Stochastic modeling of knee arthrometry for Intact, partial and ruptured ACL

Abstract

Knee pain and injuries are very common in athletes. Anterior cruciate ligament (ACL) injury is one of the most important reason of knee acute and chronic pains. As ACL plays a significant role in knee stability and due to amount of reported ACL related injuries, much effort has been placed on finding proper and on time diagnosis and treatment of these injuries. Arthrometric methods are the most non-invasive reliable diagnosis option; however they are trusted to distinguish partial injuries from intact joint and instead expensive and more time consuming imaging diagnosis techniques (i.e MRI) are used. Arthromery results can be investigated by quantifying the tests using computer simulations; so far few studies have been conducted toward this goal. In this study a two dimensional finite flement (FE) model of knee joint under standard arthrometry state (by a KT-2000 arthrometer) was built. This model was then used to evaluate the effect of precise modeling of tibiofemoral contact-surface geometry with considering flexible articular cartilage in the joint. The results revealed that exact modeling of the contact geometry highly influences the simulation results. In the next step, the built model was used in a stochastic modeling with the experimental data obtained from literature in order to simulate a real diagnosis circumstance and to provide a statistical comparison between seven different levels of ACL injury. The result of this simulation shows that although arthrometry may be able to distinguish partial injuries from intact in most cases leading to less ACLD with appropriate treatment, but it is hard to tell anything about the level of injury. To prevent the costs of imaging techniques moving towards the introduction of new measuring methods (or newly defined identifiers) or using intelligent processing of currently obtained data is suggested.