Fujifilm Sonosite, Inc.

Fujifilm Corporation

Mass General Brigham

Adam Benjamin Pely

Thomas M Duffy

Paul Tomotaro Danset

Diku Pranav Mandavia

Davinder S Dhatt

Christopher Aleksandr White

Tsuyoshi Matsumoto

Craig Chamberlain

Andrew Lundberg

Davin Dhatt

Kosei Takemoto

Christopher A White

Kyungsang Kim

Quanzheng Li

Suzannah Mckinney

Fabiola Macruz

Christopher Alexksandr White

Andrew Liteplo

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.

Automated detection of lung slide to aid in diagnosis of pneumothorax

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.

Displaying blood vessels in ultrasound images

Examples herein include methods, systems, and computer program products for utilizing neural networks in ultrasound systems. The methods include processor(s) of a computing device identifying a neural network for implementation on the computing device to generate, based on ultrasound data, inferences and confidence levels for the inferences, the computing device being communicatively coupled via a computing network to an ultrasound machine configured to generate the ultrasound data. The processor(s) implements the neural network on the computing device, including configuring the neural network to generate an inference and a confidence level for at least one image of the images. The processor(s) obtains the ultrasound data including images from the ultrasound machine. The processor(s) determines, for the at least one image, an accuracy of the inference and the confidence level. The processor(s) automatically reconfigures the neural network to increase the accuracy based on the determining the accuracy.

Neural network utilization with ultrasound technology

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.

Guiding instrument insertion

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.

Identifying blood vessels in ultrasound images

Growing community of inventors

Bothell, WA, United States of America

Adam Benjamin Pely