Exactech, Inc.

Pierre-Henri Flurin

Laurent Angibaud

Christopher P Roche

Thomas W Wright

Howard David Routman

Joseph Zuckerman

Ryan Simovitch

Vikas Kumar

Steven Overman

Ankur Teredesai

An apparatus includes a processor and a non-transitory memory. The processor is configured to receive pre-operative patient specific data. The pre-operative patient specific data is inputted to a first machine learning model to determine a first predicted post-operative joint performance data output including first predicted post-operative outcome metrics. A reconstruction plan of the joint of the patient is generated based on a medical image of the joint, and at least one arthroplasty surgical parameter obtained from the user. The at least one arthroplasty surgical parameter is inputted into a second machine learning model to determine a second predicted post-operative joint performance data output including second predicted post-operative outcome metrics. The second predicted post-operative joint performance data output is updated to include an arthroplasty surgery recommendation, in response to the user varying the at least one arthroplasty surgical parameter, before the arthroplasty surgery, during the arthroplasty surgery, or both.

Method and system for modeling predictive outcomes of arthroplasty surgical procedures

Various embodiments of the present invention are directed to a shoulder prosthesis. These embodiments may relate to a shoulder prosthesis including, for example, a humeral stem for fracture indication. Of note, certain embodiments of the present invention provide a humeral prosthesis with improved integration of the tuberosities around the humeral stem. Various methods relating to uses and applications of the prosthesis are also disclosed.

Shoulder prosthesis with humeral fracture stem

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Le Bouscat, France

Pierre-Henri Flurin