APPLICABILITY OF OPERATIONS RESEARCH AND ARTIFICIAL INTELLIGENCE APPROACHES TO NON- CONTACT ANTERIOR CRUCIATE LIGAMENT INJURY STUDIES

Abstract

Numerous problems in biomechanics can be tackled using optimization methods. The primary objective of this paper is to explore the applicability and illustrate the importance of two major optimization approaches, namely, operation research (OR) and artificial intelligence (AI) to studies in non-contact anterior cruciate ligament (ACL) injury biomechanics. This paper focuses on the applicability of the two approaches to bring attention to the enabling capabilities that can be offered to address challenges faced in non-contact ACL injury studies. The differences and similarities, as well as, advantages and disadvantages of these two approaches are discussed. Some of the key techniques covered in the two different approaches are highlighted. As well, the area in which there is a common ground for both approaches is outlined. It was determined for a small search space and highly tailored problems, classical exhaustive OR methods usually suffice; however, for large search spaces an AI technique must be employed. Thus, an AI technique is better suited to tackle the challenges faced in non-contact ACL injury studies especially given the multifaceted nature of such problems.