Abstract: Examples of the present disclosure describe systems and methods for implementing real-time artificial intelligence (AI) for physical biopsy marker detection. In aspects, the physical characteristics for one or more biopsy site markers may be used to train an AI component of an ultrasound system. The trained AI may be configured to identify deployed markers. When information relating to the characteristics of a deployed marker is input into the ultrasound system, the trained AI may process the received information to create one or more estimated images of the marker, or identify echogenic properties of the marker. During an ultrasound of the site comprising the deployed marker, the AI may use the estimated images and/or identified properties to detect the shape and location of the deployed marker.

Abstract: An ultrasonic method for quantifying the nonlinear shear wave elasticity of a medium, the method comprising the following steps: A1.—collecting a temporal succession of shear wave elasticity data from the medium, A2.—applying, to the medium, a deformation that successively changes according to a predetermined sequence of deformations, during the collection of the shear waves, A3.—observing the actual evolution of deformation, and B.—quantifying the nonlinear elasticity of the medium depending on the temporal succession of data and the evolution of deformation.