Recommender-based bone tumour classification with radiographs-a link to the past.

OBJECTIVES: To develop an algorithm to link undiagnosed patients to previous patient histories based on radiographs, and simultaneous classification of multiple bone tumours to enable early and specific diagnosis. MATERIALS AND METHODS: For this retrospective study, data from 2000 to 2021 were curated from our database by two orthopaedic surgeons, a radiologist and a data scientist. Patients with complete clinical and pre-therapy radiographic data were eligible. To ensure feasibility, the ten most frequent primary tumour entities, confirmed histologically or by tumour board decision, were included. We implemented a ResNet and transformer model to establish baseline results. Our method extracts image features using deep learning and then clusters the k most similar images to the target image using a hash-based nearest-neighbour recommender approach that performs simultaneous classification by majority voting. The results were evaluated with precision-at-k, accuracy, precision and recall. Discrete parameters were described by incidence and percentage ratios. For continuous parameters, based on a normality test, respective statistical measures were calculated. RESULTS: Included were data from 809 patients (1792 radiographs; mean age 33.73 ± 18.65, range 3-89 years; 443 men), with Osteochondroma (28.31%) and Ewing sarcoma (1.11%) as the most and least common entities, respectively. The dataset was split into training (80%) and test subsets (20%). For k = 3, our model achieved the highest mean accuracy, precision and recall (92.86%, 92.86% and 34.08%), significantly outperforming state-of-the-art models (54.10%, 55.57%, 19.85% and 62.80%, 61.33%, 23.05%). CONCLUSION: Our novel approach surpasses current models in tumour classification and links to past patient data, leveraging expert insights. CLINICAL RELEVANCE STATEMENT: The proposed algorithm could serve as a vital support tool for clinicians and general practitioners with limited experience in bone tumour classification by identifying similar cases and classifying bone tumour entities. KEY POINTS: • Addressed accurate bone tumour classification using radiographic features. • Model achieved 92.86%, 92.86% and 34.08% mean accuracy, precision and recall, respectively, significantly surpassing state-of-the-art models. • Enhanced diagnosis by integrating prior expert patient assessments.

LINAS-Score: prognostic model for mortality assessment in patients with cirrhotic liver and infected ascites.

BACKGROUND AND AIM: Patients with liver cirrhosis often face a grave threat from infected ascites (IA). However, a well-established prognostic model for this complication has not been established in routine clinical practice. Therefore, we aimed to assess mortality risk in patients with liver cirrhosis and IA. METHODS: We conducted a retrospective study across three tertiary hospitals, enrolling 534 adult patients with cirrhotic liver and IA, comprising 465 with spontaneous bacterial peritonitis (SBP), 34 with bacterascites (BA), and 35 with secondary peritonitis (SP). To determine the attributable mortality risk linked to IA, these patients were matched with 122 patients with hydropic decompensated liver cirrhosis but without IA. Clinical, laboratory, and microbiological parameters were assessed for their relation to mortality using univariable analyses and a multivariable random forest model (RFM). Least absolute shrinkage and selection operator (Lasso) regression model was used to establish an easy-to-use mortality prediction score. RESULTS: The in-hospital mortality risk was highest for SP (39.0%), followed by SBP (26.0%) and BA (25.0%). Besides illness severity markers, microbiological parameters, such as Candida spp., were identified as the most significant indicators for mortality. The Lasso model determined 15 parameters with corresponding scores, yielding good discriminatory power (area under the receiver operating characteristics curve = 0.89). Counting from 0 to 83, scores of 20, 40, 60, and 80 corresponded to in-hospital mortalities of 3.3%, 30.8%, 85.2%, and 98.7%, respectively. CONCLUSION: We developed a promising mortality prediction score for IA, highlighting the importance of microbiological parameters in conjunction with illness severity for assessing patient outcomes.

Numerical evaluation of internal femur osteosynthesis based on a biomechanical model of the loading in the proximal equine hindlimb.

Femoral fractures are often considered lethal for adult horses because femur osteosynthesis is still a surgical challenge. For equine femur osteosynthesis, primary stability is essential, but the detailed physiological forces occurring in the hindlimb are largely unknown. The objective of this study was to create a numerical testing environment to evaluate equine femur osteosynthesis based on physiological conditions. The study was designed as a finite element analysis (FEA) of the femur using a musculoskeletal model of the loading situation in stance. Relevant forces were determined in the musculoskeletal model via optimization. The treatment of four different fracture types with an intramedullary nail was investigated in FEA with loading conditions derived from the model. The analyzed diaphyseal fracture types were a transverse (TR) fracture, two oblique fractures in different orientations (OB-ML: medial-lateral and OB-AP: anterior-posterior) and a "gap" fracture (GAP) without contact between the fragments. For the native femur, the most relevant areas of increased stress were located distally to the femoral head and proximally to the caudal side of the condyles. For all fracture types, the highest stresses in the implant material were present in the fracture-adjacent screws. Maximum compressive (-348 MPa) and tensile stress (197 MPa) were found for the GAP fracture, but material strength was not exceeded. The mathematical model was able to predict a load distribution in the femur of the standing horse and was used to assess the performance of internal fixation devices via FEA. The analyzed intramedullary nail and screws showed sufficient stability for all fracture types.

The mesenteric entry site as a potential weak point in gastrointestinal anastomoses - findings from an ex-vivo biomechanical analysis.

PURPOSE: Gastrointestinal disorders frequently necessitate surgery involving intestinal resection and anastomosis formation, potentially leading to severe complications like anastomotic leakage (AL) which is associated with increased morbidity, mortality, and adverse oncologic outcomes. While extensive research has explored the biology of anastomotic healing, there is limited understanding of the biomechanical properties of gastrointestinal anastomoses, which was aimed to be unraveled in this study. METHODS: An ex-vivo model was developed for the biomechanical analysis of 32 handsewn porcine end-to-end anastomoses, using interrupted and continuous suture techniques subjected to different flow models. While multiple cameras captured different angles of the anastomosis, comprehensive data recording of pressure, time, and temperature was performed simultaneously. Special focus was laid on monitoring time, location and pressure of anastomotic leakage (LP) and bursting pressures (BP) depending on suture techniques and flow models. RESULTS: Significant differences in LP, BP, and time intervals were observed based on the flow model but not on the suture techniques applied. Interestingly, anastomoses at the insertion site of the mesentery exhibited significantly higher rates of leakage and bursting compared to other sections of the anastomosis. CONCLUSION: The developed ex-vivo model facilitated comparable, reproducible, and user-independent biomechanical analyses. Assessing biomechanical properties of anastomoses offers an advantage in identifying technical weak points to refine surgical techniques, potentially reducing complications like AL. The results indicate that mesenteric insertion serves as a potential weak spot for AL, warranting further investigations and refinements in surgical techniques to optimize outcomes in this critical area of anastomotic procedures.

Effect of different decellularization protocols on reendothelialization with human cells for a perfused renal bioscaffold of the rat.

BACKGROUND: Scaffolds for tissue engineering can be received by whole organ decellularization while maintaining the site-specific extracellular matrix and the vascular tree. One among other decellularization techniques is the perfusion-based method using specific agents e.g. SDS for the elimination of cellular components. While SDS can disrupt the composition of the extracellular matrix and impair the adherence and growth of site-specific cells there are indications that xenogeneic cell types may benefit from protein denaturation by using higher detergent concentrations. The aim of this work is to investigate the effect of two different SDS-concentrations (i.e. 0.66% and 3%) on the ability of human endothelial cells to adhere and proliferate in an acellular rat kidney scaffold. MATERIAL AND METHODS: Acellular rat kidney scaffold was obtained by perfusion-based decellularization through the renal artery using a standardized protocol including SDS at concentrations of 0.66% or 3%. Subsequently cell seeding was performed with human immortalized endothelial cells EA.hy 926 via the renal artery. Recellularized kidneys were harvested after five days of pressure-controlled dynamic culture followed sectioning, histochemical and immunohistochemical staining as well as semiquantitative analysis. RESULTS: Efficacy of decellularization was verified by absence of cellular components as well as preservation of ultrastructure and adhesive proteins of the extracellular matrix. In semiquantitative analysis of recellularization, cell count after five days of dynamic culture more than doubled when using the gentle decellularization protocol with a concentration of SDS at 0.66% compared to 3%. Detectable cells maintained their endothelial phenotype and presented proliferative behavior while only a negligible fraction underwent apoptosis. CONCLUSION: Recellularization of acellular kidney scaffold with endothelial cells EA.hy 926 seeded through the renal artery benefits from gentle decellularization procedure. Because of that, decellularization with a SDS concentration at 0.66% should be preferred in further studies and coculture experiments.

SAM-X: sorting algorithm for musculoskeletal x-ray radiography.

OBJECTIVE: To develop a two-phased deep learning sorting algorithm for post-X-ray image acquisition in order to facilitate large musculoskeletal image datasets according to their anatomical entity. METHODS: In total, 42,608 unstructured and pseudonymized radiographs were retrieved from the PACS of a musculoskeletal tumor center. In phase 1, imaging data were sorted into 1000 clusters by a self-supervised model. A human-in-the-loop radiologist assigned weak, semantic labels to all clusters and clusters with the same label were merged. Three hundred thirty-two non-musculoskeletal clusters were discarded. In phase 2, the initial model was modified by "injecting" the identified labels into the self-supervised model to train a classifier. To provide statistical significance, data split and cross-validation were applied. The hold-out test set consisted of 50% external data. To gain insight into the model's predictions, Grad-CAMs were calculated. RESULTS: The self-supervised clustering resulted in a high normalized mutual information of 0.930. The expert radiologist identified 28 musculoskeletal clusters. The modified model achieved a classification accuracy of 96.2% and 96.6% for validation and hold-out test data for predicting the top class, respectively. When considering the top two predicted class labels, an accuracy of 99.7% and 99.6% was accomplished. Grad-CAMs as well as final cluster results underlined the robustness of the proposed method by showing that it focused on similar image regions a human would have considered for categorizing images. CONCLUSION: For efficient dataset building, we propose an accurate deep learning sorting algorithm for classifying radiographs according to their anatomical entity in the assessment of musculoskeletal diseases. KEY POINTS: • Classification of large radiograph datasets according to their anatomical entity. • Paramount importance of structuring vast amounts of retrospective data for modern deep learning applications. • Optimization of the radiological workflow and increase in efficiency as well as decrease of time-consuming tasks for radiologists through deep learning.

Prediction of complications and surgery duration in primary TKA with high accuracy using machine learning with arthroplasty-specific data.

PURPOSE: The number of primary total knee arthroplasties (TKA) is expected to rise constantly. For patients and healthcare providers, the early identification of risk factors therefore becomes increasingly fundamental in the context of precision medicine. Others have already investigated the detection of risk factors by conducting literature reviews and applying conventional statistical methods. Since the prediction of events has been moderately accurate, a more comprehensive approach is needed. Machine learning (ML) algorithms have had ample success in many disciplines. However, these methods have not yet had a significant impact in orthopaedic research. The selection of a data source as well as the inclusion of relevant parameters is of utmost importance in this context. In this study, a standardized approach for ML in TKA to predict complications during surgery and an irregular surgery duration using data from two German arthroplasty-specific registries was evaluated. METHODS: The dataset is based on two initiatives of the German Society for Orthopaedics and Orthopaedic Surgery. A problem statement and initial parameters were defined. After screening, cleaning and preparation of these datasets, 864 cases of primary TKA (2016-2019) were gathered. The XGBoost algorithm was chosen and applied with a hyperparameter search, a cross validation and a loss weighting to cope with class imbalance. For final evaluation, several metrics (accuracy, sensitivity, specificity, AUC) were calculated. RESULTS: An accuracy of 92.0%, sensitivity of 34.8%, specificity of 95.8%, and AUC of 78.0% were achieved for predicting complications in primary TKA and 93.4%, 74.0%, 96.3%, and 91.6% for predicting irregular surgery duration, respectively. While traditional statistics (correlation coefficient) could not find any relevant correlation between any two parameters, the feature importance revealed several non-linear outcomes. CONCLUSION: In this study, a feasible ML model to predict outcomes of primary TKA with very promising results was built. Complex correlations between parameters were detected, which could not be recognized by conventional statistical analysis. Arthroplasty-specific data were identified as relevant by the ML model and should be included in future clinical applications. Furthermore, an interdisciplinary interpretation as well as evaluation of the results by a data scientist and an orthopaedic surgeon are of paramount importance. LEVEL OF EVIDENCE: Level IV.

Validation of a Robotic Testbench for Evaluating Biomechanical Effects of Implant Rotation in Total Knee Arthroplasty on a Cadaveric Specimen.

In this study, we developed and validated a robotic testbench to investigate the biomechanical compatibility of three total knee arthroplasty (TKA) configurations under different loading conditions, including varus-valgus and internal-external loading across defined flexion angles. The testbench captured force-torque data, position, and quaternion information of the knee joint. A cadaver study was conducted, encompassing a native knee joint assessment and successive TKA testing, featuring femoral component rotations at -5°, 0°, and +5° relative to the transepicondylar axis of the femur. The native knee showed enhanced stability in varus-valgus loading, with the +5° external rotation TKA displaying the smallest deviation, indicating biomechanical compatibility. The robotic testbench consistently demonstrated high precision across all loading conditions. The findings demonstrated that the TKA configuration with a +5° external rotation displayed the minimal mean deviation under internal-external loading, indicating superior joint stability. These results contribute meaningful understanding regarding the influence of different TKA configurations on knee joint biomechanics, potentially influencing surgical planning and implant positioning. We are making the collected dataset available for further biomechanical model development and plan to explore the 6 Degrees of Freedom (DOF) robotic platform for additional biomechanical analysis. This study highlights the versatility and usefulness of the robotic testbench as an instrumental tool for expanding our understanding of knee joint biomechanics.

Applications of machine learning for imaging-driven diagnosis of musculoskeletal malignancies-a scoping review.

Musculoskeletal malignancies are a rare type of cancer. Consequently, sufficient imaging data for machine learning (ML) applications is difficult to obtain. The main purpose of this review was to investigate whether ML is already having an impact on imaging-driven diagnosis of musculoskeletal malignancies and what the respective reasons for this might be. A scoping review was conducted by a radiologist, an orthopaedic surgeon and a data scientist to identify suitable articles based on the PRISMA statement. Studies meeting the following criteria were included: primary malignant musculoskeletal tumours, machine/deep learning application, imaging data or data retrieved from images, human/preclinical, English language and original research. Initially, 480 articles were found and 38 met the eligibility criteria. Several continuous and discrete parameters related to publication, patient distribution, tumour specificities, ML methods, data and metrics were extracted from the final articles. For the synthesis, diagnosis-oriented studies were further examined by retrieving the number of patients and labels and metric scores. No significant correlations between metrics and mean number of samples were found. Several studies presented that ML could support imaging-driven diagnosis of musculoskeletal malignancies in distinct cases. However, data quality and quantity must be increased to achieve clinically relevant results. Compared to the experience of an expert radiologist, the studies used small datasets and mostly included only one type of data. Key to critical advancement of ML models for rare diseases such as musculoskeletal malignancies is a systematic, structured data collection and the establishment of (inter)national networks to obtain substantial datasets in the future. KEY POINTS: • Machine learning does not yet significantly impact imaging-driven diagnosis for musculoskeletal malignancies compared to other disciplines such as lung, breast or CNS cancer. • Research in the area of musculoskeletal tumour imaging and machine learning is still very limited. • Machine learning in musculoskeletal tumour imaging is impeded by insufficient availability of data and rarity of the disease.

Development and evaluation of machine learning models based on X-ray radiomics for the classification and differentiation of malignant and benign bone tumors.

OBJECTIVES: To develop and validate machine learning models to distinguish between benign and malignant bone lesions and compare the performance to radiologists. METHODS: In 880 patients (age 33.1 ± 19.4 years, 395 women) diagnosed with malignant (n = 213, 24.2%) or benign (n = 667, 75.8%) primary bone tumors, preoperative radiographs were obtained, and the diagnosis was established using histopathology. Data was split 70%/15%/15% for training, validation, and internal testing. Additionally, 96 patients from another institution were obtained for external testing. Machine learning models were developed and validated using radiomic features and demographic information. The performance of each model was evaluated on the test sets for accuracy, area under the curve (AUC) from receiver operating characteristics, sensitivity, and specificity. For comparison, the external test set was evaluated by two radiology residents and two radiologists who specialized in musculoskeletal tumor imaging. RESULTS: The best machine learning model was based on an artificial neural network (ANN) combining both radiomic and demographic information achieving 80% and 75% accuracy at 75% and 90% sensitivity with 0.79 and 0.90 AUC on the internal and external test set, respectively. In comparison, the radiology residents achieved 71% and 65% accuracy at 61% and 35% sensitivity while the radiologists specialized in musculoskeletal tumor imaging achieved an 84% and 83% accuracy at 90% and 81% sensitivity, respectively. CONCLUSIONS: An ANN combining radiomic features and demographic information showed the best performance in distinguishing between benign and malignant bone lesions. The model showed lower accuracy compared to specialized radiologists, while accuracy was higher or similar compared to residents. KEY POINTS: • The developed machine learning model could differentiate benign from malignant bone tumors using radiography with an AUC of 0.90 on the external test set. • Machine learning models that used radiomic features or demographic information alone performed worse than those that used both radiomic features and demographic information as input, highlighting the importance of building comprehensive machine learning models. • An artificial neural network that combined both radiomic and demographic information achieved the best performance and its performance was compared to radiology readers on an external test set.

Dysregulation of Wnt signaling in bone of type 2 diabetes mellitus and diabetic Charcot arthropathy.

BACKGROUND: Type 2 diabetes mellitus (T2DM) patients show a markedly higher fracture risk and impaired fracture healing when compared to non-diabetic patients. However in contrast to type 1 diabetes mellitus, bone mineral density in T2DM is known to be normal or even regionally elevated, also known as diabetic bone disease. Charcot arthropathy is a severe and challenging complication leading to bone destruction and mutilating bone deformities. Wnt signaling is involved in increasing bone mineral density, bone homeostasis and apoptotic processes. It has been shown that type 2 diabetes mellitus is strongly associated with gene variants of the Wnt signaling pathway, specifically polymorphisms of TCF7L2 (transcription factor 7 like 2), which is an effector transcription factor of this pathway. METHODS: Bone samples of 19 T2DM patients and 7 T2DM patients with additional Charcot arthropathy were compared to 19 non-diabetic controls. qPCR analysis for selected members of the Wnt-signaling pathway (WNT3A, WNT5A, catenin beta, TCF7L2) and bone gamma-carboxyglutamate (BGLAP, Osteocalcin) was performed and analyzed using the 2-ΔΔCt- Method. Statistical analysis comprised one-way analysis of variance (ANOVA). RESULTS: In T2DM patients who had developed Charcot arthropathy WNT3A and WNT5A gene expression was down-regulated by 89 and 58% compared to healthy controls (p < 0.0001). TCF7L2 gene expression showed a significant reduction by 63% (p < 0.0001) and 18% (p = 0.0136) in diabetic Charcot arthropathy. In all diabetic patients BGLAP (Osteocalcin) was significantly decreased by at least 59% (p = 0.0019). CONCLUSIONS: For the first time with this study downregulation of members of the Wnt-signaling pathway has been shown in the bone of diabetic patients with and without Charcot arthropathy. This may serve as future therapeutic target for this severe disease.

Treatment of vascular graft infections: gentamicin-coated ePTFE grafts reveals strong antibacterial properties in vitro.

Vascular graft infections (VGI) are severe complications in prosthetic vascular surgery with an incidence ranging from 1 to 6%. In these cases, synthetic grafts are commonly used in combination with antimicrobial agents. Expanded polytetrafluoroethylene (ePTFE) is in clinical use as a synthetic graft material and shows promising results by influencing bacterial adhesion. However, the literature on antibiotic-bound ePTFE grafts is scarce. Gentamicin is a frequently used antibiotic for local treatment of surgical site infections, but has not been evaluated as antimicrobial agent on ePTFE grafts. In this study, we examine the antimicrobial efficacy and biocompatibility of novel types of gentamicin-coated ePTFE grafts in vitro. ePTFE grafts coated with gentamicin salt formulations with covalently-bound palmitate were evaluated in two drug concentrations (GP1.75% and GP3.5%). To investigate effects from types of formulations, also suspensions of gentamicin in palmitate as well as polylactide were used at comparable levels (GS + PA and GS + R203). Antibacterial efficacies were estimated by employing a zone of inhibition, growth inhibition and bacterial adhesion assay against Staphylococcus aureus (SA). Cytotoxicity was determined with murine fibroblasts according to the ISO standard 10993-5. Gentamicin-coated ePTFE grafts show low bacterial adherence and strong antibacterial properties in vitro against SA. Bactericidal inhibition lasted until day 11. Highest biocompatibility was achieved using gentamicin palmitate GP1.75% coated ePTFE grafts. ePTFE grafts with gentamicin-coating are effective in vitro against SA growth and adherence. Most promising results regarding antimicrobial properties and biocompatibility were shown with chemically bounded gentamicin palmitate GP1.75% coatings. Graphical abstract.

Machine learning in knee arthroplasty: specific data are key-a systematic review.

PURPOSE: Artificial intelligence (AI) in healthcare is rapidly growing and offers novel options of data analysis. Machine learning (ML) represents a distinct application of AI, which is capable of generating predictions and has already been tested in different medical specialties with various approaches such as diagnostic applications, cost predictions or identification of risk factors. In orthopaedics, this technology has only recently been introduced and the literature on ML in knee arthroplasty is scarce. In this review, we aim to investigate which predictions are already feasible using ML models in knee arthroplasty to identify prerequisites for the effective use of this novel approach. For this reason, we conducted a systematic review of ML algorithms for outcome prediction in knee arthroplasty. METHODS: A comprehensive search of PubMed, Medline database and the Cochrane Library was conducted to find ML applications for knee arthroplasty. All relevant articles were systematically retrieved and evaluated by an orthopaedic surgeon and a data scientist on the basis of the PRISMA statement. The search strategy yielded 225 articles of which 19 were finally assessed as eligible. A modified Coleman Methodology Score (mCMS) was applied to account for a methodological evaluation. RESULTS: The studies presented in this review demonstrated fair to good results (AUC median 0.76/range 0.57-0.98), while heterogeneous prediction models were analysed: complications (6), costs (4), functional outcome (3), revision (2), postoperative satisfaction (2), surgical technique (1) and biomechanical properties (1) were investigated. The median mCMS was 65 (range 40-80) points. CONCLUSION: The prediction of distinct outcomes with ML models applying specific data is already feasible; however, the prediction of more complex outcomes is still inaccurate. Registry data on knee arthroplasty have not been fully analysed yet so that specific parameters have not been sufficiently evaluated. The inclusion of specific input data as well as the collaboration of orthopaedic surgeons and data scientists are essential prerequisites to fully utilize the capacity of ML in knee arthroplasty. Future studies should investigate prospective data with specific and longitudinally recorded parameters. LEVEL OF EVIDENCE: III.

Development of an Exoskeleton Platform of the Finger for Objective Patient Monitoring in Rehabilitation.

This paper presents the application of an adaptive exoskeleton for finger rehabilitation. The system consists of a force-controlled exoskeleton of the finger and wireless coupling to a mobile application for the rehabilitation of complex regional pain syndrome (CRPS) patients. The exoskeleton has sensors for motion detection and force control as well as a wireless communication module. The proposed mobile application allows to interactively control the exoskeleton, store collected patient-specific data, and motivate the patient for therapy by means of gamification. The exoskeleton was applied to three CRPS patients over a period of six weeks. We present the design of the exoskeleton, the mobile application with its game content, and the results of the performed preliminary patient study. The exoskeleton system showed good applicability; recorded data can be used for objective therapy evaluation.

Multitask Deep Learning for Segmentation and Classification of Primary Bone Tumors on Radiographs.

Background An artificial intelligence model that assesses primary bone tumors on radiographs may assist in the diagnostic workflow. Purpose To develop a multitask deep learning (DL) model for simultaneous bounding box placement, segmentation, and classification of primary bone tumors on radiographs. Materials and Methods This retrospective study analyzed bone tumors on radiographs acquired prior to treatment and obtained from patient data from January 2000 to June 2020. Benign or malignant bone tumors were diagnosed in all patients by using the histopathologic findings as the reference standard. By using split-sample validation, 70% of the patients were assigned to the training set, 15% were assigned to the validation set, and 15% were assigned to the test set. The final performance was evaluated on an external test set by using geographic validation, with accuracy, sensitivity, specificity, and 95% CIs being used for classification, the intersection over union (IoU) being used for bounding box placements, and the Dice score being used for segmentations. Results Radiographs from 934 patients (mean age, 33 years ± 19 [standard deviation]; 419 women) were evaluated in the internal data set, which included 667 benign bone tumors and 267 malignant bone tumors. Six hundred fifty-four patients were in the training set, 140 were in the validation set, and 140 were in the test set. One hundred eleven patients were in the external test set. The multitask DL model achieved 80.2% (89 of 111; 95% CI: 72.8, 87.6) accuracy, 62.9% (22 of 35; 95% CI: 47, 79) sensitivity, and 88.2% (67 of 76; CI: 81, 96) specificity in the classification of bone tumors as malignant or benign. The model achieved an IoU of 0.52 ± 0.34 for bounding box placements and a mean Dice score of 0.60 ± 0.37 for segmentations. The model accuracy was higher than that of two radiologic residents (71.2% and 64.9%; P = .002 and P < .001, respectively) and was comparable with that of two musculoskeletal fellowship-trained radiologists (83.8% and 82.9%; P = .13 and P = .25, respectively) in classifying a tumor as malignant or benign. Conclusion The developed multitask deep learning model allowed for accurate and simultaneous bounding box placement, segmentation, and classification of primary bone tumors on radiographs. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Carrino in this issue.

Suspension button constructs restore posterior knee laxity in solid tibial avulsion of the posterior cruciate ligament.

PURPOSE: Dislocated tibial avulsions of the posterior cruciate ligament (PCL) require surgical intervention. Several arthroscopic strategies are options to fix the fragment and restore posterior laxity, including two types of suspension button devices: adjustable (self-locking) and rigid knotted systems. Our hypothesis was that a rigid knotted button construct has superior biomechanical properties regarding laxity restoration compared with an adjustable system. Both techniques were compared with standard screw fixation and the native PCL. METHODS: Sixty porcine knees were dissected. The constructs were tested for elongation, stiffness, yield force, load to failure force, and failure mode in a material testing machine. Group N (native, intact PCL) was used as a control group. In group DB (Dogbone™), TR (Tightrope™), and S (screw), a standardized block osteotomy with the osteotomized fragment attached to the PCL was set. The DB and TR groups simulated using a suspension button system with either a rigid knotted (DB) or adjustable system (TR). These groups were compared to a screw technique (S) simulating antegrade screw fixation from posterior. RESULTS: Comparing the different techniques (DB, TR, S), no significant elongation was detected; all techniques achieved a sufficient posterior laxity restoration. Significant elongation in the DB and TR group was detected compared with the native PCL (N). In contrast, screw fixation did not lead to significant elongation. The stiffness, yield load, and load to failure force did not differ significantly between the techniques. None of the techniques reached the same level of yield load and load to failure force as the intact state. CONCLUSION: Arthroscopic suspension button techniques sufficiently restore the posterior laxity and gain a comparable construct strength as an open antegrade screw fixation.

Measuring hygiene competence: the picture-based situational judgement test HygiKo.

BACKGROUND: With the onset of the COVID-19 pandemic at the beginning of 2020, the crucial role of hygiene in healthcare settings has once again become very clear. For diagnostic and for didactic purposes, standardized and reliable tests suitable to assess the competencies involved in "working hygienically" are required. However, existing tests usually use self-report questionnaires, which are suboptimal for this purpose. In the present study, we introduce the newly developed, competence-oriented HygiKo test instrument focusing health-care professionals' hygiene competence and report empirical evidence regarding its psychometric properties. METHODS: HygiKo is a Situational Judgement Test (SJT) to assess hygiene competence. The HygiKo-test consists of twenty pictures (items), each item presents only one unambiguous hygiene lapse. For each item, test respondents are asked (1) whether they recognize a problem in the picture with respect to hygiene guidelines and, (2) if yes, to describe the problem in a short verbal response. Our sample comprised n = 149 health care professionals (79.1 % female; age: M = 26.7 years, SD = 7.3 years) working as clinicians or nurses. The written responses were rated by two independent raters with high agreement (α > 0.80), indicating high reliability of the measurement. We used Item Response Theory (IRT) for further data analysis. RESULTS: We report IRT analyses that show that the HygiKo-test is suitable to assess hygiene competence and that it allows to distinguish between persons demonstrating different levels of ability for seventeen of the twenty items), especially for the range of low to medium person abilities. Hence, the HygiKo-SJT is suitable to get a reliable and competence-oriented measure for hygiene-competence. CONCLUSIONS: In its present form, the HygiKo-test can be used to assess the hygiene competence of medical students, medical doctors, nurses and trainee nurses in cross-sectional measurements. In order to broaden the difficulty spectrum of the current test, additional test items with higher difficulty should be developed. The Situational Judgement Test designed to assess hygiene competence can be helpful in testing and teaching the ability of working hygienically. Further research for validity is needed.

In Vitro Cartilage Regeneration with a Three-Dimensional Polyglycolic Acid (PGA) Implant in a Bovine Cartilage Punch Model.

Resorbable polyglycolic acid (PGA) chondrocyte grafts are clinically established for human articular cartilage defects. Long-term implant performance was addressed in a standardized in vitro model. PGA implants (+/- bovine chondrocytes) were placed inside cartilage rings punched out of bovine femoral trochleas (outer Ø 6 mm; inner defect Ø 2 mm) and cultured for 84 days (12 weeks). Cartilage/PGA hybrids were subsequently analyzed by histology (hematoxylin/eosin; safranin O), immunohistochemistry (aggrecan, collagens 1 and 2), protein assays, quantitative real-time polymerase chain reactions, and implant push-out force measurements. Cartilage/PGA hybrids remained vital with intact matrix until 12 weeks, limited loss of proteoglycans from "host" cartilage or cartilage-PGA interface, and progressively diminishing release of proteoglycans into the supernatant. By contrast, the collagen 2 content in cartilage and cartilage-PGA interface remained approximately constant during culture (with only little collagen 1). Both implants (+/- cells) displayed implant colonization and progressively increased aggrecan and collagen 2 mRNA, but significantly decreased push-out forces over time. Cell-loaded PGA showed significantly accelerated cell colonization and significantly extended deposition of aggrecan. Augmented chondrogenic differentiation in PGA and cartilage/PGA-interface for up to 84 days suggests initial cartilage regeneration. Due to the PGA resorbability, however, the model exhibits limitations in assessing the "lateral implant bonding".

Effects of focal metallic implants on opposing cartilage - an in-vitro study with an abrasion test machine.

BACKGROUND: For focal cartilage defects, biological repair might be ineffective in patients over 45 years. A focal metallic implant (FMI) (Hemi-CAP Arthrosurface Inc., Franklin, MA, USA) was designed to reduce symptoms. The aim of this study was to evaluate the effects of a FMI on the opposing tibial cartilage in a biomechanical set-up. It is hypothesized that a FMI would not damage the opposing cartilage under physiological loading conditions. METHODS: An abrasion machine was used to test the effects of cyclic loading on osteochondral plugs. The machine applied a compressive load of 33 N and sheared the samples 10 mm in the anteroposterior direction by 1 Hz. Tibial osteochondral plugs from porcine knees were placed in opposition to a FMI and cycled for 1 or 6 h. After testing each plug was fixed, stained and evaluated for cartilage damage. RESULTS: After 1 h of loading (n = 6), none of the osteochondral plugs showed histologic signs of degradation. After 6 h of loading (n = 6) three samples had histologic signs of injury in the tangential zone (grade 1) and one had signs of injury in the transitional and deep zones (grade 2). Exploration for 6 h resulted in significant more cartilage damage compared to the shorter exploration time (p = 0.06). However, no significant difference between saline and hyaluronic acid was evident (p = 0.55). CONCLUSION: Under physiologic loading conditions, contact with a FMI leads to cartilage damage in the opposing articular cartilage in six hours. In clinical practice, a thorough analysis of pre-existing defects on the opposing cartilage is recommended when FMI is considered.

Varus alignment increases medial meniscus extrusion and peak contact pressure: a biomechanical study.

PURPOSE: Assessment of medial meniscus extrusion (MME) has become increasingly popular in clinical practice to evaluate the dynamic meniscus function and diagnose meniscus pathologies. The purpose of this biomechanical study was to investigate the correlation between MME and the changes in joint contact pressure in varus and valgus alignment. It was hypothesized that varus alignment would result in significantly higher MME along with a higher joint contact pressure in the medial compartment. METHODS: Eight fresh-frozen human cadaveric knees were axially loaded, with a 750 N compressive load, in full extension with the mechanical axis shifted to intersect the tibial plateau at 30% and 40% (varus), 50% (neutral), 60% and 70% (valgus) of its width (TPW). Tibiofemoral peak contact pressure (PCP), mean contact pressure (MCP) and contact area (CA) were determined using pressure-sensitive films. MME was obtained via ultrasound at maximum load. RESULTS: MME was significantly increased from valgus (1.32 ± 0.22 mm) to varus alignment (3.16 ± 0.24 mm; p < 0.001). Peak contact pressure at 30% TPW varus alignment was significantly higher compared to 60% TPW valgus (p = 0.018) and 70% TPW valgus (p < 0.01). MME significantly correlated with PCP (r = 0.56; p < 0.001) and MCP (r = 0.47, p < 0.01) but not with CA (r = 0.23; n.s.). CONCLUSION: MME was significantly increased in varus alignment, compared to neutral or valgus alignment, with an intact medial meniscus. It was also significantly correlated with PCP and MCP within the medial compartment. However, valgus malalignment and neutral axis resulted in reduced MME and contact pressure. Lower limb alignment must be taken into account while assessing MME in clinical practice. LEVEL OF EVIDENCE: Controlled laboratory study.