Abstract: A method and apparatus for identifying blood vessels in ultrasound images and displaying blood vessels in ultrasound images are described. In some embodiments, the method is implemented by a computing device and includes receiving an ultrasound image that includes one or more blood vessels, and determining, with a neural network implemented at least partially in hardware of the computing device, diameters of the one or more blood vessels in the ultrasound image. The method includes receiving a user selection of an instrument size, and indicating, in the ultrasound image, at least one blood vessel of the one or more blood vessels based on the instrument size and the diameters of the one or more blood vessels.

Abstract: A method and apparatus for identifying blood vessels in ultrasound images and displaying blood vessels in ultrasound images are described. In some embodiments, the method is implemented by a computing device and includes assigning, with a neural network implemented at least partially in hardware of the computing device, one of a vein classification and an artery classification to one or more blood vessels in ultrasound images. The method also includes determining a misclassification for one blood vessel that denotes the neural network assigning the one of the vein classification and the artery classification to the one blood vessel in one ultrasound image and the other of the vein classification and the artery classification to the one blood vessel in additional ultrasound images. The method includes displaying, in the one ultrasound image, an indication of the other of the vein classification and the artery classification for the one blood vessel.

Abstract: A method and apparatus for identifying blood vessels in ultrasound images and displaying blood vessels in ultrasound images are described. In some embodiments, the method is implemented by a computing device and includes assigning, with a neural network implemented at least partially in hardware of the computing device, one of a vein classification and an artery classification to one or more blood vessels in ultrasound images. The method also includes determining a misclassification for one blood vessel that denotes the neural network assigning the one of the vein classification and the artery classification to the one blood vessel in one ultrasound image and the other of the vein classification and the artery classification to the one blood vessel in additional ultrasound images. The method includes displaying, in the one ultrasound image, an indication of the other of the vein classification and the artery classification for the one blood vessel.

Abstract: A method and apparatus for identifying blood vessels in ultrasound images and displaying blood vessels in ultrasound images are described. In some embodiments, the method is implemented by a computing device and includes receiving an ultrasound image that includes one or more blood vessels, and determining, with a neural network implemented at least partially in hardware of the computing device, diameters of the one or more blood vessels in the ultrasound image. The method includes receiving a user selection of an instrument size, and indicating, in the ultrasound image, at least one blood vessel of the one or more blood vessels based on the instrument size and the diameters of the one or more blood vessels.

Abstract: Methods and apparatuses for performing automated detection of lung slide using a computing device (e.g., an ultrasound system, etc.) are disclosed. In some embodiments, the techniques determine lung sliding using one or more neural networks. In some embodiments, the neural networks are part of a process that determines probabilities of the lung sliding at one or more M-lines. In some embodiments, the techniques display one or more probabilities of lung sliding in a B-mode ultrasound image.

Abstract: Methods and apparatuses for performing automated detection of lung slide using a computing device (e.g., an ultrasound system, etc.) are disclosed. In some embodiments, the techniques determine lung sliding using one or more neural networks. In some embodiments, the neural networks are part of a process that determines probabilities of the lung sliding at one or more M-lines. In some embodiments, the techniques display one or more probabilities of lung sliding in a B-mode ultrasound image.

Abstract: A method and apparatus for identifying blood vessels in ultrasound images and displaying blood vessels in ultrasound images are described. In some embodiments, the method is implemented by a computing device and includes receiving ultrasound images that include a blood vessel, and determining, with a neural network implemented at least partially in hardware of the computing device, diameters of the blood vessel in the ultrasound images. The diameters include a respective diameter of the blood vessel for each ultrasound image of the ultrasound images. The method includes determining a blood vessel diameter based on the diameters of the blood vessel, selecting a color based on the blood vessel diameter, and indicating, in one of the ultrasound images, the blood vessel with an indicator having the color.

Abstract: A method and apparatus for identifying blood vessels in ultrasound images and displaying blood vessels in ultrasound images are described. In some embodiments, the method is implemented by a computing device and includes receiving an ultrasound image that includes one or more blood vessels, and determining, with a neural network implemented at least partially in hardware of the computing device, diameters of the one or more blood vessels in the ultrasound image. The method includes receiving a user selection of an instrument size, and indicating, in the ultrasound image, at least one blood vessel of the one or more blood vessels based on the instrument size and the diameters of the one or more blood vessels.

Abstract: A method and apparatus for identifying blood vessels in ultrasound images and displaying blood vessels in ultrasound images are described. In some embodiments, the method is implemented by a computing device and includes assigning, with a neural network implemented at least partially in hardware of the computing device, one of a vein classification and an artery classification to one or more blood vessels in ultrasound images. The method also includes determining a misclassification for one blood vessel that denotes the neural network assigning the one of the vein classification and the artery classification to the one blood vessel in one ultrasound image and the other of the vein classification and the artery classification to the one blood vessel in additional ultrasound images. The method includes displaying, in the one ultrasound image, an indication of the other of the vein classification and the artery classification for the one blood vessel.

Abstract: A method of operating a portable ultrasound device to display digital video information from a video camera includes loading instructions into a memory of the device that configures processor electronics to process digital video signals. When a request for the video is received, the ultrasound device can ready itself to receive digital video information and display an image at a display of the ultrasound device based at least in part on the video information. In one embodiment, the video modality is enabled when the portable ultrasound device detects that the digital camera device has been connected to a port.