Smart Sensors for the Early Detection of Periprosthetic Joint Infection: A Translational Perspective.

Abstract

Periprosthetic joint infections (PJIs) are one of the most dreaded complications of arthroplasty. Although relatively rare with an incidence of 1-2%, the absolute burden of PJIs is growing over time as the volume of performed arthroplasties increases. PJIs are associated with significant morbidity, mortality, and healthcare costs. Treatment is challenging and may require multiple revision surgeries, reimplantation, and/or amputation. The main issue is the delayed detection of PJIs, which permits progression of the infection into robust biofilms resistant to conventional antimicrobials. Early-detection strategies should focus on identifying infection during the pre-biofilm, planktonic phase, when it remains responsive to medical therapy. Implantable smart sensors are an innovative way to obtain local, real-time monitoring of the implant microenvironment to achieve this objective. In this translational review, an overview of PJIs and biofilm formation will first be provided. The current diagnostic approach to PJIs will then be reviewed along with its limitations to highlight opportunities for innovation. Fundamentals of smart sensor technology and examples of devices designed to detect markers of early infection will then be discussed. Research on smart sensors for the post-operative monitoring of orthopedic implants is in its infancy and has yet to be widely adopted into clinical practice. Strengths, limitations, and clinical significance of smart sensors in development will be discussed to inform recommendations on future directions. STATEMENT OF SIGNIFICANCE: Periprosthetic joint infection (PJI) is one of the most serious complications after hip and knee replacement surgery, yet current diagnostic tests often fail to detect infection early, when treatment is most effective. This review is the first to comprehensively evaluate the potential of implantable "smart sensors" that can monitor the joint environment in real time to detect early signs of infection. By comparing different sensor designs and highlighting both opportunities and limitations, our work bridges orthopaedic surgery, materials science, and bioengineering. These insights are significant for guiding future biomaterial-based diagnostics, with the long-term goal of improving outcomes for the rapidly growing population of patients undergoing joint replacement worldwide.