Patient selection in unicompartmental knee arthroplasty: how to make the diagnosis for success in the clinic.

Abstract

The success of unicompartmental knee arthroplasty (UKA) for monocompartmental knee arthritis is reliant on appropriate patient selection. This article addresses the clinical challenges that may arise when attempting to identify patients likely to have favorable outcomes following UKA. Despite advancements of implant design and accuracy of surgical tools, considerable challenges persist in predicting patient-specific success and satisfaction following UKA. Variation in patient characteristics, healthcare practices, and outcomes in the literature make the establishment of a strict set of universal guidelines difficult. This article will provide a comprehensive overview of the current landscape of patient selection for UKA, acknowledging the existing clinical dilemmas and challenges faced by clinicians and proposing avenues for future research including the integration of patient predictive models, advanced imaging, and artificial intelligence to enhance predictive accuracy.