Automatic detection of pleural line and lung sliding in lung ultrasonography using convolutional neural networks

Background: Lung ultrasonography (LUS) is a valuable diagnostic tool, but there is a shortage of LUS experts with extensive knowledge and significant experience in the field. Convolutional neural networks (CNNs) have the potential to mitigate this issue by facilitating computer-aided diagnosis. Methods: We propose computer-aided system by a CNN-based method for LUS diagnosis. As the first consideration, we investigated pleural line and lung sliding. The pleural line indicates the position of pleura in an ultrasound image, and LUS is performed after first confirming the position of pleural line. Lung sliding defined as the movement of the pleural line, and the absence of this feature is associated with pneumothorax. Results: Our proposed method accurately detected pleural line and lung sliding, demonstrating its potential to provide valuable diagnostic information on lung lesions.