Variations in pelvic anatomy are a major risk factor for misplaced percutaneous sacroiliac screws used to treat unstable posterior pelvic ring injuries. A better understanding of pelvic morphology improves preoperative planning and therefore minimises the risk of malpositioned screws, neurological or vascular injuries, failed fixation or malreduction. Hence a classification system which identifies the clinically important anatomical variations of the sacrum would improve communication among pelvic surgeons and inform treatment strategy. 300 Pelvic CT scans from skeletally mature trauma patients that did not have pre-existing posterior pelvic pathology were identified. Axial and coronal transosseous corridor widths at both S1 and S2 were recorded. Additionally, the S1 lateral mass angle were also calculated. Pelvises were classified based upon the sacroiliac joint (SIJ) height using the midpoint of the anterior cortex of L5 as a reference point. Four distinct types could be identified:. Type-A – SIJ height is above the midpoint of the anterior cortex of the L5 vertebra. Type-B – SIJ height is between the midpoint and the lowest point of the anterior cortex of the L5 vertebra. Type-C – SIJ height is below the lowest point of the anterior cortex of the L5 vertebra. Type-D – a subgroup for those with a lumbosacral transitional vertebra, in particular a sacralised L5. Differences in transosseous corridor widths and lateral mass angles between classification types were assessed using two-way ANOVAs. Type-B was the most common pelvic type followed by Type-A, Type-C, and Type-D. Significant differences in the axial and coronal corridors was observed for all pelvic types at each level. Lateral mass angles increased from Types-A to C, but were smaller in Type-D. This classification system offers a guide to surgeons navigating variable pelvic anatomy and understanding how it is associated with the differences in transosseous sacral corridors. It can assist surgeons’ preoperative planning of screw position, choice of fixation or the need for technological assistance

A number of classification systems exist for posterior malleolus fractures of the ankle. The reliability of these classification systems remains unclear. The primary aim of this study was to evaluate the reliability of three commonly utilised fracture classification systems of the posterior malleolus. 60 patients across 2 hospitals sustaining an unstable ankle fracture with a posterior malleolus fragment were identified. All patients underwent radiographs and computed tomography of their injured ankle. 9 surgeons including pre-ST3 level, ST3-8 level, and consultant level applied the Haraguchi, Rammelt, and Mason & Molloy classifications to these patients, at two timepoints, at least 4 weeks apart. The order was randomised between assessments. Inter-rater reliability was assessed using Fleiss’ kappa and 95% confidence intervals (CI). Intra-rater reliability was assessed using Cohen's Kappa and standard error (SE). Inter-rater reliability (Fleiss’ Kappa) was calculated for the Haraguchi classification as 0.522 (95% CI 0.490 – 0.553), for the Rammelt classification as 0.626 (95% CI 0.600 – 0.652), and the Mason & Molloy classification as 0.541 (95% CI 0.514 – 0.569). Intra-rater reliability (Cohen's Kappa) was 0.764 (SE 0.034) for the Haraguchi, 0.763 (SE 0.031) for the Rammelt, 0.688 (SE 0.035) for the Mason & Molloy classification. This study reports the inter-rater and intra-rater reliability for three classification systems for posterior malleolus fractures. Based on definitions by Landis & Koch (1977), inter-rater reliability was rated as ‘moderate’ for the Haraguchi and Mason & Molloy classifications; and ‘substantial’ for the Rammelt classification. Similarly, the intra-rater reliability was rated as ‘substantial’ for all three classifications

Introduction. Inaccurate identification of implants on X-rays may lead to prolonged surgical duration as well as increased complexity and costs during implant removal. Deep learning models may help to address this problem, although they typically require large datasets to effectively train models in detecting and classifying objects, e.g. implants. This can limit applicability for instances when only smaller datasets are available. Transfer learning can be used to overcome this limitation by leveraging large, publicly available datasets to pre-train detection and classification models. The aim of this study was to assess the effectiveness of deep learning models in implant localisation and classification on a lower limb X-ray dataset. Method. Firstly, detection models were evaluated on their ability to localise four categories of implants, e.g. plates, screws, pins, and intramedullary nails. Detection models (Faster R-CNN, YOLOv5, EfficientDet) were pre-trained on the large, freely available COCO dataset (330000 images). Secondly, classification models (DenseNet121, Inception V3, ResNet18, ResNet101) were evaluated on their ability to classify five types of intramedullary nails. Localisation and classification accuracy were evaluated on a smaller image dataset (204 images). Result. The YOLOv5s model showed the best capacity to detect and distinguish between different types of implants (accuracy: plate=82.1%, screw=72.3%, intramedullary nail=86.9%, pin=79.9%). Screw implants were the most difficult implant to detect, likely due to overlapping screw implants visible in the image dataset. The DenseNet121 classification model showed the best performance in classifying different types of intramedullary nails (accuracy=73.7%). Therefore, a deep learning model pipeline with the YOLOv5s and DenseNet121 was proposed for the most optimal performance of automating implants localisation and classification for a relatively small dataset. Conclusion. These findings support the potential of deep learning techniques in enhancing implant detection accuracy. With further development, AI-based implant identification may benefit patients, surgeons and hospitals through improved surgical planning and efficient use of theatre time

Introduction. The increased prevalence of osteoporosis in the patient population undergoing reverse shoulder arthroplasty (RSA) results in significantly increased complication rates. Mainly demographic and clinical predictors are currently taken into the preoperative assessment for risk stratification without quantification of preoperative computed tomography (CT) data (e.g. bone density). It was hypothesized that preoperative CT bone density measures would provide objective quantification with subsequent classification of the patients’ humeral bone quality. Methods. Thirteen bone density parameters from 345 preoperative CT scans of a clinical RSA cohort represented the data set in this study. The data set was divided into testing (30%) and training data (70%), latter included an 8-fold cross validation. Variable selection was performed by choosing the variables with the highest descriptive value for each correlation clustered variables. Machine learning models were used to improve the clustering (Hierarchical Ward) and classification (Support Vector Machine (SVM)) of bone densities at risk for complications and were compared to a conventional statistical model (Logistic Regression (LR)). Results. Clustering partitioned this cohort (training data set) into a high bone density subgroup consisting of 96 patients and a low bone density subgroup consisting of 146 patients. The optimal number of clusters (n = 2) was determined based on optimization metrics. Discrimination of the cross validated classification model showed comparable performance for the training (accuracy=91.2%; AUC=0.967) and testing data (accuracy=90.5 %; AUC=0.958) while outperforming the conventional statistical model (Logistic Regression (LR)). Local interpretable model-agnostic explanations (LIME) were created for each patient to explain how the predicted output was achieved. Conclusion. The trained and tested model provides preoperative information for surgeons treating patients with potentially poor bone quality. The use of machine learning and patient-specific calibration showed that multiple 3D bone density scores improved accuracy for objective preoperative bone quality assessment

Introduction and Objective. The Cartilage Oligomeric Matrix Protein (COMP) is a glycoprotein that is elevated in patients with osteoarthritis. The elevation increases linearly with the radiological grade of osteoarthritis. The objective of this study was to study the levels of COMP in knee osteoarthritis in the Indian population and to correlate (establish ranges) with the specific radiological grade of osteoarthritis (Kellgreen and Lawrence grading). Since the radiological classification is subjective, the COMP levels would serve as a more objective way of classifying osteoarthritic joints. Materials and Methods. We analysed the COMP levels by the Enzyme Linked Immunosorbent Assay (ELISA) method in 100 patients presenting to the outpatient clinic of our hospital, after obtaining due approvals. The radiographs of these patients were classified according to the Kellgreen-Lawrence grading by a senior orthopaedic surgeon. Results. We found a linear correlation with the COMP levels and the radiological classification as established in the previous studies. We were also able to establish a range of COMP levels for each classification stage. Conclusions. This study would provide means to classify osteoarthritis without the need for radiographs thus minimising radiation to the patient. It would also help us to predict the radiological findings thus serving as a guide for further treatment planning

In a clinical setting, there is a need for simple gait kinematic measurements to facilitate objective unobtrusive patient monitoring. The objective of this study is to determine if a learned classification model's output can be used to monitor a person's recovery status post-TKA. The gait kinematics of 20 asymptomatic and 17 people with TKA were measured using a full-body Xsens model. 1. The experimental group was measured at 6 weeks, 3, 6, and 12 months post-surgery. Joint angles of the ankle, knee, hip, and spine per stride (10 strides) were extracted from the Xsens software (MVN Awinda studio 4.4). 1. . Statistical features for each subject at each evaluation moment were derived from the kinematic time-series data. We normalised the features using standard scaling. 2. We trained a logistic regression (LR) model using L1-regularisation on the 6 weeks post-surgery data2–4. After training, we applied the trained LR- model to the normalised features computed for the subsequent timepoints. The model returns a score between 0 (100% confident the person is an asymptomatic control) and 1 (100% confident this person is a patient). The decision boundary is set at 0.5. The classification accuracy of our LR-model was 94.58%. Our population's probability of belonging to the patient class decreases over time. At 12 months post-TKA, 38% of our patients were classified as asymptomatic

Introduction. Shoulder arthroplasty (SA) has been performed with different types of implants, each requiring different replacement systems. However, data on previously utilized implant types are not always available before revision surgery, which is paramount to determining the appropriate equipment and procedure. Therefore, this meta-analysis aimed to evaluate the accuracy of the AI models in classifying SA implant types. Methods. This systematic review was conducted in Pubmed, Embase, SCOPUS, and Web of Science from inception to December 2023, according to PRISMA guidelines. Peer-reviewed research evaluating the accuracy of AI-based tools on upper-limb X-rays for recognizing and categorizing SA implants was included. In addition to the overall meta-analysis, subgroup analysis was performed according to the type of AI model applied (CNN (Convolutional neural network), non-CNN, or Combination of both) and the similarity of utilized datasets between studies. Results. 13 articles were eligible for inclusion in this meta-analysis (including 138 different tests assessing models’ efficacy). Our meta-analysis demonstrated an overall sensitivity and specificity of 0.891 (95% CI:0.866-0.912) and 0.549 (95% CI:0.532,0.566) for classifying implants in SA, respectively. The results of our subgroup analyses were as follows: CNN-subgroup: a sensitivity of 0.898 (95% CI:0.873-0.919) and a specificity of 0.554 (95% CI:0.537,0.570), Non-CNN subgroup: a sensitivity of 0.809 (95% CI:0.665-0.900) and specificity of 0.522 (95% CI:0.440,0.603), combined subgroup: a sensitivity of 0.891 (95% CI:0.752-0.957) and a specificity of 0.547 (95% CI:0.463,0.629). Studies using the same dataset demonstrated an overall sensitivity and specificity of 0.881 (95% CI:0.856-0.903) and 0.542 (95% CI:0.53,0.554), respectively. Studies that used other datasets showed an overall sensitivity and specificity of 0.995 (95% CI:969,0.999) and 0.678 (95% CI:0.234, 0.936), respectively. Conclusion. AI-based classification of shoulder implant types can be considered a sensitive method. Our study showed the potential role of using CNN-based models and different datasets to enhance accuracy, which could be investigated in future studies

Arthroscopic management of femoroacetabular impingement (FAI) has become the mainstay of treatment. However, chondral lesions are frequently encountered and have become a determinant of less favourable outcomes following arthroscopic intervention. The aim of this systematic review and meta-analysis was to assess the outcomes of hip arthroscopy (HA) in patients with FAI and concomitant chondral lesions classified as per Outerbridge. A systematic search was performed using the PRISMA guidelines on four databases including MEDLINE, EMBASE, Cochrane Library and Web of Science. Studies which included HA as the primary intervention for management of FAI and classified chondral lesions according to the Outerbridge classification were included. Patients treated with open procedures, for osteonecrosis, Legg-Calve-Perthes disease, and previous ipsilateral hip fractures were excluded. From a total of 863 articles, twenty-four were included for final analysis. Demographic data, PROMs, and radiological outcomes and rates of conversion to total hip arthroplasty (THA) were collected. Risk of bias was assessed using ROBINS-I. Improved post-operative PROMs included mHHS (mean difference:-2.42; 95%CI:-2.99 to −1.85; p<0.001), NAHS (mean difference:-1.73; 95%CI: −2.23 to −1.23; p<0.001), VAS (mean difference: 2.03; 95%CI: 0.93-3.13; p<0.001). Pooled rate of revision surgery was 10% (95%CI: 7%-14%). Most of this included conversion to THA, with a 7% pooled rate (95%CI: 4%-11%). Patients had worse PROMs if they underwent HA with labral debridement (p=0.015), had Outerbridge 3 and 4 lesions (p=0.012), concomitant lesions of the femoral head and acetabulum lesions (p=0.029). Reconstructive cartilage techniques were superior to microfracture (p=0.042). Even in concomitant lesions of the femoral head and acetabulum, employing either microfracture or cartilage repair/reconstruction provided a benefit in PROMs (p=0.027). Acceptable post-operative outcomes following HA with labral repair/reconstruction and cartilage repair in patients with FAI and concomitant moderate-to-severe chondral lesions, can be achieved. Patients suffering from Outerbridge 3 and 4 lesions, concomitant acetabular rim and femoral head chondral lesions that underwent HA with labral debridement, had worse PROMs. Reconstructive cartilage techniques were superior to microfracture. Even in concomitant acetabular and femoral head chondral lesions, employing either microfracture or cartilage repair/reconstruction was deemed to provide a benefit in PROMs

Abstract. Objectives. Exploring the relationship of gait function pre and post total knee replacement (TKR) in two groups of patients. Methods. Three-dimensional gait analysis was performed at Cardiff University, UK, and Karolinska University Hospital, Sweden, on 29 and 25 non-pathological (NP) volunteers, and 39 and 28 patients with end-stage knee osteoarthritis (OA), respectively. Patients were assessed pre and one-year post-TKR. Data reduction was performed via Principal Component (PC) analysis on twenty-four kinematic and kinetic waveforms in both NP and pre/post-TKR. Cardiff's and Karolinska's cohorts were analysed separately. The Cardiff Classifier, a classification system based on the Dempster-Shafer theory, was trained with the first 3 PCs of each variable for each cohort. The Classifier classifies each participant by assigning them a belief in NP, belief in OA (BOA) and belief in uncertainty, based on their biomechanical features. The correlation between patient's BOA values (range: 0–1, 0 indicates null BOA and 1 high BOA) pre and post-TKR was tested through Spearman's correlation coefficient in each cohort. The related-samples Wilcoxon signed-rank test (α=0.05) determined the significant changes in BOA in each cohort of patients. The Mann-Whitney U test (α=0.05) was run to explore differences between the patients’ cohorts. Results. There were no significant differences between patients’ cohorts in median age (p=0.096), height (p=0.673), weight (p=0.064) or KOOS sub-scores pre or post-TKR (p-value ranged 0.069 to 0.955) but Cardiff's patients had a significantly higher BMI (p=0.047). There was a significant, median decrease of 0.12 and 0.19 in the BOA pre to post TKR (p<0.001) in Cardiff's and Karolinska's patients, respectively. There was a statistically significant, strong positive correlation between the BOA pre and post-TKR (Cardiff:r. s. =0.706, p<0.001; Karolinska:r. s. =0.669, p<0.001). Conclusions. In two distinct cohorts of patients, having a more compromised gait function in end-stage knee OA was correlated with poorer gait function post-TKR

The aim of this study was to establish a classification system for the acromioclavicular joint using cadaveric dissection and radiological analyses of both reformatted computed tomographic scans and conventional radiographs centred on the joint. This classification should be useful for planning arthroscopic procedures or introducing a needle and in prospective studies of biomechanical stresses across the joint which may be associated with the development of joint pathology. We have demonstrated three main three-dimensional morphological groups namely flat, oblique and curved, on both cadaveric examination and radiological assessment. These groups were recognised in both the coronal and axial planes and were independent of age

Summary. The ankle X-ray has moderate diagnostic power to identify syndesmotic instability, showing large sensitivity ranges between observers. Classification systems and radiographic measurements showed moderate to high interobserver agreement, with extended classifications performing worse. Introduction. There is no consensus regarding the diagnosis and treatment of ankle fractures with respect to syndesmotic injury. The diagnosis of syndesmotic injury is currently based on intraoperative findings. Surgical indication is mainly made by ankle X-ray assessment, by several classification systems and radiographic measurements. Misdiagnosis of the injury results in suboptimal treatment, which may lead to chronic complaints, like instability and osteoarthritis. This study investigates the diagnostic power and interobserver agreement of three classification methods and radiographic measures, currently used to assess X-ankles and to identify syndesmotic injury. Patients and Methods. Twenty patients (43.2 ± 15.3yrs) with an ankle fracture, indicated for surgery, were prospectively included. All patients received a preoperative ankle X-ray, which was assessed by several observers: two orthopaedic surgeons, one trauma surgeon and two radiologists. The ankle X-ray was assessed on syndesmotic injury/stability and presence of fractures (fibula, medial/tertius malleolus). Three classification systems were used: Weber, AO-Müller (short-version n=3 options; extended-version n=27 options), Lauge-Hansen (short-version n=5 options; extended-version n=17 options) and two radiographic measurements were done: tibiofibular overlap (TFO) and ratio medial clearspace/superior clear space (MCS/SCS). All observers were instructed about the assessments before the measurements. During surgery, a proper intraoperative description of the syndesmosis was noted. Agreement (%), Intraclass Correlation Coefficients (ICC) and Kappa were calculated to determine interobserver agreement. Kappa statistic was interpreted according to Landis and Koch. To test the diagnostic power of ankle X-rays to identify syndesmotic instability, sensitivity and specificity were calculated with intraoperative findings serving as golden standard. Results. Six of 20 ankles showed syndesmotic instability intraoperatively. An overall sensitivity of 43% (specificity: 78) was found for X-rays in identifying syndesmotic instability, showing a wide range in sensitivity between observers (17–83%), with radiologists performing better (range 50–83%) than surgeons (range: 17–33%). Overall, substantial to perfect interobserver agreement (range 70–100%) was found for all short classification systems, showing an average kappa ≥0.60. The agreement reduced for more extended classification systems. E.g. observer agreement for the AO-Muller classification with 3, 9 and 27 options was respectively 85% (kappa 0.66), 68% (kappa 0.57) and 55% (kappa 0.51). One observer deviated slightly from others in all classification assessments. Removing this observer resulted in excellent agreement for all classification systems (>90%). Radiographic measurements showed moderate to high interobserver agreement, with TFO performing best (avg. ICC 0.88). Discussion/Conclusion. In ankle fractures, a preoperative X-ray has low sensitivity in detecting syndesmotic instability, showing large sensitivity ranges between observers. Further study is needed to investigate the contribution of classification systems in determining the best treatment method for syndesmotic injury. Ankle X-ray assessment using the three classification systems and radiographic measures was consistent among observers. Disagreement between observers can be attributed to intrinsic differences among the systems (e.g. stepwise classification vs. single assessment). No preference for one specific classification was found, as all showed comparable interobserver agreement. However classification systems with few options are recommended, as the observer agreement reduced with more extending classifications

INTRODUCTION. Motion analysis is routinely used in the clinical and research sectors to quantify joint biomechanics. It plays an important role in clinical assessments by aiding the physician to distinguish between primary movement abnormalities and any secondary compensatory mechanisms that may overshadow the cause of the problem. During a data collection session, a wealth of biomechanical data regarding joint and segment kinematics and kinetics are collected from patients performing daily activities. Objective classification can be used to automate a diagnosis from this data and has been used previously to analyse measurements of level gait [1]. It is of interest to assess the knee during stair-gait as this activity involves greater range of motion (ROM) of the lower limbs, larger forces and moments acting at the knee. AIM. The aim of the current study is to explore the use of an objective classifier [1] to characterise knee osteoarthritis (OA) and monitor functional recovery following a total knee replacement (TKR) using measurements from stair-gait. METHODS. Motion analysis techniques were used to quantify knee OA kinematics and kinetics during stair-gait for six patients with knee osteoarthritis (OA) and nine subjects without pathology (NP). One OA subject, forming a TKR sample, was also assessed at 4, 8 and 12 post-operatively. Each subject performed three trials of stair ascent and descent. 3D motion capture was performed using 8 Qualisys MCUs, capturing at 60Hz and a 1000Hz force plate (Bertec Corporation). Forces were measured from the first step of the staircase [2]. Independent t-tests were performed on biomechanical measures to compare the NP and OA cohorts (p<0.05). This identified the adaptations associated with knee OA. Principal components of salient kinematic and kinetic waveforms were used as inputs to train the classifier and subsequently characterise recovery of the TKR sample. RESULTS. The OA cohort adapted their stair-gait by reducing their peak: (i) external flexion moment in stance during both stair ascent and descent; (ii) medial ground reaction force (GRF) (iii) vertical GRF during stair descent and increasing their external adduction moment during stair ascent. The classifier was used to characterise knee function of the OA and NP subjects with 100% classification accuracy, defined using a Leave-one-out cross-validation. The TKR sample was classified as having dominant OA functional characteristics pre-operatively. At all subsequent measurements the subject was classified as having NP stair-gait characteristics. These changes correlated significantly with Knee Outcome Survey and Oxford Knee scores. CONCLUSION. Classification is a powerful tool for characterising data into two groups where a simplex plot provides a simple clinical interpretation of the results from a motion analysis assessment. This study demonstrates the use of objective classification to quantify NP, OA and TKR function from stair-gait. It also demonstrates its capability to monitor functional changes during a subject's recovery

Background. Evidence suggests classification system (CS) guided treatments are more effective than generalized and practice guidelines based treatments for low back pain (LBP) patients. This study evaluated clinicians' and managers' attitudes towards LBP classification and its usefulness in guiding LBP management. Methods. Data from 3 semi-structured interviews with physiotherapy service managers and advanced spinal physiotherapy practitioner and a focus group (5 physiotherapists) in two NHS Health Boards, South Wales, UK, was thematically analysed. Results. Five themes emerged. CS knowledge: Clinicians and managers know different CSs and agree with its usefulness. Clinicians have specific CSs knowledge, managers viewed classification related to referral pathways and prognosis. Current CS use: Clinicians classify using their experience and clinical reasoning skills shifting between multiple CSs. Managers are confident that staff provide evidence-based service though believe classification is not always practiced across services. CS advantages/disadvantages: Effectively targeting the right patients for right treatments using evidence-based practice is advantageous. Prevalence of “guru led” CSs developed for research and of limited clinical use is disadvantageous. Barriers: Patients' treatment expectations, threat to clinical autonomy, lack of sufficiently complex CSs, lack of resources to up-skill clinicians and overall CSs fit into complex referral pathways. Enablers: CSs sufficiently complex & placed within clinical reasoning process, mentoring for inexperienced staff, positive engagement with all stakeholders and patients. Conclusion. Clinicians and managers are aware of CSs and agree with its usefulness to guide LBP management. Clinicians classify LBP though there is no formalized CS process in place. Whilst clinicians view classification as the relationship between patients and physiotherapy managers have a broader, whole service view. Conflicts of interest: None. Sources of funding: Wales School of Primary Care Research, Cardiff, UK. This abstract has not been previously published in whole or substantial part nor has been presented previously at a national meeting

Background, Context and Motivation. “Increases in reconstructive orthopaedic surgery, resulting from advances in surgical practice and the ageing population, have lead to a demand for bone graft that far exceeds supply.”…Traditional bone grafting methods have been linked with a number of negative issues including increased morbidity due to secondary operation site and action as a vector for spread of disease. (Hing 2004). A solution to these insufficiencies would be the creation of a synthetic osteoinductive bone graft material. This would vastly improve bone graft surgery success rates and expedite post-op recovery times. The aim of this study was to classify then explore the dissolution rates of three experimental hydroxyapatite/silicate apatite synthetic bonegrafts in physiological solutions, (phosphate buffered saline, (PBS) +/− serum proteins, (PBS +FCS). The overall objective being to identify whether there is an explainable significant difference in ion exchange that could be behind the osteoinductive phenomena. Methods Used. Classification of the apatite samples, (HA, SA1 and SA2), was conducted via X-Ray diffraction, FTIR-PAS Spectroscopy and SEM with EDS analysis. A dissolution experiment of the experimental apatites was conducted in PBS and PBS + FCS solutions, over time periods of 1, 2 and 4 hours, and at 1, 2, 4 and 8 days, with repeat measures. Results and Conclusions. Silicon both free in solution and at the apatite surface was found to be key for osteoinduction and its presence at both these sites increases the rates of bone apposition around a synthetic graft. Experimental Samples HA, SA1 and SA2 share the same crystalline structure as Hydroxyapatite and are phase pure. Sample SA1 showed a %wt of silicon of 0.9%wt and SA2 showed a %wt of silicon of 0.6% wt. Review of the literature indicates that samples SA1 and SA2 are within an osteoinductive range for %wt of silicon. All three samples exhibit porosity within the most bioactive levels (150-500μm). Dissolution reactions for silicon are present and faster than experienced in the literature, this leads us to hypothesise that bone apposition rates would be high in all samples as silicon is available both free in solution and at the apatite surface, indicating all experimental samples possess an osteoinductive effect. Further work would involve exposing these samples to solutions containing osteoblast like cells and comparing the levels of activity found to those of traditional bone grafts currently used

With an aging population and increase in total knee arthroplasty, periprosthetic distal femur fractures (PDFFs) have increased. The differences between these fractures and native distal femur fractures (NDFF) have not been comprehensively investigated. The purpose of this study was to compare the demographic, fracture, and treatment details of PDFFs compared to NDFFs. A retrospective study of patients ≥ 18 years old who underwent surgical treatment for either a NDFF or a PDFF from 2010 to 2020 at a level 1 trauma center was performed. Demographics, AO/OTA fracture classification, quality of reduction, fixation constructs, and unplanned revision reoperation were compared between PDFF patients and NDFF patients using t-test and Fisher's exact test. 209 patients were identified with 70 patients having a PDFF and 139 patients having a NDFF. Of note, 48% of NDFF had a concomitant fracture of the ipsilateral knee (14%) or tibial plateau (15%). The most common AO/OTA classification for PDFFs was 33A3.3 (71%). NDFFs had two main AO/OTA classifications of 33C2.2 (28%) or 33A3.2. (25%). When controlling for patient age, bone quality, fracture classification, and fixation, the PDFF group had increased revision reoperation rate compared to NDFF (P < 0.05). PDFFs tend to occur in elderly patients with low bone quality, have complete metaphyseal comminution, and be isolated; whereas, NDFF tend to occur in younger patients, have less metaphyseal comminution, and be associated with other fractures. When controlling for variables, PDFF are at increased risk of unplanned revision reoperation. Surgeons should be aware of these increased risks in PDFFs and future research should focus on these unique fracture characteristics to improve outcomes

Physiotherapy is a critical element in successful conservative management of low back pain (LBP). The aim of this study was to develop and evaluate a system with wearable inertial sensors to objectively detect sitting postures and performance of unsupervised exercises containing movement in multiple planes (flexion, extension, rotation). A set of 8 inertial sensors were placed on 19 healthy adult subjects. Data was acquired as they performed 7 McKenzie low-back exercises and 3 sitting posture positions. This data was used to train two models (Random Forest (RF) and XGBoost (XGB)) using engineered time series features. In addition, a convolutional neural network (CNN) was trained directly on the time series data. A feature importance analysis was performed to identify sensor locations and channels that contributed most to the models. Finally, a subset of sensor locations and channels was included in a hyperparameter grid search to identify the optimal sensor configuration and the best performing algorithm(s) for exercise classification. Models were evaluated using F1-score in a 10-fold cross validation approach. The optimal hardware configuration was identified as a 3-sensor setup using lower back, left thigh, and right ankle sensors with acceleration, gyroscope, and magnetometer channels. The XBG model achieved the highest exercise (F1=0.94±0.03) and posture (F1=0.90±0.11) classification scores. The CNN achieved similar results with the same sensor locations, using only the accelerometer and gyroscope channels for exercise classification (F1=0.94±0.02) and the accelerometer channel alone for posture classification (F1=0.91±0.03). This study demonstrates the potential of a 3-sensor lower body wearable solution (e.g. smart pants) that can identify proper sitting postures and exercises in multiple planes, suitable for low back pain. This technology has the potential to improve the effectiveness of LBP rehabilitation by facilitating quantitative feedback, early problem diagnosis, and possible remote monitoring

The use of intraoperative navigation and robotic surgery for minimally invasive lumbar fusion has been increasing over the past decade. The aim of this study is to evaluate postoperative clinical outcomes, intraoperative parameters, and accuracy of pedicle screw insertion guided by intraoperative navigation in patients undergoing lumbar interbody fusion for spondylolisthesis. Patients who underwent posterior lumbar fusion interbody using intraoperative 3D navigation since December 2021 were included. Visual Analogue Scale (VAS), Oswestry Disability Index (ODI), and Short Form Health Survey-36 (SF-36) were assessed preoperatively and postoperatively at 1, 3, and 6 months. Screw placement accuracy, measured by Gertzbein and Robbins classification, and facet joint infringement, measured by Yson classification, were assessed by intraoperative Cone Beam CT scans performed at the end of instrumentation. Finally, operation time, intraoperative blood loss, hospital stay, and screw insertion time were evaluated. This study involved 50 patients with a mean age of 63.7 years. VAS decreased from 65.8±23 to 20±22 (p<.01). ODI decreased from 35.4%±15 to 11.8%±14 (p<.01). An increase of SF-36 from 51.5±14 to 76±13 (p<.01) was demonstrated. The accuracy of “perfect” and “clinically acceptable” pedicle screw fixation was 89.5% and 98.4%, respectively. Regarding facet violation, 96.8% of the screws were at grade 0. Finally, the average screw insertion time was 4.3±2 min, hospital stay was 4.2±0.8 days, operation time was 205±53 min, and blood loss was 169±107 ml. Finally, a statistically significant correlation of operation time with hospital stay, blood loss and placement time per screw was found. We demonstrated excellent results for accuracy of pedicle screw fixation and violation of facet joints. VAS, ODI and SF-36 showed statistically significant improvements from the control at one month after surgery. Navigation with intraoperative 3D images represents an effective system to improve operative performance in the surgical treatment of spondylolisthesis

Abstract. Objective. This study assesses the prevalence of major and minor discordance between hip and spine T scores using Radiofrequency Echographic Multi-spectrometry (REMS). REMS is a novel technology that uses ultrasound and radiofrequency analysis to measure bone density and bone fragility at the hip and lumbar spine. The objective was to compare the results with the existing literature on Dual-Energy X-ray Absorptiometry (DEXA) the current “gold standard” for bone densitometry. REMS and DEXA have been shown to have similar diagnostic accuracy, however, REMS has less human input when carrying out the scan, therefore the rates of discordance might be expected to be lower than for DEXA. Discordance poses a risk of misclassification of patients’ bone health status, causing diagnostic ambiguity and potentially sub-optimal management decisions. Reduction of discordance rates therefore has the potential to significantly improve treatment and patient outcomes. Methods. Results from 1,855 patients who underwent REMS investigations between 2018 and 2022 were available. Minor discordance is defined as a difference of one World Health Organisation (WHO) diagnostic classification (Normal / Osteopenia or Osteopenia / Osteoporosis). Major discordance is defined as a difference of two WHO diagnostic classifications (Normal / Osteoporosis). The results were compared with reported DEXA discordance rates. Results. 1,732 individuals had both hip and spine T scores available for analysis. There were 267 cases of discordance. No instances of major discordance were observed. The minor discordance rate was 15.4%. 6.5% of the REMS scans with minor discordance showed > 1.0 standard deviation (SD) difference between the T scores of the hip and spine. 19.4% had differences of between 0.6 SD and 1.0 SD while 73.9% had ≤ 0.5 SD or less. In 24.5% of the cases of REMS discordance the hip T scores were greater than the spine and in 75.5% of cases the spine T score was greater than the hip. Conclusions. The current analysis is the largest of its kind. It demonstrates that REMS has an overall lower rate of discordance than reported DEXA rates. Major discordance rates with DEXA range from 2–17%, but REMS avoids many of the positioning problems and post-processing errors inherent in DEXA scanning, which might account for the absence of major discordance. Rates of minor discordance in DEXA scans range between 38–51%. The REMS minor discordance rate being much lower than these rates suggests that it has the potential to enhance diagnostic accuracy considerably. Most REMS discordance results showed ≤ 0.5 SD variance between the T scores of the two sites, indicating close correlation in the bone densitometry analysis. Most studies of DEXA discordant results confirm that spinal T scores are more often higher than at the hip. The REMS results concur with this observation. Considering the comparable accuracy rates that have been shown between REMS and DEXA, with its much lower discordance rate, REMS can potentially improve current medical practice and enhance patient care. Declaration of Interest. (b) declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported:I declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research project

To conduct a meta-analysis for intertrochanteric hip fractures comparing in terms of efficacy and safety short versus long intralomedullary nails. A pubmed search of the last 10 years for intertrochanteric fracture 31A1-31A3 according to the AO/OTA classification was performed. Baseline characteristics of each article were obtained, complication measures were analyzed: Peri-implant fracture, reoperations, deep/superficial infection, and mortality. Clinical variables consisted of blood loss (mL), length of stay (days), time of surgery (min) and nº of transfusions. Functional outcomes were also recorded. A meta-analysis was performed with Review Manager 5.4. Twelve studies were included, nine were retrospective. The reoperations rate was lower in the short nail group and the peri-implant fracture rate was lower in the long nail group (OR 0.58, 95% CI 0.38 to 0.88) (OR 1.88, 95% CI 1.04 to 3.43). Surgery time and blood loss was significantly higher in the long nail group (MD −12.44, 95% CI −14.60 to −10.28) (MD −19.36, 95% CI −27.24 to −11.48). There were no differences in functional outcomes. The short intramedullary nail has a higher risk of peri-implant fracture; however, the reoperation rate is lower compared to the long nail. Blood loss and surgery time was higher in the long nail group

Access to health care, including physiotherapy, is increasingly occurring through virtual formats. At-home adherence to physical therapy programs is often poor and few tools exist to objectively measure low back physiotherapy exercise participation without the direct supervision of a medical professional. The aim of this study was to develop and evaluate the potential for performing automatic, unsupervised video-based monitoring of at-home low back physiotherapy exercises using a single mobile phone camera. 24 healthy adult subjects performed seven exercises based on the McKenzie low back physiotherapy program while being filmed with two smartphone cameras. Joint locations were automatically extracted using an open-source pose estimation framework. Engineered features were extracted from the joint location time series and used to train a support vector machine classifier (SVC). A convolutional neural network (CNN) was trained directly on the joint location time series data to classify exercises based on a recording from a single camera. The models were evaluated using a 5-fold cross validation approach, stratified by subject, with the class-balanced accuracy used as the performance metric. Optimal performance was achieved when using a total of 12 pose estimation landmarks from the upper and lower body, with the SVC model achieving a classification accuracy of 96±4% and the CNN model an accuracy of 97±2%. This study demonstrates the feasibility of using a smartphone camera and a supervised machine learning model to effectively assess at-home low back physiotherapy adherence. This approach could provide a low-cost, scalable method for tracking adherence to physical therapy exercise programs in a variety of settings