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.

Clavicle fracture and triathlon performance: a case report.

BACKGROUND: Collarbone fracture is a common injury, particularly among athletes involved in contact sports and participating in endurance activities. Conventional treatment requires surgery and postoperative immobilization, resulting in an average return-to-sport timeframe of approximately 13 weeks. This case challenges the established treatment protocols, aiming to expedite recovery and enable a quicker resumption of high-intensity athletic activities. CASE PRESENTATION: A 24-year-old Caucasian athlete completed a Half-Ironman Triathlon (70.3) merely three weeks post-collarbone fracture. Utilizing Extracorporeal Magneto-Transduction Therapy (EMTT) alongside surgical intervention, the patient achieved accelerated healing and remarkable performance outcomes without encountering any adverse effects. CONCLUSIONS: The integration of EMTT into the treatment paradigm for bone fractures alters the traditional understanding of recovery timelines and rehabilitation strategies. This case highlights the potential benefits of electromagnetic wave therapy in expediting the healing process and enabling athletes to resume high-level sports activities at an earlier stage.

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.

Multicentric development and validation of a multi-scale and multi-task deep learning model for comprehensive lower extremity alignment analysis.

Osteoarthritis of the knee, a widespread cause of knee disability, is commonly treated in orthopedics due to its rising prevalence. Lower extremity misalignment, pivotal in knee injury etiology and management, necessitates comprehensive mechanical alignment evaluation via frequently-requested weight-bearing long leg radiographs (LLR). Despite LLR's routine use, current analysis techniques are error-prone and time-consuming. To address this, we conducted a multicentric study to develop and validate a deep learning (DL) model for fully automated leg alignment assessment on anterior-posterior LLR, targeting enhanced reliability and efficiency. The DL model, developed using 594 patients' LLR and a 60%/10%/30% data split for training, validation, and testing, executed alignment analyses via a multi-step process, employing a detection network and nine specialized networks. It was designed to assess all vital anatomical and mechanical parameters for standard clinical leg deformity analysis and preoperative planning. Accuracy, reliability, and assessment duration were compared with three specialized orthopedic surgeons across two distinct institutional datasets (136 and 143 radiographs). The algorithm exhibited equivalent performance to the surgeons in terms of alignment accuracy (DL: 0.21 ± 0.18°to 1.06 ± 1.3°vs. OS: 0.21 ± 0.16°to 1.72 ± 1.96°), interrater reliability (ICC DL: 0.90 ± 0.05 to 1.0 ± 0.0 vs. ICC OS: 0.90 ± 0.03 to 1.0 ± 0.0), and clinically acceptable accuracy (DL: 53.9%-100% vs OS 30.8%-100%). Further, automated analysis significantly reduced analysis time compared to manual annotation (DL: 22 ± 0.6 s vs. OS; 101.7 ± 7 s, p ≤ 0.01). By demonstrating that our algorithm not only matches the precision of expert surgeons but also significantly outpaces them in both speed and consistency of measurements, our research underscores a pivotal advancement in harnessing AI to enhance clinical efficiency and decision-making in orthopaedics.

Individualized Training of Back Muscles Using Iterative Learning Control of a Compliant Balance Board.

Here we present the GyroTrainer, a bespoke mechatronic balance board system designed to trigger activation of the back muscles while the user engages in a balance-challenging game. The GyroTrainer uses admittance control coupled with an iterative learning approach so as to tailor the admittance control parameters, i.e. difficulty level, according to the user's skill. Our experimental evaluation demonstrated that an individualized admittance control stiffness could be identified for each user, which corresponds with a desired level of difficulty and increased back muscle activity. A first game implementation demonstrates the feasibility of utilizing the GyroTrainer system and the individually identified admittance control stiffness for gamification of back muscle training.

Are enterococcal bloodstream infections an independent risk factor for a poorer 5-year survival or just a marker for severity of illness?-The Munich multicentric enterococci cohort.

IMPORTANCE: The present study provides a substantial contribution to literature, showing that patients with enterococcal bloodstream infections (BSI) have a lower survival rate than those with Escherichia coli (E. coli) bloodstream infections after adjusting for 17 limiting prognostic factors and excluding patients with a limited life expectancy [metastatic tumor disease, Charlson Comorbidity Index (CCI) (greater than or equal to) 5]. This difference in the 5-year long-term survival was mainly driven by Enterococcus faecium (ECFM) bloodstream infections, with vancomycin resistance not being a significant contributing factor. Our findings imply that E. faecium bloodstream infections seem to be an independent risk factor for poor long-term outcomes. As such, future research should confirm this relationship and prioritize investigating its causality through prospective studies.

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.

Deep Learning-Based Detection of Bone Tumors around the Knee in X-rays of Children.

Even though tumors in children are rare, they cause the second most deaths under the age of 18 years. More often than in other age groups, underage patients suffer from malignancies of the bones, and these mostly occur in the area around the knee. One problem in the treatment is the early detection of bone tumors, especially on X-rays. The rarity and non-specific clinical symptoms further prolong the time to diagnosis. Nevertheless, an early diagnosis is crucial and can facilitate the treatment and therefore improve the prognosis of affected children. A new approach to evaluating X-ray images using artificial intelligence may facilitate the detection of suspicious lesions and, hence, accelerate the referral to a specialized center. We implemented a Vision Transformer model for image classification of healthy and pathological X-rays. To tackle the limited amount of data, we used a pretrained model and implemented extensive data augmentation. Discrete parameters were described by incidence and percentage ratio and continuous parameters by median, standard deviation and variance. For the evaluation of the model accuracy, sensitivity and specificity were computed. The two-entity classification of the healthy control group and the pathological group resulted in a cross-validated accuracy of 89.1%, a sensitivity of 82.2% and a specificity of 93.2% for test groups. Grad-CAMs were created to ensure the plausibility of the predictions. The proposed approach, using state-of-the-art deep learning methodology to detect bone tumors on knee X-rays of children has achieved very good results. With further improvement of the algorithm, enlargement of the dataset and removal of potential biases, this could become a useful additional tool, especially to support general practitioners for early, accurate and specific diagnosis of bone lesions in young patients.

The minimum required overlap length for tendon transfer A biomechanical study on human tendons.

In tendon transfer surgeries sufficient stability of the tenorrhaphy is essential. In addition to the choice of a suitable technique, adequate overlap of donor and recipient tendons must be ensured. The aim of this study was to investigate the tensile strength with regard to tendon overlap of a recently published tenorrhaphy, termed Woven-Fridén (WF) tenorrhaphy, which displayed higher tensile strength and lower bulk when compared to the established Pulvertaft technique. For this purpose, WF tenorrhaphies with 1.5 cm, 2 cm, and 3 cm tendon overlap were performed and subsequently tested for different biomechanical properties by tensile testing. Among others, the parameters of ultimate load and stiffness were collected. Native tendons served as controls. A formula was derived to quantify the relation between tendon overlap and ultimate load. We observed that sufficient tensile strength (mean ultimate load of 217 N) is already given with a 2 cm tendon overlap. In addition, with more than 3 cm overlap length only little additional tensile strength is to be expected as the calculated ultimate load of 4 cm overlap (397 N) is approaching the plateau of the maximal ultimate load of 435 N (native tendons).

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.

Differentiation of Spontaneous Bacterial Peritonitis from Secondary Peritonitis in Patients with Liver Cirrhosis: Retrospective Multicentre Study.

Ascitic fluid infection is a serious complication of liver cirrhosis. The distinction between the more common spontaneous bacterial peritonitis (SBP) and the less common secondary peritonitis in patients with liver cirrhosis is crucial due to the varying treatment approaches. This retrospective multicentre study was conducted in three German hospitals and analysed 532 SBP episodes and 37 secondary peritonitis episodes. Overall, >30 clinical, microbiological, and laboratory parameters were evaluated to identify key differentiation criteria. Microbiological characteristics in ascites followed by severity of illness and clinicopathological parameters in ascites were the most important predictors identified by a random forest model to distinguish between SBP and secondary peritonitis. To establish a point-score model, a least absolute shrinkage and selection operator (LASSO) regression model selected the ten most promising discriminatory features. By aiming at a sensitivity of 95% either to rule out or rule in SBP episodes, two cut-off scores were defined, dividing patients with infected ascites into a low-risk (score ≥ 45) and high-risk group (score < 25) for secondary peritonitis. Overall, the discrimination of secondary peritonitis from SBP remains challenging. Our univariable analyses, random forest model, and LASSO point score may help clinicians with the crucial differentiation between SBP and secondary peritonitis.

Biomechanical Properties of Repair Cartilage Tissue Are Superior Following Microdrilling Compared to Microfracturing in Critical Size Cartilage Defects.

BACKGROUND/AIM: Common surgical treatment options for large focal chondral defects (FCDs) in the knee include microfracturing (MFX) and microdrilling (DRL). Despite numerous studies addressing MFX and DRL of FDCs, no in vivo study has focused on biomechanical analysis of repair cartilage tissue in critical size FCDs with different amounts of holes and penetration depths. MATERIALS AND METHODS: Two round FCDs (d=6 mm) were created on the medial femoral condyle in 33 adult merino sheep. All 66 defects were randomly assigned to 1 control or 4 different study groups: 1) MFX1, 3 holes, 2 mm depth; 2) MFX2, 3 holes, 4 mm depth; 3) DRL1, 3 holes, 4 mm depth; and 4) DRL2, 6 holes, 4 mm depth. Animals were followed up for 1 year. Following euthanasia, quantitative optical analysis of defect filling was performed. Biomechanical properties were analysed with microindentation and calculation of the elastic modulus. RESULTS: Quantitative assessment of defect filling showed significantly better results in all treatment groups compared to untreated FCDs in the control group (p<0.001), with the best results for DRL2 (84.2% filling). The elastic modulus of repair cartilage tissue in the DRL1 and DRL2 groups was comparable to the adjacent native hyaline cartilage, while significantly inferior results were identified in both MFX groups (MFX1: p=0.002; MFX2: p<0.001). CONCLUSION: More defect filling and better biomechanical properties of the repair cartilage tissue were identified for DRL compared to MFX, with the best results for 6 holes and 4 mm of penetration depth. These findings are in contrast to the current clinical practice with MFX as the gold standard and suggest a clinical return to DRL.

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.

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.

A Novel Machine Learning-Based Point-Score Model as a Non-Invasive Decision-Making Tool for Identifying Infected Ascites in Patients with Hydropic Decompensated Liver Cirrhosis: A Retrospective Multicentre Study.

This study is aimed at assessing the distinctive features of patients with infected ascites and liver cirrhosis and developing a scoring system to allow for the accurate identification of patients not requiring abdominocentesis to rule out infected ascites. A total of 700 episodes of patients with decompensated liver cirrhosis undergoing abdominocentesis between 2006 and 2020 were included. Overall, 34 clinical, drug, and laboratory features were evaluated using machine learning to identify key differentiation criteria and integrate them into a point-score model. In total, 11 discriminatory features were selected using a Lasso regression model to establish a point-score model. Considering pre-test probabilities for infected ascites of 10%, 15%, and 25%, the negative and positive predictive values of the point-score model for infected ascites were 98.1%, 97.0%, 94.6% and 14.9%, 21.8%, and 34.5%, respectively. Besides the main model, a simplified model was generated, containing only features that are fast to collect, which revealed similar predictive values. Our point-score model appears to be a promising non-invasive approach to rule out infected ascites in clinical routine with high negative predictive values in patients with hydropic decompensated liver cirrhosis, but further external validation in a prospective study is needed.

Two-Phase Deep Learning Algorithm for Detection and Differentiation of Ewing Sarcoma and Acute Osteomyelitis in Paediatric Radiographs.

BACKGROUND/AIM: Ewing sarcoma is a highly malignant tumour predominantly found in children. The radiological signs of this malignancy can be mistaken for acute osteomyelitis. These entities require profoundly different treatments and result in completely different prognoses. The purpose of this study was to develop an artificial intelligence algorithm, which can determine imaging features in a common radiograph to distinguish osteomyelitis from Ewing sarcoma. MATERIALS AND METHODS: A total of 182 radiographs from our Sarcoma Centre (118 healthy, 44 Ewing, 20 osteomyelitis) from 58 different paediatric (≤18 years) patients were collected. All localisations were taken into consideration. Cases of acute, acute on chronic osteomyelitis and intraosseous Ewing sarcoma were included. Chronic osteomyelitis, extra-skeletal Ewing sarcoma, malignant small cell tumour and soft tissue-based primitive neuroectodermal tumours were excluded. The algorithm development was split into two phases and two different classifiers were built and combined with a Transfer Learning approach to cope with the very limited amount of data. In phase 1, pathological findings were differentiated from healthy findings. In phase 2, osteomyelitis was distinguished from Ewing sarcoma. Data augmentation and median frequency balancing were implemented. A data split of 70%, 15%, 15% for training, validation and hold-out testing was applied, respectively. RESULTS: The algorithm achieved an accuracy of 94.4% on validation and 90.6% on test data in phase 1. In phase 2, an accuracy of 90.3% on validation and 86.7% on test data was achieved. Grad-CAM results revealed regions, which were significant for the algorithms decision making. CONCLUSION: Our AI algorithm can become a valuable support for any physician involved in treating musculoskeletal lesions to support the diagnostic process of detection and differentiation of osteomyelitis from Ewing sarcoma. Through a Transfer Learning approach, the algorithm was able to cope with very limited data. However, a systematic and structured data acquisition is necessary to further develop the algorithm and increase results to clinical relevance.

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.

Biopsies of osseous jaw lesions using 3D-printed surgical guides: a clinical study.

BACKGROUND: Bone biopsies are often necessary to make a diagnosis in the case of irregular bone structures of the jaw. A 3D-printed surgical guide may be a helpful tool for enhancing the accuracy of the biopsy and for ensuring that the tissue of interest is precisely removed for examination. This study was conducted to compare the accuracy of biopsies performed with 3D-printed surgical guides to that of free-handed biopsies. METHODS: Computed tomography scans were performed on patients with bony lesions of the lower jaw. Surgical guides were planned via computer-aided design and manufactured by a 3D-printer. Biopsies were performed with the surgical guides. Bone models of the lower jaw with geometries identical to the patients' lower jaws were produced using a 3D-printer. The jaw models were fitted into a phantom head model and free-handed biopsies were taken as controls. The accuracy of the biopsies was evaluated by comparing the parameters for the axis, angle and depth of the biopsies to the planned parameters. RESULTS: Eight patients were included. The mean deviation between the biopsy axes was significantly lower in guided procedures than in free-handed biopsies (1.4 mm ± 0.9 mm; 3.6 mm ± 1.0 mm; p = 0.0005). The mean biopsy angle deviation was also significantly lower in guided biopsies than in free-handed biopsies (6.8° ± 4.0; 15.4° ± 3.6; p = 0.0005). The biopsy depth showed no significant difference between the guided and the free-handed biopsies. CONCLUSIONS: Computer-guided biopsies allow significantly higher accuracy than free-handed procedures.

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.

Coating of Intestinal Anastomoses for Prevention of Postoperative Leakage: A Systematic Review and Meta-Analysis.

Background: For several decades, scientific efforts have been taken to develop strategies and medical aids for the reduction of anastomotic complications after intestinal surgery. Still, anastomotic leakage (AL) represents a frequently occurring postoperative complication with serious consequences on health, quality of life, and economic aspects. Approaches using collagen and/or fibrin-based sealants to cover intestinal anastomoses have shown promising effects toward leak reduction; however, they have not reached routine use yet. To assess the effects of covering intestinal anastomoses with collagen and/or fibrin-based sealants on postoperative leakage, a systematic review and meta-analysis were conducted. Method: PubMed, Web of Science, Cochrane Library, and Scopus (01/01/1964 to 17/01/2022) were searched to identify studies investigating the effects of coating any intestinal anastomoses with collagen and/or fibrin-based sealants on postoperative AL, reoperation rates, Clavien-Dindo major complication, mortality, and hospitalization length. Pooled odds ratios (ORs) with 95% confidence intervals (CIs) were calculated. Results: Overall, 15 studies (five randomized controlled trials, three nonrandomized intervention studies, six observational cohort studies) examining 1,387 patients in the intervention group and 2,243 in the control group were included. Using fixed-effects meta-analysis (I 2 < 50%), patients with coated intestinal anastomoses presented significantly lower AL rates (OR = 0.37; 95% CI 0.27-0.52; p < 0.00001), reoperation rates (OR, 0.21; 95% CI, 0.10-0.47; p = 0.0001), and Clavien-Dindo major complication rates (OR, 0.54; 95% CI, 0.35-0.84; p = 0.006) in comparison to controls, with results remaining stable in sensitivity and subgroup analyses (stratified by study design, age group, intervention used, location of anastomoses, and indication for surgery). The length of hospitalization was significantly shorter in the intervention group (weighted mean difference (WMD), -1.96; 95% CI, -3.21, -0.71; p = 0.002) using random-effects meta-analysis (I 2 ≥ 50%), especially for patients with surgery of upper gastrointestinal malignancy (WMD, -4.94; 95% CI, -7.98, -1.90; p = 0.001). Conclusion: The application of collagen-based laminar biomaterials or fibrin sealants on intestinal anastomoses can significantly reduce postoperative rates of AL and its sequelae. Coating of intestinal anastomoses could be a step toward effective and sustainable leak prevention. To assess the validity and robustness of these findings, further clinical studies need to be conducted.