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Tendinopathy describes a complex multifaceted pathology of the tendon, characterized by pain, decline in function and reduced exercise tolerance. The most common overuse tendinopathies involve the rotator cuff tendon, medial and lateral elbow epicondyles, patellar tendon, gluteal tendons and the Achilles tendon. The prominent histological and molecular features of tendinopathy include disorganization of collagen fibres, an increase in the microvasculature and sensory nerve innervation, dysregulated extracellular matrix homeostasis, increased immune cells and inflammatory mediators, and enhanced cellular apoptosis. Although diagnosis is mostly achieved based on clinical symptoms, in some cases, additional pain-provoking tests and imaging might be necessary. Management consists of different exercise and loading programmes, therapeutic modalities and surgical interventions; however, their effectiveness remains ambiguous. Future research should focus on elucidating the key functional pathways implicated in clinical disease and on improved rehabilitation protocols. Tendinopathy is a multifaceted pathology of the tendon, characterized by pain and reduced function. This Primer summarizes the epidemiology, pathophysiology, diagnosis and the latest insights in the management of this disorder. Finally, the authors discuss the outstanding issues that will help achieve better outcomes in patients with tendinopathy.

The human skeleton has remarkable regenerative properties, being one of the few structures in the body that can heal by recreating its normal cellular composition, orientation and mechanical strength. When the healing process of a fractured bone fails owing to inadequate immobilization, failed surgical intervention, insufficient biological response or infection, the outcome after a prolonged period of no healing is defined as non-union. Non-union represents a chronic medical condition not only affecting function but also potentially impacting the individual’s psychosocial and economic well-being. This Primer provides the reader with an in-depth understanding of our contemporary knowledge regarding the important features to be considered when faced with non-union. The normal mechanisms involved in bone healing and the factors that disrupt the normal signalling mechanisms are addressed. Epidemiological considerations and advances in the diagnosis and surgical therapy of non-union are highlighted and the need for greater efforts in basic, translational and clinical research are identified. Non-union, defined as a fractured bone that does not heal within the expected time or is deemed unable to heal without intervention, represents a complex chronic medical condition characterized by pain and functional and psychosocial disability. In this Primer, Jupiter and colleagues discuss the epidemiology, advances in pathophysiology, diagnosis and management, and the quality of life of patients with non-union.

Pelvic fracture is one of the leading causes of death in the elderly, carrying a high risk of death within 1 year of fracture. This study proposes an automated method to detect pelvic fractures on 3-dimensional computed tomography (3D-CT). Deep convolutional neural networks (DCNNs) have been used for lesion detection on 2D and 3D medical images. However, training a DCNN directly using 3D images is complicated, computationally costly, and requires large amounts of training data. We propose a method that evaluates multiple, 2D, real-time object detection systems (YOLOv3 models) in parallel, in which each YOLOv3 model is trained using differently orientated 2D slab images reconstructed from 3D-CT. We assume that an appropriate reconstruction orientation would exist to optimally characterize image features of bone fractures on 3D-CT. Multiple YOLOv3 models in parallel detect 2D fracture candidates in different orientations simultaneously. The 3D fracture region is then obtained by integrating the 2D fracture candidates. The proposed method was validated in 93 subjects with bone fractures. Area under the curve (AUC) was 0.824, with 0.805 recall and 0.907 precision. The AUC with a single orientation was 0.652. This method was then applied to 112 subjects without bone fractures to evaluate over-detection. The proposed method successfully detected no bone fractures in all except 4 non-fracture subjects (96.4%).

For the generation of multi-layered full thickness osteochondral tissue substitutes with an individual geometry based on clinical imaging data, combined extrusion-based 3D printing (3D plotting) of a bioink laden with primary chondrocytes and a mineralized biomaterial phase was introduced. A pasty calcium phosphate cement (CPC) and a bioink based on alginate-methylcellulose (algMC) – both are biocompatible and allow 3D plotting with high shape fidelity – were applied in monophasic and combinatory design to recreate osteochondral tissue layers. The capability of cells reacting to chondrogenic biochemical stimuli inside the algMC-based 3D hydrogel matrix was assessed. Towards combined osteochondral constructs, the chondrogenic fate in the presence of CPC in co-fabricated and biphasic mineralized pattern was evaluated. Majority of expanded and algMC-encapsulated cells survived the plotting process and the cultivation period, and were able to undergo redifferentiation in the provided environment to produce their respective extracellular matrix (ECM) components (i.e. sulphated glycosaminoglycans, collagen type II), examined after 3 weeks. The presence of a mineralized zone as located in the physiological calcified cartilage region suspected to interfere with chondrogenesis, was found to support chondrogenic ECM production by altering the ionic concentrations of calcium and phosphorus in in vitro culture conditions.

Sensing the proper signal could be a vital piece of the solution to the much evading attributes of prosthetic hands, such as robustness to noise, ease of connectivity, and intuitive movement. Towards this end, magnetics tags have been recently suggested as an alternative sensing mechanism to the more common EMG signals. Such sensing technology, however, is inherently invasive and hence only in simulation stages of magnet localization to date. Here, for the first time, we report on the clinical implementation of implanted magnetic tags for an amputee's prosthetic hand from both the medical and engineering perspectives. Specifically, the proposed approach introduces a flexor–extensor tendon transfer surgical procedure to implant the tags, artificial neural networks to extract human intention directly from the implanted magnet's magnetic fields -in short KineticoMyoGraphy (KMG) signals- rather than localizing them, and a game strategy to examine the proposed algorithms and rehabilitate the patient with his new prosthetic hand. The bionic hand's ability is then tested following the patient's intended gesture type and grade. The statistical results confirm the possible utility of surgically implanted magnetic tags as an accurate sensing interface for recognizing the intended gesture and degree of movement between an amputee and his bionic hand.

Recent evidence supports the use of immersive virtual reality (immersive VR) as a means of applying visual feedback techniques in neurorehabilitation. In this study, we investigated the benefits of an embodiment-based immersive VR training program for orthopedic upper limb rehabilitation, with the aim of improving the motor functional ability of the arm and accelerating the rehabilitation process in patients with a conservatively managed distal radius fracture. We designed a rehabilitation program based on developing ownership over a virtual arm and then exercising it in immersive VR. We carried out a between 3-group controlled trial with 54 patients (mean age = 61.80 ± 14.18): 20 patients were assigned to the experimental training group (immersive VR), 20 to the conventional digit mobilization (CDM) training control group, and 14 to a non-immersive (non-immersive VR) training control group. We found that functional recovery of the arm in the immersive VR group was correlated with the ownership and agency scores over the virtual arm. We also found larger range of joint movements and lower disability of the fractured arm compared with patients in the Non-immersive VR and CDM groups. Feeling embodied in a virtual body can be used as a rehabilitation tool to speed up and improve motor functional recovery of a fractured arm after the immobilization period.

Developmental dysplasia of the hip (DDH) is a cluster of hip development disorders and one of the most common hip diseases in infants. Hip radiography is a convenient diagnostic tool for DDH, but its diagnostic accuracy is dependent on the interpreter’s level of experience. The aim of this study was to develop a deep learning model for detecting DDH. Patients younger than 12 months who underwent hip radiography between June 2009 and November 2021 were selected. Using their radiography images, transfer learning was performed to develop a deep learning model using the “You Only Look Once” v5 (YOLOv5) and single shot multi-box detector (SSD). A total of 305 anteroposterior hip radiography images (205 normal and 100 DDH hip images) were collected. Of these, 30 normal and 17 DDH hip images were used as the test dataset. The sensitivity and the specificity of our best YOLOv5 model (YOLOv5l) were 0.94 (95% confidence interval [CI] 0.73–1.00) and 0.96 (95% CI 0.89–0.99), respectively. This model also outperformed the SSD model. This is the first study to establish a model for detecting DDH using YOLOv5. Our deep learning model provides good diagnostic performance for DDH. We believe our model is a useful diagnostic assistant tool.

Intraoperative radiography imaging is essential for accurate spinal implant placement. Hazards caused by ionizing radiation raised concern on personnel’s work life long exposure in the operating room (OR). To particularize a cumulative risk estimation of radiation of personnel and patient, depending on used methods (C-arm fluoroscopy, O-arm navigation) and patient characteristics during spinal surgery, detailed investigation of radiation exposure in a clinical setting is required. Lumbosacral dorsal spinal fusion was performed in 37 patients (19 navigated, 18 fluoroscopy) during this prospective study. Radiation exposure was measured on several body regions with thermoluminescent dosimeters on patient and OR personnel (surgeon, assistant, sterile nurse, radiology technologist). Comparison between patient characteristics and radiation exposure was included. The highest patients values were measured in the surgery field and gonads area during navigation (43.2 ± 19.4 mSv; fluoroscopy: 27.7 ± 31.3 mSv; p = 0.02), followed by the thoracic region during fluoroscopy (7.7 ± 14.8 mSv; navigation: 1.1 ± 1.0 mSv; p = 0.06), other measured regions can be considered marginal in comparison. Amongst OR personnel exposure of the surgeon was significant higher during fluoroscopy (right hand: 566 ± 560 µSv and thoracic region: 275 ± 147 µSv; followed by thyroid and forehead) compared to navigation (right finger: 49 ± 19 µSv; similar levels for all regions; p < 0.001 in all regions). When compared to the surgeon, other OR personnel had significantly lower radiation doses on all body regions using fluoroscopy, and similar dose during navigation. The highest eye’s lens region value was measured during fluoroscopy for the patient (185 ± 165 µSv; navigation: 205 ± 60 µSv; p = 0.57) and the surgeon (164 ± 74 µSv; navigation: 92 ± 41 µSv; p < 0.001). There was a significant correlation between patient BMI and radiation exposure to the surgery field during fluoroscopy. To our knowledge, these data present the first real life, detailed comparison of radiation exposure on OR personnel and patients between clinical use of navigation and fluoroscopy. Although patient’s radiation dose is approximately 3-fold during navigation compared to the fluoroscopy, we found that a spinal surgeon could perform up to 10-fold number of surgeries (10.000 versus 883) until maximum permissible annual effective radiation dose would be reached. Especially for a spinal surgeon, who is mainly exposed amongst OR personnel, radiation prevention and protection must remain a main issue.

Venous thromboembolism (VTE) and major bleeding (MBE) are feared complications that are influenced by numerous host and surgical related factors. Using machine learning on contemporary data, our aim was to develop and validate a practical, easy-to-use algorithm to predict risk for VTE and MBE following total joint arthroplasty (TJA). This was a single institutional study of 35,963 primary and revision total hip (THA) and knee arthroplasty (TKA) patients operated between 2009 and 2020. Fifty-six variables related to demographics, comorbidities, operative factors as well as chemoprophylaxis were included in the analysis. The cohort was divided to training (70%) and test (30%) sets. Four machine learning models were developed for each of the outcomes assessed (VTE and MBE). Models were created for all VTE grouped together as well as for pulmonary emboli (PE) and deep vein thrombosis (DVT) individually to examine the need for distinct algorithms. For each outcome, the model that best performed using repeated cross validation was chosen for algorithm development, and predicted versus observed incidences were evaluated. Of the 35,963 patients included, 308 (0.86%) developed VTE (170 PE’s, 176 DVT’s) and 293 (0.81%) developed MBE. Separate models were created for PE and DVT as they were found to outperform the prediction of VTE. Gradient boosting trees had the highest performance for both PE (AUC-ROC 0.774 [SD 0.055]) and DVT (AUC-ROC 0.759 [SD 0.039]). For MBE, least absolute shrinkage and selection operator (Lasso) analysis had the highest AUC (AUC-ROC 0.803 [SD 0.035]). An algorithm that provides the probability for PE, DVT and MBE for each specific patient was created. All 3 algorithms had good discriminatory capability and cross-validation showed similar probabilities comparing predicted and observed failures indicating high accuracy of the model. We successfully developed and validated an easy-to-use algorithm that accurately predicts VTE and MBE following TJA. This tool can be used in every-day clinical decision making and patient counseling.

During progression of knee osteoarthritis (OA), gait biomechanics changes three-dimensionally; however, its characteristics and trunk posture according to OA severity remain unknown. The present study investigated three-dimensional knee joint biomechanics and trunk posture according to knee OA severity. Overall, 75 patients (93 knees) with medial knee OA [Kellgren-Lawrence grade ≥ 2, grade 2: 20 patients with 24 knees (mean 60.0 years old); grade 3: 25 with 28 knees (mean 62.0 years old); grade 4: 30 with 41 knees (mean 67.9 years old)] and 14 healthy controls (23 knees, mean 63.6 years old) underwent gait analysis using an optical motion capture system and point cluster technique. In grade 2 knee OA, the relative contribution of the knee adduction moment (KAM) increased significantly ( P  < 0.05), and that of the knee flexion moment decreased ( P  < 0.05) prior to significant progression of varus knee deformity. Grade 3 knee OA showed significant exacerbation of varus knee deformity ( P  < 0.01) and KAM increase ( P  < 0.001). The maximum knee extension angle decreased ( P  < 0.05) and trunk flexion increased during gait in grade 4 knee OA ( P  < 0.001). Our study clarified the kinematics and kinetics of medial knee OA with trunk flexion according to severity. Kinetic conversion occurred in grade 2 knees prior to progression of varus deformities, knee flexion contractures, and sagittal imbalance during gait in patients with severe knee OA.