavicular block (n=881). Deep learning-based computer-aided diagnosis (CADx) system vs. Expert labeling (ground truth) was evaluated on Optimal view detection performance (AUROC) (AUROC 0.936). A deep learning-based computer-aided diagnosis system using ResNet34 and gated recurrent units with augmentation accurately detected the optimal view for supraclavicular block with an average AUROC of 0.936.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: