In an example method, a computer system receives one or more images of one or more bones of a patient. The one or more images are generated by a magnetic resonance imaging (MRI). The computer system determines one or more metrics indicative of an image texture of the one or more images; and determines at least one of a bone risk or a bone health of the patient based on the one or more metrics.

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.

Aspects of the present disclosure involve systems, methods, computer program products, and the like, for utilizing a series of images of a patient's anatomy to determine a cut plane for use during a knee procedure. To determine a cut plane for use during a knee replacement procedure, the 2D images may be analyzed by a computer program to determine a best fit plane through one or more points along the proximal surface of the tibia and to determine one or more features of depressions within the proximal surface. With these landmarks identified in the images, a cut plane through the tibia for use during a TKA procedure may be determined. Further, the location of these features in the images may be determined by analyzing the gray scale value of one or more pixels around a selected point on the image. The pixel with the lowest gray scale value may then be assumed to be the edge of the cortical bone in the 2D image.

Implantable sensors for determining bone health that can be utilized in conjunction with orthopedic implants are described. The sensors can include passive strain gauges or passive chemical sensors that can be read by radiographic imaging techniques. Sensors can be affixed to implantable support devices so as to non-invasively monitor the effect of load on the implant; for instance, to provide a quantitative assessment of when a fracture is sufficiently healed to allow safe weight-bearing upon the limb. Alternatively, sensors can monitor the health of a local implant area; for instance, to monitor the implant area of early stage infection or healing of a fusion procedure.

The invention relates to a method of determining the laxity of a joint (9, 15) of a human (5) or an animal. The method comprises providing at least one patient-specific geometrical model (1) of at least one bone and/or at least one prosthesis comprised by the joint. Known loads are applied to the joint or to a part of the body connected to the joint, and a series of actual images (16) of the joint are obtained while the loads are applied. Then the at least one patient-specific geometrical model (1) is registered onto the actual images (16). Based thereon relative displacement and/or rotation of the at least one bone and/or at least one prosthesis is calculated as a function of the applied loads, and based thereon a measure of the laxity of the joint is determined. The invention further relates to a system for performing such a method and to a computer readable medium for performing such a method.

Example implants, systems and methods using sensors for orthopedic surgical assessment and/or planning are described herein. An example system can include a wearable sensor device for pre-operative use by a patient before an orthopedic surgery to generate pre-operative sensor data. The system can also include an implantable sensor device (e.g., a bone implant) to generate and aggregate post-operative sensor data associated with the patient after the surgery. The system can retrieve the pre-operative sensor data and the post-operative sensor data and predict, analyze or assess an outcome of the surgery.

A system for detecting nonunion or malunion fractures can include an implant, a sensor, and a controller. The implant can be securable to a bone of a patient in a location near a fracture of the bone. The sensor can be connectable to the implant and can be configured to produce a sensor signal based on a condition of the implant near the fracture. The controller can be configured to determine a nonunion or malunion of the fracture based on the sensor signal.

A method and an osseointegrated prosthesis system having an osseointegrated prosthesis member are provided having a monitoring system operably coupled to the osseointegrated prosthesis member configured to quantitatively assess the osseointegration of the osseointegrated prosthesis member, a wave-generating element coupled to the osseointegrated prosthesis member and configured to output guided waves along the osseointegrated prosthesis member interrogating an interface between bone and the osseointegrated prosthesis member, and a sensing system configured to sense a condition of the interface between bone and the prosthesis.

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.

A method of generating a 3-D patient-specific bone model, the method comprising: (a) acquiring a plurality of raw radiofrequency (“RF”) signals from an A-mode ultrasound scan of a patient's bone at a plurality of locations using an ultrasound probe that comprises a transducer array; (b) tracking the acquiring of the plurality of raw RF signals in 3-D space and generating corresponding tracking data; (c) transforming each of the plurality of raw RF signals into an envelope comprising a plurality of peaks by applying an envelope detection algorithm to each of the plurality of raw RF signals, each peak corresponding with a tissue interface echo; (d) identifying a bone echo from the tissue interface echoes of each of the plurality of raw RF signals to comprise a plurality of bone echoes by selecting the last peak having a normalized envelope amplitude above a preset threshold, wherein the envelope amplitude is normalized with respect to a maximum peak existing in the envelope; (e) determining a 2-D bone contour from the plurality of bone echoes corresponding to each location of the ultrasound probe to comprise 2-D bone contours; (f) transforming the 2-D bone contours into an integrated 3-D point cloud using the tracking data; and, (g) deforming a non-patient specific 3-D bone model corresponding to the patient's bone in correspondence with the integrated 3-D point cloud to generate a 3-D patient-specific bone model.