Numerical study on lung ultrasound B-line formation as a function of imaging frequency and alveolar geometries.

Lung ultrasound (LUS) has become a widely adopted diagnostic method for several lung diseases. However, the presence of air inside the lung does not allow the anatomical investigation of the organ. Therefore, LUS is mainly based on the interpretation of vertical imaging artifacts, called B-lines.

Deep Learning for Classification and Localization of COVID-19 Markers in Point-of-Care Lung Ultrasound.

Deep learning (DL) has proved successful in medical imaging and, in the wake of the recent COVID-19 pandemic, some works have started to investigate DL-based solutions for the assisted diagnosis of lung diseases. While existing works focus on CT scans, this paper studies the application of DL techniques for the analysis of lung ultrasonography (LUS) images. Specifically, we present a novel fully-annotated dataset of LUS images collected from several Italian hospitals, with labels indicating the degree of disease severity at a frame-level, video-level, and pixel-level (segmentation masks).