Abstract: Portable ultrasound apparatus, systems methods are disclosed for scanning anatomical regions of interest. The apparatus includes a housing adapted to be fixed in place over the region, a plurality of transducers positioned in a geometrical arrangement within the housing, and configured to emit ultrasound waves and receive echoes. The system also includes an ultrasound probe including the transducers, at least one processor, and a display. The at least one processor analyzes raw data received from the transducers to identify the likelihood of a condition affecting the anatomical region of interest and provides an indication when the likelihood exceeds a predetermined threshold.

Abstract: Systems and methods for automatically masking an ultrasound image are provided. A computing system is configured to: automatically compute within the ultrasound image a set of keypoints that define an estimated shape of an ultrasound beam image within the ultrasound image; automatically select, using the set of keypoints, a beam geometry from amongst a set of known beam geometries; automatically modify the set of keypoints based on the beam geometry to generate a modified set of keypoints that defines a modified shape of the ultrasound beam image; automatically compute a binary mask configured to mask pixels around the modified shape of the ultrasound beam image using at least the modified set of keypoints and the beam geometry; and automatically compute and output a masked ultrasound image using at least the binary mask.

Abstract: Systems and methods for detecting an absent lung sliding condition from B-mode or M-mode ultrasound imaging. Pleural line detection and M-mode designation are performed to enhance classifier performance. A convolutional neural network binary classifier operates to predict lung sliding or absent lung sliding for a series of clips. A further algorithm uses constituent M-mode-level prediction confidences from the series of clips to produce a final prediction.

Abstract: Methods and systems for processing data to distinguish between a plurality of conditions in lung ultrasound images and, in particular, lung ultrasound images containing B lines. Neural network systems and methods, in which the processor is trained using lung ultrasound images to distinguish between acute respiratory distress syndrome due to COVID-19, acute respiratory distress syndrome due to non-COVID-19 causes, and hydrostatic pulmonary edema.