Abstract: A computing system is configured to determine a location of a humerus anatomical neck. The computing system receives a three-dimensional (3D) representation of a humerus, and applies a machine learning model to the three-dimensional representation to determine a segmentation of the 3D representation of the humerus. Based on the segmentation, the computing system determines a location of the humerus anatomical neck on the 3D representation of the humerus based on the segmentation, and generates a labeled 3D representation of the humerus that indicates the location of the humerus anatomical neck.

Abstract: A computing system may generate an initial segmentation mask by applying a neural network to a 3D image of a set of objects. The initial segmentation mask associates voxels of the 3D image with individual objects of the set of objects. Additionally, the computing system generates a refined segmentation mask. As part of generating the refined segmentation mask, the computing system performs, for each respective object, a front propagation process for the respective object. The front propagation process for the respective object uses input voxel data to relabel, in the refined segmentation mask, voxels of the 3D image as being associated with the respective object. A stopping condition of a path evaluated by the front propagation process for the respective object occurs when the front propagation process evaluates a voxel identified in the initial segmentation mask as being associated with a different one of the objects from the respective object.

Abstract: A surgical assistance system obtains patient-specific values of a plurality of physical characteristics of a humeral bone of a patient. The surgical assistance system predicts, based on the patient-specific values, a humeral prosthesis (202) for the patient from among a plurality of humeral prostheses, wherein the predicted humeral prosthesis has a filling ratio less than a remodeling threshold for the patient. The filling ratio of the humeral prosthesis is a ratio of (i) a radial distance from a lengthwise central axis of a stem (206) of the humeral prosthesis to an outer surface of the stem, to (ii) a radial distance from the lengthwise central axis of the stem to an inner surface of the intramedullary canal (208) of a humerus of the patient. The remodeling threshold for the patient is a filling ratio above which the humeral prosthesis would cause remodeling in the patient.

Abstract: The disclosure describes examples of machine-learned model based techniques. A computing system may obtain patient characteristics of a patient and implant characteristics of an implant. The computing system may determine information indicative of an operational duration of the implant based on the patient characteristics and the implant characteristics and output the information indicative of the operational duration of the implant. In some examples, one or more processors may be configured to receive, with a machine-learned model of the computing system, implant characteristics of an implant to be manufactured, apply model parameters of the machine-learned model to the implant characteristics, determine information indicative of dimensions of the implant to be manufactured based on the applying of the model parameters of the machine-learned model, and output the information indicative of the dimensions of the implant to be manufactured.

Abstract: Techniques are described for closed surface fitting (CSF). Processing circuitry may determine a plurality of points on a shape, determine a contour, used to determine a shape of an anatomical object, from image information of one or more images of a patient, and determine corresponding points on the contour that correspond to the plurality of points on the shape based on at least one of respective normal vectors projected from points on the shape and normal vectors projected from points on the contour. The processing circuitry may generate a plurality of intermediate points between the points on the shape and the corresponding points on the contour, generate an intermediate shape based on plurality of intermediate points, and generate a mask used to determine the shape of the anatomical object based on the intermediate shape.

Abstract: A surgical planning system for use in surgical procedures to repair an anatomy of interest includes a preplanning system to generate a virtual surgical plan and a mixed reality system that includes a visualization device wearable by a user to view the virtual surgical plan projected in a real environment. The virtual surgical plan includes a 3D virtual model of the anatomy of interest. When wearing the visualization device, the user can align the 3D virtual model with the real anatomy of interest, thereby achieving a registration between details of the virtual surgical plan and the real anatomy of interest. The registration enables a surgeon to implement the virtual surgical plan on the real anatomy of interest without the use of tracking markers.

Abstract: A surgical planning system for use in surgical procedures to repair an anatomy of interest includes a preplanning system to generate a virtual surgical plan and a mixed reality system that includes a visualization device wearable by a user to view the virtual surgical plan projected in a real environment. The virtual surgical plan includes a 3D virtual model of the anatomy of interest. When wearing the visualization device, the user can align the 3D virtual model with the real anatomy of interest, thereby achieving a registration between details of the virtual surgical plan and the real anatomy of interest. The registration enables a surgeon to implement the virtual surgical plan on the real anatomy of interest without the use of tracking markers.

Abstract: A surgical planning system for use in surgical procedures to repair an anatomy of interest includes a preplanning system to generate a virtual surgical plan and a mixed reality system that includes a visualization device wearable by a user to view the virtual surgical plan projected in a real environment. The virtual surgical plan includes a 3D virtual model of the anatomy of interest. When wearing the visualization device, the user can align the 3D virtual model with the real anatomy of interest, thereby achieving a registration between details of the virtual surgical plan and the real anatomy of interest. The registration enables a surgeon to implement the virtual surgical plan on the real anatomy of interest without the use of tracking markers.

Abstract: A surgical planning system for use in surgical procedures to repair an anatomy of interest includes a preplanning system to generate a virtual surgical plan and a mixed reality system that includes a visualization device wearable by a user to view the virtual surgical plan projected in a real environment. The virtual surgical plan includes a 3D virtual model of the anatomy of interest. When wearing the visualization device, the user can align the 3D virtual model with the real anatomy of interest, thereby achieving a registration between details of the virtual surgical plan and the real anatomy of interest. The registration enables a surgeon to implement the virtual surgical plan on the real anatomy of interest without the use of tracking markers.

Abstract: A surgical planning system for use in surgical procedures to repair an anatomy of interest includes a preplanning system to generate a virtual surgical plan and a mixed reality system that includes a visualization device wearable by a user to view the virtual surgical plan projected in a real environment. The virtual surgical plan includes a 3D virtual model of the anatomy of interest. When wearing the visualization device, the user can align the 3D virtual model with the real anatomy of interest, thereby achieving a registration between details of the virtual surgical plan and the real anatomy of interest. The registration enables a surgeon to implement the virtual surgical plan on the real anatomy of interest without the use of tracking markers.

Abstract: A computing system generates a plurality of training datasets from past shoulder surgery cases and uses the plurality of training datasets to train a neural network. Each output layer neuron in a plurality of output layer neurons of the neural network corresponds to a different class in one or more shoulder pathology classification systems. The computing system may obtain a current input vector that corresponds to a current patient. Additionally, the computing system may apply the neural network to the current input vector to generate a current output vector. The computing system may determine, based on the current output vector, a recommended type of shoulder surgery for the current patient.

Abstract: A computing system generates a plurality of training datasets from past shoulder surgery cases and uses the plurality of training datasets to train a neural network. Each output layer neuron in a plurality of output layer neurons of the neural network corresponds to a different class in one or more shoulder pathology classification systems. The computing system may obtain a current input vector that corresponds to a current patient. Additionally, the computing system may apply the neural network to the current input vector to generate a current output vector. The computing system may determine, based on the current output vector, a classification of a shoulder condition of the current patient.

Abstract: A computing system obtains motion data describing a movement of an appendage of a patient. Additionally, the computing system determine, based on the motion data, a range of motion of the appendage. Furthermore, the computing system generates, for display by an extended reality visualization device, an extended reality visualization of the range of motion of the appendage superimposed on an image of the patient or an avatar of the patient.