Preclinical computational models: predictors of tibial insert damage patterns in total knee arthroplasty: AAOS exhibit selection.

Abstract

Computational models that predict clinical surface damage of the tibial insert during activities of daily living are emerging as powerful tools to assess the safety and efficacy of contemporary total knee arthroplasty designs. These models have the advantage of quickly determining the performance of new designs at low cost, and they allow direct comparison with the performance of classic, clinically successful designs. This study validated finite element and kinematic modeling predictions through comparison with preclinical physical testing results, damage patterns on retrieved tibial inserts, and clinically measured knee motion. There is a mounting body of evidence to support the role of computational modeling as a preclinical tool that enables the optimization of total knee arthroplasty designs and the auditing of component quality control before large-scale manufacturing is undertaken.