Gait analysis is an indispensable tool for scientific assessment and treatment of individuals whose ability to walk is impaired. The high cost of installation and operation are a major limitation for wide-spread use in clinical routine. Advances in Artificial Intelligence (AI) could significantly reduce the required instrumentation. A mobile phone could be all equipment necessary for 3D gait analysis. MediaPipe Pose provided by Google Research is such a Machine Learning approach for human body tracking from monocular RGB video frames that is detecting 3D-landmarks of the human body. Aim of this study was to analyze the accuracy of gait phase detection based on the joint landmarks identified by the AI system. Motion data from 10 healthy volunteers walking on a treadmill with a fixed speed of 4.5km/h (Callis, Sprintex, Germany) was sampled with a mobile phone (iPhone SE 2nd Generation, Apple). The video was processed with Mediapipe Pose (Version 0.9.1.0) using custom python software. Gait phases (Initial Contact - IC and Toe Off - TO) were detected from the angular velocities of the lower legs. For the determination of ground truth, the movement was simultaneously recorded with the AS-200 System (LaiTronic GmbH, Innsbruck, Austria). The number of detected strides, the error in IC detection and stance phase duration was calculated. In total, 1692 strides were detected from the reference system during the trials from which the AI-system identified 679 strides. The absolute mean error (AME) in IC detection was 39.3 ± 36.6 ms while the AME for stance duration was 187.6 ± 140 ms. Landmark detection is a challenging task for the AI-system as can clearly be seen be the rate of only 40% detected strides. As mentioned by Fadillioglu et al., error in TO-detection is higher than in IC-detection

Introduction:. Most of the published papers on AI based diagnosis have focused on the algorithm's diagnostic performance in a ‘binary’ setting (i.e. disease vs no disease). However, no study evaluated the actual value for the clinicians of an AI based approach in diagnostic. Detection of Traumatic thoracolumbar (TL) fractures is challenging on planar radiographs, resulting in significant rates of missed diagnoses (30-60%), thus constituting a field in which a performance improvement is needed. Aim of this study is therefore to evaluate the value provided by AI generated saliency maps (SM), i.e. the maps that highlight the AI identified region of interests. Methods:. An AI model aimed at identifying TL fractures on plain radiographs was trained and tested on 567 single vertebrae images. Three expert spine surgeons established the Ground Truth (GT) using CT and MRI to confirm the presence of the fracture. From the test set, 12 cases (6 with a GT of fracture and 6 with a GT of no fracture, associated with varying levels of algorithm confidence) were selected and the corresponding SMs were generated and shown to 7 independent evaluators with different grade of experience; the evaluators were requested to: (1) identify the presence or absence of a fracture before and after the saliency map was shown; (2) grade, with a score from 1 (low) to 6 (high) the pertinency (correlation between the map and the human diagnosis), and the utility (the perceived utility in confirming or not the initial diagnosis) of the SM. Furthermore, the usefulness of the SM was evaluated through the rate of correct change in diagnosis after the maps had been shown. Finally, the obtained scores were correlated with the algorithm confidence for the specific case. Results:. Of the selected maps, 8 had an agreement between the AI diagnosis and the GT, while in 4 the diagnosis was discordant (67% accuracy). The pertinency of the map was found higher when the AI diagnosis was the same as the GT and the human diagnosis (respectively p-value = .021 and <.000). A positive and significant correlation between the AI confidence score and the perceived utility (Spearman: 27%, p-value=.0-27) was found. Furthermore, evaluator with experience < 5 year found the maps more useful than the experts (z-score=2.004; p-value=.0455). Among the 84 evaluation we found 12 diagnostic errors in respect to the GT, 6 (50%) of which were reverted after the saliency map evaluation (z statistic = 1.25 and p-value = .21). Discussion:. The perceived utility of AI generated SM correlate with the model confidence in the diagnosis. This highlights the fact that to be considered helpful, the AI must provide not only the diagnosis but also the case specific confidence. Furthermore, the perceived utility was higher among less experienced users, but overall, the SM were useful in improving the human diagnostic accuracy. Therefore, in this setting, the AI enhanced approach provides value in improving the human performance

Acetabular morphology and orientation differs from ethnic group to another. Thus, investigating the normal range of the parameters that are used to assess both was a matter of essence. Nevertheless, the main aim of this study was clarification the relationship between acetabular inclination (AI) and acetabular and femoral head arcs’ radii (AAR and FHAR). A cross-sectional retrospective study that had been done in a tertiary center where Computed tomography abdomen scouts’ radiographs of non-orthopedics patients were included. They had no history of pelvic or hips’ related symptoms or fractures in femur or pelvis. A total of 84 patients was included with 52% of them were females. The mean of age was 30.38± 5.48. Also, Means of AI were 38.02±3.89 and 40.15±4.40 (P 0.02, significant gender difference) for males and females, respectively. Nonetheless, Head neck shaft angle (HNSA) means were 129.90±5.55 and 130.72±6.62 for males and females, respectively. However, AAR and FHAR means for males and females were 21.3±3.1mm, 19.9±3.1mm, P 0.04 and 19.7±3.1mm, 18.1±2.7mm, P 0.019, respectively. In addition, negative significant correlations were detected between AI against AAR, FHAR, HNSA and body mass index (BMI) (r 0.529, P ≤0.0001, r 0.445, P ≤0.0001, r 0.238, P 0.029, r 0.329, P ≤0.007, respectively). On the other hand, high BMI was associated with AAR and FHAR (r 0.577, P 0.0001 and r 0.266, p 0.031, respectively). This study shows that high AI is correlated with lower AAR, FHAR. Each ethnic group has its own normal values that must be studied to tailor the path for future implications in clinical setting

Critical shoulder angle (CSA), lateral acromial angle (LAA), and acromion index (AI) are common radiologic parameters used to distinguish between patients with rotator cuff tears (RCT) and those with an intact rotator cuff. This study aims to assess the predictive power of these parameters in degenerative RCT. This retrospective study included data from 92 patients who were divided into two groups: the RCT group, which included 47 patients with degenerative full-thickness supraspinatus tendon tears, and a control group of 45 subjects without tears. CSA, AI, and LAA measurements from standardized true anteroposterior radiographs were independently derived and analyzed by two orthopedic surgeons. Receiver operating characteristic (ROC) analyses were performed to determine the cutoff values. No significant differences were found between patients in the RCT and control groups in age (p = 0.079), gender (p = 0.804), or injury side (p = 0.552). Excellent inter-observer reliability was seen for CSA, LAA, and AI values. Mean CSA (38.1°) and AI (0.72) values were significantly larger in the RCT group than in the control group (34.56° and 0.67°, respectively, p < 0.001) with no significant difference between groups for LAA (RCT, 77.99° vs. control, 79.82°; p = 0.056). ROC analysis yielded an area under the curve (AUC) of 0.815 for CSA with a cutoff value of 37.95°, and CSA was found to be the strongest predictor of the presence of a RCT, followed by AI with an AUC of 0.783 and a cutoff value of 0.705. We conclude that CSA and AI may be useful predictive factors for degenerative RCT in the Turkish population

Introduction. The recent introduction of Chatbots has provided an interactive medium to answer patient questions. The accuracy of responses with these programs in limb lengthening and reconstruction surgery has not previously been determined. Therefore, the purpose of this study was to assess the accuracy of answers from 3 free AI chatbot platforms to 23 common questions regarding treatment for limb lengthening and reconstruction. Method. We generated a list of 23 common questions asked by parents before their child's limb lengthening and reconstruction surgery. Each question was posed to three different AI chatbots (ChatGPT 3.5 [OpenAI], Google Bard, and Microsoft Copilot [Bing!]) by three different answer retrievers on separate computers between November 17 and November 18, 2023. Responses were only asked one time to each chatbot by each answer retriever. Nine answers (3 answer retrievers × 3 chatbots) were randomized and platform-blinded prior to rating by three orthopedic surgeons. The 4-point rating system reported by Mika et al. was used to grade all responses. Result. ChatGPT had the best response accuracy score (RAS) with a mean score of 1.73 ± 0.88 across all three raters (range of means for all three raters – 1.62 – 1.81) and a median score of 2. The mean response accuracy scores for Google Bard and Microsoft Copilot were 2.32 ± 0.97 and 3.14 ± 0.82, respectively. This ranged from 2.10 – 2.48 and 2.86 – 3.54 for Google Bard and Microsoft Copilot, respectively. The differences between the mean RAS scores were statistically significant (p < 0.0001). The median scores for Google Bard and Microsoft Copilot were 2 and 3, respectively. Conclusion. Using the Response Accuracy Score, the responses from ChatGPT were determined to be satisfactory, requiring minimal clarification, while the responses from Microsoft Copilot were either satisfactory, requiring moderate clarification, or unsatisfactory, requiring substantial clarification

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

Purpose. Collagen-rich structures of the knee are prone to damage through acute injury or chronic “wear and tear”. Collagen becomes more disorganised in degenerative tissue e.g. osteoarthritis. An alignment index (AI) used to analyse orientation distribution of collagen-rich structures is presented. Method. A healthy caprine knee was scanned in a Siemens Verio 3T Scanner. The caprine knee was rotated and scanned in nine directions to the main magnetic field B. 0. A 3D PD SPACE sequence with isotropic 1×1×1mm voxels (TR1300ms, TE13ms, FOV256mm,) was optimised to allow for a greater angle-sensitive contrast. For each collagen-rich voxel the orientation vector is computed using Szeverenyi and Bydder's method. Each orientation vector reflects the net effect of all the fibres comprised within a voxel. The assembly of all unit vectors represents the fibre orientation map. Alignment Index (AI) in any direction is defined as a ratio of the fraction of orientations within 20° (solid angle) centred in that direction to the same fraction in a random (flat) case. In addition, AI is normalised in such a way that AI=0 indicates isotropic collagen alignment. Increasing AI values indicate increasingly aligned structures: AI=1 indicates that all collagen fibres are orientated within the cone of 20° centred at the selected direction. AI = (nM - nRnd)/(nTotal - nRnd) if nM >= nRnd. AI = 0 if nM < nRnd. Where:. nM is a number of reconstructed orientations that are within a cone of 20° centred in selected direction. nRnd is a number of random orientations within a cone of 20° around selected direction. nTotal is a number of collagen reach voxels. By computing AI for a regular gridded orientation space we are able to visualise change of AI on a hemisphere facilitating understanding of the collagen fibre orientation distribution. Results. The patella tendon had an AI=0.6453. The Anterior Cruciate Ligament (ACL) had an AI=0.2732. The meniscus had an AI=0.1847. Discussion. The most aligned knee structure is the patella tendon where the collagen fibres align with the skeleton to transmit forces through bones and muscles. This structure had the AI closest to 1. The ACL had the second highest AI and is composed of two fibre bundles aligned diagonally across the knee. The meniscus acts as a shock absorber and is made up of vertical, radial and circumferential fibres which disperse forces more equally. The complexity of the meniscal structure resulted in the lowest AI. To date, this technique has only been performed with healthy tissue; the AI may become closer to zero if there is damage disrupting the collagen fibre alignment. The AI can further our understanding of collagen orientation distribution and could be used as a quantitative, non-invasive measure of structural health

In the last decades, the use of artificial intelligence (AI) has been increasingly investigated in intervertebral disc degeneration (IDD) and chronic low back pain (LBP) research. To date, several AI-based cutting-edge technologies, such as computer vision, computer-assisted diagnosis, decision support system and natural language processing have been utilized to optimize LBP prevention, diagnosis, and treatment. This talk will provide an outline on contemporary AI applications to IDD and LBP research, with a particular attention towards actual knowledge gaps and promising innovative tools

Artificial Intelligence (AI) is becoming more powerful but is barely used to counter the growth in health care burden. AI applications to increase efficiency in orthopedics are rare. We questioned if (1) we could train machine learning (ML) algorithms, based on answers from digitalized history taking questionnaires, to predict treatment of hip osteoartritis (either conservative or surgical); (2) such an algorithm could streamline clinical consultation. Multiple ML models were trained on 600 annotated (80% training, 20% test) digital history taking questionnaires, acquired before consultation. Best performing models, based on balanced accuracy and optimized automated hyperparameter tuning, were build into our daily clinical orthopedic practice. Fifty patients with hip complaints (>45 years) were prospectively predicted and planned (partly blinded, partly unblinded) for consultation with the physician assistant (conservative) or orthopedic surgeon (operative). Tailored patient information based on the prediction was automatically sent to a smartphone app. Level of evidence: IV. Random Forest and BernoulliNB were the most accurate ML models (0.75 balanced accuracy). Treatment prediction was correct in 45 out of 50 consultations (90%), p<0.0001 (sign and binomial test). Specialized consultations where conservatively predicted patients were seen by the physician assistant and surgical patients by the orthopedic surgeon were highly appreciated and effective. Treatment strategy of hip osteoartritis based on answers from digital history taking questionnaires was accurately predicted before patients entered the hospital. This can make outpatient consultation scheduling more efficient and tailor pre-consultation patient education

The aim of this scoping review is to understand the extent and type of evidence in relation to the use of guided growth for correcting rotational deformities of long bones. Guided growth is routinely used to correct angular deformities in long bones in children. It has also been proven to be a viable method to correct rotational deformities, but the concept is not yet fully examined. Databases searched include Medline, Embase, Cochrane Library, Web of Science and Google Scholar. All identified citations were uploaded into Rayyan.ai and screened by at least two reviewers. The search resulted in 3569 hits. 14 studies were included: 1 review, 3 clinical trials and 10 pre-clinical trials. Clinical trials: a total of 21 children (32 femurs and 5 tibiae) were included. Surgical methods were 2 canulated screws connected by cable, PediPlates obliquely oriented, and separated Hinge Plates connected by FiberTape. Rotation was achieved in all but 1 child. Adverse effects reported include limb length discrepancy (LLD), knee stiffness and rebound of rotation after removal of tethers. 2 pre-clinical studies were ex-vivo studies, 1 using 8-plates on Sawbones and 1 using a novel z-shaped plates on human cadaver femurs. There were 5 lapine studies (2 using femoral plates, 2 using tibial plates and 1 using an external device on tibia), 1 ovine (external device on tibia), 1 bovine (screws and cable on metacarp) and a case-report on a dog that had an external device spanning from femur to tibia. Rotation was achieved in all studies. Adverse effects reported include implant extrusions, LLD, articular deformities, joint stiffness and rebound. All included studies conclude that guided growth is a viable treatment for rotational deformities of long bones, but there is great variation in models and surgical methods used, and in reported adverse effects

Introduction. Patients (2.7M in EU) with positive cancer prognosis frequently develop metastases (≈1M) in their remaining lifetime. In 30-70% cases, metastases affect the spine, reducing the strength of the affected vertebrae. Fractures occur in ≈30% patients. Clinicians must choose between leaving the patient exposed to a high fracture risk (with dramatic consequences) and operating to stabilise the spine (exposing patients to unnecessary surgeries). Currently, surgeons rely on their sole experience. This often results in to under- or over-treatment. The standard-of-care are scoring systems (e.g. Spine Instability Neoplastic Score) based on medical images, with little consideration of the spine biomechanics, and of the structure of the vertebrae involved. Such scoring systems fail to provide clear indications in ≈60% patients. Method. The HEU-funded METASTRA project is implemented by biomechanicians, modellers, clinicians, experts in verification, validation, uncertainty quantification and certification from 15 partners across Europe. METASTRA aims to improve the stratification of patients with vertebral metastases evaluating their risk of fracture by developing dedicated reliable computational models based on Explainable Artificial Intelligence (AI) and on personalised Physiology-based biomechanical (VPH) models. Result. The METASTRA-AI model is expected to be able to stratify most patients with limited effort end cost, based on parameters extracted semi-automatically from the medical files and images. The cases which are not reliably stratified through the AI model, are examined through a more detailed and personalised biomechanical VPH model. These METASTRA numerical tools are trained through an unprecedentedly large multicentric retrospective study (2000 cases) and validated against biomechanical ex vivo experiments (120 specimens). Conclusion. The METASTRA decision support system is tested in a multicentric prospective observational study (200 patients). The METASTRA approach is expected to cut down the indeterminate diagnoses from the current 60% down to 20% of cases. METASTRA project funded by the European Union, HEU topic HLTH-2022-12-01, grant 101080135

Chronic inflammatory events have been associated to almost every chronic disease, including cardiovascular-, neurodegenerative- and autoimmune- diseases, cancer, and host-implant rejection. Given the toll of chronic inflammation in healthcare and socioeconomical costs developing strategies to resolve and control chronic states of inflammation remain a priority for the significant benefit of patients.

Macrophages (Mφ) hold a central role both in the initiation and resolution of inflammatory events, assuming different functional profiles. The outstanding features of Mφ counting with the easy access to tissues, and the extended networking make Mφ excellent candidates for precision therapy. Moreover, sophisticated macrophage-oriented systems could offer innovative immune-regulatory alternatives to effectively regulate chronic environments that traditional pharmacological agents cannot provide.

We propose magnetically assisted systems for balancing Mφ functions at the injury site. This platform combines polymers, inflammatory miRNA antagonists and magnetically responsive nanoparticles to stimulate Mφ functions towards pro-regenerative phenotypes. Strategies with magnetically assisted systems include contactless presentation of immune-modulatory molecules, cell internalization of regulatory agents for functional programming via magnetofection, and multiple payload delivery and release.

Overall, Mφ-oriented systems stimulated pro-regenerative functions of Mφ supporting magnetically assisted theranostic nanoplatforms for precision therapies, envisioning safer and more effective control over the distribution of sensitive nanotherapeutics for the treatments of chronical inflammatory conditions.

Acknowledgements: ERC CoG MagTendon No.772817; FCT Doctoral Grant SFRD/BD/144816/2019, and TERM

RES Hub (Norte-01-0145-FEDER-022190).

Introduction and Objective. Up to 30% of thoracolumbar (TL) fractures are missed in the emergency room. Failure to identify these fractures can result in neurological injuries up to 51% of the casesthis article aimed to clarify the incidence and risk factors of traumatic fractures in China. The China National Fracture Study (CNFS. Obtaining sagittal and anteroposterior radiographs of the TL spine are the first diagnostic step when suspecting a traumatic injury. In most cases, CT and/or MRI are needed to confirm the diagnosis. These are time and resource consuming. Thus, reliably detecting vertebral fractures in simple radiographic projections would have a significant impact. We aim to develop and validate a deep learning tool capable of detecting TL fractures on lateral radiographs of the spine. The clinical implementation of this tool is anticipated to reduce the rate of missed vertebral fractures in emergency rooms. Materials and Methods. We collected sagittal radiographs, CT and MRI scans of the TL spine of 362 patients exhibiting traumatic vertebral fractures. Cases were excluded when CT and/or MRI where not available. The reference standard was set by an expert group of three spine surgeons who conjointly annotated (fracture/no-fracture and AO Classification) the sagittal radiographs of 171 cases. CT and/or MRI were used confirm the presence and type of the fracture in all cases. 302 cropped vertebral images were labelled “fracture” and 328 “no fracture”. After augmentation, this dataset was then used to train, validate, and test deep learning classifiers based on the ResNet18 and VGG16 architectures. To ensure that the model's prediction was based on the correct identification of the fracture zone, an Activation Map analysis was conducted. Results. Vertebras T12 to L2 were the most frequently involved, accounting for 48% of the fractures. Accuracies of 88% and 84% were obtained with ResNet18 and VGG16 respectively. The sensitivity was 89% with both architectures but ResNet18 had a significantly higher specificity (88%) compared to VGG16 (79%). The fracture zone used was precisely identified in 81% of the heatmaps. Conclusions. Our AI model can accurately identify anomalies suggestive of TL vertebral fractures in sagittal radiographs precisely identifying the fracture zone within the vertebral body

Introduction

Guidelines from the North American Spine Society (2009 and 2013) are the best evidence-based instructions on venous thromboembolism (VTE) and antibiotic prophylaxis in spinal surgery. NICE guidelines exist for VTE prophylaxis but do not specifically address spinal surgery. In addition, the ruling of the UK Supreme Court in 2015 resulted in new guidance on consent being published by the Royal College of Surgeons of England (RCSEng). This study assesses our compliance in antibiotic, VTE prophylaxis and consent in spinal surgery against both US and UK standards.

Tendon injuries are a worldwide problem affecting several age groups and stem cell based therapies hold potential for tendon strategies guiding tendon regeneration.

Tendons rely on mechano-sensing mechanisms that regulate homeostasis and influence regeneration. The mechanosensitive receptors available in cell membranes sense the external stimuli and initiate mechanotransduction processes. Activins are members of the TGF-β superfamily which participate in several tendon biological processes. It is envisioned that the activation of the activin receptor, trigger downstream Smad2/3 pathway thus regulating the transcription of tenogenic genes driving stem cell differentiation.

In this work, we propose to target the Activin receptor type IIA (ActRIIA) in human adipose stem cells (hASCs), inducing hASCs commitment towards the tenogenic lineage. Since mechanotransduction can be remotely triggered through magnetic actuation combined with magnetic nanoparticles (MNPs), we stimulated hASCs tagged complexes using a vertical oscillating magnetic bioreactor (MICA Biosystems Ltd). Carboxyl functionalised MNPs (Micromod) were coated with anti-ActRIIA antibody (Abcam) by carbodiimide activation. hASCs were then cultured with MNPs-anti-ActRIIA for 14days with or without magnetic exposure (1Hz, 1h/every other day). hASCs cultured alone in αMEM (negative control) or in αMEM supplemented with ActivinA (R&D systems) (positive control of ActRIIA activation) were used as experimental controls. The tenogenic commitment of hASCs was assessed by real time RT-PCR, immunocytochemistry and quantification of collagen and non-collagenous proteins. Moreover, the phosphorylation of Smad2/3 was also evaluated on hASCs incubated for 2, 10, or 30min under magnetic stimulated (1Hz) and non-stimulated conditions.

The increased gene expression of tendon related markers and higher ECM proteins deposition suggests that remote magnetic activation of ActRIIA promotes effectively hASCs tenogenic commitment. Furthermore, the detection of phospho-Smad2/3 proteins by ELISA (Cell Signaling Technology) was significantly more intense after 10min in hASCs under magnetic stimulation and in comparison to the control groups. These outcomes suggest that ActRIIA is a mechanosensitive receptor that can be remotely activated upon magnetic stimulation.

In conclusion, remotely activation of MNPs tagged hASCs has potential for modulating tenogenic differentiation of stem cells envisioning successful cell therapies for tendon regeneration.

Platelet-leucocyte gel (PLG), a new biotechnological blood product, has hitherto been used primarily to treat chronic ulcers and to promote soft-tissue and bone regeneration in a wide range of medical fields. In this study, the antimicrobial efficacy of PLG against Staphylococcus aureus (ATCC 25923) was investigated in a rabbit model of osteomyelitis. Autologous PLG was injected into the tibial canal after inoculation with Staph. aureus. The prophylactic efficacy of PLG was evaluated by microbiological, radiological and histological examination. Animal groups included a treatment group that received systemic cefazolin and a control group that received no treatment.

Treatment with PLG or cefazolin significantly reduced radiological and histological severity scores compared to the control group. This result was confirmed by a significant reduction in the infection rate and the number of viable bacteria. Although not comparable to cefazolin, PLG exhibited antimicrobial efficacy in vivo and therefore represents a novel strategy to prevent bone infection in humans.

We investigated the effect of stimulation with a pulsed electromagnetic field on the osseointegration of hydroxyapatite in cortical bone in rabbits. Implants were inserted into femoral cortical bone and were stimulated for six hours per day for three weeks.

Electromagnetic stimulation improved osseointegration of hydroxyapatite compared with animals which did not receive this treatment in terms of direct contact with the bone, the maturity of the bone and mechanical fixation. The highest values of maximum push-out force (F_max) and ultimate shear strength (σ_u) were observed in the treated group and differed significantly from those of the control group at three weeks (F_max; p < 0.0001; σ_u, p < 0.0005).