Prognosis of Anterior Cruciate Ligament (ACL) Reconstruction: A Data   Driven Approach
  Individuals who suffer anterior cruciate ligament (ACL) injury are at higher risk of developing knee osteoarthritis (OA) and almost 50% display symptoms 10 to 20 years post injury. Anterior cruciate ligament reconstruction (ACLR) often does not protect against knee OA development. Accordingly, a multiscale formulation for Data Driven Prognosis (DDP) of post ACLR is developed. Unlike traditional predictive strategies that require controlled off-line measurements or training for determination of constitutive parameters to derive the transitional statistics, the proposed DDP algorithm relies solely on in situ measurements. The proposed DDP scheme is capable of predicting onset of instabilities. Since the need for off line testing (or training) is obviated, it can be easily implemented for ACLR, where such controlled a priori testing is almost impossible to conduct. The DDP algorithm facilitates hierarchical handling of the large data set, and can assess the state of recovery in post ACLR conditions based on data collected from stair ascent and descent exercises of subjects. The DDP algorithm identifies inefficient knee varus motion and knee rotation as primary difficulties experienced by some of the post ACLR population. In such cases, levels of energy dissipation rate at the knee, and its fluctuation may be used as measures for assessing progress after ACL reconstruction. 
Abhijit Chandra, Oliva Kar, Kuan-Chuen Wu, Michelle Hall and Jason   Gillette
cs.CE