Abstract. Robotic-assisted total knee arthroplasty (TKA) has proven higher accuracy, fewer alignment outliers, and improved short-term clinical outcomes when compared to conventional TKA. However, evidence of cost-effectiveness and individual superiority of one system over another is the subject of further research. Despite its growing adoption rate, published results are still limited and comparative studies are scarce. This review compares characteristics and performance of five currently available systems, focusing on the information and feedback each system provides to the surgeon, what the systems allow the surgeon to modify during the operation, and how each system then aids execution of the surgical plan. Cite this article: Bone Jt Open 2023;4(1):13–18

Aims. The aims of this systematic review were to assess the learning curve of semi-active robotic arm-assisted total hip arthroplasty (rTHA), and to compare the accuracy, patient-reported functional outcomes, complications, and survivorship between rTHA and manual total hip arthroplasty (mTHA). Methods. Searches of PubMed, Medline, and Google Scholar were performed in April 2020 in line with the Preferred Reporting Items for Systematic Review and Meta-Analysis statement. Search terms included “robotic”, “hip”, and “arthroplasty”. The criteria for inclusion were published clinical research articles reporting the learning curve for rTHA (robotic arm-assisted only) and those comparing the implantation accuracy, functional outcomes, survivorship, or complications with mTHA. Results. There were 501 articles initially identified from databases and references. Following full text screening, 17 articles that satisfied the inclusion criteria were included. Four studies reported the learning curve of rTHA, 13 studies reported on implant positioning, five on functional outcomes, ten on complications, and four on survivorship. The meta-analysis showed a significantly greater number of cases of acetabular component placement in the safe zone compared with the mTHA group (95% confidence interval (CI) 4.10 to 7.94; p < 0.001) and that rTHA resulted in a significantly better Harris Hip Score compared to mTHA in the short- to mid-term follow-up (95% CI 0.46 to 5.64; p = 0.020). However, there was no difference in infection rates, dislocation rates, overall complication rates, and survival rates at short-term follow-up. Conclusion. The learning curve of rTHA was between 12 and 35 cases, which was dependent on the assessment goal, such as operating time, accuracy, and team working. Robotic arm-assisted total hip arthroplasty was associated with improved accuracy of component positioning and functional outcome, however no difference in complication rates or survival were observed at short- to mid-term follow-up. Overall, there remains an absence of high-quality level I evidence and cost analysis comparing rTHA and mTHA. Cite this article: Bone Joint J 2021;103-B(6):1009–1020

Aims. The purpose of this study was to develop a personalized outcome prediction tool, to be used with knee arthroplasty patients, that predicts outcomes (lengths of stay (LOS), 90 day readmission, and one-year patient-reported outcome measures (PROMs) on an individual basis and allows for dynamic modifiable risk factors. Methods. Data were prospectively collected on all patients who underwent total or unicompartmental knee arthroplasty at a between July 2015 and June 2018. Cohort 1 (n = 5,958) was utilized to develop models for LOS and 90 day readmission. Cohort 2 (n = 2,391, surgery date 2015 to 2017) was utilized to develop models for one-year improvements in Knee Injury and Osteoarthritis Outcome Score (KOOS) pain score, KOOS function score, and KOOS quality of life (QOL) score. Model accuracies within the imputed data set were assessed through cross-validation with root mean square errors (RMSEs) and mean absolute errors (MAEs) for the LOS and PROMs models, and the index of prediction accuracy (IPA), and area under the curve (AUC) for the readmission models. Model accuracies in new patient data sets were assessed with AUC. Results. Within the imputed datasets, the LOS (RMSE 1.161) and PROMs models (RMSE 15.775, 11.056, 21.680 for KOOS pain, function, and QOL, respectively) demonstrated good accuracy. For all models, the accuracy of predicting outcomes in a new set of patients were consistent with the cross-validation accuracy overall. Upon validation with a new patient dataset, the LOS and readmission models demonstrated high accuracy (71.5% and 65.0%, respectively). Similarly, the one-year PROMs improvement models demonstrated high accuracy in predicting ten-point improvements in KOOS pain (72.1%), function (72.9%), and QOL (70.8%) scores. Conclusion. The data-driven models developed in this study offer scalable predictive tools that can accurately estimate the likelihood of improved pain, function, and quality of life one year after knee arthroplasty as well as LOS and 90 day readmission. Cite this article: Bone Joint J 2020;102-B(9):1183–1193

The February 2023 Spine Roundup. 360. looks at: S2AI screws: At what cost?; Just how good is spinal deformity surgery?; Is 80 years of age too late in the day for spine surgery?; Factors affecting the accuracy of pedicle screw placement in robot-assisted surgery; Factors causing delay in discharge in patients eligible for ambulatory lumbar fusion surgery; Anterior cervical discectomy or fusion and selective laminoplasty for cervical spondylotic myelopathy; Surgery for cervical radiculopathy: what is the complication burden?; Hypercholesterolemia and neck pain; Return to work after surgery for cervical radiculopathy: a nationwide registry-based observational study

Aims. Ultrasound (US)-guided injections are widely used in patients with conditions of the shoulder in order to improve their accuracy. However, the clinical efficacy of US-guided injections compared with blind injections remains controversial. The aim of this study was to compare the accuracy and efficacy of US-guided compared with blind corticosteroid injections into the glenohumeral joint in patients with primary frozen shoulder (FS). Methods. Intra-articular corticosteroid injections were administered to 90 patients primary FS, who were randomly assigned to either an US-guided (n = 45) or a blind technique (n = 45), by a shoulder specialist. Immediately after injection, fluoroscopic images were obtained to assess the accuracy of the injection. The outcome was assessed using a visual analogue scale (VAS) for pain, the American Shoulder and Elbow Surgeons (ASES) score, the subjective shoulder value (SSV) and range of movement (ROM) for all patients at the time of presentation and at three, six, and 12 weeks after injection. Results. The accuracy of injection in the US and blind groups was 100% (45/45) and 71.1% (32/45), respectively; this difference was significant (p < 0.001). Both groups had significant improvements in VAS pain score, ASES score, SSV, forward flexion, abduction, external rotation, and internal rotation throughout follow-up until 12 weeks after injection (all p < 0.001). There were no significant differences between the VAS pain scores, the ASES score, the SSV and all ROMs between the two groups at the time points assessed (all p > 0.05). No injection-related adverse effects were noted in either group. Conclusion. We found no significant differences in pain and functional outcomes between the two groups, although an US-guided injection was associated with greater accuracy. Considering that it is both costly and time-consuming, an US-guided intra-articular injection of corticosteroid seems not always to be necessary in the treatment of FS as it gives similar outcomes as a blind injection. Cite this article: Bone Joint J 2021;103-B(2):353–359

The October 2023 Oncology Roundup. 360. looks at: Are pathological fractures in patients with osteosarcoma associated with worse survival outcomes?; Spotting the difference: how secondary osteosarcoma manifests in retinoblastoma survivors versus conventional cases; Accuracy of MRI scans in predicting intra-articular joint involvement in high-grade sarcomas around the knee; Endoprosthetic reconstruction for lower extremity soft-tissue sarcomas with bone involvement; Local relapse of soft-tissue sarcoma of the extremities or trunk wall operated on with wide margins without radiation therapy; 3D-printed, custom-made prostheses in patients who had resection of tumours of the hand and foot; Long-term follow-up for low-grade chondrosarcoma; Evaluation of local recurrence and diagnostic discordance in chondrosarcoma patients undergoing preoperative biopsy; Radiological scoring and resection grade for intraosseous chondrosarcoma

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. The sensitivity and specificity of electrodiagnostic parameters in diagnosing carpal tunnel syndrome (CTS) have been reported differently, and this study aims to address this gap. Methods. This case-control study was conducted on 57 cases with CTS and 58 controls without complaints, such as pain or paresthesia on the median nerve. The main assessed electrodiagnostic parameters were terminal latency index (TLI), residual latency (RL), median ulnar F-wave latency difference (FdifMU), and median sensory latency-ulnar motor latency difference (MSUMLD). Results. The mean age in cases and controls were 50.7 years (SD 9.9) and 47.9 years (SD 12.1), respectively. The CTS severity was mild in 20 patients (34.4%), moderate in 19 patients (32.8%), and severe in 19 patients (32.8%). The sensitivity and specificity of the electrodiagnostic parameters in diagnosing CTS were as follows: TLI 75.4% and 87.8%; RL 85.9% and 82.5%; FdifMU 87.9% and 82.9%; and MSUMLD 94.8% and 60.0%, respectively. Conclusion. Our findings indicated that electrodiagnostic parameters are significantly associated with the clinical manifestation of CTS, and are associated with high diagnostic accuracy in CTS diagnosis. However, further studies are required to highlight the role of electrodiagnostic parameters and their combination in CTS detection. Cite this article: Bone Jt Open 2024;5(10):898–903

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. Disorders of bone integrity carry a high global disease burden, frequently requiring intervention, but there is a paucity of methods capable of noninvasive real-time assessment. Here we show that miniaturized handheld near-infrared spectroscopy (NIRS) scans, operated via a smartphone, can assess structural human bone properties in under three seconds. Methods. A hand-held NIR spectrometer was used to scan bone samples from 20 patients and predict: bone volume fraction (BV/TV); and trabecular (Tb) and cortical (Ct) thickness (Th), porosity (Po), and spacing (Sp). Results. NIRS scans on both the inner (trabecular) surface or outer (cortical) surface accurately identified variations in bone collagen, water, mineral, and fat content, which then accurately predicted bone volume fraction (BV/TV, inner R. 2. = 0.91, outer R. 2. = 0.83), thickness (Tb.Th, inner R. 2. = 0.9, outer R. 2. = 0.79), and cortical thickness (Ct.Th, inner and outer both R. 2. = 0.90). NIRS scans also had 100% classification accuracy in grading the quartile of bone thickness and quality. Conclusion. We believe this is a fundamental step forward in creating an instrument capable of intraoperative real-time use. Cite this article: Bone Jt Open 2023;4(4):250–261

The February 2024 Foot & Ankle Roundup. 360. looks at: Survival of revision ankle arthroplasty; Tibiotalocalcaneal nail for the management of open ankle fractures in the elderly patient; Accuracy of a patient-specific total ankle arthroplasty instrumentation; Fusion after failed primary ankle arthroplasty: can it work?; Treatment options for osteochondral lesions of the talus; Managing hair tourniquet syndrome of toe: a rare emergency; Ultrasound-guided collagenase therapy for recurrent plantar fibromatosis: a promising line of therapy?

Telehealth has the potential to change the way we approach patient care. From virtual consenting to reducing carbon emissions, costs, and waiting times, it is a powerful tool in our clinical armamentarium. There is mounting evidence that remote diagnostic evaluation and decision-making have reached an acceptable level of accuracy and can safely be adopted in orthopaedic surgery. Furthermore, patients’ and surgeons’ satisfaction with virtual appointments are comparable to in-person consultations. Challenges to the widespread use of telehealth should, however, be acknowledged and include the cost of installation, training, maintenance, and accessibility. It is also vital that clinicians are conscious of the medicolegal and ethical considerations surrounding the medium and adhere strictly to the relevant data protection legislation and storage framework. It remains to be seen how organizations harness the full spectrum of the technology to facilitate effective patient care. Cite this article: Bone Joint J 2023;105-B(8):843–849

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. The aim of this study was to evaluate the diagnostic accuracy of the absolute synovial polymorphonuclear neutrophil cell (PMN) count for the diagnosis or exclusion of periprosthetic joint infection (PJI) after total hip (THA) or knee arthroplasty (TKA). Methods. In this retrospective cohort study, 147 consecutive patients with acute or chronic complaints following THA and TKA were included. Diagnosis of PJI was established based on the 2018 International Consensus Meeting criteria. A total of 39 patients diagnosed with PJI (32 chronic and seven acute) and 108 patients with aseptic complications were surgically revised. Results. Using receiver operating characteristic curves and calculating the area under the curve (AUC), an optimal synovial cut-off value of 2,000 PMN/µl was determined (AUC 0.978 (95% confidence interval (CI) 0.946 to 1)). Using this cut-off, sensitivity and specificity of absolute synovial PMN count for PJI were 97.4% (95% CI 91.2 to 100) and 93.5% (95% CI 88.9 to 98.1), respectively. Positive and negative predictive value were 84.4% (95% CI 72.7 to 93.9) and 99.0% (95% CI 96.7 to 100), respectively. Exclusion of 20 patients with acute complications improved specificity to 97.9% (95% CI 94.6 to 100). Different cut-off values for THA (< 3,600 PMN/µl) and TKA (< 2,000 PMN/µl) were identified. Absolute synovial PMN count correlated strongly with synovial alpha-defensin (AD) (r = 0.759; p < 0.001). With a positive AD result, no additional PJI could be identified in any case. Conclusion. Absolute synovial PMN count is a widely available, rapid, cost-effective, and accurate marker in PJI diagnostics, whereas synovial AD appears to be a surrogate parameter of absolute synovial PMN count. Despite limitations in the early postoperative phase, wear, and rheumatic diseases in confirming PJI, an absolute synovial PMN count below 2,000/µl is highly suitable for ruling out PJI, with specific cut-off values for THA and TKA. Cite this article: Bone Joint J 2023;105-B(4):373–381

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

Acute bone and joint infections in children are serious, and misdiagnosis can threaten limb and life. Most young children who present acutely with pain, limping, and/or loss of function have transient synovitis, which will resolve spontaneously within a few days. A minority will have a bone or joint infection. Clinicians are faced with a diagnostic challenge: children with transient synovitis can safely be sent home, but children with bone and joint infection require urgent treatment to avoid complications. Clinicians often respond to this challenge by using a series of rudimentary decision support tools, based on clinical, haematological, and biochemical parameters, to differentiate childhood osteoarticular infection from other diagnoses. However, these tools were developed without methodological expertise in diagnostic accuracy and do not consider the importance of imaging (ultrasound scan and MRI). There is wide variation in clinical practice with regard to the indications, choice, sequence, and timing of imaging. This variation is most likely due to the lack of evidence concerning the role of imaging in acute bone and joint infection in children. We describe the first steps of a large UK multicentre study, funded by the National Institute for Health Research, which seeks to integrate definitively the role of imaging into a decision support tool, developed with the assistance of individuals with expertise in the development of clinical prediction tools. Cite this article: Bone Joint J 2023;105-B(3):227–229

Aims. Histology is widely used for diagnosis of persistent infection during reimplantation in two-stage revision hip and knee arthroplasty, although data on its utility remain scarce. Therefore, this study aims to assess the predictive value of permanent sections at reimplantation in relation to reinfection risk, and to compare results of permanent and frozen sections. Methods. We retrospectively collected data from 226 patients (90 hips, 136 knees) with periprosthetic joint infection who underwent two-stage revision between August 2011 and September 2021, with a minimum follow-up of one year. Histology was assessed via the SLIM classification. First, we analyzed whether patients with positive permanent sections at reimplantation had higher reinfection rates than patients with negative histology. Further, we compared permanent and frozen section results, and assessed the influence of anatomical regions (knee versus hip), low- versus high-grade infections, as well as first revision versus multiple prior revisions on the histological result at reimplantation. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), chi-squared tests, and Kaplan-Meier estimates were calculated. Results. Overall, the reinfection rate was 18%. A total of 14 out of 82 patients (17%) with positive permanent sections at reimplantation experienced reinfection, compared to 26 of 144 patients (18%) with negative results (p = 0.996). Neither permanent sections nor fresh frozen sections were significantly associated with reinfection, with a sensitivity of 0.35, specificity of 0.63, PPV of 0.17, NPV of 0.81, and accuracy of 58%. Histology was not significantly associated with reinfection or survival time for any of the analyzed sub-groups. Permanent and frozen section results were in agreement for 91% of cases. Conclusion. Permanent and fresh frozen sections at reimplantation in two-stage revision do not serve as a reliable predictor for reinfection. Cite this article: Bone Joint J 2024;106-B(4):372–379

Aims. To identify variables independently associated with same-day discharge (SDD) of patients following revision total knee arthroplasty (rTKA) and to develop machine learning algorithms to predict suitable candidates for outpatient rTKA. Methods. Data were obtained from the American College of Surgeons National Quality Improvement Programme (ACS-NSQIP) database from the years 2018 to 2020. Patients with elective, unilateral rTKA procedures and a total hospital length of stay between zero and four days were included. Demographic, preoperative, and intraoperative variables were analyzed. A multivariable logistic regression (MLR) model and various machine learning techniques were compared using area under the curve (AUC), calibration, and decision curve analysis. Important and significant variables were identified from the models. Results. Of the 5,600 patients included in this study, 342 (6.1%) underwent SDD. The random forest (RF) model performed the best overall, with an internally validated AUC of 0.810. The ten crucial factors favoring SDD in the RF model include operating time, anaesthesia type, age, BMI, American Society of Anesthesiologists grade, race, history of diabetes, rTKA type, sex, and smoking status. Eight of these variables were also found to be significant in the MLR model. Conclusion. The RF model displayed excellent accuracy and identified clinically important variables for determining candidates for SDD following rTKA. Machine learning techniques such as RF will allow clinicians to accurately risk-stratify their patients preoperatively, in order to optimize resources and improve patient outcomes. Cite this article: Bone Jt Open 2023;4(6):399–407

Aims. Conventional patient-reported surveys, used for patients undergoing total hip arthroplasty (THA), are limited by subjectivity and recall bias. Objective functional evaluation, such as gait analysis, to delineate a patient’s functional capacity and customize surgical interventions, may address these shortcomings. This systematic review endeavours to investigate the application of objective functional assessments in appraising individuals undergoing THA. Methods. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were applied. Eligible studies of THA patients that conducted at least one type of objective functional assessment both pre- and postoperatively were identified through Embase, Medline/PubMed, and Cochrane Central database-searching from inception to 15 September 2023. The assessments included were subgrouped for analysis: gait analysis, motion analysis, wearables, and strength tests. Results. A total of 130 studies using 15 distinct objective functional assessment methods (FAMs) were identified. The most frequently used method was instrumented gait/motion analysis, followed by the Timed-Up-and-Go test (TUG), 6 minute walk test, timed stair climbing test, and various strength tests. These assessments were characterized by their diagnostic precision and applicability to daily activities. Wearables were frequently used, offering cost-effectiveness and remote monitoring benefits. However, their accuracy and potential discomfort for patients must be considered. Conclusion. The integration of objective functional assessments in THA presents promise as a progress-tracking modality for improving patient outcomes. Gait analysis and the TUG, along with advancing wearable sensor technology, have the potential to enhance patient care, surgical planning, and rehabilitation. Cite this article: Bone Joint J 2024;106-B(8):764–774