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.

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.

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.

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.

Prediction of Complications and Surgery Duration in Primary Total Hip Arthroplasty Using Machine Learning: The Necessity of Modified Algorithms and Specific Data.

BACKGROUND: Machine Learning (ML) in arthroplasty is becoming more popular, as it is perfectly suited for prediction models. However, results have been heterogeneous so far. We hypothesize that an accurate ML model for outcome prediction in THA must be able to compute arthroplasty-specific data. In this study, we evaluate a ML approach applying data from two German arthroplasty-specific registries to predict adverse outcomes after THA, after careful evaluations of ML algorithms, outcome and input variables by an interdisciplinary team of data scientists and surgeons. METHODS: Data of 1217 cases of primary THA from a single center were derived from two German arthroplasty-specific registries between 2016 to 2019. The XGBoost algorithm was adjusted and applied. Accuracy, sensitivity, specificity and AUC were calculated. RESULTS: For the prediction of complications, the ML algorithm achieved an accuracy of 80.3%, a sensitivity of 31.0%, a specificity of 89.4% and an AUC of 64.1%. For the prediction of surgery duration, the ML algorithm yielded an accuracy of 81.7%, a sensitivity of 58.2%, a specificity of 91.6% and an AUC of 89.1%. The feature importance indicated non-linear outcomes for age, height, weight and surgeon. No relevant linear correlations were found. CONCLUSION: The attunement of input and output data as well as the modifications of the ML algorithm permitted the development of a feasible ML model for the prediction of complications and surgery duration.

Emergency room as primary point of access in the German healthcare system : Objective evaluation and interview of motivation for ER entrance of 235 ER patients in a German hospital.

BACKGROUND: The importance of emergency rooms (ERs) as everyday healthcare suppliers is growing. Due to increasing patient flows, hospitals are forced to raise physicians' and caregivers' headcount continuously to meet the new demand of patients seeing the ER as primary point of contact in non-emergency situations. Patients from various cultural and educational backgrounds approach the ER for different reasons. Detailed understanding of these reasons and their roots is key to be able to offer guidance for patients as well as planning and staffing of hospitals in the future. AIM: This study examines motivation for the entrance to the medical system via the ER in Germany via an anonymized patient survey. Evaluation in regard to socioeconomic and medical reasons is taken into account. MATERIALS AND METHODS: Over the course of 210 h in the ER, a total of 235 patients were interviewed in the surgical emergency room of Klinikum rechts der Isar in the year 2016. Focus was set on standard cases to allow for facilitated comparability. Heavily injured patients were excluded from the study. RESULTS: The main reasons for patients entering the ER were immediate help (45.9%) and treatment by a specialist (35.4%). Furthermore, the location/good accessibility (47.9%) and prior positive experience with the emergency room (20.7%) were decisive reasons for choosing the hospital over the outpatient sector. Analysis of demands of patients in relation to their migration background and their religious confession showed no significant difference between groups. CONCLUSION: Younger patients tend to more often access the ER instead of an outpatient clinic or doctor in private practice. As a survey suits the less urgent patients, our research describes this population in detail. The need for better information of patients regarding treatment options becomes apparent. The study's outcomes aim to teach physicians as well as operators how to influence resource management in the healthcare system by meaningful information of patients. Further research may evaluate long-term results of information measures.

Treatment of fracture-related infection of the lower extremity with antibiotic-eluting ceramic bone substitutes: case series of 35 patients and literature review.

INTRODUCTION: The current treatment concepts of fracture-related infection (FRI) [Consensus Conference (Anti-Infection Task Force (AITF)) on the definition of acute or chronic osteomyelitis (cOM)] are associated with unsolved challenges and problems, underlining the need for ongoing medical research. METHOD: Literature review of treatments for FRI and description of own cases. RESULTS: We could include eight papers with 394 patients reporting treatments and outcome in FRI. The infection was resolved in 92.9% (mean) of all treatments. The mean follow-up was 25 months with a persistent non-union in 7% of the patients. We diagnosed 35 (19f/16m; 56.4 ± 18.6 years) patients with bone infections anatomically allocated to the proximal and distal femur (12×), the pelvis (2×), distal tibia (3×), tibial diaphysis (11×), the ankle joint (4×) and calcaneus (3×). These 35 patients were treated (1) with surgical debridement; (2) with antibiotic-eluting ceramic bone substitutes; (3) bone stabilization (including nail fixation, arthrodesis nails, plates, or external ring fixation), (4) optionally negative pressure wound therapy (NPWT) and (5) optionally soft tissue closure with local or free flaps. The mean follow-up time was 14.9 ± 10.6 months (min/max: 2/40 month). The overall recurrence rate is low (8.5%, 3/35). Prolonged wound secretion was observed in six cases (17.1%, 6/35). The overall number of surgeries was a median of 2.5. CONCLUSION: The results in the literature and in our case series are explicitly promising regarding the treatment of posttraumatic fracture-related infection.

Healthcare IT Utilization and Penetration among Physicians: Novel IT Solutions in Healthcare - Use and Acceptance in Hospitals.

BACKGROUND: Healthcare IT (HIT) increasingly gains public attention and clinical daily relevance. A growing number of patients and physicians increasingly relies on IT services to monitor and support well-being and recovery both in their private and professional environment. This is assumed to develop rapidly in the upcoming years. OBJECTIVE: This study examines the current status of HIT, its use and penetration among physicians in hospitals and researches utilization as well as future expectations regarding HIT. METHODS: Physicians in Germany, Austria and Switzerland were addressed via e-mail to answer a standardized Internet-based questionnaire consisting of 17 multiple-choice and 3 open text questions. Parameters were evaluated in 5 categories: general use, frequency, acceptance, IT needs and future expectations. RESULTS: An overall 234 physicians (response rate 83.6%) with a median age of 45 (range 25-60) responded and filled out the entire online questionnaire. A significant correlation between parameters gender, age and level of training (resident, specialist, consultant etc.) was proven. The professional, medical employment of technology shows a strong correlation with age as well as level of training. Whereas increasing age among physicians is associated with a decreasing level of application of HIT, a higher training level is accompanied by an increasing level of professional application of IT services and tools within the healthcare context. Routine employment of HIT is regarded as a necessary and positive standard. Most users assume the importance of HIT to strongly grow in the future in comparison to current use. A clear lack of trust towards data security and storage is recognized on both patient and physician sides. Needs are currently satisfied by employing privately acquired IT in the professional setup rather than the hospitals'. Future expectations from HIT show a clear demand for interoperability and exchangeability of data. CONCLUSIONS: The results display a clear gap between demand and expectations of IT for medical purposes. The rate of use of HIT applications generally correlates with age, gender as well as role within the hospital and type of employment within the healthcare sector. The current offering does not satisfy the needs of healthcare professionals.

Effect of private versus emergency medical systems transportation in trauma patients in a mostly physician based system- a retrospective multicenter study based on the TraumaRegister DGU®.

BACKGROUND: The effects of private transportation (PT) to definitive trauma care in comparison to transportation using Emergency Medical Services (EMS) have so far been addressed by a few studies, with some of them finding a beneficial effect on survival. The aim of the current study was to investigate epidemiology, pre- and in-hospital times as well as outcomes in patients after PT as compared to EMS recorded in the TraumaRegister DGU®. METHODS: All patients in the database of the TraumaRegister DGU® (TR-DGU) from participating European trauma centers treated in 2009 to 2013 with available data on the mode of transportation, ISS ≥ 4 and ICU treatment were included in the study. Epidemiological data, pre- and in-hospital times were analysed. Outcomes were analysed after adjustment for RISC-II scores. RESULTS: 76,512 patients were included in the study, of which 1,085 (1.4 %) were private transports. Distribution of ages and trauma mechanisms showed a markedly different pattern following PT, with more children < 15 years treated following PT (3.3 % EMS vs. 9.6 for PT) and more elderly patients of 65 years or older (26.6 vs 32.4 %). Private transportation to trauma care was by far more frequent in Level 2 and 3 hospitals (41.2 % in EMS group vs 73.7 %). Median pre-hospital times were also reduced following PT (59 min for EMS vs. 46 for PT). In-hospital time in the trauma room (66 for EMS vs. 103 min for PT) and time to diagnostics were prolonged following PT. Outcome analysis after adjustment for RISC-II scores showed a survival benefit of PT over EMS transport (SMR for EMS 1.07 95 % CI 1.05-1.09; for PT 0.85 95 % CI 0.62-1.08). DISCUSSION: The current study shows a distinct pattern concerning epidemiology and mechanism of injury following PT. PT accelerates the median pre-hospital times, but prolongs time to diagnostic measures and time in the trauma room. CONCLUSIONS: In this distinct collective, PT seemed to lead to a small benefit in terms of mortality, which may reflect pre-hospital times, pre-hospital interventions or other confounders.