Abstract: A method of assessing hip joint kinematics based on a spinopelvic condition of a patient is provided. The method comprises receiving a three-dimensional model of a human anatomy and receiving input related to a spinopelvic condition of a patient. The method further comprises determining a sitting sacral slope and a standing sacral slope of the patient based on the input and classifying the spinopelvic condition of the patient based on at least one of the sitting sacral slope and a standing sacral slope. The method further comprises modifying the three-dimensional model according to the spinopelvic condition and performing at least one simulation of one or more activities with the modified three-dimensional model. The method further comprises and displaying hip joint kinematic information from the simulations on a display device.

Abstract: A computer-implemented method for creating an activity-optimized cutting guides for surgical procedures includes receiving one or more pre-operative images depicting one or more anatomical joints of a patient, and creating a three-dimensional anatomical model of the one or more anatomical joints based on the one or more pre-operative images. One or more patient-specific anatomical measurements are determined based on the three-dimensional anatomical model. A statistical model of joint performance is applied to the patient-specific anatomical measurements to identify one or more cut angles for performing a surgical procedure. A patient-specific cutting guide is created that comprises one or more apertures positioned based on the one or more cut angles.

Abstract: A computer-implemented method for creating an activity-optimized cutting guides for surgical procedures includes receiving one or more pre-operative images depicting one or more anatomical joints of a patient, and creating a three-dimensional anatomical model of the one or more anatomical joints based on the one or more pre-operative images. One or more patient-specific anatomical measurements are determined based on the three-dimensional anatomical model. A statistical model of joint performance is applied to the patient-specific anatomical measurements to identify one or more cut angles for performing a surgical procedure. A patient-specific cutting guide is created that comprises one or more apertures positioned based on the one or more cut angles.

Abstract: A method for creating a patient-specific surgical plan includes receiving one or more pre-operative images of a patient having one or more infirmities affecting one or more anatomical joints. three-dimensional anatomical model of the one or more anatomical joints is created based on the one or more pre-operative images. One or more transfer functions and the three-dimensional anatomical model are used to identify a patient-specific implantation geometry that corrects the one or more infirmities. The transfer functions model performance of the one or more anatomical joints as a function of anatomical geometry and anatomical implantation features. surgical plan comprising the patient-specific implantation geometry may then be displayed.

Abstract: A method for creating a patient-specific surgical plan includes receiving one or more pre-operative images of a patient having one or more infirmities affecting one or more anatomical joints, three-dimensional anatomical model of the one or more anatomical joints is created based on the one or more pre-operative images. One or more transfer functions and the three-dimensional anatomical model are used to identify a patient-specific implantation geometry that corrects the one or more infirmities. The transfer functions model performance of the one or more anatomical joints as a function of anatomical geometry and anatomical implantation features, surgical plan comprising the patient-specific implantation geometry may then be displayed.

Abstract: Methods, non-transitory computer readable media, and surgical computing devices are illustrated that improve surgical planning using machine learning. With this technology, a machine learning model is trained based on historical case log data sets associated with patients that have undergone a surgical procedure. The machine learning model is applied to current patient data for a current patient to generate a predictor equation. The current patient data comprises anatomy data for an anatomy of the current patient. The predictor equation is optimized to generate a size, position, and orientation of an implant, and resections required to achieve the position and orientation of the implant with respect to the anatomy of the current patient, as part of a surgical plan for the current patient.

Abstract: A method and system for performing hip arthroplasty include analyzing images of a patient's hip joint in a plurality of positions to identify preoperative hip geometry. A statistical patient model predicts prosthetic hip implant performance based on the preoperative knee geometry and given prosthetic knee implant implantation parameters for a plurality of selected patient activities, each having a predefined motion profile to calculate an optimized surgical plan for performing the procedure using a computer assisted surgical system, which may use fiducial markers affixed to patient tissue. Hip geometry can be determined by angles between landmarks in the images, including sacral tilt, pelvic incidence, pelvic femoral angle, and ante-inclination angle in x-ray images. Implant performance criteria can include, for example, edge loading and range of motion of implant components.

Abstract: Methods and systems for performing a knee arthroplasty procedure include analyzing images of a patient's patellofemoral and femoral-tibial joint in a plurality of flexion positions to identify preoperative knee geometry. A statistical patient model predicts prosthetic knee implant performance based on the preoperative knee geometry and given prosthetic knee implant implantation parameters to calculate an optimized surgical plan for performing the procedure using a computer assisted surgical system, which may use fiducial markers affixed to patient tissue. The model can include selectable patient activities to adjust the motion profile for plan optimization.

Abstract: Methods, non-transitory computer readable media, and surgical computing devices are illustrated that improve robotic surgical systems. With this technology, one or more machine learning models are trained based on historical state data obtained for a computer-assisted surgical system (CASS) at each of a plurality of time periods during a plurality of historical knee arthroplasty surgical procedures. One or more of the machine learning models are applied to initial state data for a current knee arthroplasty surgical procedure to generate robotic commands required to achieve one or more future states of the CASS. The initial state data comprises a surgical plan. One or more surgical tools of the CASS are then manipulated based on the robotic commands to achieve the one or more future states of the CASS and thereby carry out at least a portion of the surgical plan.

Abstract: A method for creating a patient-specific surgical plan includes receiving one or more pre-operative images of a patient having one or more infirmities affecting one or more anatomical joints. three-dimensional anatomical model of the one or more anatomical joints is created based on the one or more pre-operative images. One or more transfer functions and the three-dimensional anatomical model are used to identify a patient-specific implantation geometry that corrects the one or more infirmities. The transfer functions model performance of the one or more anatomical joints as a function of anatomical geometry and anatomical implantation features. surgical plan comprising the patient-specific implantation geometry may then be displayed.

Abstract: A computer-implemented method for creating an activity-optimized cutting guides for surgical procedures includes receiving one or more pre-operative images depicting one or more anatomical joints of a patient, and creating a three-dimensional anatomical model of the one or more anatomical joints based on the one or more pre-operative images. One or more patient-specific anatomical measurements are determined based on the three-dimensional anatomical model. A statistical model of joint performance is applied to the patient-specific anatomical measurements to identify one or more cut angles for performing a surgical procedure. A patient-specific cutting guide is created that comprises one or more apertures positioned based on the one or more cut angles.

Abstract: A computer-implemented method for creating an activity-optimized cutting guides for surgical procedures includes receiving one or more pre-operative images depicting one or more anatomical joints of a patient, and creating a three-dimensional anatomical model of the one or more anatomical joints based on the one or more pre-operative images. One or more patient-specific anatomical measurements are determined based on the three-dimensional anatomical model. A statistical model of joint performance is applied to the patient-specific anatomical measurements to identify one or more cut angles for performing a surgical procedure. A patient-specific cutting guide is created that comprises one or more apertures positioned based on the one or more cut angles.

Abstract: A computer-implemented method for creating an activity-optimized cutting guides for surgical procedures includes receiving one or more pre-operative images depicting one or more anatomical joints of a patient, and creating a three-dimensional anatomical model of the one or more anatomical joints based on the one or more pre-operative images. One or more patient-specific anatomical measurements are determined based on the three-dimensional anatomical model. A statistical model of joint performance is applied to the patient-specific anatomical measurements to identify one or more cut angles for performing a surgical procedure. A patient-specific cutting guide is created that comprises one or more apertures positioned based on the one or more cut angles.