US Government As Represented by the Secretary of the Army

Mt Innovations, Llc.

USA As Represented by Secretary of the Navy

Decisionq Corporation

The Solsidan Group, LLC

Jonathan A Forsberg

Thomas A Davis

John S Eberhardt, Iii

Eric Elster

Trevor S Brown

Doug K Tadaki

Alexander Stojadinovic

Jason Hawksworth

Roslyn Bernstein Mannon

An orthopaedic fixation assembly for prosthetic biologic attachment. The orthopaedic fixation assembly may include a main body with a longitudinally-extending stem having a proximal end, a distal end, and a cavity body. An anchor plug may be configured to be received within the stem cavity, and securable thereto via complementary mating surfaces. A spindle structure may be fixedly attached to the proximal end of the longitudinally-extending stem and protrude outwardly therefrom such that a portion of the structure extends externally beyond the resected cavity of the bone that may prevent rotational motion of the spindle. The spindle structure may have at least one compliant biasing member configured to apply a compressive force to the surrounding bone. A porous coating may be at the juncture between stem and spindle structure, on the spindle, and the splines and anti-rotation chocks, improving the initial stability of the implant and facilitating long-term bone ingrowth.

Orthopaedic fixation assembly, system, and method of use

An embodiment of the invention provides a method for determining a patient-specific probability of disease. The method collects clinical parameters from a plurality of patients to create a training database. A fully unsupervised Bayesian Belief Network model is created using data from the training database; and, the fully unsupervised Bayesian Belief Network is validated. Clinical parameters are collected from an individual patient; and, such clinical parameters are input into the fully unsupervised Bayesian Belief Network model via a graphical user interface. The patient-specific probability of the healing rate of an acute traumatic wound is output from the fully unsupervised Bayesian Belief Network model and sent to the graphical user interface for use by a clinician in pre-operative planning. The fully unsupervised Bayesian Belief Network model is updated using the clinical parameters from the individual patient and the patient-specific probability of the healing rate of an acute traumatic wound.

Clinical decision model

An embodiment of the invention provides a method for determining a patient-specific probability of transplant glomerulopathy. The method collects clinical parameters from a plurality of patients to create a training database. A fully unsupervised Bayesian Belief Network model is created using data from the training database; and, the fully unsupervised Bayesian Belief Network is validated. Clinical parameters are collected from an individual patient; and, such clinical parameters are input into the fully unsupervised Bayesian Belief Network model via a graphical user interface. The patient-specific probability of transplant glomerulopathy is output from the fully unsupervised Bayesian Belief Network model and sent to the graphical user interface for use by a clinician in pre-operative planning. The fully unsupervised Bayesian Belief Network model is updated using the clinical parameters from the individual patient and the patient-specific probability of transplant glomerulopathy.

Bayesian clinical decision model for determining probability of transplant glomerulopathy

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Kensington, MD, United States of America

Jonathan A Forsberg