Example methods and systems may be used intraoperatively to help surgeons perform accurate and replicable surgeries, such as knee arthroplasty surgeries. An example system combines real time measurement and tracking components with functionality to compile data collected by the hardware to register a bone of a patient, calculate an axis (e.g., mechanical axis) of the leg of the patient, assist a surgeon in placing cut guides, and verify a placement of an inserted prosthesis.

Parametric model based computer implemented methods for determining the stiffness of a bone and systems for estimating the stiffness of a bone in vivo. The computer implemented methods include determining a complex compliance frequency response function Y(f) and an associated complex stiffness frequency response function H(f) and independently fitting a parametric mathematical model to Y(f) and to H(f). The systems include a device for measuring the stiffness of the bone in vivo and a data analyzer to determine a complex compliance frequency response function Y(f) and an associated complex stiffness frequency response function H(f).

Particular embodiments described herein provide for a wearable electronic device, such as a bracelet, coupled to a plurality of electronic components (which may include any type of components, elements, circuitry, etc.). One particular implementation of a wearable electronic device may include a plurality of sensors configured to measure at least one health parameter of a first user associated with the wearable electronic device, and a control module in communication with the plurality of sensors. The control module includes a processor configured to receive a plurality of health parameter measurements from at least a subset of the plurality of sensors, and determine a general health state of the first user based upon the received health parameter measurements.

An orthopedic system to monitor a parameter related to the muscular-skeletal system is disclosed. The orthopedic system includes electronic circuitry, at least one sensor, and a computer to receive measurement data in real-time. The orthopedic system comprises a first plurality of shims of a first type, a second plurality of a second type, a measurement module, and the computer. The measurement module houses the electronic circuitry and at least one sensor. The measurement module is adapted to be used with the first plurality of shims and the second plurality of shims. The measurement module has a medial surface that differs from a lateral surface by shape, size, or contour.

Provided is an apparatus for measuring implant osseointegration, and the apparatus for measuring implant osseointegration includes: a vibration generation unit configured to apply multiple vibrations with frequencies in different bands, respectively, to an implant fixture; a vibration sensor configured to measure three-axis vibration information of the implant fixture caused by the vibrations from the vibration generation unit; and a control unit configured to determine the degree of osseointegration based on the measured vibration information.

A method, system, and computer program product for implementing machine learning visual code and action generation is provided. The method includes receiving from a plurality of hardware and software sources, digital description data associated with visual presentations and an action for execution. A resulting code-based class for each portion of the digital description data is generated with respect to the visual presentation. Self learning software code is executed and a type of visual presentation is selected with respect to associated visual features and the code-based class. Additionally, a visual presentation is selected and an action is executed resulting in hardware and software of a server hardware device being operationally modified. The visual presentation is presented to a user.

The present invention relates generally to the field of orthopedics, and more particularly to a pelvic referencing guide and methods of using the guide in orthopaedic surgery, specifically total hip arthroplasty.

This disclosure includes surgical depth gauges and related methods. Some gauges have a probe assembly including a probe having proximal and distal ends, the distal end including one or more prongs, each having a distal-most tip, being movable between a retracted position and a deployed position in which the tip is farther from a longitudinal axis of the probe than when the prong is in the retracted position, and being biased toward the deployed position. Some probe assemblies include a cannula having proximal and distal ends, where the probe is disposable within the cannula such that moving the distal end of the probe toward and away from the distal end of the cannula causes each of the prongs to move between the retracted and deployed positions. Some depth gauges include a sleeve disposable over the probe assembly.

Exemplary quantitative susceptibility mapping methods, systems and computer-accessible medium can be provided to generate images of tissue magnetism property from complex magnetic resonance imaging data using the Bayesian inference approach, which minimizes a cost function consisting of a data fidelity term and two regularization terms. The data fidelity term is constructed directly from the complex magnetic resonance imaging data. The first prior is constructed from matching structures or information content in known morphology. The second prior is constructed from a region having an approximately homogenous and known susceptibility value and a characteristic feature on anatomic images. The quantitative susceptibility map can be determined by minimizing the cost function. Thus, according to the exemplary embodiment, system, method and computer-accessible medium can be provided for determining magnetic susceptibility information associated with at least one structure.

Segmentation of bony regions in MRI images of joints is automated using a two-stage process. In a first stage, a machine-learning image-slice categorizer is used to categorize image slices of the MRI image data as belonging to one of a set of image-slice categories, depending on presence or absence of bone and/or tendon in the image slice. In a second stage, a first instance of a machine-learning segmentation classifier is used to segment image slices that contain both bone and tendon into bone and non-bone regions, and a second instance of a machine-learning segmentation classifier is used to segment image slices that contain bone but not tendon into bone regions and non-bone regions. Results from the two segmentation classifiers can be combined across image slices to provide a final segmentation of the bony structures, including inflammatory regions, in the image data.