Key result
Deep learning CADx system identifies optimal supraclavicular block views with ~0.94 AUROC accuracy.
Why the study?
Successful ultrasound-guided supraclavicular block requires understanding sonoanatomy and identifying the optimal view, but CNN segmentation is limited in clearly determining this view.
Does a deep learning-based computer-aided diagnosis system accurately determine the optimal view for ultrasound-guided supraclavicular block?
Observational (n=881)
No
Does a deep learning-based computer-aided diagnosis system accurately determine the optimal view for ultrasound-guided supraclavicular block?
A deep learning-based computer-aided diagnosis system demonstrated high accuracy and AUROC in determining the optimal view for ultrasound-guided supraclavicular block, potentially aiding non-experts in real-time settings.
Answers are grounded in this paper and cite their sources.
May enable real-time optimal SCB view detection; hypothesis-generating and requires prospective clinical validation.
Background 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.Objective 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.Methods 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.Results 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.Conclusions 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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Jo et al. (2023) conducted an observational in Patients requiring ultrasound-guided supraclavicular block for upper limb surgery (n=881). Deep learning-based computer-aided diagnosis (CADx) system (ResNet34 classification approach) vs. Segmentation approach (U-Net with ResNet34) was evaluated on Diagnostic performance (AUROC) for determining the optimal view for supraclavicular block (AUROC 0.936). A deep learning-based computer-aided diagnosis system using a classification approach (ResNet34 with gated recurrent units and augmentation) achieved an average accuracy of 0.901 and AUROC of 0.936 in determining the optimal view for ultrasound-guided supraclavicular block.
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