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.
There is limited information on outcomes of revision ACL reconstruction (rACLR) in soccer (association football) athletes, particularly on return to sport and the rate of additional knee surgery. The purpose of this study was to report return to soccer after rACLR, and to test the hypothesis that patient sex and graft choice are associated with return to play and the likelihood of future knee surgery in soccer players undergoing rACLR. Soccer athletes enrolled in a prospective multicentre cohort were contacted to collect ancillary data on their participation in soccer and their return to play following rACLR. Information regarding if and when they returned to play and their current playing status was recorded. If they were not currently playing soccer, they were asked the primary reason they stopped playing. Information on any subsequent knee surgery following their index rACLR was also collected. Player demographic data and graft choice were collected from their baseline enrolment data at rACLR.Aims
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