Optimal view detection for ultrasound-guided supraclavicular block using convolutional neural networks

The objective of this study is to develop a computer-aided diagnosis (CADx) system for successful ultrasound-guided supraclavicular block (SCB). The retrospectively collected ultrasound videos were from 800 patients to develop the CADx system (600 to the training and validation set, and two test sets of 100 each). The proposed method consists of classification and segmentation approaches using convolutional neural networks (CNN). As for the classification method, a ResNet-based model using augmentation technique, GRU module, and self-supervised learning method were added to the comparison experiment. The segmentation approach used ResNet as encoder for U-Net, and it is a cascaded structure that trains the classification model successively by using the prediction result of the U-Net as pseudo labels. As a result of the classification and segmentation approaches, the ResNet layer did not improve performance further in layers deeper than 34, but applying the augmentation methods was effective. In addition, it was confirmed that the classification approach improved performance when the GRU modules were added, but was not suitable for real-time setting. The proposed approaches showed the highest performance with accuracy 0.88, 0.883, precision 0.578, 0.621, recall 0.712, 0.601, F1-score 0.639, 0.609, and AUROC 0.913, 0.919, respectively.