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. 2025 Aug 14;27(3):38. doi: 10.1007/s10544-025-00768-9

Microfluidic and lab-on-a-chip devices for detection and diagnosis of periprosthetic joint infections

Luca Pellegrino 1,3,✉, Alberto Bulgarelli 1, Cristina Belgiovine 2, Mattia Loppini 1,3,4, Roberto Rusconi 1,3
PMCID: PMC12354561  PMID: 40810870

Abstract

Abstract

Periprosthetic joint infection (PJI) is a serious complication of prosthetic joint implantation, which poses a significant burden on both individuals and society. Effective treatment relies on the rapid identification of the underlying cause; however, the diagnosis of PJI remains challenging, inefficient, and time-consuming. Current detection protocols based on clinical signs and conventional cultures often fail to provide definitive results. Additionally, advanced molecular analyses of synovial fluid samples, while effective, require specialized personnel and are impractical for on-site applications. This review aims to highlight the potential of microfluidic and lab-on-a-chip technologies in enhancing the identification of PJI, offering a rapid and accurate diagnostic method.

Graphical abstract

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Keywords: Microfluidic devices, Biosensors, Total hip arthroplasty, Microbial infections

Introduction

Total joint arthroplasty (TJA) is a surgical procedure in which degenerated bone and cartilage are removed and replaced with articulating prosthetic components. It helps restore function and alleviate pain, and remains one of the most successful procedures in the history of orthopedic surgery (Singh 2011; Patel et al. 2023; Carulli et al. 2011). Recent advances in preoperative planning and implant design have led to increased patient satisfaction and longer-lasting devices. Additionally, the use of computer-assisted navigation systems and robotic-assisted surgery has been shown to enhance implant placement accuracy and precision, leading to better functional outcomes (Gallo, Nieslanikova 2020). However, TJA can still be associated with serious complications that adversely affect outcomes and increase the risk of higher morbidity and mortality (Belmont Jr et al. 2014; Gómez-Barrena and García-Rey 2019; Caldara et al. 2022).

Periprosthetic joint infection (PJI) is a common complication that affects approximately 1–2% of TJA cases (Beam, Osmon 2018; Kurtz et al. 2012; Senthi et al. 2011; Zardi, Franceschi 2020). The rate of PJI has risen notably in the last few years, primarily because of the aging population and increasing antibiotic resistance. PJIs can be caused by bacteria or fungi and present with various clinical pictures depending on underlying etiology (Gómez-Barrena and García-Rey 2019). Classification is based on the time of onset. Early PJIs occur within the first three months from index surgery and present with clear local and systemic signs (e.g., fever, pain, warmth, redness). Early PJIs often stem from perioperative contamination and are caused by highly virulent bacteria like Staphylococcus aureus or Streptococci. In contrast, delayed PJIs have a more subtle course. Patients typically experience persistent joint pain with few or no physical examination findings (e.g., sinus tract, early implant loosening). The most common route of infection is still perioperative contamination. However, delayed PJIs are usually caused by less virulent organisms such as coagulase-negative Staphylococci or Cutibacterium species (Izakovicova et al. 2019; Tande, Patel 2014; Shoji, Chen 2020; Belgiovine et al. 2023).

Management of PJI is complex and relies on a combination of surgery, debridement, and prolonged antibiotic therapy. The treatment algorithm depends on a number of factors, including the duration of symptoms, microbiology, the conditions of the implant, and individual patient circumstances. Early PJI can often be treated with debridement, antibiotics, and implant retention. In contrast, delayed PJI typically requires resection arthroplasty, with re-implantation performed either simultaneously (one-stage exchange) or later (two-stage exchange) (Izakovicova et al. 2019).

The cost of treating PJIs per patient is substantial, with the total burden on the healthcare system estimated in the billions of dollars annually (Premkumar et al. 2021). Eradicating PJI is challenging, often requiring multiple surgeries and prolonged intravenous antibiotic therapy. Additionally, PJI can present at any time index arthroplasty, sometimes without obvious signs of infection, which can delay diagnosis and treatment. The fact that cultures are negative in up to 15% of cases further complicates the matter (Tubb et al. 2020). These challenges in management of PJI, combined with prolonged immobility and soft tissue damage during treatment, contribute to high morbidity rates and significantly impact patients’ overall health outcomes (Tubb et al. 2020; Kurtz et al. 2012). While prevention remains essential to avoid PJI, prompt and accurate diagnosis and treatment are equally critical in reducing infection severity and preventing long-term complications.

Classification and diagnosis of PJIs

There is currently no universally accepted classification system for periprosthetic joint infections (PJI). New definitions continue to emerge each year, and even the most widely used classification systems, though similar in focus, emphasize distinct aspects (Romanò et al. 2011). Traditionally, PJIs have been classified based on the onset of symptoms (acute < 3 months post-placement, chronic > 3 months post-placement) and the positivity of intra-operative cultures (Pellegrini et al. 2022). The Society of Musculoskeletal Infections (MSIS) proposed the first consensus criteria for defining and classifying PJIs in 2011 (Parvizi et al. 2012). The MSIS criteria rely on a combination of clinical, laboratory, and microbiological parameters. In recent years, efforts have been made to develop more comprehensive classification systems (Pellegrini et al. 2022; Romanò et al. 2019). For example, Romano et al. proposed a seven-point classification of PJI in 2011, which includes timing, route of infection, histology, type of host, microbiology, and the type of bone and soft tissue defect (Romanò et al. 2011). More recently, the World Association against Infection in Orthopedics and Trauma (W.A.I.O.T.) introduced a definition of high-grade and low-grade PJIs, suggesting a simplified classification based on clinical manifestations, the relative sensitivity and specificity of available pre-/intraoperative tests, and postoperative confirmation (cultures, histology) (Romanò et al. 2019). Despite these advancements, ongoing efforts aim to resolve ambiguities in current guidelines and address cases that fall outside existing classifications. A recent proposal by Pellegrini et al., for example, emphasizes the importance of distinguishing infection patterns based on the topography of the infectious process, i.e., whether it involves the joint space or the bone-implant interface (Pellegrini et al. 2022).

The classification of PJI put forward by the MSIS in 2011 (Parvizi, Gehrke 2014) marks the first effort to standardize the definition of PJI using both clinical and laboratory data. Initially, this classification enhanced diagnostic accuracy and informed treatment decisions. However, as time passed, concerns about its limited sensitivity, particularly in detecting low-grade infections, led to further revisions, including the updates from the 2018 International Consensus Meeting (ICM). According to the MSIS definition of 2011, the diagnosis of PJI requires the presence of at least one major criterion or four minor criteria. Major criteria include the isolation of the same organism from two or more cultures of joint fluid or tissue, or the detection of a sinus tract connected to the joint cavity. Minor criteria include elevated levels of serum C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR), increased leukocyte count in synovial fluid (SF), a high percentage of synovial fluid polymorphonuclear neutrophils (PMN%), joint effusion with purulent material, isolation of a microorganism in a single periprosthetic sample, and histologic analysis showing over five neutrophils per high-power field in five separate fields at 400x magnification.

The 2018 ICM (Schwarz et al. 2019) simplified the preoperative diagnosis of PJI. The new definition, not only, took in consideration the relative weight of each new (i.e., D-dimer, leukocyte esterase test, alpha-defensin, interleukin-6) and old marker, but also factored in the invasiveness of each test. Diagnosis of PJI can be confirmed if one of the major criteria is met or if a combination of minor criteria reaches a specific threshold. During the same year, also the European Bone and Joint Infection Society (EBJIS) published consensus criteria for the diagnosis of PJI (Renz et al. 2018). It used lower cutoff values for synovial fluid count and incorporated sonication as a technique to disrupt biofilms and lower the high rate of false negatives from traditional tissue culture. The EBJIS criteria are 4 and only 1 is required to make a diagnosis of PJI. However, recognizing the limitations of a binary “infected” versus “not infected” approach, the EBJIS proposed in 2019 a revised definition of PJI based on a nuanced “traffic light” system to classify infections as “unlikely”, “likely”, or “confirmed” (McNally et al. 2021).

To address the increasing complexity of PJI diagnosis, the 2019 W.A.I.O.T. guidelines introduced a diagnostic framework based on Rule OUT (RO) and Rule IN (RI) tests (Romanò et al. 2019). These tests are evaluated according to their sensitivity and specificity: a negative result on an RO test (sensitivity over 90%) decreases the diagnostic score by–1, while a positive RI test (specificity over 90%) increases the score by +1. This scoring system, when combined with clinical presentation and postoperative findings, enables classification of cases into five categories: high-grade PJI, low-grade PJI, biofilm-related implant malfunction, contamination, or no infection. The method incorporates a range of diagnostic indicators, including novel markers such as procalcitonin levels, evidence of antibiotic failure, and radiological signs of implant loosening.

Challenges in the diagnosis of PJIs

Diagnosing PJIs presents unique and persistent challenges, largely due to the ability of bacteria to adhere to implant surfaces and form biofilms. Within these biofilms, bacteria can survive in a metabolically quiescent state, significantly reducing the effectiveness of conventional culture techniques that depend on the proliferation of actively dividing organisms (Fernández-Rodríguez et al. 2021). Moreover, the biofilm matrix provides a protective barrier that shields pathogens from both host immune defenses and antimicrobial agents, thereby contributing to persistent infection and frequent treatment failure (Koo et al. 2017).

An additional complication arises from the spatial heterogeneity of biofilm formation (Gu et al. 2013; Jang et al. 2017). Biofilms often develop in localized patches on the prosthesis, resulting in uneven bacterial colonization. Consequently, diagnostic samples such as synovial fluid or periprosthetic tissue biopsies may fail to capture the infected sites, particularly in chronic or indolent PJIs where the planktonic bacterial load is minimal. This challenge is further amplified in patients who have received empirical antibiotic treatment prior to sampling, which can suppress microbial growth and reduce culture sensitivity without eradicating the infection. Moreover, the encapsulation of pathogens within biofilms limits the release of bacterial byproducts and target molecules into surrounding tissues or fluids, thereby lowering the concentrations detectable by standard diagnostic assays. The high viscosity of synovial fluid also complicates sample handling in traditional laboratory settings. Conventional microbial detection methods—such as Gram staining, culturing, and biochemical assays—are inherently time-consuming, often requiring 2 to 4 days or longer to yield results (Traore et al. 2019). This diagnostic latency significantly limits their utility in urgent clinical settings or during intraoperative decision-making. More advanced approaches, including flow cytometry (Reis et al. 2021; Fisher, Patel 2023), polymerase chain reaction (PCR) (Borde et al. 2015; Jacovides et al. 2012; Barghouthi 2011; Schoenmakers et al. 2023), enzyme-linked immunosorbent assays (ELISA) (Pang et al. 2018; Grassi et al. 2022), and chemical sensor-based platforms, offer higher sensitivity and specificity. However, these techniques typically require expensive instrumentation, complex protocols, and specialized personnel, thereby restricting their use in point-of-care or intraoperative scenarios.

In contrast, microfluidic technologies offer a promising alternative by miniaturizing and integrating multiple analytical steps—including sample preparation, bacterial isolation, cell lysis, nucleic acid amplification, and detection—onto a single compact platform. These lab-on-a-chip systems provide distinct advantages in terms of speed, automation, and minimal sample volume requirements. Notably, their ability to concentrate low-abundance analytes and perform efficiently at the point of care makes them particularly well-suited for rapid and decentralized diagnosis of PJIs (Zhou et al. 2020), especially in cases complicated by biofilm formation or prior antibiotic exposure.

Microfluidic-based PJI detection approaches

Microfluidics is a multidisciplinary field that integrates biology, engineering, and materials science to manipulate and analyze small volumes of fluids within microscale devices, which typically include microchannels, chambers, and pumps (Hajam, Khan 2023). These devices handle fluids at the microliter or nanoliter scale, allowing precise control over fluid flow and enabling the creation of highly controlled microenvironments (Rusconi et al. 2014). In microbial ecology (Rusconi et al. 2014), bacterial contamination (Giacomo et al. 2017; Ranjbaran, Verma 2022; Lonchamps et al. 2022), and biofilm formation (Yawata et al. 2016; Yuan et al. 2023), microfluidics is increasingly used as an alternative and complementary technology to conventional microbiological assays and millimeter-scale flow cells. Indeed, microfluidics offers several fundamental advantages that make it particularly suitable for PJI detection:

  • Low sample volume: Microfluidic devices are designed to operate with minimal sample volumes, which is especially beneficial in medical contexts where obtaining sufficient quantities of synovial fluid or tissue samples can be challenging. This capability ensures that even small amounts of biological material can be effectively analyzed, making the technology accessible for various clinical settings.

  • High surface area-to-volume ratio: The diminutive size of microfluidic channels significantly increases the surface area relative to the volume of fluid. This high surface area-to-volume ratio enhances the interaction between target molecules and detection elements, such as antibodies or nucleic acid probes. Consequently, microfluidic devices can achieve higher sensitivity and specificity in detecting pathogens or biomarkers associated with PJI.

  • Rapid reaction times: The confined spaces within microchannels ensure efficient diffusion and mixing, leading to faster reaction kinetics. This rapid processing capability translates to quicker diagnostic results, which is crucial for timely clinical decision-making and improving patient outcomes.

  • Automation and integration: Microfluidic devices can be seamlessly integrated with automation systems, minimizing manual intervention and thereby reducing the risks of contamination and human error. Automated microfluidic platforms are capable of executing multiple steps of the diagnostic workflow– including sample preparation (e.g., reducing the viscosity of synovial fluid), bacterial isolation, and detection– within a closed, self-contained system. This level of integration not only enhances assay reproducibility and reliability but also enables high-throughput screening and real-time monitoring, which are essential for efficiently processing large volumes of clinical samples.

These features make microfluidics a powerful tool for studying microbial infections, offering rapid, sensitive, and specific analysis that can improve diagnostics and therapeutic strategies.

Nucleic acid-based detection of PJIs

The creation of integrated microfluidic sensing platforms presents a promising solution for the rapid detection of live bacteria in clinical PJI samples. Research has shown that bacterial ribosomal ribonucleic acids (rRNAs) are excellent targets for infection diagnosis due to their high conservation among bacterial species and their abundance. Using universal primers, bacterial rRNA can be amplified through reverse-transcription polymerase chain reaction (RT-PCR). The RT-PCR method for detecting bacterial rRNA is highly sensitive, with a limit of detection (LOD) as low as a picogram. However, RT-PCR can only indirectly differentiate between live and dead bacteria based on rRNA degradation in the tissue, and the entire detection process for 16s rRNA RT-PCR is labor-intensive (Zegaer et al. 2014; Tkadlec et al. 2019; Morgenstern et al. 2018; Schoenmakers et al. 2023).

Chang et al. (2014, 2015) developed a system that uses an ethidium monoazide (EMA)-based assay and PCR with universal bacterial primers and probes to isolate and detect live bacteria commonly associated with PJI. This system is capable of detecting live bacteria in human joint fluids, specifically MRSA (methicillin-resistant Staphylococcus aureus), coagulase-negative Staphylococcus, Staphylococcus aureus, Enterococcus sp., and Pseudomonas syringae, with a detection limit of Inline graphic colony-forming units per mL. The microfluidic system was also able to simultaneously analyze nine clinical samples. The results from the microfluidic system were negative for all culture-negative cases, suggesting its potential to reduce false-positive results. The study also highlighted the importance of the EMA sample pre-treatment process for accurately detecting live bacteria. Specifically, the system diagnosed culture-positive cases as positive only when clinical samples were treated with EMA immediately after collection from patients.

An alternative approach to address the limitations of DNA-based molecular diagnostics and to enhance the ability to differentiate between live and dead bacteria involves integrating loop-mediated isothermal amplification (LAMP) with the EMA assay in a single microfluidic system for comprehensive molecular diagnostics in PJI analysis (Chen et al. 2017). This integrated microfluidic system automates all diagnostic processes, including bacteria isolation, cell lysis, DNA amplification, and optical detection (Figure 1A). The system uses four primers complementary to six regions of the target genes, thereby improving the detection limit through the use of LAMP. In the case of the multiple EMA-LAMP assay, the limit of detection could be as low as 5 fg/reaction (approximately 1 CFU/reaction) when an optimized primer set is chosen, as demonstrated in the detection of the mecA gene. This method combines the high specificity and sensitivity of LAMP with the ability of the EMA assay to distinguish between live and dead bacteria, providing a robust and efficient diagnostic tool for PJI.

Fig. 1.

Fig. 1

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Mosaic of representative images of microfluidic devices employed for bacterial detection. A) A schematic illustration of the entire experimental process performed on the integrated microfluidic chip for PJI analysis, comprising of bacteria isolation, cell lysis, DNA amplification and optical detection. Modified with permission from Chen et al. (2017). B) An illustration (a) and a photograph (b) of the microfluidic chip developed by Liu et al. (2019). It is comprised of several normally closed microvalves, micropumps, and a waste unit for fluid transportation. N: PCR reaction chamber for the negative control, P: PCR reaction chamber for the positive control, and S: PCR reaction chamber for the sample. C) Schematic of the Raman-optofluidic platform reported by Hunter et al. (2019) and detection performance of the optofluidic platform for monocultures of bacteria in fetal bovine serum. Modified with permission from (Hunter et al. 2019)

Liu et al. (2019) developed another integrated microfluidic system capable of detecting and identifying bacteria from synovial fluid. This system automates the entire molecular diagnostic process on a single chip, including sample treatment, bacterial isolation, bacterial lysis, nucleic acid amplification (via PCR), and optical detection (Fig. 1B). Initially, N-acetyl-L-cysteine (NALC) is used to reduce the high viscosity of SF samples, improving bacterial isolation with vancomycin-coated nano-magnetic beads. Following this, a universal 16S ribosomal RNA PCR primer set and four species-specific primer sets are used for PCR-based detection and identification of four common bacteria associated with PJIs: Staphylococcus aureus, methicillin-resistant S. aureus, Escherichia coli, and Acinetobacter baumannii. This approach achieves a detection limit as low as 100 colony-forming units (CFUs) per milliliter (or 20 CFUs per reaction), making it suitable for clinical diagnostics and guiding post-operative antibiotic decisions. Importantly, bacterial detection and identification data can be obtained within 90 minutes, significantly faster than traditional culture-based methods.

Hunter et al. (2019) designed an optofluidic Raman detection platform that utilizes a microfluidic-driven hollow-core photonic crystal fiber (Figure 1C). When combined with silver nanoparticles, this platform provides a substantial enhancement to the Raman signal. By confining both light and cells within this fiber, the spectral events generated by the flowing cells enable a novel method of cell counting for simultaneously quantifying and qualifying infections. The counting process is automated through a genetically optimized support vector machine learning algorithm developed by the group. The microfluidic system is capable of being regenerated multiple times and allows for the online detection of planktonic bacteria to levels as low as 4 CFU/mL in just 15 minutes. This compares favorably to other methods currently under development, such as qPCR and biosensing techniques. Additionally, Raman spectral differences between bacteria enable inherent multiplexed detection in serum, adding another layer to the learning algorithm. Further development of this device holds promising potential as a rapid point-of-care system for infection management in clinical settings.

The molecular diagnosis of biofilm-related genes (BRGs) in bacteria commonly responsible for PJIs offers significant potential for clinical applications. In a study by Yu and colleagues (Yu et al. 2018), several BRGs, such as ica, fnbA, and fnbB, were rapidly detected within an hour using an integrated microfluidic system. This system employed mannose-binding lectin (MBL)-coated magnetic beads to isolate the bacteria, followed by on-chip nucleic acid amplification via polymerase chain reaction (PCR) to detect the BRGs. Both eukaryotic and prokaryotic MBLs were effective in isolating common bacterial strains, regardless of their antibiotic resistance. The limits of detection were as low as 3 CFU for methicillin-resistant Staphylococcus aureus and 9 CFU for Escherichia coli, using a universal 16S rRNA PCR assay for bacterial identification. The entire process—including bacteria isolation with MBL-coated beads, on-chip PCR, and fluorescent signal detection—was completed within an hour. This highlights the potential of this integrated microfluidic system for monitoring BRG profiles, thereby providing clinicians with valuable insights for making informed clinical decisions.

Biomarker-based detection of PJIs

Cytological analysis of synovial fluid is a standard component of clinical practice for evaluating joint health and diagnosing PJIs. Traditionally, such analysis focuses on determining the total white blood cell (WBC) count and differential cell composition, as elevated WBC counts in synovial fluid are strongly associated with infection. However, the diagnostic utility of conventional cytology is limited by several practical and technical constraints. Most notably, standard laboratory methods often require a relatively large volume of synovial fluid, which can be difficult to obtain in patients with dry joints or those who have undergone prior joint aspirations. Moreover, these methods provide limited information about cell morphology, activation state, or other inflammatory biomarkers that may be critical in distinguishing infection from aseptic inflammation. Emerging microfluidic point-of-care (POC) platforms are addressing these limitations by enabling high-resolution, multiplexed analysis of minute fluid volumes.

Krebs et al. (2017) developed a microfluidic platform called Synovial Chip, which enables repeatable and standardized analysis of synovial fluid using specific cell surface markers (Fig. 2A). The platform utilizes microfluidic channels functionalized with antibodies to capture WBC subpopulations, including CD4+ T-helper cells, CD8+ cytotoxic T cells, and CD66b+ neutrophils, from small volumes (100 µL) of synovial fluid. Fluorescent labeling of captured cells indicated approximately 90% specificity for these subpopulations. The study also explored the effect of synovial fluid viscosity on capture efficiency, finding that enzyme treatment with hyaluronidase improved efficiency to over 60%. Hyaluronic acid is one of the main components contributing to the high viscosity that is observed in SF; hyaluronidase enzyme has been commonly used to digest hyaluronic acid and shown to improve performance of cellular, protein, and crystal analyse. Another advanced microfluidic system developed by Chen et al. (2023) focuses on the automated detection of synovial fluid biomarkers associated with PJIs, such as alpha-defensin human neutrophil peptide 1 (HNP-1) and C-reactive protein (CRP). This system uses a magnetic bead-based one-aptamer-one-antibody assay, which operates automatically within 45 minutes on a single chip. It allows simultaneous detection of both biomarkers within concentration ranges of 0.01–50 mg/L (HNP-1) and 1–100 mg/L (CRP). The technology has shown potential for early and efficient joint infection diagnosis, validated with 20 clinical samples and compared with standard kits.

Fig. 2.

Fig. 2

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Mosaic of representative images of microfluidic devices for biomarker detection. (A) The synovial chip, reported from Krebs et al., allows for the capture of white blood cells (WBC) subpopulations from synovial fluid samples in serially connected microchannel functionalized via specific antibodies binding. Cell capture specificity is evaluated by fluorescent labelling of isolated cells. Adapted with permission from Krebs et al. (2017). (B) A schematic and photograph of the microfluidic on-chip SELEX reported by Gandotra et al. The chip bears a automated microfluidic control system for automated SELEX, or selection of PJI biomarkers-specific aptamers. Modified with permission from Gandotra et al. (2022). (C) Specificity tests for the aptamer-ased ELISA-like assay for HPN-1, and IgG and (D) HPN-1 measurements from the aptamer-based ELISA-like assay while using clinical PJI samples. Adapted with permission from Gandotra et al. (2022)

Aptamer-based technologies have also been integrated into microfluidic devices for detecting PJIs. Gandotra et al. (2022) employed the SELEX method to identify an aptamer with high affinity for HNP-1, a PJI biomarker. Their compact microfluidic system, equipped with Peltier devices and a magnetic control module, allowed for precise aptamer selection and validation, achieving a dissociation constant of 19 nM. The aptamer was then used in an enzyme-linked immunosorbent assay (ELISA)-like assay to diagnose PJI, showing promise for future clinical applications. In a follow-up study (Gandotra et al. 2023), the same group enhanced their system by incorporating a paper-based aptamer-sandwich assay with a nitrocellulose membrane, improving signal clarity and simplifying the assay. This microfluidic device achieved 100% accuracy in detecting up to four clinical samples within 42 minutes, as validated against the “gold standard”. They also explored using magnetic beads coated with the primary aptamer probe for high-affinity binding of target proteins, combined with a fluorescently labeled secondary aptamer for quantification, further advancing the diagnostic capabilities of their microfluidic system.

Tsai et al. (2019) developed a multi-target lateral flow immunoassay strip (msLFIA) with a stacking pad design for rapid on-site PJI diagnosis. This method simultaneously detects alpha-defensin and CRP in synovial fluid, providing results consistent with commercial enzyme-linked immunosorbent assay (ELISA) kits. The msLFIA reduces test time to 20 minutes due to its enhanced specificity and sensitivity, achieved through the stacking pad design and a quick preincubation step. Yin et al. (2022) introduced a tape-integrated microfluidic chip for detecting PJI biomarkers. They incorporated commercially available 3M tapes into a self-designed microfluidic chip (KVMC), enabling detection of biomarkers in serum over a wide range, from pg/mL to µg/mL. This system demonstrated limits of detection of 0.23 µg/mL for CRP, 0.14 ng/mL for procalcitonin (PCT), and 12.53 pg/mL for interleukin-6 (IL-6), offering a promising method for early sepsis diagnosis.

Rheological-based detection of PJIs

Beyond molecular and biomarker-based techniques, recent research has investigated the diagnostic potential of rheological analysis for PJIs. The viscosity and viscoelastic properties of synovial fluid are highly sensitive to biochemical alterations that occur during infection. Indeed, biofluids such as synovial fluid, blood plasma, and saliva exhibit non-Newtonian behavior due to their high protein content. The concentration and structural organization of these macromolecules have been shown to correlate not only with joint infections (Fu et al. 2019) but also with a range of pathological conditions (Beris et al. 2021). However, traditional biochemical assays for detecting such molecules are often time-consuming, labor-intensive, and reliant on specialized laboratory infrastructure, underscoring the need for more efficient and accessible diagnostic alternatives.

A promising approach was recently demonstrated by Del Giudice, Barnes (2022), who developed a microfluidic rheometer capable of rapidly and simultaneously measuring shear viscosity and the longest relaxation time of non-Newtonian fluids across a range of temperatures. By integrating machine learning algorithms, the system achieves a turnaround time of approximately two minutes, markedly reducing analysis time compared to conventional rheometric methods. The device was validated using aqueous solutions of poly(ethylene oxide) and hyaluronic acid, both representative of clinically relevant concentration ranges found in synovial fluid. In this context, the high viscosity of synovial fluid, typically considered a challenge for sample handling, becomes a valuable diagnostic parameter. The results demonstrated a strong correlation between macromolecular concentration and rheological properties, supporting the emerging concept of “rheo-markers”– rheological properties used as biomarkers– for disease diagnosis, including PJIs(Figure 3).

Fig. 3.

Fig. 3

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Comparative analysis of microfluidic diagnostic platforms developed for PJI detection

Conclusions

Diagnosing PJIs necessitates a combination of clinical, laboratory, and microbiological parameters. Traditional diagnostic methods, remain the clinical standard but suffer from prolonged turnaround times and reduced sensitivity in chronic or biofilm-associated infections. Their limitations are particularly evident when bacterial loads are low or suppressed by prior antibiotic treatment. In response to these challenges, microfluidic-based technologies have emerged as a promising alternative, offering rapid, sensitive, and decentralized diagnostic capabilities. Microfluidic systems provide substantial advantages over conventional techniques, including low sample volume requirements, reduced processing times, automation, and the ability to perform multiplexed analyses. Recent advances have demonstrated the potential of microfluidics in detecting live bacteria using molecular diagnostic techniques such as reverse-transcription polymerase chain reaction (RT-PCR), loop-mediated isothermal amplification (LAMP), and aptamer-based assays. These methods offer high specificity and can even discriminate between live and dead bacteria—a crucial feature for managing biofilm-related infections. Moreover, microfluidic platforms facilitate the simultaneous detection of multiple biomarkers associated with PJIs, such as alpha-defensin, C-reactive protein (CRP), and interleukin-6 (IL-6). These biomarkers offer critical insights into the inflammatory response and the presence of infection, enabling more comprehensive diagnostic capabilities.

The spatial heterogeneity and low biomass typical of biofilm-associated infections impose additional diagnostic hurdles, often resulting in false negatives with conventional sampling. These limitations highlight the need for highly sensitive diagnostic tools capable of detecting minute concentrations of bacterial or host-derived markers. Microfluidic devices, particularly those coupled with advanced sensing technologies and machine learning algorithms, are uniquely equipped to meet this need. Their ability to rapidly process complex biofluids and integrate sample preparation, target amplification, and detection into a single chip makes them ideal candidates for intraoperative or bedside applications.

Looking ahead, the integration of microfluidic diagnostics into clinical workflows holds the potential to improve the management of PJIs by enabling more timely and targeted interventions. These platforms can deliver rapid, point-of-care data that support more effective antibiotic stewardship and early clinical decision-making. To facilitate broader adoption, future work should focus on clinical validation, cost-effectiveness, and scalability for routine use. Additionally, developing multiplexed systems that combine nucleic acid amplification, biomarker detection, and rheological analysis may offer a more comprehensive approach to diagnosis by capturing multiple dimensions of the infection process.

In parallel with diagnostic applications, microfluidic systems are increasingly being explored as platforms to study PJI pathophysiology under controlled and physiologically relevant conditions. These devices can be designed to mimic aspects of the periprosthetic environment, including implant surfaces, fluid flow, and the presence of microbial and immune components. Such models enable the investigation of biofilm formation, immune interactions, and antimicrobial efficacy in a reproducible manner, complementing insights from animal studies and conventional in vitro assays. As these platforms continue to evolve, they may support both translational research and the development of more targeted therapeutic strategies. In conclusion, microfluidic-based diagnostics represent a significant leap forward in the detection and management of PJIs. By addressing the limitations of current methods and enabling point-of-care testing, these innovations hold the potential to improve patient outcomes, reduce healthcare costs, and transform infection control practices in orthopedic surgery.

Author Contributions

L.P. and A.B. wrote the main manuscript text and L.P. prepared figures. All authors reviewed the manuscript.

Data Availability

No datasets were generated or analysed during the current study.

Declarations

Competing interests

The authors declare no competing interests.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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