Exactech, Inc.

Joseph Zuckerman

Christopher P Roche

Thomas W Wright

C Michael Mauldin

Cary M Mauldin

Pierre Flurin

Thomas Wright

Howard David Routman

Pierre-Henri Flurin

Steven Overman

Ankur Teredesai

Ryan Simovitch

Vikas Kumar

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 relate to an apparatus and method for reverse shoulder arthroplasty (e.g., reverse total shoulder arthroplasty). In one specific example, a glenoid component used to resurface the scapula may be provided. Of note, unlike traditional total shoulder arthroplasty the glenoid component in a reverse shoulder is convex rather than concave; it acts as a physical stop to prevent the superior migration of the humeral head&#x2014;a typical occurrence in patients suffering from rotator cuff tear arthropathy (CTA).

Reverse shoulder prosthesis

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New York, NY, United States of America

Joseph Zuckerman