Aims. The aim of this study was to systematically compare the safety and accuracy of robot-assisted (RA) technique with conventional freehand with/without fluoroscopy-assisted (CT) pedicle screw insertion for spine disease. Methods. A systematic search was performed on PubMed, EMBASE, the Cochrane Library, MEDLINE, China National Knowledge Infrastructure (CNKI), and WANFANG for randomized controlled trials (RCTs) that investigated the safety and accuracy of RA compared with conventional freehand with/without fluoroscopy-assisted pedicle screw insertion for spine disease from 2012 to 2019. This meta-analysis used Mantel-Haenszel or inverse variance method with mixed-effects model for heterogeneity, calculating the odds ratio (OR), mean difference (MD), standardized mean difference (SMD), and 95% confidence intervals (CIs). The results of heterogeneity, subgroup analysis, and risk of bias were analyzed. Results. Ten RCTs with 713 patients and 3,331 pedicle screws were included. Compared with CT, the accuracy rate of RA was superior in Grade A with statistical significance and Grade A + B without statistical significance. Compared with CT, the operating time of RA was longer. The difference between RA and CT was statistically significant in radiation dose. Proximal facet joint violation occurred less in RA than in CT. The postoperative Oswestry Disability Index (ODI) of RA was smaller than that of CT, and there were some interesting outcomes in our subgroup analysis. Conclusion. RA technique could be viewed as an accurate and safe pedicle screw implantation method compared to CT. A robotic system equipped with optical intraoperative navigation is superior to CT in accuracy. RA pedicle screw insertion can improve accuracy and maintain stability for some challenging areas. Cite this article: Bone Joint Res 2020;9(10):653–666

Aims. Hip arthroplasty aims to accurately recreate joint biomechanics. Considerable attention has been paid to vertical and horizontal offset, but femoral head centre in the anteroposterior (AP) plane has received little attention. This study investigates the accuracy of restoration of joint centre of rotation in the AP plane. Methods. Postoperative CT scans of 40 patients who underwent unilateral uncemented total hip arthroplasty were analyzed. Anteroposterior offset (APO) and femoral anteversion were measured on both the operated and non-operated sides. Sagittal tilt of the femoral stem was also measured. APO measured on axial slices was defined as the perpendicular distance between a line drawn from the anterior most point of the proximal femur (anterior reference line) to the centre of the femoral head. The anterior reference line was made parallel to the posterior condylar axis of the knee to correct for rotation. Results. Overall, 26/40 hips had a centre of rotation displaced posteriorly compared to the contralateral hip, increasing to 33/40 once corrected for sagittal tilt, with a mean posterior displacement of 7 mm. Linear regression analysis indicated that stem anteversion needed to be increased by 10.8° to recreate the head centre in the AP plane. Merely matching the native version would result in a 12 mm posterior displacement. Conclusion. This study demonstrates the significant incidence of posterior displacement of the head centre in uncemented hip arthroplasty. Effects of such displacement include a reduction in impingement free range of motion, potential alterations in muscle force vectors and lever arms, and impaired proprioception due to muscle fibre reorientation. Cite this article: Bone Joint Res 2022;11(3):180–188

Objectives. To assess the accuracy of patient-specific instruments (PSIs) versus standard manual technique and the precision of computer-assisted planning and PSI-guided osteotomies in pelvic tumour resection. Methods. CT scans were obtained from five female cadaveric pelvises. Five osteotomies were designed using Mimics software: sacroiliac, biplanar supra-acetabular, two parallel iliopubic and ischial. For cases of the left hemipelvis, PSIs were designed to guide standard oscillating saw osteotomies and later manufactured using 3D printing. Osteotomies were performed using the standard manual technique in cases of the right hemipelvis. Post-resection CT scans were quantitatively analysed. Student’s t-test and Mann–Whitney U test were used. Results. Compared with the manual technique, PSI-guided osteotomies improved accuracy by a mean 9.6 mm (p < 0.008) in the sacroiliac osteotomies, 6.2 mm (p < 0.008) and 5.8 mm (p < 0.032) in the biplanar supra-acetabular, 3 mm (p < 0.016) in the ischial and 2.2 mm (p < 0.032) and 2.6 mm (p < 0.008) in the parallel iliopubic osteotomies, with a mean linear deviation of 4.9 mm (p < 0.001) for all osteotomies. Of the manual osteotomies, 53% (n = 16) had a linear deviation > 5 mm and 27% (n = 8) were > 10 mm. In the PSI cases, deviations were 10% (n = 3) and 0 % (n = 0), respectively. For angular deviation from pre-operative plans, we observed a mean improvement of 7.06° (p < 0.001) in pitch and 2.94° (p < 0.001) in roll, comparing PSI and the standard manual technique. Conclusion. In an experimental study, computer-assisted planning and PSIs improved accuracy in pelvic tumour resections, bringing osteotomy results closer to the parameters set in pre-operative planning, as compared with standard manual techniques. Cite this article: A. Sallent, M. Vicente, M. M. Reverté, A. Lopez, A. Rodríguez-Baeza, M. Pérez-Domínguez, R. Velez. How 3D patient-specific instruments improve accuracy of pelvic bone tumour resection in a cadaveric study. Bone Joint Res 2017;6:577–583. DOI: 10.1302/2046-3758.610.BJR-2017-0094.R1

Objectives. The accuracy and precision of two new methods of model-based radiostereometric analysis (RSA) were hypothesised to be superior to a plain radiograph method in the assessment of polyethylene (PE) wear. Methods. A phantom device was constructed to simulate three-dimensional (3D) PE wear. Images were obtained consecutively for each simulated wear position for each modality. Three commercially available packages were evaluated: model-based RSA using laser-scanned cup models (MB-RSA), model-based RSA using computer-generated elementary geometrical shape models (EGS-RSA), and PolyWare. Precision (95% repeatability limits) and accuracy (Root Mean Square Errors) for two-dimensional (2D) and 3D wear measurements were assessed. Results. The precision for 2D wear measures was 0.078 mm, 0.102 mm, and 0.076 mm for EGS-RSA, MB-RSA, and PolyWare, respectively. For the 3D wear measures the precision was 0.185 mm, 0.189 mm, and 0.244 mm for EGS-RSA, MB-RSA, and PolyWare respectively. Repeatability was similar for all methods within the same dimension, when compared between 2D and 3D (all p > 0.28). For the 2D RSA methods, accuracy was below 0.055 mm and at least 0.335 mm for PolyWare. For 3D measurements, accuracy was 0.1 mm, 0.2 mm, and 0.3 mm for EGS-RSA, MB-RSA and PolyWare respectively. PolyWare was less accurate compared with RSA methods (p = 0.036). No difference was observed between the RSA methods (p = 0.10). Conclusions. For all methods, precision and accuracy were better in 2D, with RSA methods being superior in accuracy. Although less accurate and precise, 3D RSA defines the clinically relevant wear pattern (multidirectional). PolyWare is a good and low-cost alternative to RSA, despite being less accurate and requiring a larger sample size

Aims. 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. Methods. 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. Results. A total of 49 PJI and 21 noninfection patients were finally included. Of the 39 culture-positive PJI cases, mNGS results were positive in 37 patients (94.9%), and were consistent with culture results at the genus level in 32 patients (86.5%) and at the species level in 27 patients (73.0%). Metagenomic next-generation sequencing additionally identified 15 pathogens from five culture-positive and all ten culture-negative PJI cases, and even one pathogen from one noninfection patient, while yielding no positive findings in any primary arthroplasty. However, seven pathogens identified by culture were missed by mNGS. The sensitivity of mNGS for diagnosing PJI was 95.9%, which was significantly higher than that of comprehensive culture (79.6%; p = 0.014). The specificity is similar between mNGS and comprehensive culture (95.2% and 95.2%, respectively; p = 1.0). Conclusion. Metagenomic next-generation sequencing can effectively identify pathogens from synovial fluid of PJI patients, and demonstrates high accuracy in diagnosing PJI. Cite this article: Bone Joint Res 2020;9(7):440–449

Aims. Histology is an established tool in diagnosing periprosthetic joint infections (PJIs). Different thresholds, using various infection definitions and histopathological criteria, have been described. This study determined the performance of different thresholds of polymorphonuclear neutrophils (≥ 5 PMN/HPF, ≥ 10 PMN/HPF, ≥ 23 PMN/10 HPF) , when using the European Bone and Joint Infection Society (EBJIS), Infectious Diseases Society of America (IDSA), and the International Consensus Meeting (ICM) 2018 criteria for PJI. Methods. A total of 119 patients undergoing revision total hip (rTHA) or knee arthroplasty (rTKA) were included. Permanent histology sections of periprosthetic tissue were evaluated under high power (400× magnification) and neutrophils were counted per HPF. The mean neutrophil count in ten HPFs was calculated (PMN/HPF). Based on receiver operating characteristic (ROC) curve analysis and the z-test, thresholds were compared. Results. Using the EBJIS criteria, a cut-off of ≥ five PMN/HPF showed a sensitivity of 93% (95% confidence interval (CI) 81 to 98) and specificity of 84% (95% CI 74 to 91). The optimal threshold when applying the IDSA and ICM criteria was ≥ ten PMN/HPF with sensitivities of 94% (95% CI 79 to 99) and 90% (95% CI 76 to 97), and specificities of 86% (95% CI 77 to 92) and 92% (95% CI 84 to 97), respectively. In rTKA, a better performance of histopathological analysis was observed in comparison with rTHA when using the IDSA criteria (p < 0.001). Conclusion. With high accuracy, histopathological analysis can be supported as a confirmatory criterion in diagnosing periprosthetic joint infections. A threshold of ≥ five PMN/HPF can be recommended to distinguish between septic and aseptic loosening, with an increased possibility of detecting more infections caused by low-virulence organisms. However, neutrophil counts between one and five should be considered suggestive of infection and interpreted carefully in conjunction with other diagnostic test methods. Cite this article: Bone Joint Res 2021;10(8):536–547

Objective . Dunkin Hartley guinea pigs, a commonly used animal model of osteoarthritis, were used to determine if high frequency ultrasound can ensure intra-articular injections are accurately positioned in the knee joint. Methods. A high-resolution small animal ultrasound system with a 40 MHz transducer was used for image-guided injections. A total of 36 guinea pigs were anaesthetised with isoflurane and placed on a heated stage. Sterile needles were inserted directly into the knee joint medially, while the transducer was placed on the lateral surface, allowing the femur, tibia and fat pad to be visualised in the images. B-mode cine loops were acquired during 100 µl. We assessed our ability to visualise 1) important anatomical landmarks, 2) the needle and 3) anatomical changes due to the injection. . Results. From the ultrasound images, we were able to visualise clearly the movement of anatomical landmarks in 75% of the injections. The majority of these showed separation of the fat pad (67.1%), suggesting the injections were correctly delivered in the joint space. We also observed dorsal joint expansion (23%) and patellar tendon movement (10%) in a smaller subset of injections. Conclusion. The results demonstrate that this image-guided technique can be used to visualise the location of an intra-articular injection in the joints of guinea pigs. Future studies using an ultrasound-guided approach could help improve the injection accuracy in a variety of anatomical locations and animal models, in the hope of developing anti-arthritic therapies. Cite this article: Bone Joint Res 2015;4:1–5

Aims. This study aimed to evaluate the clinical application of the PJI-TNM classification for periprosthetic joint infection (PJI) by determining intraobserver and interobserver reliability. To facilitate its use in clinical practice, an educational app was subsequently developed and evaluated. Methods. A total of ten orthopaedic surgeons classified 20 cases of PJI based on the PJI-TNM classification. Subsequently, the classification was re-evaluated using the PJI-TNM app. Classification accuracy was calculated separately for each subcategory (reinfection, tissue and implant condition, non-human cells, and morbidity of the patient). Fleiss’ kappa and Cohen’s kappa were calculated for interobserver and intraobserver reliability, respectively. Results. Overall, interobserver and intraobserver agreements were substantial across the 20 classified cases. Analyses for the variable ‘reinfection’ revealed an almost perfect interobserver and intraobserver agreement with a classification accuracy of 94.8%. The category 'tissue and implant conditions' showed moderate interobserver and substantial intraobserver reliability, while the classification accuracy was 70.8%. For 'non-human cells,' accuracy was 81.0% and interobserver agreement was moderate with an almost perfect intraobserver reliability. The classification accuracy of the variable 'morbidity of the patient' reached 73.5% with a moderate interobserver agreement, whereas the intraobserver agreement was substantial. The application of the app yielded comparable results across all subgroups. Conclusion. The PJI-TNM classification system captures the heterogeneity of PJI and can be applied with substantial inter- and intraobserver reliability. The PJI-TNM educational app aims to facilitate application in clinical practice. A major limitation was the correct assessment of the implant situation. To eliminate this, a re-evaluation according to intraoperative findings is strongly recommended. Cite this article: Bone Joint Res 2024;13(1):19–27

Aims. 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. Methods. 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. Results. At the time of the study, the CNN model showed an area under the receiver operating curve of 0.97. AI assistance improved the physician’s sensitivity (correct fracture detection) from 80% to 87%, and the specificity (correct fracture exclusion) from 91% to 95%. The overall error rate (combined false positive and false negative) was reduced from 14% without AI to 9% with AI. Conclusion. The use of a CNN model as a second opinion can improve the diagnostic accuracy of DRF detection in the study setting. Cite this article: Bone Joint Res 2024;13(10):588–595

Aims. Manual impaction, with a mallet and introducer, remains the standard method of installing cementless acetabular cups during total hip arthroplasty (THA). This study aims to quantify the accuracy and precision of manual impaction strikes during the seating of an acetabular component. This understanding aims to help improve impaction surgical techniques and inform the development of future technologies. Methods. Posterior approach THAs were carried out on three cadavers by an expert orthopaedic surgeon. An instrumented mallet and introducer were used to insert cementless acetabular cups. The motion of the mallet, relative to the introducer, was analyzed for a total of 110 strikes split into low-, medium-, and high-effort strikes. Three parameters were extracted from these data: strike vector, strike offset, and mallet face alignment. Results. The force vector of the mallet strike, relative to the introducer axis, was misaligned by an average of 18.1°, resulting in an average wasted strike energy of 6.1%. Furthermore, the mean strike offset was 19.8 mm from the centre of the introducer axis and the mallet face, relative to the introducer strike face, was misaligned by a mean angle of 15.2° from the introducer strike face. Conclusion. The direction of the impact vector in manual impaction lacks both accuracy and precision. There is an opportunity to improve this through more advanced impaction instruments or surgical training. Cite this article: Bone Joint Res 2024;13(4):193–200

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 diagnostic performances were calculated. The areas under the curve (AUCs) were compared using the z-test. Results. AGR, CAR, and CRP were associated with PJI (p < 0.001). Sensitivities were 62.5% (95% CI 54.3 to 70.0), 73.6% (95% CI 65.8 to 80.1), and 71.5% (95% CI 63.6 to 78.3), respectively. Specificities were calculated with 84.7% (95% CI 77.5 to 89.9), 86.3% (95% CI 79.2 to 91.2), and 87.8% (95% CI 80.9 to 92.4), respectively. The AUC of CRP (0.797 (95% CI 0.750 to 0.843)) was significantly higher than the AUC of AGR (0.736 (95% CI 0.686 to 0.786), p < 0.001), and similar to AUC of CAR (0.799 (95% CI 0.753 to 0.846), p = 0.832). Decreased sensitivities were observed in PJIs caused by low-virulence organisms (AGR: 60%, CAR: 78%) compared to high-virulence pathogens (AGR: 80%, p = 0.042; CAR: 88%, p = 0.158). Higher sensitivities were seen in acute haematogenous (AGR: 83%, CAR: 96%) compared to chronic PJIs (AGR: 54%, p = 0.001; CAR: 65%, p < 0.001). Conclusion. Serum AGR and CAR showed limited diagnostic accuracy (especially in low-grade and chronic infections) and did not outperform the established marker CRP in our study. Hence, neither parameter can be recommended as an additional tool for diagnosing PJI. Cite this article: Bone Joint Res 2024;13(8):372–382

Aims. The Oxford Shoulder Score (OSS) is a 12-item measure commonly used for the assessment of shoulder surgeries. This study explores whether computerized adaptive testing (CAT) provides a shortened, individually tailored questionnaire while maintaining test accuracy. Methods. A total of 16,238 preoperative OSS were available in the National Joint Registry (NJR) for England, Wales, Northern Ireland, the Isle of Man, and the States of Guernsey dataset (April 2012 to April 2022). Prior to CAT, the foundational item response theory (IRT) assumptions of unidimensionality, monotonicity, and local independence were established. CAT compared sequential item selection with stopping criteria set at standard error (SE) < 0.32 and SE < 0.45 (equivalent to reliability coefficients of 0.90 and 0.80) to full-length patient-reported outcome measure (PROM) precision. Results. Confirmatory factor analysis (CFA) for unidimensionality exhibited satisfactory fit with root mean square standardized residual (RSMSR) of 0.06 (cut-off ≤ 0.08) but not with comparative fit index (CFI) of 0.85 or Tucker-Lewis index (TLI) of 0.82 (cut-off > 0.90). Monotonicity, measured by H value, yielded 0.482, signifying good monotonic trends. Local independence was generally met, with Yen’s Q3 statistic > 0.2 for most items. The median item count for completing the CAT simulation with a SE of 0.32 was 3 (IQR 3 to 12), while for a SE of 0.45 it was 2 (IQR 2 to 6). This constituted only 25% and 16%, respectively, when compared to the 12-item full-length questionnaire. Conclusion. Calibrating IRT for the OSS has resulted in the development of an efficient and shortened CAT while maintaining accuracy and reliability. Through the reduction of redundant items and implementation of a standardized measurement scale, our study highlights a promising approach to alleviate time burden and potentially enhance compliance with these widely used outcome measures. Cite this article: Bone Joint Res 2024;13(8):392–400

Aims. To determine the major risk factors for unplanned reoperations (UROs) following corrective surgery for adult spinal deformity (ASD) and their interactions, using machine learning-based prediction algorithms and game theory. Methods. Patients who underwent surgery for ASD, with a minimum of two-year follow-up, were retrospectively reviewed. In total, 210 patients were included and randomly allocated into training (70% of the sample size) and test (the remaining 30%) sets to develop the machine learning algorithm. Risk factors were included in the analysis, along with clinical characteristics and parameters acquired through diagnostic radiology. Results. Overall, 152 patients without and 58 with a history of surgical revision following surgery for ASD were observed; the mean age was 68.9 years (SD 8.7) and 66.9 years (SD 6.6), respectively. On implementing a random forest model, the classification of URO events resulted in a balanced accuracy of 86.8%. Among machine learning-extracted risk factors, URO, proximal junction failure (PJF), and postoperative distance from the posterosuperior corner of C7 and the vertical axis from the centroid of C2 (SVA) were significant upon Kaplan-Meier survival analysis. Conclusion. The major risk factors for URO following surgery for ASD, i.e. postoperative SVA and PJF, and their interactions were identified using a machine learning algorithm and game theory. Clinical benefits will depend on patient risk profiles. Cite this article: Bone Joint Res 2023;12(4):245–255

Aims. An objective technological solution for tracking adherence to at-home shoulder physiotherapy is important for improving patient engagement and rehabilitation outcomes, but remains a significant challenge. The aim of this research was to evaluate performance of machine-learning (ML) methodologies for detecting and classifying inertial data collected during in-clinic and at-home shoulder physiotherapy exercise. Methods. A smartwatch was used to collect inertial data from 42 patients performing shoulder physiotherapy exercises for rotator cuff injuries in both in-clinic and at-home settings. A two-stage ML approach was used to detect out-of-distribution (OOD) data (to remove non-exercise data) and subsequently for classification of exercises. We evaluated the performance impact of grouping exercises by motion type, inclusion of non-exercise data for algorithm training, and a patient-specific approach to exercise classification. Algorithm performance was evaluated using both in-clinic and at-home data. Results. The patient-specific approach with engineered features achieved the highest in-clinic performance for differentiating physiotherapy exercise from non-exercise activity (area under the receiver operating characteristic (AUROC) = 0.924). Including non-exercise data in algorithm training further improved classifier performance (random forest, AUROC = 0.985). The highest accuracy achieved for classifying individual in-clinic exercises was 0.903, using a patient-specific method with deep neural network model extracted features. Grouping exercises by motion type improved exercise classification. For at-home data, OOD detection yielded similar performance with the non-exercise data in the algorithm training (fully convolutional network AUROC = 0.919). Conclusion. Including non-exercise data in algorithm training improves detection of exercises. A patient-specific approach leveraging data from earlier patient-supervised sessions should be considered but is highly dependent on per-patient data quality. Cite this article: Bone Joint Res 2023;12(3):165–177

Aims. This study aimed to develop and validate a fully automated system that quantifies proximal femoral bone mineral density (BMD) from CT images. Methods. The study analyzed 978 pairs of hip CT and dual-energy X-ray absorptiometry (DXA) measurements of the proximal femur (DXA-BMD) collected from three institutions. From the CT images, the femur and a calibration phantom were automatically segmented using previously trained deep-learning models. The Hounsfield units of each voxel were converted into density (mg/cm. 3. ). Then, a deep-learning model trained by manual landmark selection of 315 cases was developed to select the landmarks at the proximal femur to rotate the CT volume to the neutral position. Finally, the CT volume of the femur was projected onto the coronal plane, and the areal BMD of the proximal femur (CT-aBMD) was quantified. CT-aBMD correlated to DXA-BMD, and a receiver operating characteristic (ROC) analysis quantified the accuracy in diagnosing osteoporosis. Results. CT-aBMD was successfully measured in 976/978 hips (99.8%). A significant correlation was found between CT-aBMD and DXA-BMD (r = 0.941; p < 0.001). In the ROC analysis, the area under the curve to diagnose osteoporosis was 0.976. The diagnostic sensitivity and specificity were 88.9% and 96%, respectively, with the cutoff set at 0.625 g/cm. 2. . Conclusion. Accurate DXA-BMD measurements and diagnosis of osteoporosis were performed from CT images using the system developed herein. As the models are open-source, clinicians can use the proposed system to screen osteoporosis and determine the surgical strategy for hip surgery. Cite this article: Bone Joint Res 2023;12(9):590–597

Aims. The use of 3D printing has become increasingly popular and has been widely used in orthopaedic surgery. There has been a trend towards an increasing number of publications in this field, but existing literature incorporates limited high-quality studies, and there is a lack of reports on outcomes. The aim of this study was to perform a scoping review with Level I evidence on the application and effectiveness of 3D printing. Methods. A literature search was performed in PubMed, Embase, and Web of Science databases. The keywords used for the search criteria were ((3d print*) OR (rapid prototyp*) OR (additive manufactur*)) AND (orthopaedic). The inclusion criteria were: 1) use of 3D printing in orthopaedics, 2) randomized controlled trials, and 3) studies with participants/patients. Risk of bias was assessed with Cochrane Collaboration Tool and PEDro Score. Pooled analysis was performed. Results. Overall, 21 studies were included in our study with a pooled total of 932 participants. Pooled analysis showed that operating time (p < 0.001), blood loss (p < 0.001), fluoroscopy times (p < 0.001), bone union time (p < 0.001), pain (p = 0.040), accuracy (p < 0.001), and functional scores (p < 0.001) were significantly improved with 3D printing compared to the control group. There were no significant differences in complications. Conclusion. 3D printing is a rapidly developing field in orthopaedics. Our findings show that 3D printing is advantageous in terms of operating time, blood loss, fluoroscopy times, bone union time, pain, accuracy, and function. The use of 3D printing did not increase the risk of complications. Cite this article: Bone Joint Res 2021;10(12):807–819

Aims. We aimed to assess the reliability and validity of OpenPose, a posture estimation algorithm, for measurement of knee range of motion after total knee arthroplasty (TKA), in comparison to radiography and goniometry. Methods. In this prospective observational study, we analyzed 35 primary TKAs (24 patients) for knee osteoarthritis. We measured the knee angles in flexion and extension using OpenPose, radiography, and goniometry. We assessed the test-retest reliability of each method using intraclass correlation coefficient (1,1). We evaluated the ability to estimate other measurement values from the OpenPose value using linear regression analysis. We used intraclass correlation coefficients (2,1) and Bland–Altman analyses to evaluate the agreement and error between radiography and the other measurements. Results. OpenPose had excellent test-retest reliability (intraclass correlation coefficient (1,1) = 1.000). The R. 2. of all regression models indicated large correlations (0.747 to 0.927). In the flexion position, the intraclass correlation coefficients (2,1) of OpenPose indicated excellent agreement (0.953) with radiography. In the extension position, the intraclass correlation coefficients (2,1) indicated good agreement of OpenPose and radiography (0.815) and moderate agreement of goniometry with radiography (0.593). OpenPose had no systematic error in the flexion position, and a 2.3° fixed error in the extension position, compared to radiography. Conclusion. OpenPose is a reliable and valid tool for measuring flexion and extension positions after TKA. It has better accuracy than goniometry, especially in the extension position. Accurate measurement values can be obtained with low error, high reproducibility, and no contact, independent of the examiner’s skills. Cite this article: Bone Joint Res 2023;12(5):313–320

Aims. This study aimed to explore the diagnostic value of synovial fluid neutrophil extracellular traps (SF-NETs) in periprosthetic joint infection (PJI) diagnosis, and compare it with that of microbial culture, serum ESR and CRP, synovial white blood cell (WBC) count, and polymorphonuclear neutrophil percentage (PMN%). Methods. In a single health centre, patients with suspected PJI were enrolled from January 2013 to December 2021. The inclusion criteria were: 1) patients who were suspected to have PJI; 2) patients with complete medical records; and 3) patients from whom sufficient synovial fluid was obtained for microbial culture and NET test. Patients who received revision surgeries due to aseptic failure (AF) were selected as controls. Synovial fluid was collected for microbial culture and SF-WBC, SF-PNM%, and SF-NET detection. The receiver operating characteristic curve (ROC) of synovial NET, WBC, PMN%, and area under the curve (AUC) were obtained; the diagnostic efficacies of these diagnostic indexes were calculated and compared. Results. The levels of SF-NETs in the PJI group were significantly higher than those of the AF group. The AUC of SF-NET was 0.971 (95% confidence interval (CI) 0.903 to 0.996), the sensitivity was 93.48% (95% CI 82.10% to 98.63%), the specificity was 96.43% (95% CI 81.65% to 99.91%), the accuracy was 94.60% (95% CI 86.73% to 98.50%), the positive predictive value was 97.73%, and the negative predictive value was 90%. Further analysis showed that SF-NET could improve the diagnosis of culture-negative PJI, patients with PJI who received antibiotic treatment preoperatively, and fungal PJI. Conclusion. SF-NET is a novel and ideal synovial fluid biomarker for PJI diagnosis, which could improve PJI diagnosis greatly. Cite this article: Bone Joint Res 2023;12(2):113–120

Objectives. The aim of this study was to assess the clinical application of, and optimize the variables used in, the BACH classification of long-bone osteomyelitis. Methods. A total of 30 clinicians from a variety of specialities classified 20 anonymized cases of long-bone osteomyelitis using BACH. Cases were derived from patients who presented to specialist centres in the United Kingdom between October 2016 and April 2017. Accuracy and Fleiss’ kappa (Fκ) were calculated for each variable. Bone involvement (B-variable) was assessed further by nine clinicians who classified ten additional cases of long bone osteomyelitis using a 3D clinical imaging package. Thresholds for defining multidrug-resistant (MDR) isolates were optimized using results from a further analysis of 253 long bone osteomyelitis cases. Results. The B-variable had a classification accuracy of 77.0%, which improved to 95.7% when using a 3D clinical imaging package (p < 0.01). The A-variable demonstrated difficulty in the accuracy of classification for increasingly resistant isolates (A1 (non-resistant), 94.4%; A2 (MDR), 46.7%; A3 (extensively or pan-drug-resistant), 10.0%). Further analysis demonstrated that isolates with four or more resistant test results or less than 80% sensitive susceptibility test results had a 98.1% (95% confidence interval (CI) 96.6 to 99.6) and 98.8% (95% CI 98.1 to 100.0) correlation with MDR status, respectively. The coverage of the soft tissues (C-variable) and the host status (H-variable) both had a substantial agreement between users and a classification accuracy of 92.5% and 91.2%, respectively. Conclusions. The BACH classification system can be applied accurately by users with a variety of clinical backgrounds. Accuracy of B-classification was improved using 3D imaging. The use of the A-variable has been optimized based on susceptibility testing results. Cite this article: A. J. Hotchen, M. Dudareva, J. Y. Ferguson, P. Sendi, M. A. McNally. The BACH classification of long bone osteomyelitis. Bone Joint Res 2019;8:459–468. DOI: 10.1302/2046-3758.810.BJR-2019-0050.R1

Aims. This study aimed to evaluate the accuracy of implant placement with robotic-arm assisted total hip arthroplasty (THA) in patients with developmental dysplasia of the hip (DDH). Methods. The study analyzed a consecutive series of 69 patients who underwent robotic-arm assisted THA between September 2018 and December 2019. Of these, 30 patients had DDH and were classified according to the Crowe type. Acetabular component alignment and 3D positions were measured using pre- and postoperative CT data. The absolute differences of cup alignment and 3D position were compared between DDH and non-DDH patients. Moreover, these differences were analyzed in relation to the severity of DDH. The discrepancy of leg length and combined offset compared with contralateral hip were measured. Results. The mean values of absolute differences (postoperative CT-preoperative plan) were 1.7° (standard deviation (SD) 2.0) (inclination) and 2.5° (SD 2.1°) (anteversion) in DDH patients, and no significant differences were found between non-DDH and DDH patients. The mean absolute differences for 3D cup position were 1.1 mm (SD 1.0) (coronal plane) and 1.2 mm (SD 2.1) (axial plane) in DDH patients, and no significant differences were found between two groups. No significant difference was found either in cup alignment between postoperative CT and navigation record after cup screws or in the severity of DDH. Excellent restoration of leg length and combined offset were achieved in both groups. Conclusion. We demonstrated that robotic-assisted THA may achieve precise cup positioning in DDH patients, and may be useful in those with severe DDH. Cite this article: Bone Joint Res 2021;10(10):629–638