Smith + Nephew

Smith & Nephew Orthopaedics Ag

Smith Nephew Asia Pacific Pte. Limited

Shawn P Mcguan

Elizabeth Duxbury

Constantinos Nikou

Ryan Lloyd Landon

Daniel Farley

Randy C Winebarger

Ruxandra Cristiana Marinescu Tanasoca

William L Bowers, Jr

Brian W Mckinnon

Branislav Jaramaz

Zachary Christopher Wilkinson

Sied William Janna

Sean M Haddock

Nathaniel Milton Lenz

Scott Kennedy Laster

James Bennett Wiebe, Iii

A method of determining patient-specific implant parameters for an implant used in a surgical procedure is described. A surgical system receives one or more initial transfer functions and one or more preoperative input factors for a patient and generates a surgical plan comprising one or more patient-specific implant parameters based on the one or more initial transfer functions and the one or more preoperative input factors for the patient. The surgical system further receives one or more intraoperative input factors for the patient and updates the one or more patient-specific implant parameters based on the one or more intraoperative input factors for the patient. An implant for the patient is selected based on the one or more updated patient-specific implant parameters.

Patient-specific surgical method and system

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. The machine learning model is updated based on the current patient data and current outcome.

Methods for improved surgical planning using machine learning and devices thereof

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.

Patient-specific simulation data for robotic surgical planning

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.

Algorithm-based optimization, tool and selectable simulation data for total hip arthroplasty

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San Clemente, CA, United States of America

Shawn P Mcguan