The present disclosure includes a method of diagnosing a condition of bodily tissue using a computer, the method comprising comparing, using a computer, a 3D tissue model derived from an ultrasound scan of the bodily tissue with at least one 3D tissue model having common tissue with the bodily tissue, and diagnosing a condition of the bodily tissue responsive to comparing the 3D tissue models.

A system and related methods for performing nerve detection during surgical access using ultrasound testing during surgery.

A method of calculating leg length discrepancy of a patient including: receiving patient bone data associated with a lower body of the patient; identifying anatomical landmarks in the patient bone data; orienting a first proximal landmark and a second proximal landmark relative to each other and an origin in a coordinate system; aligning a first axis associated with a first femur and a second axis associated with a second femur with a longitudinal axis extending in a distal-proximal direction, wherein the first and second distal landmarks are adjusted according to the alignment of the first and second axes; calculating a distance between the first and second distal landmarks in the distal-proximal direction along the longitudinal axis; and displaying at least one of the distance or a portion of the patient bone data on a display screen.

Prostheses are described for stabilizing dysfunctional sacroiliac (SI) joints. The prostheses are sized and configured to be press-fit into surgically created pilot SI joint openings in dysfunctional SI joint structures. The prostheses have a pontoon shape with opposed elongated partially cylindrical sections connected by a bridge section. The partially cylindrical sections and, in some instances, the bridge section have a porous structure.

An embodiment of wearable imaging system implements a set of sensors distributed around the joint of a user with advanced software machine learning techniques to deliver accurate measurements of bone-to-bone displacement and angle. A first subset of the distributed sensors emit ultrasound signals towards the joint of the user and a second subset detects ultrasound signals traveling through and reflected off structures of the joint. A controller of the wearable imaging system extracts physiological properties of the joint from the detected ultrasound signals. The controller inputs the physiological properties of the joint and properties of the detected ultrasound signals to a machine-learned displacement model to generate a bone displacement measurement at the joint.

Methods and apparatus for treating a patient. The method includes acquiring a plurality of radio frequency (RF) signals with an ultrasound transducer, each RF signal representing one or more return echoes from a scan line of a pulse-mode echo ultrasound scan. A position of the ultrasound transducer corresponding to each of the acquired RF signals is determined, and a plurality of contour lines generated from the plurality of RF signals. The method estimates a 3-D shape and position of an anatomical feature, such as a joint of patient based on the generated contour lines and corresponding ultrasound transducer positions. An apparatus, or computer includes a processor and a memory with instructions that, when executed by the processor, perform the aforementioned method.

The position and orientation of an ultrasound probe is tracked in three dimensions to provide highly-accurate three-dimensional bone surface images that can be used for anatomical assessment and/or procedure guidance. The position and orientation of a therapy applicator can be tracked in three dimensions to provide feedback to align the projected path of the therapy applicator with a desired path for the therapy applicator or to provide feedback to align the potential therapy field of a therapy applicator with a target anatomical site. The three-dimensional bone surface images can be fit to a three-dimensional model of the anatomical site to provide or display additional information to the user to improve the accuracy of the anatomical assessment and/or procedure guidance.

A method for acquiring image data of a body part of a patient by means of a ultrasonography device comprising the following steps: providing a transducer of the ultrasonography device, said transducer comprising a first orientation sensor; attaching a second orientation sensor to the skin of the patient above the body part; detecting the orientation of the first orientation sensor relative to the second orientation sensor and verifying, whether the relative orientation corresponds to a target value; and acquiring image data of the body part, once the relative orientation corresponds to the target value.

Systems are described for conducting minimally invasive medical interventions utilizing instruments and assemblies thereof to stabilize and/or fixate a dysfunctional sacroiliac (SI) joint. The systems include a drill guide having a bone dislodging member adapted to create a pilot SI joint opening in the dysfunctional SI joint through an incision comprising a length no greater than 3.0 cm; portions of the pilot SI joint opening being disposed in the sacrum and ilium bone structures. The drill guide includes a tri-mode fixation system adapted to position and stabilize the drill guide during creation of the pilot SI joint opening in the dysfunctional SI joint and delivery of the SI joint prosthesis therein. The systems also include a SI joint prosthesis configured to be inserted into the pilot SI joint opening of the dysfunctional SI joint, a prosthesis deployment assembly configured to engage the SI joint prosthesis and advance the SI joint prosthesis into the dysfunctional SI joint, and a bone harvesting assembly adapted to extract and collect dislodge bone material from the bone dislodging member after creation of the pilot SI joint opening.

During acquisition of an ultrasound image feed, ultrasound control data frames are acquired that may be interspersed amongst the ultrasound data frames. The control data frames may use consistent reference scan parameters, irrespective of the scanner settings, and may not need to be converted to image frames. The control data frames can be passed to an artificial intelligence model, which predicts the suitable settings for scanning the anatomy that is being scanned. The artificial intelligence model can be trained with a dataset containing different classes of ultrasound control data frames for different settings, where substantially all the ultrasound control data frames in the dataset are consistently acquired using the reference scan parameters.