Aims. Custom triflange acetabular components (CTACs) play an important role in reconstructive orthopaedic surgery, particularly in revision total hip arthroplasty (rTHA) and pelvic tumour resection procedures. Accurate CTAC positioning is essential to successful surgical outcomes. While prior studies have explored CTAC positioning in rTHA, research focusing on tumour cases and implant flange positioning precision remains limited. Additionally, the impact of intraoperative navigation on positioning accuracy warrants further investigation. This study assesses CTAC positioning accuracy in tumour resection and rTHA cases, focusing on the differences between preoperative planning and postoperative implant positions. Methods. A multicentre observational cohort study in Australia between February 2017 and March 2021 included consecutive patients undergoing acetabular reconstruction with CTACs in rTHA (Paprosky 3A/3B defects) or tumour resection (including Enneking P2 peri-acetabular area). Of 103 eligible patients (104 hips), 34 patients (35 hips) were analyzed. Results. CTAC positioning was generally accurate, with minor deviations in cup inclination (mean 2.7°; SD 2.84°), anteversion (mean 3.6°; SD 5.04°), and rotation (mean 2.1°; SD 2.47°). Deviation of the hip centre of rotation (COR) showed a mean vector length of 5.9 mm (SD 7.24). Flange positions showed small deviations, with the ischial flange exhibiting the largest deviation (mean vector length of 7.0 mm; SD 8.65). Overall, 83% of the implants were accurately positioned, with 17% exceeding malpositioning thresholds. CTACs used in tumour resections exhibited higher positioning accuracy than rTHA cases, with significant differences in inclination (1.5° for tumour vs 3.4° for rTHA) and rotation (1.3° for tumour vs 2.4° for rTHA). The use of intraoperative navigation appeared to enhance positioning accuracy, but this did not reach statistical significance. Conclusion. This study demonstrates favourable CTAC positioning accuracy, with potential for improved accuracy through intraoperative navigation. Further research is needed to understand the implications of positioning accuracy on implant performance and long-term survival. Cite this article: Bone Jt Open 2024;5(4):260–268

Aims. Navigation devices are designed to improve a surgeon’s accuracy in positioning the acetabular and femoral components in total hip arthroplasty (THA). The purpose of this study was to both evaluate the accuracy of an optical computer-assisted surgery (CAS) navigation system and determine whether preoperative spinopelvic mobility (categorized as hypermobile, normal, or stiff) increased the risk of acetabular component placement error. Methods. A total of 356 patients undergoing primary THA were prospectively enrolled from November 2016 to March 2018. Clinically relevant error using the CAS system was defined as a difference of > 5° between CAS and 3D radiological reconstruction measurements for acetabular component inclination and anteversion. Univariate and multiple logistic regression analyses were conducted to determine whether hypermobile (. Δ. sacral slope(SS). stand-sit. > 30°), or stiff (. ∆. SS. stand-sit. < 10°) spinopelvic mobility contributed to increased error rates. Results. The paired absolute difference between CAS and postoperative imaging measurements was 2.3° (standard deviation (SD) 2.6°) for inclination and 3.1° (SD 4.2°) for anteversion. Using a target zone of 40° (± 10°) (inclination) and 20° (± 10°) (anteversion), postoperative standing radiographs measured 96% of acetabular components within the target zone for both inclination and anteversion. Multiple logistic regression analysis controlling for BMI and sex revealed that hypermobile spinopelvic mobility significantly increased error rates for anteversion (odds ratio (OR) 2.48, p = 0.009) and inclination (OR 2.44, p = 0.016), whereas stiff spinopelvic mobility increased error rates for anteversion (OR 1.97, p = 0.028). There were no dislocations at a minimum three-year follow-up. Conclusion. Despite high reliability in acetabular positioning for inclination in a large patient cohort using an optical CAS system, hypermobile and stiff spinopelvic mobility significantly increased the risk of clinically relevant errors. In patients with abnormal spinopelvic mobility, CAS systems should be adjusted for use to avoid acetabular component misalignment and subsequent risk for long-term dislocation. Cite this article: Bone Jt Open 2022;3(6):475–484

Aims. National joint registries under-report revisions for periprosthetic joint infection (PJI). We aimed to validate PJI reporting to the Australian Orthopaedic Association National Joint Arthroplasty Registry (AOANJRR) and the factors associated with its accuracy. We then applied these data to refine estimates of the total national burden of PJI. Methods. A total of 561 Australian cases of confirmed PJI were captured by a large, prospective observational study, and matched to data available for the same patients through the AOANJRR. Results. In all, 501 (89.3%) cases of PJI recruited to the prospective observational study were successfully matched with the AOANJRR database. Of these, 376 (75.0%) were captured by the registry, while 125 (25.0%) did not have a revision or reoperation for PJI recorded. In a multivariate logistic regression analysis, early (within 30 days of implantation) PJIs were less likely to be reported (adjusted odds ratio (OR) 0.56; 95% confidence interval (CI) 0.34 to 0.93; p = 0.020), while two-stage revision procedures were more likely to be reported as a PJI to the registry (OR 5.3 (95% CI 2.37 to 14.0); p ≤ 0.001) than debridement and implant retention or other surgical procedures. Based on this data, the true estimate of the incidence of PJI in Australia is up to 3,900 cases per year. Conclusion. In Australia, infection was not recorded as the indication for revision or reoperation in one-quarter of those with confirmed PJI. This is better than in other registries, but suggests that registry-captured estimates of the total national burden of PJI are underestimated by at least one-third. Inconsistent PJI reporting is multifactorial but could be improved by developing a nested PJI registry embedded within the national arthroplasty registry. Cite this article: Bone Jt Open 2022;3(5):367–373

Aims. Precise implant positioning, tailored to individual spinopelvic biomechanics and phenotype, is paramount for stability in total hip arthroplasty (THA). Despite a few studies on instability prediction, there is a notable gap in research utilizing artificial intelligence (AI). The objective of our pilot study was to evaluate the feasibility of developing an AI algorithm tailored to individual spinopelvic mechanics and patient phenotype for predicting impingement. Methods. This international, multicentre prospective cohort study across two centres encompassed 157 adults undergoing primary robotic arm-assisted THA. Impingement during specific flexion and extension stances was identified using the virtual range of motion (ROM) tool of the robotic software. The primary AI model, the Light Gradient-Boosting Machine (LGBM), used tabular data to predict impingement presence, direction (flexion or extension), and type. A secondary model integrating tabular data with plain anteroposterior pelvis radiographs was evaluated to assess for any potential enhancement in prediction accuracy. Results. We identified nine predictors from an analysis of baseline spinopelvic characteristics and surgical planning parameters. Using fivefold cross-validation, the LGBM achieved 70.2% impingement prediction accuracy. With impingement data, the LGBM estimated direction with 85% accuracy, while the support vector machine (SVM) determined impingement type with 72.9% accuracy. After integrating imaging data with a multilayer perceptron (tabular) and a convolutional neural network (radiograph), the LGBM’s prediction was 68.1%. Both combined and LGBM-only had similar impingement direction prediction rates (around 84.5%). Conclusion. This study is a pioneering effort in leveraging AI for impingement prediction in THA, utilizing a comprehensive, real-world clinical dataset. Our machine-learning algorithm demonstrated promising accuracy in predicting impingement, its type, and direction. While the addition of imaging data to our deep-learning algorithm did not boost accuracy, the potential for refined annotations, such as landmark markings, offers avenues for future enhancement. Prior to clinical integration, external validation and larger-scale testing of this algorithm are essential. Cite this article: Bone Jt Open 2024;5(8):671–680

Aims. While internet search engines have been the primary information source for patients’ questions, artificial intelligence large language models like ChatGPT are trending towards becoming the new primary source. The purpose of this study was to determine if ChatGPT can answer patient questions about total hip (THA) and knee arthroplasty (TKA) with consistent accuracy, comprehensiveness, and easy readability. Methods. We posed the 20 most Google-searched questions about THA and TKA, plus ten additional postoperative questions, to ChatGPT. Each question was asked twice to evaluate for consistency in quality. Following each response, we responded with, “Please explain so it is easier to understand,” to evaluate ChatGPT’s ability to reduce response reading grade level, measured as Flesch-Kincaid Grade Level (FKGL). Five resident physicians rated the 120 responses on 1 to 5 accuracy and comprehensiveness scales. Additionally, they answered a “yes” or “no” question regarding acceptability. Mean scores were calculated for each question, and responses were deemed acceptable if ≥ four raters answered “yes.”. Results. The mean accuracy and comprehensiveness scores were 4.26 (95% confidence interval (CI) 4.19 to 4.33) and 3.79 (95% CI 3.69 to 3.89), respectively. Out of all the responses, 59.2% (71/120; 95% CI 50.0% to 67.7%) were acceptable. ChatGPT was consistent when asked the same question twice, giving no significant difference in accuracy (t = 0.821; p = 0.415), comprehensiveness (t = 1.387; p = 0.171), acceptability (χ. 2. = 1.832; p = 0.176), and FKGL (t = 0.264; p = 0.793). There was a significantly lower FKGL (t = 2.204; p = 0.029) for easier responses (11.14; 95% CI 10.57 to 11.71) than original responses (12.15; 95% CI 11.45 to 12.85). Conclusion. ChatGPT answered THA and TKA patient questions with accuracy comparable to previous reports of websites, with adequate comprehensiveness, but with limited acceptability as the sole information source. ChatGPT has potential for answering patient questions about THA and TKA, but needs improvement. Cite this article: Bone Jt Open 2024;5(2):139–146

Aims. Accurate skeletal age and final adult height prediction methods in paediatric orthopaedics are crucial for determining optimal timing of growth-guiding interventions and minimizing complications in treatments of various conditions. This study aimed to evaluate the accuracy of final adult height predictions using the central peak height (CPH) method with long leg X-rays and four different multiplier tables. Methods. This study included 31 patients who underwent temporary hemiepiphysiodesis for varus or valgus deformity of the leg between 2014 and 2020. The skeletal age at surgical intervention was evaluated using the CPH method with long leg radiographs. The true final adult height (FH. TRUE. ) was determined when the growth plates were closed. The final height prediction accuracy of four different multiplier tables (1. Bayley and Pinneau; 2. Paley et al; 3. Sanders – Greulich and Pyle (SGP); and 4. Sanders – peak height velocity (PHV)) was then compared using either skeletal age or chronological age. Results. All final adult height predictions overestimated the FH. TRUE. , with the SGP multiplier table having the lowest overestimation and lowest absolute deviation when using both chronological age and skeletal age. There were no significant differences in final height prediction accuracy between using skeletal age and chronological age with PHV (p = 0.652) or SGP multiplier tables (p = 0.969). Adult height predictions with chronological age and SGP (r = 0.769; p ≤ 0.001), as well as chronological age and PHV (r = 0.822; p ≤ 0.001), showed higher correlations with FH. TRUE. than predictions with skeletal age and SGP (r = 0.657; p ≤ 0.001) or skeletal age and PHV (r = 0.707; p ≤ 0.001). Conclusion. There was no significant improvement in adult height prediction accuracy when using the CPH method compared to chronological age alone. The study concludes that there is no advantage in routinely using the CPH method for skeletal age determination over the simple use of chronological age. The findings highlight the need for more accurate methods to predict final adult height in contemporary patient populations. Cite this article: Bone Jt Open 2023;4(10):750–757

Aims. The diagnosis of periprosthetic joint infection (PJI) can be challenging as the symptoms are similar to other conditions, and the markers used for diagnosis have limited sensitivity and specificity. Recent research has suggested using blood cell ratios, such as platelet-to-volume ratio (PVR) and platelet-to-lymphocyte ratio (PLR), to improve diagnostic accuracy. The aim of the study was to further validate the effectiveness of PVR and PLR in diagnosing PJI. Methods. A retrospective review was conducted to assess the accuracy of different marker combinations for diagnosing chronic PJI. A total of 573 patients were included in the study, of which 124 knees and 122 hips had a diagnosis of chronic PJI. Complete blood count and synovial fluid analysis were collected. Recently published blood cell ratio cut-off points were applied to receiver operating characteristic curves for all markers and combinations. The area under the curve (AUC), sensitivity, specificity, and positive and negative predictive values were calculated. Results. The results of the analysis showed that the combination of ESR, CRP, synovial white blood cell count (Syn. WBC), and polymorphonuclear neutrophil percentage (PMN%) with PVR had the highest AUC of 0.99 for knees, with sensitivity of 97.73% and specificity of 100%. Similarly, for hips, this combination had an AUC of 0.98, sensitivity of 96.15%, and specificity of 100.00%. Conclusion. This study supports the use of PVR calculated from readily available complete blood counts, combined with established markers, to improve the accuracy in diagnosing chronic PJI in both total hip and knee arthroplasties. Cite this article: Bone Jt Open 2023;4(11):881–888

Aims. Glenoid bone loss is a significant problem in the management of shoulder instability. The threshold at which the bone loss is considered “critical” requiring bony reconstruction has steadily dropped and is now approximately 15%. This necessitates accurate measurement in order that the correct operation is performed. CT scanning is the most commonly used modality and there are a number of techniques described to measure the bone loss however few have been validated. The aim of this study was to assess the accuracy of the most commonly used techniques for measuring glenoid bone loss on CT. Methods. Anatomically accurate models with known glenoid diameter and degree of bone loss were used to determine the mathematical and statistical accuracy of six of the most commonly described techniques (relative diameter, linear ipsilateral circle of best fit (COBF), linear contralateral COBF, Pico, Sugaya, and circle line methods). The models were prepared at 13.8%, 17.6%, and 22.9% bone loss. Sequential CT scans were taken and randomized. Blinded reviewers made repeated measurements using the different techniques with a threshold for theoretical bone grafting set at 15%. Results. At 13.8%, only the Pico technique measured under the threshold. At 17.6% and 22.9% bone loss all techniques measured above the threshold. The Pico technique was 97.1% accurate, but had a high false-negative rate and poor sensitivity underestimating the need for grafting. The Sugaya technique had 100% specificity but 25% of the measurements were incorrectly above the threshold. A contralateral COBF underestimates the area by 16% and the diameter by 5 to 7%. Conclusion. No one method stands out as being truly accurate and clinicians need to be aware of the limitations of their chosen technique. They are not interchangeable, and caution must be used when reading the literature as comparisons are not reliable. Cite this article: Bone Jt Open 2023;4(7):478–489

Aims. This systematic review aims to identify 3D predictors derived from biplanar reconstruction, and to describe current methods for improving curve prediction in patients with mild adolescent idiopathic scoliosis. Methods. A comprehensive search was conducted by three independent investigators on MEDLINE, PubMed, Web of Science, and Cochrane Library. Search terms included “adolescent idiopathic scoliosis”,“3D”, and “progression”. The inclusion and exclusion criteria were carefully defined to include clinical studies. Risk of bias was assessed with the Quality in Prognostic Studies tool (QUIPS) and Appraisal tool for Cross-Sectional Studies (AXIS), and level of evidence for each predictor was rated with the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) approach. In all, 915 publications were identified, with 377 articles subjected to full-text screening; overall, 31 articles were included. Results. Torsion index (TI) and apical vertebral rotation (AVR) were identified as accurate predictors of curve progression in early visits. Initial TI > 3.7° and AVR > 5.8° were predictive of curve progression. Thoracic hypokyphosis was inconsistently observed in progressive curves with weak evidence. While sagittal wedging was observed in mild curves, there is insufficient evidence for its correlation with curve progression. In curves with initial Cobb angle < 25°, Cobb angle was a poor predictor for future curve progression. Prediction accuracy was improved by incorporating serial reconstructions in stepwise layers. However, a lack of post-hoc analysis was identified in studies involving geometrical models. Conclusion. For patients with mild curves, TI and AVR were identified as predictors of curve progression, with TI > 3.7° and AVR > 5.8° found to be important thresholds. Cobb angle acts as a poor predictor in mild curves, and more investigations are required to assess thoracic kyphosis and wedging as predictors. Cumulative reconstruction of radiographs improves prediction accuracy. Comprehensive analysis between progressive and non-progressive curves is recommended to extract meaningful thresholds for clinical prognostication. Cite this article: Bone Jt Open 2024;5(3):243–251

Aims. The modified Radiological Union Scale for Tibia (mRUST) fractures score was developed in order to assess progress to union and define a numerical assessment of fracture healing of metadiaphyseal fractures. This score has been shown to be valuable in predicting radiological union; however, there is no information on the sensitivity, specificity, and accuracy of this index for various cut-off scores. The aim of this study is to evaluate sensitivity, specificity, accuracy, and cut-off points of the mRUST score for the diagnosis of metadiaphyseal fractures healing. Methods. A cohort of 146 distal femur fractures were retrospectively identified at our institution. After excluding AO/OTA type B fractures, nonunions, follow-up less than 12 weeks, and patients aged less than 16 years, 104 sets of radiographs were included for analysis. Anteroposterior and lateral femur radiographs at six weeks, 12 weeks, 24 weeks, and final follow-up were separately scored by three surgeons using the mRUST score. The sensitivity and specificity of mean mRUST score were calculated using clinical and further radiological findings as a gold standard for ultimate fracture healing. A receiver operating characteristic curve was also performed to determine the cut-off points at each time point. Results. The mean mRUST score of ten at 24 weeks revealed a 91.9% sensitivity, 100% specificity, and 92.6% accuracy of predicting ultimate fracture healing. A cut-off point of 13 points revealed 41.9% sensitivity, 100% specificity, and 46.9% accuracy at the same time point. Conclusion. The mRUST score of ten points at 24 weeks can be used as a viable screening method with the highest sensitivity, specificity, and accuracy for healing of metadiaphyseal femur fractures. However, the cut-off point of 13 increases the specificity to 100%, but decreases sensitivity. Furthermore, the mRUST score should not be used at six weeks, as results show an inability to accurately predict eventual fracture healing at this time point. Cite this article: Bone Jt Open 2021;2(10):796–805

Aims. Posterior column plating through the single anterior approach reduces the morbidity in acetabular fractures that require stabilization of both the columns. The aim of this study is to assess the effectiveness of posterior column plating through the anterior intrapelvic approach (AIP) in the management of acetabular fractures. Methods. We retrospectively reviewed the data from R G Kar Medical College, Kolkata, India, from June 2018 to April 2023. Overall, there were 34 acetabulum fractures involving both columns managed by medial buttress plating of posterior column. The posterior column of the acetabular fracture was fixed through the AIP approach with buttress plate on medial surface of posterior column. Mean follow-up was 25 months (13 to 58). Accuracy of reduction and effectiveness of this technique were measured by assessing the Merle d’Aubigné score and Matta’s radiological grading at one year and at latest follow-up. Results. Immediate postoperative radiological Matta’s reduction accuracy showed anatomical reduction (0 to 1 mm) in 23 cases (67.6%), satisfactory (2 to 3 mm) in nine (26.4%), and unsatisfactory (> 3 mm) in two (6%). Merle d’Aubigné score at the end of one year was calculated to be excellent in 18 cases (52.9%), good in 11 (32.3%), fair in three (8.8%), and poor in two (5.9%). Matta’s radiological grading at the end of one year was calculated to be excellent in 16 cases (47%), good in nine (26.4%), six in fair (17.6%), and three in poor (8.8%). Merle d’Aubigné score at latest follow-up deteriorated by one point in some cases, but the grading remained the same; Matta’s radiological grading at latest follow-up also remained unchanged. Conclusion. Stabilization of posterior column through AIP by medial surface plate along the sciatic notch gives good stability to posterior column, and at the same time can avoid morbidity of the additional lateral window. Cite this article: Bone Jt Open 2024;5(2):147–153

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. 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

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. The aim of this study was to develop and evaluate machine-learning-based computerized adaptive tests (CATs) for the Oxford Hip Score (OHS), Oxford Knee Score (OKS), Oxford Shoulder Score (OSS), and the Oxford Elbow Score (OES) and its subscales. Methods. We developed CAT algorithms for the OHS, OKS, OSS, overall OES, and each of the OES subscales, using responses to the full-length questionnaires and a machine-learning technique called regression tree learning. The algorithms were evaluated through a series of simulation studies, in which they aimed to predict respondents’ full-length questionnaire scores from only a selection of their item responses. In each case, the total number of items used by the CAT algorithm was recorded and CAT scores were compared to full-length questionnaire scores by mean, SD, score distribution plots, Pearson’s correlation coefficient, intraclass correlation (ICC), and the Bland-Altman method. Differences between CAT scores and full-length questionnaire scores were contextualized through comparison to the instruments’ minimal clinically important difference (MCID). Results. The CAT algorithms accurately estimated 12-item questionnaire scores from between four and nine items. Scores followed a very similar distribution between CAT and full-length assessments, with the mean score difference ranging from 0.03 to 0.26 out of 48 points. Pearson’s correlation coefficient and ICC were 0.98 for each 12-item scale and 0.95 or higher for the OES subscales. In over 95% of cases, a patient’s CAT score was within five points of the full-length questionnaire score for each 12-item questionnaire. Conclusion. Oxford Hip Score, Oxford Knee Score, Oxford Shoulder Score, and Oxford Elbow Score (including separate subscale scores) CATs all markedly reduce the burden of items to be completed without sacrificing score accuracy. Cite this article: Bone Jt Open 2022;3(10):786–794

Aims. To report the development of the technique for minimally invasive lumbar decompression using robotic-assisted navigation. Methods. Robotic planning software was used to map out bone removal for a laminar decompression after registration of CT scan images of one cadaveric specimen. A specialized acorn-shaped bone removal robotic drill was used to complete a robotic lumbar laminectomy. Post-procedure advanced imaging was obtained to compare actual bony decompression to the surgical plan. After confirming accuracy of the technique, a minimally invasive robotic-assisted laminectomy was performed on one 72-year-old female patient with lumbar spinal stenosis. Postoperative advanced imaging was obtained to confirm the decompression. Results. A workflow for robotic-assisted lumbar laminectomy was successfully developed in a human cadaveric specimen, as excellent decompression was confirmed by postoperative CT imaging. Subsequently, the workflow was applied clinically in a patient with severe spinal stenosis. Excellent decompression was achieved intraoperatively and preservation of the dorsal midline structures was confirmed on postoperative MRI. The patient experienced improvement in symptoms postoperatively and was discharged within 24 hours. Conclusion. Minimally invasive robotic-assisted lumbar decompression utilizing a specialized robotic bone removal instrument was shown to be accurate and effective both in vitro and in vivo. The robotic bone removal technique has the potential for less invasive removal of laminar bone for spinal decompression, all the while preserving the spinous process and the posterior ligamentous complex. Spinal robotic surgery has previously been limited to the insertion of screws and, more recently, cages; however, recent innovations have expanded robotic capabilities to decompression of neurological structures. Cite this article: Bone Jt Open 2024;5(9):809–817

Aims. Accurate identification of the ankle joint centre is critical for estimating tibial coronal alignment in total knee arthroplasty (TKA). The purpose of the current study was to leverage artificial intelligence (AI) to determine the accuracy and effect of using different radiological anatomical landmarks to quantify mechanical alignment in relation to a traditionally defined radiological ankle centre. Methods. Patients with full-limb radiographs from the Osteoarthritis Initiative were included. A sub-cohort of 250 radiographs were annotated for landmarks relevant to knee alignment and used to train a deep learning (U-Net) workflow for angle calculation on the entire database. The radiological ankle centre was defined as the midpoint of the superior talus edge/tibial plafond. Knee alignment (hip-knee-ankle angle) was compared against 1) midpoint of the most prominent malleoli points, 2) midpoint of the soft-tissue overlying malleoli, and 3) midpoint of the soft-tissue sulcus above the malleoli. Results. A total of 932 bilateral full-limb radiographs (1,864 knees) were measured at a rate of 20.63 seconds/image. The knee alignment using the radiological ankle centre was accurate against ground truth radiologist measurements (inter-class correlation coefficient (ICC) = 0.99 (0.98 to 0.99)). Compared to the radiological ankle centre, the mean midpoint of the malleoli was 2.3 mm (SD 1.3) lateral and 5.2 mm (SD 2.4) distal, shifting alignment by 0.34. o. (SD 2.4. o. ) valgus, whereas the midpoint of the soft-tissue sulcus was 4.69 mm (SD 3.55) lateral and 32.4 mm (SD 12.4) proximal, shifting alignment by 0.65. o. (SD 0.55. o. ) valgus. On the intermalleolar line, measuring a point at 46% (SD 2%) of the intermalleolar width from the medial malleoli (2.38 mm medial adjustment from midpoint) resulted in knee alignment identical to using the radiological ankle centre. Conclusion. The current study leveraged AI to create a consistent and objective model that can estimate patient-specific adjustments necessary for optimal landmark usage in extramedullary and computer-guided navigation for tibial coronal alignment to match radiological planning. Cite this article: Bone Jt Open 2022;3(10):767–776

Aims. The aims of this study were: 1) to describe extended restricted kinematic alignment (E-rKA), a novel alignment strategy during robotic-assisted total knee arthroplasty (RA-TKA); 2) to compare residual medial compartment tightness following virtual surgical planning during RA-TKA using mechanical alignment (MA) and E-rKA, in the same set of osteoarthritic varus knees; 3) to assess the requirement of soft-tissue releases during RA-TKA using E-rKA; and 4) to compare the accuracy of surgical plan execution between knees managed with adjustments in component positioning alone, and those which require additional soft-tissue releases. Methods. Patients who underwent RA-TKA between January and December 2022 for primary varus osteoarthritis were included. Safe boundaries for E-rKA were defined. Residual medial compartment tightness was compared following virtual surgical planning using E-rKA and MA, in the same set of knees. Soft-tissue releases were documented. Errors in postoperative alignment in relation to planned alignment were compared between patients who did (group A) and did not (group B) require soft-tissue releases. Results. The use of E-rKA helped restore all knees within the predefined boundaries, with appropriate soft-tissue balancing. E-rKA compared with MA resulted in reduced residual medial tightness following surgical planning, in full extension (2.71 mm (SD 1.66) vs 5.16 mm (SD 3.10), respectively; p < 0.001), and 90° of flexion (2.52 mm (SD 1.63) vs 6.27 mm (SD 3.11), respectively; p < 0.001). Among the study population, 156 patients (78%) were managed with minor adjustments in component positioning alone, while 44 (22%) required additional soft-tissue releases. The mean errors in postoperative alignment were 0.53 mm and 0.26 mm among patients in group A and group B, respectively (p = 0.328). Conclusion. E-rKA is an effective and reproducible alignment strategy during RA-TKA, permitting a large proportion of patients to be managed without soft-tissue releases. The execution of minor alterations in component positioning within predefined multiplanar boundaries is a better starting point for gap management than soft-tissue releases. Cite this article: Bone Jt Open 2024;5(8):628–636

Aims. Iliopsoas impingement occurs in 4% to 30% of patients after undergoing total hip arthroplasty (THA). Despite a relatively high incidence, there are few attempts at modelling impingement between the iliopsoas and acetabular component, and no attempts at modelling this in a representative cohort of subjects. The purpose of this study was to develop a novel computational model for quantifying the impingement between the iliopsoas and acetabular component and validate its utility in a case-controlled investigation. Methods. This was a retrospective cohort study of patients who underwent THA surgery that included 23 symptomatic patients diagnosed with iliopsoas tendonitis, and 23 patients not diagnosed with iliopsoas tendonitis. All patients received postoperative CT imaging, postoperative standing radiography, and had minimum six months’ follow-up. 3D models of each patient’s prosthetic and bony anatomy were generated, landmarked, and simulated in a novel iliopsoas impingement detection model in supine and standing pelvic positions. Logistic regression models were implemented to determine if the probability of pain could be significantly predicted. Receiver operating characteristic curves were generated to determine the model’s sensitivity, specificity, and area under the curve (AUC). Results. Highly significant differences between the symptomatic and asymptomatic cohorts were observed for iliopsoas impingement. Logistic regression models determined that the impingement values significantly predicted the probability of groin pain. The simulation had a sensitivity of 74%, specificity of 100%, and an AUC of 0.86. Conclusion. We developed a computational model that can quantify iliopsoas impingement and verified its accuracy in a case-controlled investigation. This tool has the potential to be used preoperatively, to guide decisions about optimal cup placement, and postoperatively, to assist in the diagnosis of iliopsoas tendonitis. Cite this article: Bone Jt Open 2023;4(1):3–12

Limb alignment in total knee arthroplasty (TKA) influences periarticular soft-tissue tension, biomechanics through knee flexion, and implant survival. Despite this, there is no uniform consensus on the optimal alignment technique for TKA. Neutral mechanical alignment facilitates knee flexion and symmetrical component wear but forces the limb into an unnatural position that alters native knee kinematics through the arc of knee flexion. Kinematic alignment aims to restore native limb alignment, but the safe ranges with this technique remain uncertain and the effects of this alignment technique on component survivorship remain unknown. Anatomical alignment aims to restore predisease limb alignment and knee geometry, but existing studies using this technique are based on cadaveric specimens or clinical trials with limited follow-up times. Functional alignment aims to restore the native plane and obliquity of the joint by manipulating implant positioning while limiting soft tissue releases, but the results of high-quality studies with long-term outcomes are still awaited. The drawbacks of existing studies on alignment include the use of surgical techniques with limited accuracy and reproducibility of achieving the planned alignment, poor correlation of intraoperative data to long-term functional outcomes and implant survivorship, and a paucity of studies on the safe ranges of limb alignment. Further studies on alignment in TKA should use surgical adjuncts (e.g. robotic technology) to help execute the planned alignment with improved accuracy, include intraoperative assessments of knee biomechanics and periarticular soft-tissue tension, and correlate alignment to long-term functional outcomes and survivorship