In various embodiments, the invention teaches systems and methods for magnetic resonance imaging. In some embodiments, the invention teaches systems and methods for determining the source of pain in intervertebral discs by measuring one or more physiological biomarkers associated with disc pain and/or disc degeneration.

Some embodiments provide systems, assemblies, and methods of analyzing patient anatomy including providing an analysis of a patient's spine. The systems, assemblies, and/or methods can include obtaining initial patient data, and acquiring spinal alignment contour information. Further, the systems, assemblies, and/or methods can assess localized anatomical features of the patient, and obtain anatomical region data. The system, assemblies, and/or method can analyze the localized anatomy and therapeutic device location and contouring. Further, the system, assemblies, and/or method can output localized anatomical analyses and therapeutic device contouring data and/or imagery on a display.

A system and method for determining patient-specific anatomical parameters to improve surgical outcomes. Some embodiments include processes for predicting the parameters of occluded anatomy. Some embodiment includes processes for more accurately identifying a center point of a ball and socket joint, such as a center point or center of rotation of a femoral head. Some embodiments include processes for identify a patient-specific spinal curvature, including more precisely determining patient specific spinal inflection points. The various steps can be performed automatically through trained computing devices and graphically presented to a surgeon for review and any necessary modifications.

A computer system receives readings from sensors embedded in a spinal implant implanted in a patient during surgery. The sensor readings are indicative of a load applied by a spine of the patient on the spinal implant. The load causes physical discomfort to the patient. A feature vector is extracted from the implant sensor readings using a machine learning module. The feature vector is indicative of the physical discomfort caused by the load. Electrical signals are generated using the machine learning module based on the feature vector. The machine learning module is trained based on patient data sets to generate the electrical signals to balance the load, such that the physical discomfort is reduced. The electrical signals are transmitted to one or more actuators embedded in the spinal implant to cause the one or more actuators to configure the spinal implant, such that the load is balanced.

A load sensing assembly for a spinal implant includes a set screw having a central opening that extends from a first end of the set screw toward a second end of the set screw. The second end of the set screw is configured to engage with an anchoring member. The load sensing assembly includes an antenna, an integrated circuit in communication with the antenna, where the integrated circuit is positioned within the central opening of the set screw, and a strain gauge in connection with the integrated circuit. The strain gauge is located within the central opening of the set screw in proximity to the second end of the set screw.

Systems and methods to monitor and track the treatment of bones using a bone correction system are provided. The method includes implanting growth modulating implants of a bone correction system in two or more bones of a patient. Each growth modulating implant includes an implant body having at least one sensor device embedded in the implant body. The method includes receiving sensor data from the sensor devices and determining an operational status of the growth modulating implants, based on the received sensor data. The method includes determining, by the processor, a longitudinal growth or growth rate between the two or more bones, based on the received sensor data and causing a display device to selectively display a graphical user interface (GUI) representative of at least one of the longitudinal growth and the growth rate of the patient.

A medical device for penetrating a bone structure including a processing unit having a transfer function that associates an electrical conductivity value S with a depth value d, wherein the processing unit is configured to detect a threshold selected from amongst an absolute threshold, a relative threshold and a critical gradient, and to emit a warning signal and/or control signal responsive to detection of the threshold.

A system and method for performing range of motion measurements in an unsupervised manner. A user equipment with one or more cameras may be used with an application on the UE using the one or more cameras to measure range of motion of a user of the UE. The patient may record movement of the part of the body of interest, such as the head. The movement may be recorded in three dimensions (e.g., calculated from two dimensional coordinates of an image) and processed by the application, and the three rotational angles calculated corresponding to the difference between the final position of the head and the initial position of the head at the beginning of the recording. As a result, facial recognition is used to improve monitoring of range of motion during surgical recovery in an unsupervised environment.

The present disclosure provides a system and method for image data acquisition. The method may include obtaining image data of a subject including a first type of tissue and a second type of tissue. The method may include determining, based on the image data of the subject, a target portion including at least a portion of at least one of the first type of tissue or the second type of tissue. The method may include determining, based at least in part on the target portion represented in the image data, a scan mode corresponding to the target portion. The method may include causing an imaging device to acquire, based on the scan mode, image data of the target portion.

Systems and methods for establishing and managing a patient-device network on or about a body of a patient are disclosed. One or more of the devices implanted into, affixed to, and/or carried by a patient may be configured to establish and manage a communication network. For example, one or more of the implants may include networking mechanisms that autonomously connect to each other, thereby establishing and managing a mesh network or an ad-hoc network on or about a portion of the patient body. The networked devices can communicate with each other and/or to other external devices.