Reducing uncertainty when using knee-specific finite element models by assessing the effect of input parameters.

Little is known about knee-specific factors that influence contact mechanics. Finite Element (FE) models offer a powerful tool to study contact mechanics, but there often exists ambiguity in the exact values of the inputs (e.g., tissue properties), which can result in a range of output values. Our objective was to quantify the reduction in the range of output values (defined herein as "uncertainty") from FE models of the human knee joint when known pre-defined values are used for clinically measurable inputs. To achieve this goal, we applied a statistically augmented FE approach to three human cadaveric knees for which full geometric and kinematic data were available. Two sets of conditions were simulated: All model inputs, clinically measurable or not, were varied to represent a "normal" patient population (Condition 1); subsets of clinically measurable variable inputs were fixed at specific values (called "patient derived inputs," or PDIs) while the other variables were varied over "normal" values (Condition 2). We found that by fixing body mass index and the anterior-posterior position of the meniscal-bony insertion points, model output uncertainty was reduced by one- to three-fifths. The magnitude of uncertainty reduction was strongly influenced by the individual knee. It was observed that knees with great anterior-posterior translation during gait had greater reductions in uncertainty when PDIs were used. This study represents the first step in developing FE models of the human knee joint based on inputs that can be derived from patients in a clinical setting.