bluuebunny/crossref_metadata_2025_split huggingface dataset:
BIOMECHANICAL ASSESSMENT OF NOVEL DYNAMIC VERSUS CONVENTIONAL HIGH-STRENGTH SUTURES IN DISTAL BICEPS TENDON REPAIR
Authors: Zderic I. , Kraus M. , Rossenberg L. V. , Gueorguiev B. , Richards G. , Pape H. C. , Pastor T. , Pastor T. 
Published: November 2024

IntroductionTendon ruptures are a common injury and often require surgical intervention to heal. A refixation is commonly performed with high-strength suture material. However, slipping of the thread is unavoidable even at 7 knots potentially leading to reduced compression of the sutured tendon at its footprint. This study aimed to evaluate the biomechanical properties and effectiveness of a novel dynamic high-strength suture, featuring self-tightening properties.MethodDistal biceps tendon rupture tenotomies and subsequent repairs were performed in sixteen paired human forearms using either conventional or the novel dynamic high-strength sutures in a paired design. Each tendon repair utilized an intramedullary biceps button for radial fixation. Biomechanical testing aimed to simulate an aggressive postoperative rehabilitation protocol stressing the repaired constructs. For that purpose, each specimen underwent in nine sequential days a daily mobilization over 300 cycles under 0-50 N loading, followed by a final destructive test.ResultAfter the ninth day of cyclic loading, specimens treated with the dynamic suture exhibited significantly less tendon elongation at both proximal and distal measurement sites (-0.569±2.734 mm and 0.681±1.871 mm) compared to the conventional suture group (4.506±2.169 mm and 3.575±1.716 mm), p=0.003/p<0.002. Gap formation at the bone-tendon interface was significantly lower following suturing using dynamic suture (2.0±1.6 mm) compared to conventional suture (4.5±2.2 mm), p=0.04. The maximum load at failure was similar in both treatment groups (dynamic suture: 374± 159 N; conventional suture: 379± 154 N), p=0.925. The predominant failure mechanism was breakout of the button from the bone (dynamic suture: 5/8; conventional suture: 6/8), followed by suture rupturing, suture unraveling and tendon cut-through.ConclusionFrom a biomechanical perspective, the novel dynamic high-strength suture demonstrated higher resistance against gap formation at the bone tendon interface compared to the conventional suture, which may contribute to better postoperative tendon integrity and potentially quicker functional recovery in the clinical setting.
REDUCED HEART RATE VARIABILITY AND INCREASED STRESS HORMONE LEVELS INDICATE AN AUTONOMIC DYSFUNCTION IN OSTEOARTHRITIS PATIENTS
Authors: Sohn R. , Assar T. , Kaufhold I. , Brenneis M. , Braun S. , Junker M. , Zaucke F. , Pongratz G. , Jenei-Lanzl Z. 
Published: November 2024

IntroductionRecent studies suggested that the progression of osteoarthritis (OA), a chronic degenerative joint disease, may be affected by the autonomic nervous system (ANS). Under healthy conditions, the sympathetic (SNS) and parasympathetic (PNS) branches of the ANS are well coordinated to maintain homeostasis. However, pathological conditions are frequently associated with a disturbance of this fine-tuned balance. Therefore, we analyzed whether an autonomic dysfunction occurs in OA patients.Method225 participants with early- or late-stage knee OA as well as 40 healthy age-matched probands were included in this study. Autonomic activity was investigated by analyzing heart rate variability (HRV), which decreases under chronic sympathetic overactivity. Time- and frequency-domain HRV indices SDRR, RMSSD, pRR50 and LF were examined. Linear regression analysis was performed to adjust for clinical characteristics, such as age, sex, BMI, or medication. Moreover, perceived chronic stress (PSQ) and pain (WOMAC) were assessed via questionnaires. In addition, the serum stress hormones cortisol, DHEA-S and IL-6 were analyzed via ELISA.ResultSDRR, RMSSD, and pRR50 were slightly reduced in the early stage of OA and showed significant decrease in the later stage of the disease. Also LF decreased significantly with OA progression. HRV was significantly influenced by the grade of OA, but not other patient characteristics. Moreover, late-stage OA patients demonstrated significantly higher PSQ and WOMAC levels compared to healthy controls. In addition, cortisol/DHEA-S ratio and IL-6 serum concentrations were significantly higher in late-stage than in early-stage OA patients and healthy controls.ConclusionReduced HRV, increased cortisol/DHEA-S ratio and PSQ level as well as elevated systemic IL-6 concentration indicated an autonomic shift towards a more pronounced SNS activity due to PNS deficiency in OA patients, particularly in the late-stage of the disease. Therefore, modulation of the ANS, for example by vagus nerve stimulation, might be a potential treatment strategy for of knee OA patients.
DEVIATION FROM PREOPERATIVE PLANNING AND ACCURACY OF TRANSPEDICULAR SCREW FIXATION USING INTRAOPERATIVE 3D NAVIGATION FOR LUMBAR SPONDYLOLISTHESIS
Authors: Vadalà G. , Papalia G. F. , Russo F. , Nardi N. , Ambrosio L. , Papalia R. , Denaro V. 
Published: November 2024

IntroductionIntraoperative navigation systems for lumbar spine surgery allow to perform preoperative planning and visualize the real-time trajectory of pedicle screws. The aim of this study was to evaluate the deviation from preoperative planning and the correlations between screw deviation and accuracy.MethodPatients affected by degenerative spondylolisthesis who underwent posterior lumbar interbody fusion using intraoperative 3D navigation since April 2022 were included. Intraoperative cone-beam computed tomography (CBCT) was performed before screw planning and following implantation. The deviation from planning was calculated as linear, angular, and 3D discrepancies between planned and implanted screws. Accuracy and facet joint violation (FJV) were evaluated using Gertzbein-Robbins system (GRS) and Yson classification, respectively. Statistical analysis was performed using SPSS version28. One-way ANOVA followed by Bonferroni post-hoc tests were performed to evaluate the association between GRS, screw deviation and vertebral level. Statistical significance was set at p<0.05.ResultThis study involved 34 patients, for a total of 154 pedicle screws. Mean age was 62.6±8.9 years. The mean two-dimensional screw tip deviation in mediolateral (ML), craniocaudal (CC), and anteroposterior (AP) was 2.6±2.45mm, 1.6±1.7mm, and 3.07±2.9mm, respectively. The mean screw tip 3D deviation was 5±3.3mm. The mean two-dimensional screw head deviation in ML, CC and AP was 1.83±1.8mm, 1.7±1.67mm and 3.6±3.1mm, respectively. The mean screw head 3D deviation was 4.94±3.2mm. 98% of screws were clinically acceptable (grade A+B), and grade 0 for FJV. Significant results were found between GRS and ML (p=0.005), AP (p=0.01) and 3D (p=0.003) tip deviations, and between GRS and AP and 3D head deviations (both p=0). Moreover, a significant correlation was found between GRS and vertebral level (p=0).ConclusionOur results showed a reasonable rate of discrepancy between planned and positioned screws. However, accuracy was clinically acceptable in almost all cases. Therefore, pedicle screw fixation using intraoperative CBCT, 3D navigation and screw planning is safe and accurate.
IMPLANT DETECTION AND CLASSIFICATION FROM A SMALL DATASET OF LOWER LIMB RADIOGRAPHS: PERFORMANCE OF DEEP LEARNING MODELS PRE-TRAINED ON LARGER DATASETS
Authors: Birkholtz F. , Eken M. , Swanevelder M. , Engelbrecht A. 
Published: November 2024

IntroductionInaccurate identification of implants on X-rays may lead to prolonged surgical duration as well as increased complexity and costs during implant removal. Deep learning models may help to address this problem, although they typically require large datasets to effectively train models in detecting and classifying objects, e.g. implants. This can limit applicability for instances when only smaller datasets are available. Transfer learning can be used to overcome this limitation by leveraging large, publicly available datasets to pre-train detection and classification models. The aim of this study was to assess the effectiveness of deep learning models in implant localisation and classification on a lower limb X-ray dataset.MethodFirstly, detection models were evaluated on their ability to localise four categories of implants, e.g. plates, screws, pins, and intramedullary nails. Detection models (Faster R-CNN, YOLOv5, EfficientDet) were pre-trained on the large, freely available COCO dataset (330000 images). Secondly, classification models (DenseNet121, Inception V3, ResNet18, ResNet101) were evaluated on their ability to classify five types of intramedullary nails. Localisation and classification accuracy were evaluated on a smaller image dataset (204 images).ResultThe YOLOv5s model showed the best capacity to detect and distinguish between different types of implants (accuracy: plate=82.1%, screw=72.3%, intramedullary nail=86.9%, pin=79.9%). Screw implants were the most difficult implant to detect, likely due to overlapping screw implants visible in the image dataset. The DenseNet121 classification model showed the best performance in classifying different types of intramedullary nails (accuracy=73.7%). Therefore, a deep learning model pipeline with the YOLOv5s and DenseNet121 was proposed for the most optimal performance of automating implants localisation and classification for a relatively small dataset.ConclusionThese findings support the potential of deep learning techniques in enhancing implant detection accuracy. With further development, AI-based implant identification may benefit patients, surgeons and hospitals through improved surgical planning and efficient use of theatre time.
WEARABLE INERTIAL SENSORS AND DEEP LEARNING TO DETECT CLINICALLY MEANINGFUL REAL-WORLD WALKING SURFACE CHARACTERISTICS
Authors: Vinco G. , Ley C. , Dixon P. , Grimm B. 
Published: November 2024

IntroductionThe ability to walk over various surfaces such as cobblestones, slopes or stairs is a very patient centric and clinically meaningful mobility outcome. Current wearable sensors only measure step counts or walking speed regardless of such context relevant for assessing gait function. This study aims to improve deep learning (DL) models to classify surfaces of walking by altering and comparing model features and sensor configurations.MethodUsing a public dataset, signals from 6 IMUs (Movella DOT) worn on various body locations (trunk, wrist, right/left thigh, right/left shank) of 30 subjects walking on 9 surfaces were analyzed (flat ground, ramps (up/down), stairs (up/down), cobblestones (irregular), grass (soft), banked (left/right)). Two variations of a CNN Bi-directional LSTM model, with different Batch Normalization layer placement (beginning vs end) as well as data reduction to individual sensors (versus combined) were explored and model performance compared in-between and with previous models using F1 scores.ResultThe Bi-LSTM architecture improved performance over previous models, especially for subject-wise data splitting and when combining the 6 sensor locations (e.g. F1=0.94 versus 0.77). Placement of the Batch Normalization layer at the beginning, prior to the convolutional layer, enhanced model understanding of participant gait variations across surfaces. Single sensor performance was best on the right shank (F1=0.88).ConclusionWalking surface detection using wearable IMUs and DL models shows promise for clinically relevant real-world applications, achieving high F1 levels (>0.9) even for subject-wise data splitting enhancing the model applicability in real-world scenarios. Normalization techniques, such as Batch Normalization, seem crucial for optimizing model performance across diverse participant data. Also single-sensor set-ups can give acceptable performance, in particular for specific surface types of potentially high clinical relevance (e.g. stairs, ramps), offering practical and cost-effective solutions with high usability. Future research will focus on collecting ground-truth labeled data to investigate system performance in real-world settings.
FRACTURE DETECTION IN WRIST TRAUMA RADIOGRAPH: OPTIMIZING ALGORITHM PERFORMANCE USING TRANSFER LEARNING
Authors: Birkholtz F. , Eken M. , Boyes A. , Engelbrecht A. 
Published: November 2024

IntroductionWith advances in artificial intelligence, the use of computer-aided detection and diagnosis in clinical imaging is gaining traction. Typically, very large datasets are required to train machine-learning models, potentially limiting use of this technology when only small datasets are available. This study investigated whether pretraining of fracture detection models on large, existing datasets could improve the performance of the model when locating and classifying wrist fractures in a small X-ray image dataset. This concept is termed “transfer learning”.MethodFirstly, three detection models, namely, the faster region-based convolutional neural network (faster R-CNN), you only look once version eight (YOLOv8), and RetinaNet, were pretrained using the large, freely available dataset, common objects in context (COCO) (330000 images). Secondly, these models were pretrained using an open-source wrist X-ray dataset called “Graz Paediatric Wrist Digital X-rays” (GRAZPEDWRI-DX) on a (1) fracture detection dataset (20327 images) and (2) fracture location and classification dataset (14390 images). An orthopaedic surgeon classified the small available dataset of 776 distal radius X-rays (Arbeidsgmeischaft für Osteosynthesefragen Foundation / Orthopaedic Trauma Association; AO/OTA), on which the models were tested.ResultDetection models without pre-training on the large datasets were the least precise when tested on the small distal radius dataset. The model with the best accuracy to detect and classify wrist fractures was the YOLOv8 model pretrained on the GRAZPEDWRI-DX fracture detection dataset (mean average precision at intersection over union of 50=59.7%). This model showed up to 33.6% improved detection precision compared to the same models with no pre-training.ConclusionOptimisation of machine-learning models can be challenging when only relatively small datasets are available. The findings of this study support the potential of transfer learning from large datasets to improve model performance in smaller datasets. This is encouraging for wider application of machine-learning technology in medical imaging evaluation, including less common orthopaedic pathologies.
ANALYSIS OF REOPERATIONS FOLLOWING POSTERIOR PEDICLE FIXATION IN SPINAL SURGERY
Authors: Bulut H. , Maestre M. , Tomey D. 
Published: November 2024

IntroductionUnplanned reoperations (UROs) following corrective surgery for adult spinal deformity (ASD) present significant challenges for both patients and surgeons. Understanding the specific UROs types is crucial for improving patient outcomes and refining surgical strategies in ASD correction.MethodThis retrospective analysis utilized data from the American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) database spanning from 2017 to 2021. Patient information was extracted using specific CPT codes related to posterior pedicle fixation.ResultIn a cohort of 1088 patients undergoing posterior spinal deformity corrections, we examined various preoperative factors to discern their correlation with reoperation prevalence. Our analysis revealed no statistically significant differences in reoperation prevalence concerning gender (male: 4.0%, p=0.131) or ethnicity (Hispanic: 4.2%, p=0.192). Similarly, no notable associations were identified for diabetes mellitus, smoking status, dyspnea, history of severe COPD, hypertension, ASA classification, or functional health status before surgery, with reoperation prevalences ranging from 3.2% to 8.8% and p-values spanning from 0.146 to 0.744. Overall, the reoperation prevalence within the entire cohort stood at 5.2% (55 cases). In terms of the types of reoperations investigated, spinal-related procedures emerged as the most prevalent, accounting for 43.7% (24 cases), followed closely by wound site revisions at 23.6% (13 cases). Additionally, gastrointestinal-related procedures and various other miscellaneous interventions, such as uroscopy, demonstrated reoperation prevalences of 7.2% (4 cases) and 25.5% (14 cases), respectively.Conclusionour findings highlight the diverse spectrum of reoperation procedures encountered following posterior spinal deformity corrections, with wound site revisions and spinal-related interventions being the most prevalent categories. These results emphasize the complexity of managing UROs in spinal surgery and the need for tailored approaches and infection/incision protocols to address the specific challenges associated with each type of reoperation.
AUTOMATIC MULTI-CLASS SEGMENTATION OF HIP JOINT SPACE USING 3D DEEP LEARNING ALGORITHMS
Authors: Sawant S. , Borotikar B. , Raghu V. , Audenaert E. , Khanduja V. 
Published: November 2024

IntroductionThree-dimensional (3D) morphological understanding of the hip joint, specifically the joint space and surrounding anatomy, including the proximal femur and the pelvis bone, is crucial for a range of orthopedic diagnoses and surgical planning. While deep learning algorithms can provide higher accuracy for segmenting bony structures, delineating hip joint space formed by cartilage layers is often left for subjective manual evaluation. This study compared the performance of two state-of-the-art 3D deep learning architectures (3D UNET and 3D UNETR) for automated segmentation of proximal femur bone, pelvis bone, and hip joint space with single and multi-class label segmentation strategies.MethodA dataset of 56 3D CT images covering the hip joint was used for the study. Two bones and hip joint space were manually segmented for training and evaluation. Deep learning models were trained and evaluated for a single-class approach for each label (proximal femur, pelvis, and the joint space) separately, and for a multi-class approach to segment all three labels simultaneously. A consistent training configuration of hyperparameters was used across all models by implementing the AdamW optimizer and Dice Loss as the primary loss function. Dice score, Root Mean Squared Error, and Mean Absolute Error were utilized as evaluation metrics.ResultsBoth the models performed at excellent levels for single-label segmentations in bones (dice > 0.95), but single-label joint space performance remained considerably lower (dice < 0.87). Multi-class segmentations remained at lower performance (dice < 0.88) for both models. Combining bone and joint space labels may have introduced a class imbalance problem in multi-class models, leading to lower performance.ConclusionIt is not clear if 3D UNETR provides better performance as the selection of hyperparameters was the same across the models and was not optimized. Further evaluations will be needed with baseline UNET and nnUNET modeling architectures.
PATIENT-SPECIFIC TIBIAL IMPLANTS: ALGORITHMIC DESIGN PROCESSES FOR CLINICAL CASES
Authors: Kühl J. , Grocholl J. , Seekamp A. , Klüter T. , Fuchs S. 
Published: November 2024

IntroductionThe surgical treatment of critical-sized bone defects with complex three-dimensional (3D) geometries is a challenge for the treating surgeon. Additive manufacturing such as 3D printing enables the production of highly individualized bone implants meeting the shape of the patient's bone defect and including a tunable internal structure. In this study, we showcase the design process for patient-specific implants with critical-sized tibia defects.MethodsTwo clinical cases of patients with critical tibia defects (size 63×20×21 mm and 50×24×17 mm) were chosen. Brainlab software was used for segmentation of CT data generating 3D models of the defects. The implant construction involves multiple stages. Initially, the outer shell is precisely defined. Subsequently, the specified volume is populated with internal structures using Voronoi, Gyroid, and NaCl crystal structures. Variation in pore size (1.6 mm and 1.0 mm) was accomplished by adjusting scaffold size and material thickness.ResultsAn algorithmic design process in Rhino and Grasshopper was successfully applied to generate model implants for the tibia from Ct data. By integrating a precise mesh into an outer shell, a scaffold with controlled porosity was designed. In terms of the internal design, both Voronoi and Gyroid form macroscopically homogeneous properties, while NaCl, exhibits irregularities in density and consequently, in the strength of the structure. Data implied that Voronoi and Gyroid structures adapt more precisely to complex and irregular outer shapes of the implants.ConclusionIn proof-of-principle studies customized tibia implants were successfully generated and printed as model implants based on resin. Further studies will include more patient data sets to refine the workflows and digital tools for a broader spectrum of bone defects. The algorithm-based design might offer a tremendous potential in terms of an automated design process for 3D printed implants which is essential for clinical application.
DEVELOPMENT OF TISSUE-ENGINEERED SUBSTITUTES FOR THE REGENERATION OF TENDON TOTAL RUPTURES
Authors: Llucia A. , Espinosa S. C. 
Published: November 2024

IntroductionTendon ruptures represent one of the most common acute tendon injuries in adults worldwide, affecting millions of people anually and becoming more prevalent due to longer life expectancies and sports activities. Current clinical treatments for full tears are unable to completely restore the torn tendons to their native composition, structure and mechanical properties.To address this clinical challenge, tissue-engineered substitutes will be developed to serve as functional replacements for total tendon ruptures that closely resemble the original tissue, restoring functionality.MethodWater borne polyurethanes (WBPU) containing acrylate groups, specifically polyethylene glycol methacrylate (PEGMA) or 2-hydroxyethyl methacrylate (HEMA), were combined with mouse mesenchymal stem cells (MoMSCs) and heparin sodium to formulate bioinks for the fabrication of scaffolds via extrusion-based 3D bioprinting.ResultThe biocompatibility of acrylated-WBPUs was confirmed in 2D with MoMSCs using lactate dehydrogenase assay, DNA assay and live/dead assays. Cell-laden scaffolds were 3D-bioprinted by encapsulating MoMSCs at varying cell densities within the acrylated WBPUs. The resulting 3D structures support cell viability and proliferation within the scaffolds, as confirmed by live/dead assay, lactate dehydrogenase assay and DNA assays. Differentiation studies in the 3D-bioprinted scaffolds demonstrated the phenotype transition of MoMSCs toward tenocytes through gene expression and protein deposition analysis. The inclusion of sodium heparin in the bioinks revealed increased synthesis of matrix assembly proteins within the 3D-bioprinted constructs.ConclusionThe developed bioinks were biocompatible and printable, supporting cell viability within the 3D-bioprinted scaffold. The fabricated cell-laden constructs sustained cell proliferation, differentiation, and tissue formation. The addition of heparin sodium enhanced tissue formation and organization, showing promising results for the regeneration of tendon total ruptures.Principio del formularioThis work was supported by the Spanish State Research Agency (AEI) under grant No CPP2021-008754. The authors would like to thank their partners in the project, which are in charge of the synthesis of heparin sodium and acrylated-WBPUs.