Digital Subtraction Angiography of Cerebral Arteries: Influence of Cranial Dimensions on X-ray Tube Performance.

(1) Background. Digital subtraction angiography (DSA) is indispensable for diagnosing cerebral aneurysms due to its superior imaging precision. However, optimizing X-ray parameters is crucial for accurate diagnosis, with X-ray tube settings significantly influencing image quality. Understanding the relationship between skull dimensions and X-ray parameters is pivotal for tailoring imaging protocols to individual patients. (2) Methods. A retrospective analysis of DSA data from a single center was conducted, involving 251 patients. Cephalometric measurements and statistical analyses were performed to assess correlations between skull dimensions and X-ray tube parameters (voltage and current). (3) Results. The study revealed significant correlations between skull dimensions and X-ray tube parameters, highlighting the importance of considering individual anatomical variations. Gender-based differences in X-ray parameters were observed, emphasizing the need for personalized imaging protocols. (4) Conclusions. Personalized approaches to DSA imaging, integrating individual anatomical variations and gender-specific differences, are essential for optimizing diagnostic outcomes. While this study provides valuable insights, further research across multiple centers and diverse imaging equipment is warranted to validate these findings.

Is there a role of calprotectin testing in the diagnosis of surgical site infections after total hip and knee arthroplasty? A preliminary study.

BACKGROUND: Recent studies have revealed the usefulness of synovial calprotectin (CLP) in diagnosing chronic periprosthetic joint infections (PJIs). However, there is still a lack of evidence to support the use of serum CLP in the diagnosis of early PJIs and surgical site infections (SSIs) after total joint arthroplasties (TJAs). OBJECTIVES: The primary aim of this study is to investigate the standard kinetics of CLP concentrations in the blood during the very early postoperative period after non-complicated total hip arthroplasty (THA) and total knee arthroplasty (TKA). The secondary aim was to perform a preliminary comparison of CLP concentrations between non-infected patients and patients with recognized SSIs. MATERIAL AND METHODS: A total of 64 consecutive patients who underwent primary THA and TKA were included in this prospective research. Sixty patients (30 THA and 30 TKA) were scheduled to determine the standard shape of the blood CLP curve and the expected concentrations during the first 5 postoperative days after non-complicated TJAs. In 4 additonal patients, early SSI was confirmed, and they were included in a separate SSI subgroup. RESULTS: Calprotectin demonstrated a linear increase during the first 5 postoperative days. Statistically significant differences in CLP concentrations between non-infected cases and SSIs were not observed. The preoperative median results with interquartile range (Q1-Q3) were 0.52 (0.39-0.64) mg/dL and 0.5 (0.47-0.52) mg/dL (p = 0.77), while post operation they were as follows: on postoperative day 1: 0.88 (0.53-1.3) mg/dL and 0.86 (0.62-1.1) mg/dL (p = 0.84), on postoperative day 3: 1.77 (1.29-2.08) mg/dL and 1.85 (1.70-1.95) mg/dL (p = 0.72), and on postoperative day 5: 2.32 (1.79-2.67) mg/dL and 2.56 (2.25-2.83) mg/dL (p = 0.55), respectively. CONCLUSION: Serial CLP measurements during the early postoperative period revealed a linear (statistically significant) increase in concentration to postoperative day 5 without an evident point of decrease. A significant difference in median values and the course of curve patterns between the non-complicated and SSI groups was not observed.

Mobile applications in radiology: own study based on polish data.

As the number of smartphones increases, so does the number of medical apps. Medical mobile applications are widely used in many medical fields by both patients and doctors. However, there are still few approved mobile applications that can be used in the diagnostic-therapeutic process and radiological apps are affected as well. We conducted our research by classifying radiological applications from the Google Play® store into appropriate categories, according to our own qualification system developed by researchers for the purposes of this study. In addition, we also evaluated apps from the App Store®. The radiology application rating system we created has not been previously used in other articles. Out of 228 applications from the Google Play store, only 6 of them were classified as "A" category with the highest standard. Apps from the App Store (157) were not categorized due to the lack of download counts, which was necessary in our app-rating system. The vast majority of applications are for educational purposes and are not used in clinical practice. This is due to the need of obtaining special permits and certificates from relevant institutions in order to use them in medical practice. We recommend applications from the Google Play store that have been classified in the "A" category, evaluating them as the most valuable. App Store apps data is described and presented in the form of diagrams and tables.

Chest X-ray Foreign Objects Detection Using Artificial Intelligence.

Diagnostic imaging has become an integral part of the healthcare system. In recent years, scientists around the world have been working on artificial intelligence-based tools that help in achieving better and faster diagnoses. Their accuracy is crucial for successful treatment, especially for imaging diagnostics. This study used a deep convolutional neural network to detect four categories of objects on digital chest X-ray images. The data were obtained from the publicly available National Institutes of Health (NIH) Chest X-ray (CXR) Dataset. In total, 112,120 CXRs from 30,805 patients were manually checked for foreign objects: vascular port, shoulder endoprosthesis, necklace, and implantable cardioverter-defibrillator (ICD). Then, they were annotated with the use of a computer program, and the necessary image preprocessing was performed, such as resizing, normalization, and cropping. The object detection model was trained using the You Only Look Once v8 architecture and the Ultralytics framework. The results showed not only that the obtained average precision of foreign object detection on the CXR was 0.815 but also that the model can be useful in detecting foreign objects on the CXR images. Models of this type may be used as a tool for specialists, in particular, with the growing popularity of radiology comes an increasing workload. We are optimistic that it could accelerate and facilitate the work to provide a faster diagnosis.

Autoimmune polyendocrine syndromes associated with autoimmune rheumatic diseases.

Autoimmune polyendocrine syndromes (APSs), also called autoimmune polyglandular syndromes, are a group of autoimmune diseases characterized by the co-occurrence of dysfunctions of several (at least two) endocrine glands. They develop under the influence of environmental factors in genetically predisposed people. Autoimmune polyendocrine syndromes may accompany autoimmune rheumatic diseases and worsen their course - APS-2 and APS-3 are the most common. The APS-2 includes the coexistence of, e.g. Hashimoto's disease, celiac disease and rheumatoid arthritis (RA). In APS-3, rheumatic diseases such as RA, systemic lupus erythematosus, and Sjögren's syndrome may coexist with Hashimoto's disease, type 1 diabetes and hypogonadism or other endocrinopathies. Undiagnosed endocrine diseases may be the reason for the intensification of metabolic disorders observed in the course of rheumatic diseases, cause the ineffectiveness of rheumatological treatment and also increase the frequency of bone fractures due to osteoporosis, cardiovascular complications and even miscarriages when coexistent, e.g. Hashimoto's disease with hypothyroiditis, which increases the risk of pregnancy loss. It is important to be able to conduct an extensive interview, paying attention to the symptoms of possible endocrinopathy as well as the features of other autoimmune disorders in the physical examination (e.g. vitiligo or darkening of the skin in Addison's disease). Depending on the history and physical examination, screening for various APSs is advised.

Obesity in rheumatological practice.

Obesity is a chronic disease that leads to the development of secondary metabolic disturbances and diseases and strongly contributes to increased morbidity and mortality. On the other hand, musculoskeletal disorders are currently the main cause of disability and the second most frequent reason for visits to the doctor. Many studies clearly show that excessive body weight adversely affects the course of almost all musculoskeletal system diseases, from osteoarthritis, through metabolic, systemic connective tissue, to rarely diagnosed diseases. The impact of increased fat mass on the musculoskeletal system is presumably complex in nature and involves the influence of biomechanical, dietary, genetic, inflammatory and metabolic factors. Due to the epidemic nature of obesity and its serious health consequences, this disease requires energetic treatment. It is always based on lifestyle modification enriched, if necessary, by pharmacological and, in justified cases, surgical treatment.

What Is Machine Learning, Artificial Neural Networks and Deep Learning?-Examples of Practical Applications in Medicine.

Machine learning (ML), artificial neural networks (ANNs), and deep learning (DL) are all topics that fall under the heading of artificial intelligence (AI) and have gained popularity in recent years. ML involves the application of algorithms to automate decision-making processes using models that have not been manually programmed but have been trained on data. ANNs that are a part of ML aim to simulate the structure and function of the human brain. DL, on the other hand, uses multiple layers of interconnected neurons. This enables the processing and analysis of large and complex databases. In medicine, these techniques are being introduced to improve the speed and efficiency of disease diagnosis and treatment. Each of the AI techniques presented in the paper is supported with an example of a possible medical application. Given the rapid development of technology, the use of AI in medicine shows promising results in the context of patient care. It is particularly important to keep a close eye on this issue and conduct further research in order to fully explore the potential of ML, ANNs, and DL, and bring further applications into clinical use in the future.

Malodour classification with low-cost flexible electronics.

Understanding body malodour in a measurable manner is essential for developing personal care products. Body malodour is the result of bodily secretion of a highly complex mixture of volatile organic compounds. Current body malodour measurement methods are manual, time consuming and costly, requiring an expert panel of assessors to assign a malodour score to each human test subject. This article proposes a technology-based solution to automate this task by developing a custom-designed malodour score classification system comprising an electronic nose sensor array, a sensor readout interface and a machine learning hardware fabricated on low-cost flexible substrates. The proposed flexible integrated smart system is to augment the expert panel by acting like a panel assessor but could ultimately replace the panel to reduce the test and measurement costs. We demonstrate that it can classify malodour scores as good as or even better than half of the assessors on the expert panel.

Lateral or Medial Parapatellar Surgical Approach to the Valgus Osteoarthritic Knee? A Retrospective Single-Center Study.

Aims: Total knee arthroplasty in patients with fixed valgus deformity is a demanding procedure. The aim of this study was to compare the clinical results of using the lateral approach [LA] versus the medial approach [MA] in the treatment of fixed valgus knee deformities. Methods: This single-center study compared the results of 143 consecutive patients with fixed valgus deformity (mean 21.55° valgus, mean age 68.2 years) undergoing LA Total Knee Arthroplasty [TKA] to 50 patients (mean 16.58° valgus, mean age 67.2 years) undergoing MA TKA. The mean follow-up period was 5.1 years (2−10 years). Data was collected from operative notes, routine postoperative visits, and radiological findings. Apart from a radiological evaluation, patients were clinically assessed both pre- and postoperatively using the Knee Society Score [KSS]. Descriptive statistics together with the Kolmogorov-Smirnov test, the Student's t-test for independent samples, and the Mann-Whitney U test were used. The level of significance in this study was α = 0.05. Results: In the LA group, the KSS Knee was significantly higher than in the MA group [85.31 vs. 77.42, respectively, p-value < 0.001]. The difference was also in the KSS total but with no statistical significance [155.17 vs. 149.22, p-value 0.087]. The surgery time in the LA group was shorter than in the MA group [81 vs. 91 min, respectively, p-value­0.002]. The complication rate after surgery was higher in the MA group than in the LA group (14% vs. 9%, respectively). Conclusions: The lateral approach is a good alternative to the standard medial parapatellar approach in the treatment of fixed valgus knee deformities. A higher postoperative KSS Knee, shorter surgery time, and similar complication rate make the lateral approach a valuable option for treating patients with osteoarthritis and fixed valgus knee deformity.

Technological Optimization of the Stirrup Casting Process with the Use of Computer Simulations.

The article presents the optimization of high-pressure die casting process technology for equestrian stirrups with the application of computer simulation. In the initial stage, the output technology was analyzed, and on the basis of a series of virtual experiments the cause of defects in the casting was highlighted. The optimization process includes different designs of a gating system. Additionally, the casting application properties were evaluated in an exploitation simulation, taking into account predicted defects resulting from the casting and solidification process. Based on the conducted analyses, technological changes were made to the casting technology design allowing the defects occurring in the original technological concept to be removed.

Usability of Mobile Solutions Intended for Diagnostic Images-A Systematic Review.

Despite the growing popularity of mobile devices, they still have not found widespread use in medicine. This is due to the procedures in a given place, differences in the availability of mobile devices between individual institutions or lack of appropriate legal regulations and accreditation by relevant institutions. Numerous studies have been conducted and compared the usability of mobile solutions designed for diagnostic images evaluation on various mobile devices and applications with classic stationary descriptive stations. This study is an attempt to compare the usefulness of currently available mobile applications which are used in the medical industry, focusing on imaging diagnostics. As a consequence of the healthcare sector's diversity, it is also not possible to design a universal mobile application, which results in a multitude of software available on the market and makes it difficult to reliably compile and compare studies included in this systematic review. Despite these differences, it was possible to identify both positive and negative features of portable methods analyzing radiological images. The mobile application of the golden mean in hospital infrastructure should be widely available, with convenient and simple usage. Our future research will focus on development in the use of mobile devices and applications in the medical sector.

Application of artificial intelligence in diagnosing COVID-19 disease symptoms on chest X-rays: A systematic review.

This systematic review focuses on using artificial intelligence (AI) to detect COVID-19 infection with the help of X-ray images. Methodology: In January 2022, the authors searched PubMed, Embase and Scopus using specific medical subject headings terms and filters. All articles were independently reviewed by two reviewers. All conflicts resulting from a misunderstanding were resolved by a third independent researcher. After assessing abstracts and article usefulness, eliminating repetitions and applying inclusion and exclusion criteria, six studies were found to be qualified for this study. Results: The findings from individual studies differed due to the various approaches of the authors. Sensitivity was 72.59%-100%, specificity was 79%-99.9%, precision was 74.74%-98.7%, accuracy was 76.18%-99.81%, and the area under the curve was 95.24%-97.7%. Conclusion: AI computational models used to assess chest X-rays in the process of diagnosing COVID-19 should achieve sufficiently high sensitivity and specificity. Their results and performance should be repeatable to make them dependable for clinicians. Moreover, these additional diagnostic tools should be more affordable and faster than the currently available procedures. The performance and calculations of AI-based systems should take clinical data into account.

On-sensor binarized CNN inference with dynamic model swapping in pixel processor arrays.

Many types of Convolutional Neural Network (CNN) models and training methods have been proposed in recent years aiming to provide efficiency for embedded and edge devices with limited computation and memory resources. The wide variety of architectures makes this a complex task that has to balance generality with efficiency. Among the most interesting camera-sensor architectures are Pixel Processor Arrays (PPAs). This study presents two methods that are useful for embedded CNNs in general but particularly suitable for PPAs. The first is for training purely binarized CNNs, the second is for deploying larger models with a model swapping paradigm that loads model components dynamically. Specifically, this study trains and implements networks with batch normalization and adaptive threshold for binary activations. Then, we convert batch normalization and binary activations into a bias matrix which can be parallelly implemented by an add/sub operation. For dynamic model swapping, we propose to decompose applications that are beyond the capacity of a PPA into sub-tasks that can be solved by tree networks that can be loaded dynamically as needed. We demonstrate our approaches to various tasks including classification, localization, and coarse segmentation on a highly resource constrained PPA sensor-processor.

Periprosthetic Joint Infections Caused by Candida Species-A Single-Center Experience and Systematic Review of the Literature.

BACKGROUND: The aim of this study was to analyze the treatment results of fungal periprosthetic joint infections (PJI) caused by Candida species from a single orthopedic center and to compare them with reports from other institutions. METHODS: Eight patients operated on from January 2014 to December 2021 met the inclusion criteria and were analyzed in terms of clinical outcomes. A systematic review of the literature identified 153 patients with Candida PJIs extracted from 12 studies according to the PRISMA (Preferred Reporting Item for Systematic Reviews and Meta-Analyses) guidelines. RESULTS: The success rate of the treatment in the case series was 50%. The most frequent pathogens were Candida albicans (three cases; 37.5%) and Candida parapsilosis (three cases; 37.5%). In one patient (12.5%), bacterial co-infection was noted, and in five patients (62.5%) significant risk factors of PJI were confirmed. The overall success rate on the basis of data collected for systematic review was 65.5%. A sub-analysis of 127 patients revealed statistically significant differences (p = 0.02) with a higher success rate for the knees (77.6%) than for the hips (58%). In 10 studies the analysis of risk factors was performed and among 106 patients in 77 (72.6%) comorbidities predispose to fungal PJI were confirmed. Bacterial co-infection was noted in 84 patients (54.9%). In 93 patients (60.7%) Candida albicans was the culprit pathogen, and in 39 patients (25.5%) Candida parapsilosis was the culprit pathogen. Based on these two most frequent Candida species causing PJI, the success rate of the treatment was statistically different (p = 0.03), and was 60.3% and 83.3%, respectively. The two-stage strategy was more favorable for patients with Candida parapsilosis infections (94.4% success rate) than the one-stage protocol (50% success rate; p = 0.02); as well as in comparison to the two-stage treatment of Candida albicans (65% success rate; p = 0.04). CONCLUSIONS: The analysis of the literature showed no differences in the overall success rate between one- and two-stage surgical strategies for all Candida species, but differed significantly comparing the two most frequent strains and concerning PJI localization. The frequent presence of bacterial co-infections makes it necessary to consider the additional administration of antibiotics in the case of fungal PJI.

Sensor-level computer vision with pixel processor arrays for agile robots.

Vision processing for control of agile autonomous robots requires low-latency computation, within a limited power and space budget. This is challenging for conventional computing hardware. Parallel processor arrays (PPAs) are a new class of vision sensor devices that exploit advances in semiconductor technology, embedding a processor within each pixel of the image sensor array. Sensed pixel data are processed on the focal plane, and only a small amount of relevant information is transmitted out of the vision sensor. This tight integration of sensing, processing, and memory within a massively parallel computing architecture leads to an interesting trade-off between high performance, low latency, low power, low cost, and versatility in a machine vision system. Here, we review the history of image sensing and processing hardware from the perspective of in-pixel computing and outline the key features of a state-of-the-art smart camera system based on a PPA device, through the description of the SCAMP-5 system. We describe several robotic applications for agile ground and aerial vehicles, demonstrating PPA sensing functionalities including high-speed odometry, target tracking, obstacle detection, and avoidance. In the conclusions, we provide some insight and perspective on the future development of PPA devices, including their application and benefits within agile, robust, adaptable, and lightweight robotics.

Inhibin-B and FSH Are Good Indicators of Spermatogenesis but Not the Best Indicators of Fertility.

Biochemical markers of spermatogenesis and fertility assessment are important in the practical management of infertile males and the determination of an individual's prognosis. We performed an analysis on 100 males with a male infertility factor. The following study inclusion parameters were analyzed: seminogram, FSH, LH, testosterone, estradiol, prolactin, TSH, and inhibin B concentrations. The patients were subsequently treated by reproductive endocrinologists in accordance with AUA/ASRM and EAU guidelines. The reproductive status was evaluated over a period of 3 years. We found a strong correlation of sperm count with inhibin B (r = 0.74, p < 0.001) and FSH concentration levels (r = −0.46, p < 0.001). Among 95 patients at follow-up, pregnancies occurred for 59 of their partners (48 spontaneous, 5 after IVF−ET, and 6 after IUI). Thirty-six patients remained childless despite the therapy. Sperm count and inhibin B level were the best predictors of natural fertilization (ROC AUC: 0.86 and 0.84; cut-off: 2.7 mln/mL and 45 pg/mL). Although inhibin B and FSH can be used to evaluate spermatogenesis and fertility, the initial sperm concentration appeared to be the best predictor of success. Pregnancy was achieved in a surprisingly large proportion of patients with a very low concentration of inhibin B and a low initial sperm count. It is noteworthy that 81% of the pregnancies were achieved without medically assisted reproduction.

NiO-Ba0.95Ca0.05Ce0.9Y0.1O3-δ as a Modified Anode Material Fabricated by the Tape Casting Method.

The development of new chemically resistant anodes for protonic ceramic fuel cells (PCFCs) is urgently required to avoid the costly deep hydrogen purification method. Ba0.95Ca0.05Ce0.9Y0.1O3-δ (5CBCY), which is more chemically resistant than BaCaCe0.9Y0.1O3-δ, was here tested as a component of a composite NiO-5CBCY anode material. A preparation slurry comprising 5CBCY, NiO, graphite, and an organic medium was tape cast, sintered and subjected to thermal treatment in 10 vol.% H2 in Ar at 700 °C. Differential thermal analysis, thermogravimetry, quadrupole mass spectrometry, X-ray diffraction analysis, scanning electron microscopy, the AC four-probe method and electrochemical impedance spectroscopy were used for the investigation. The electrical conductivity of the Ni-5CBCY in H2-Ar at 700 °C was 1.1 S/cm. In the same gas atmosphere but with an additional 5 vol.% CO2, it was slightly lower, at 0.8 S/cm. The Ni-5CBCY cermet exhibited repeatable electrical conductivity values during Ni-to-NiO oxidation cycles and NiO-to-Ni reduction in the 5CBCY matrix, making it sufficient for preliminary testing in PCFCs.

Shaft Fractures in Patients Requiring Primary or Revision Total Knee Arthroplasty Can Be Successfully Treated with Long-Stemmed Implants without Additional Fixation.

The aim of this study was to evaluate the bone union, complication rate, clinical and functional outcomes of long-stemmed total knee arthroplasty (TKA) in patients with periprosthetic femoral or tibial shaft fractures and in patients with femoral or tibial shaft fractures with coexisting advanced knee osteoarthritis (OA). This retrospective study comprised 25 patients who underwent surgery due to tibial or femoral shaft fractures: (1) with coexisting severe knee OA or (2) with a periprosthetic fracture requiring implant exchange. In all cases, fracture stabilization was performed intramedullary with the use of long-stemmed implants without the use of additional fixation material (plates, screws, or cerclage). Bone union was achieved in 22/25 patients (88%). One patient required revision with additional plate stabilization due to non-union, and asymptomatic partial bone union was observed in two cases. The group with periprosthetic fractures demonstrated good clinical (mean 73.1 ± 13.3) and moderate functional (mean 59.2 ± 18.8) outcomes in the Knee Society Scoring system (KSS). In the group with shaft fracture and coexisting OA significantly higher clinical (excellent results, mean 84.1 ± 11; p = 0.03) and functional (good results, mean 76.2 ± 20.6; p = 0.04) results were noted. There were no statistically significant differences in terms of range of motion (ROM) or complication rate between these two groups. One-stage TKA with a long-stemmed implant without the use of additional fixation material is an effective method for the treatment of femoral or tibial shaft fractures in patients who require joint replacement. Despite being technically demanding, the approach yields bone union and moderate to excellent clinical and functional outcomes with a relatively low complication rate.

The Utilisation of Solid Fuels Derived from Waste Pistachio Shells in Direct Carbon Solid Oxide Fuel Cells.

The comprehensive results regarding the physicochemical properties of carbonaceous materials that are obtained from pistachio shells support their usage as solid fuels to supply direct carbon solid oxide fuel cells (DC-SOFCs). The influence of preparation conditions on variations in the chemical composition, morphology of the biochar powders, and degree of graphitization of carbonaceous materials were investigated. Based on structural investigations (X-ray diffraction analysis and Raman spectroscopy), it was observed that disordered carbon particles developed during the application of thermal treatments. The use of X-ray fluorescence enabled a comparative analysis of the chemical composition of the inorganic matter in biocarbon-based samples. Additionally, the gasification of carbonaceous-based samples vs. time at a temperature of 850 °C was investigated in a H2O or CO2 gas atmosphere. The analysis demonstrated the conversion rate of biochar obtained from pistachio shells to H2, CH4 and CO during steam gasification. The electrochemical investigations of the DC-SOFCs that were supplied with biochars obtained from pistachio shells were characterized by satisfactory values for the current and power densities at a temperature range of 700-850 °C. However, a higher power output of the DC-SOFCs was observed when CO2 was introduced to the anode chamber. Therefore, the impact of the Boudouard reaction on the performance of DC-SOFCs was confirmed. The chars that were prepared from pistachio shells were adequate for solid fuels for utilization in DC-SOFCs.

Elevated plasma D-dimer concentration has higher efficacy for the diagnosis of periprosthetic joint infection of the knee than of the hip-A single-center, retrospective study.

The aim of this study is to evaluate the value of D-dimers in the diagnosis of periprosthetic joint infection (PJI). The analysis was performed for revision total hip (rTHA) and revision total knee arthroplasty (rTKA) together and separately with two thresholds, one calculated by statistical methods and the second adopted from the ICM 2018 definition. The study group comprised 133 patients who underwent rTHA or rTKA: 68 patients diagnosed according to the ICM 2018 definition (PJI group) and 65 with aseptic implant loosening, instability, malposition, or implant failure with the exclusion of infection (aseptic revision total joint arthroplasty or arTJA group). Mean D-dimer concentrations were 0.36 ± 0.25 µg/ml in the arTJA group and 0.87 ± 0.78 µg/ml in the PJI group (p < .001). For rTHA and rTKA together, the sensitivity and specificity of the evaluation were 75% and 73.8% according to the calculated cut-off value (0.45 µg/ml), and 33.8% and 95.4% based on the ICM 2018 threshold (0.85 µg/ml). Separately, for rTHA, sensitivity and specificity were 62.5% and 62.1% for the calculated value (0.43 µg/ml) and 6.3% and 96.6% for the ICM 2018 threshold; for rTKA, sensitivity was 86.1% and specificity was 88.9% for the calculated threshold (0.48 µg/ml) and 58.3% and 94.4% for the ICM 2018 value. Our findings indicate that plasma D-dimers have potential as markers of knee PJI, but moderate to low value for hip PJI.