Anterior cruciate ligament (ACL) graft failure from rupture, attenuation, or malposition may cause recurrent subjective instability and objective laxity, and occurs in 3% to 22% of ACL reconstruction (ACLr) procedures. Revision ACLr is often indicated to restore knee stability, improve knee function, and facilitate return to cutting and pivoting activities. Prior to reconstruction, a thorough clinical and diagnostic evaluation is required to identify factors that may have predisposed an individual to recurrent ACL injury, appreciate concurrent intra-articular pathology, and select the optimal graft for revision reconstruction. Single-stage revision can be successful, although a staged approach may be used when optimal tunnel placement is not possible due to the position and/or widening of previous tunnels. Revision ACLr often involves concomitant procedures such as meniscal/chondral treatment, lateral extra-articular augmentation, and/or osteotomy. Although revision ACLr reliably restores knee stability and function, clinical outcomes and reoperation rates are worse than for primary ACLr. Cite this article:
Artificial intelligence and machine-learning analytics have gained extensive popularity in recent years due to their clinically relevant applications. A wide range of proof-of-concept studies have demonstrated the ability of these analyses to personalize risk prediction, detect implant specifics from imaging, and monitor and assess patient movement and recovery. Though these applications are exciting and could potentially influence practice, it is imperative to understand when these analyses are indicated and where the data are derived from, prior to investing resources and confidence into the results and conclusions. In this article, we review the current benefits and potential limitations of machine-learning for the orthopaedic surgeon with a specific emphasis on data quality.
