Fujifilm Sonosite, Inc.

Fujifilm Corporation

Mass General Brigham

Davinder S Dhatt

Thomas M Duffy

Paul Tomotaro Danset

Diku Pranav Mandavia

Adam Benjamin Pely

Christopher Aleksandr White

Tsuyoshi Matsumoto

Andrew Lundberg

Kosei Takemoto

Saeed Aliakbari

Kyungsang Kim

Quanzheng Li

Thomas Endres

Fabiola Macruz

Christopher Alexksandr White

Andrew Liteplo

Suzannah Mckinney

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 are disclosed herein for controlling an ultrasound machine using one or more touchless inputs. In one embodiment, the method for controlling operation of the ultrasound machine comprises obtaining one or more touchless inputs; determining one or more operations to control the ultrasound machine based on the one or more touchless inputs and machine state of the ultrasound machine; and controlling the ultrasound machine using the one or more operations.

Touchless input ultrasound control

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

Systems and methods to detect electromagnetic (EM) emissions on ultrasound systems are described. In some embodiments, an ultrasound system includes an ultrasound scanner that is configured to generate ultrasound data based on reflections of ultrasound signals transmitted by the ultrasound scanner. An ultrasound machine is coupled to the ultrasound scanner and configured to generate an ultrasound image based on the ultrasound data. A circuit is coupled to the ultrasound machine and configured to make a determination whether the ultrasound image is corrupted by a noise process.

Detecting electromagnetic emissions on ultrasound systems

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

Davinder S Dhatt