[Computer-assisted sensing in periprosthetic joint infection : Current evidence and future perspectives for...

Periprosthetic joint infection (PJI) is among the most serious complications after hip and knee arthroplasty and requires timely diagnosis and a close follow-up. In parallel with the digital transformation of medicine, wearables (e.g., inertial sensors) and computer-assisted sensing are increasingly being used to generate objective data on function, mobility, and physiological parameters throughout the entire treatment pathway.

In arthroplasty, current evidence for wearables is strongest in rehabilitation and outcome monitoring, although important limitations remain, including device heterogeneity, patient adherence, and the lack of standardized assessment protocols. At present, only a few studies have addressed their role in the prediction, diagnosis, prevention, and rehabilitation of PJI. Potential applications therefore appear to lie less in direct infection detection than in the identification of nonspecific warning signals, such as persistently reduced activity or disturbed circadian patterns, which may trigger structured diagnostic work-up and longitudinal follow-up within established PJI frameworks. Comparable concepts have already been explored in the diagnosis and management of sepsis.

For PJI-specific monitoring, implantable sensing concepts ("smart implants") appear particularly promising from a translational perspective, as local parameters such as pH, temperature, and metabolites can already be assessed in experimental as well as early preclinical and clinical settings. This article summarizes the current evidence on wearables in arthroplasty with a focus on PJI and discusses key requirements for clinical utility, validation, data security, and implementation.