Aims. The number of convolutional neural networks (CNN) available for fracture detection and classification is rapidly increasing. External validation of a CNN on a temporally separate (separated by time) or geographically separate (separated by location) dataset is crucial to assess generalizability of the CNN before application to clinical practice in other institutions. We aimed to answer the following questions: are current CNNs for fracture recognition externally valid?; which methods are applied for external validation (EV)?; and, what are reported performances of the EV sets compared to the internal validation (IV) sets of these CNNs?. Methods. The PubMed and Embase databases were systematically searched from January 2010 to October 2020 according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. The type of EV, characteristics of the external dataset, and
Aims. Serum inflammatory parameters are widely used to aid in diagnosing a periprosthetic joint infection (PJI). Due to their limited performances in the literature, novel and more accurate biomarkers are needed. Serum albumin-to-globulin ratio (AGR) and serum CRP-to-albumin ratio (CAR) have previously been proposed as potential new parameters, but results were mixed. The aim of this study was to assess the diagnostic accuracy of AGR and CAR in diagnosing PJI and to compare them to the established and widely used marker CRP. Methods. From 2015 to 2022, a consecutive series of 275 cases of revision total hip (n = 129) and knee arthroplasty (n = 146) were included in this retrospective cohort study. Based on the 2021 European Bone and Joint Infection Society (EBJIS) definition, 144 arthroplasties were classified as septic. Using receiver operating characteristic curve (ROC) analysis, the ideal thresholds and
Objectives. Nasal carriers of Staphylococcus (S.) aureus (MRSA and MSSA) have an increased risk for healthcare-associated infections. There are currently limited national screening policies for the detection of S. aureus despite the World Health Organization’s recommendations. This study aimed to evaluate the
The aim of this study was to create artificial intelligence (AI) software with the purpose of providing a second opinion to physicians to support distal radius fracture (DRF) detection, and to compare the accuracy of fracture detection of physicians with and without software support. The dataset consisted of 26,121 anonymized anterior-posterior (AP) and lateral standard view radiographs of the wrist, with and without DRF. The convolutional neural network (CNN) model was trained to detect the presence of a DRF by comparing the radiographs containing a fracture to the inconspicuous ones. A total of 11 physicians (six surgeons in training and five hand surgeons) assessed 200 pairs of randomly selected digital radiographs of the wrist (AP and lateral) for the presence of a DRF. The same images were first evaluated without, and then with, the support of the CNN model, and the diagnostic accuracy of the two methods was compared.Aims
Methods
This study aimed to explore the biological and clinical importance of dysregulated key genes in osteoarthritis (OA) patients at the cartilage level to find potential biomarkers and targets for diagnosing and treating OA. Six sets of gene expression profiles were obtained from the Gene Expression Omnibus database. Differential expression analysis, weighted gene coexpression network analysis (WGCNA), and multiple machine-learning algorithms were used to screen crucial genes in osteoarthritic cartilage, and genome enrichment and functional annotation analyses were used to decipher the related categories of gene function. Single-sample gene set enrichment analysis was performed to analyze immune cell infiltration. Correlation analysis was used to explore the relationship among the hub genes and immune cells, as well as markers related to articular cartilage degradation and bone mineralization.Aims
Methods
Treatment of high-grade limb bone sarcoma that invades a joint requires en bloc extra-articular excision. MRI can demonstrate joint invasion but is frequently inconclusive, and its predictive value is unknown. We evaluated the diagnostic accuracy of direct and indirect radiological signs of intra-articular tumour extension and the performance characteristics of MRI findings of intra-articular tumour extension. We performed a retrospective case-control study of patients who underwent extra-articular excision for sarcoma of the knee, hip, or shoulder from 1 June 2000 to 1 November 2020. Radiologists blinded to the pathology results evaluated preoperative MRI for three direct signs of joint invasion (capsular disruption, cortical breach, cartilage invasion) and indirect signs (e.g. joint effusion, synovial thickening). The discriminatory ability of MRI to detect intra-articular tumour extension was determined by receiver operating characteristic analysis.Aims
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We aimed to evaluate the utility of 68Ga-citrate positron emission tomography (PET)/CT in the differentiation of periprosthetic joint infection (PJI) and aseptic loosening (AL), and compare it with 99mTc-methylene bisphosphonates (99mTc-MDP) bone scan. We studied 39 patients with suspected PJI or AL. These patients underwent 68Ga-citrate PET/CT, 99mTc-MDP three-phase bone scan and single-photon emission CT (SPECT)/CT. PET/CT was performed at ten minutes and 60 minutes after injection, respectively. Images were evaluated by three nuclear medicine doctors based on: 1) visual analysis of the three methods based on tracer uptake model, and PET images attenuation-corrected with CT and those not attenuation-corrected with CT were analyzed, respectively; and 2) semi-quantitative analysis of PET/CT: maximum standardized uptake value (SUVmax) of lesions, SUVmax of the lesion/SUVmean of the normal bone, and SUVmax of the lesion/SUVmean of the normal muscle. The final diagnosis was based on the clinical and intraoperative findings, and histopathological and microbiological examinations.Aims
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This study evaluated the definitions developed by the European Bone and Joint Infection Society (EBJIS) 2021, the International Consensus Meeting (ICM) 2018, and the Infectious Diseases Society of America (IDSA) 2013, for the diagnosis of periprosthetic joint infection (PJI). In this single-centre, retrospective analysis of prospectively collected data, patients with an indicated revision surgery after a total hip or knee arthroplasty were included between 2015 and 2020. A standardized diagnostic workup was performed, identifying the components of the EBJIS, ICM, and IDSA criteria in each patient.Aims
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Artificial intelligence and machine-learning analytics have gained extensive popularity in recent years due to their clinically relevant applications. A wide range of proof-of-concept studies have demonstrated the ability of these analyses to personalize risk prediction, detect implant specifics from imaging, and monitor and assess patient movement and recovery. Though these applications are exciting and could potentially influence practice, it is imperative to understand when these analyses are indicated and where the data are derived from, prior to investing resources and confidence into the results and conclusions. In this article, we review the current benefits and potential limitations of machine-learning for the orthopaedic surgeon with a specific emphasis on data quality.
Synovial fluid white blood cell (WBC) count and percentage of polymorphonuclear cells (%PMN) are elevated at periprosthetic joint infection (PJI). Leucocytes produce different interleukins (IL), including IL-6, so we hypothesized that synovial fluid IL-6 could be a more accurate predictor of PJI than synovial fluid WBC count and %PMN. The main aim of our study was to compare the predictive performance of all three diagnostic tests in the detection of PJI. Patients undergoing total hip or knee revision surgery were included. In the perioperative assessment phase, synovial fluid WBC count, %PMN, and IL-6 concentration were measured. Patients were labeled as positive or negative according to the predefined cut-off values for IL-6 and WBC count with %PMN. Intraoperative samples for microbiological and histopathological analysis were obtained. PJI was defined as the presence of sinus tract, inflammation in histopathological samples, and growth of the same microorganism in a minimum of two or more samples out of at least four taken.Aims
Methods
The diagnosis of periprosthetic joint infection (PJI) has always been challenging. Recently, D-dimer has become a promising biomarker in diagnosing PJI. However, there is controversy regarding its diagnostic value. We aim to investigate the diagnostic value of D-dimer in comparison to ESR and CRP. PubMed, Embase, and the Cochrane Library were searched in February 2020 to identify articles reporting on the diagnostic value of D-dimer on PJI. Pooled analysis was conducted to investigate the diagnostic value of D-dimer, CRP, and ESR.Aims
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The lack of disease-modifying treatments for osteoarthritis (OA) is linked to a shortage of suitable biomarkers. This study combines multi-molecule synovial fluid analysis with machine learning to produce an accurate diagnostic biomarker model for end-stage knee OA (esOA). Synovial fluid (SF) from patients with esOA, non-OA knee injury, and inflammatory knee arthritis were analyzed for 35 potential markers using immunoassays. Partial least square discriminant analysis (PLS-DA) was used to derive a biomarker model for cohort classification. The ability of the biomarker model to diagnose esOA was validated by identical wide-spectrum SF analysis of a test cohort of ten patients with esOA.Aims
Methods
Microbiological culture is a key element in the diagnosis of periprosthetic joint infection (PJI). However, cultures of periprosthetic tissue do not have optimal sensitivity. One of the main reasons for this is that microorganisms are not released from the tissues, either due to biofilm formation or intracellular persistence. This study aimed to optimize tissue pretreatment methods in order to improve detection of microorganisms. From December 2017 to September 2019, patients undergoing revision arthroplasty in a single centre due to PJI and aseptic failure (AF) were included, with demographic data and laboratory test results recorded prospectively. Periprosthetic tissue samples were collected intraoperatively and assigned to tissue-mechanical homogenization (T-MH), tissue-manual milling (T-MM), tissue-dithiothreitol (T-DTT) treatment, tissue-sonication (T-S), and tissue-direct culture (T-D). The yield of the microbial cultures was then analyzed.Aims
Methods
The aim of this study was to evaluate the performance of metagenomic next-generation sequencing (mNGS) in detecting pathogens from synovial fluid of prosthetic joint infection (PJI) patients. A group of 75 patients who underwent revision knee or hip arthroplasties were enrolled prospectively. Ten patients with primary arthroplasties were included as negative controls. Synovial fluid was collected for mNGS analysis. Optimal thresholds were determined to distinguish pathogens from background microbes. Synovial fluid, tissue, and sonicate fluid were obtained for culture.Aims
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This pilot study tested the performance of a rapid assay for diagnosing prosthetic joint infection (PJI), which measures synovial fluid calprotectin from total hip and knee revision patients. A convenience series of 69 synovial fluid samples from revision patients at the Norfolk and Norwich University Hospital were collected intraoperatively (52 hips, 17 knees) and frozen. Synovial fluid calprotectin was measured retrospectively using a new commercially available lateral flow assay for PJI diagnosis (Lyfstone AS) and compared to International Consensus Meeting (ICM) 2018 criteria and clinical case review (ICM-CR) gold standards.Aims
Methods
Preoperative diagnosis is important for revision surgery after prosthetic joint infection (PJI). The purpose of our study was to determine whether reverse transcription-quantitative polymerase chain reaction (RT-qPCR), which is used to detect bacterial ribosomal RNA (rRNA) preoperatively, can reveal PJI in low volumes of aspirated fluid. We acquired joint fluid samples (JFSs) by preoperative aspiration from patients who were suspected of having a PJI and failed arthroplasty; patients with preoperative JFS volumes less than 5 ml were enrolled. RNA-based polymerase chain reaction (PCR) and bacterial culture were performed, and diagnostic efficiency was compared between the two methods.According to established Musculoskeletal Infection Society (MSIS) criteria, 21 of the 33 included patients were diagnosed with PJI.Aims
Methods
The aim of this study was to review the current evidence and future application for the role of diagnostic and therapeutic ultrasound in fracture management. A review of relevant literature was undertaken, including articles indexed in PubMed with keywords “ultrasound” or “sonography” combined with “diagnosis”, “fracture healing”, “impaired fracture healing”, “nonunion”, “microbiology”, and “fracture-related infection”.Objectives
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The diagnosis of periprosthetic joint infection (PJI) is difficult and requires a battery of tests and clinical findings. The purpose of this review is to summarize all current evidence for common and new serum biomarkers utilized in the diagnosis of PJI. We searched two literature databases, using terms that encompass all hip and knee arthroplasty procedures, as well as PJI and statistical terms reflecting diagnostic parameters. The findings are summarized as a narrative review.Objectives
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The objective of this study was to develop a test for the rapid (within 25 minutes) intraoperative detection of bacteria from synovial fluid to diagnose periprosthetic joint infection (PJI). The 16s rDNA test combines a polymerase chain reaction (PCR) for amplification of 16s rDNA with a lateral flow immunoassay in one fully automated system. The synovial fluid of 77 patients undergoing joint aspiration or primary or revision total hip or knee surgery was prospectively collected. The cohort was divided into a proof-of-principle cohort (n = 17) and a validation cohort (n = 60). Using the proof-of-principle cohort, an optimal cut-off for the discrimination between PJI and non-PJI samples was determined. PJI was defined as detection of the same bacterial species in a minimum of two microbiological samples, positive histology, and presence of a sinus tract or intra-articular pus.Objectives
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