Journal of Orthopaedic Research®最新文献

Magnetic resonance imaging (MRI) of the knee is the recommended diagnostic method before invasive arthroscopy surgery. Nevertheless, interpreting knee MRI scans is a time-consuming process that is vulnerable to inaccuracies and inconsistencies. We proposed a multitask learning network MCSNet_att which efficiently introduces segmentation prior features and enhances classification results through multiscale feature fusion and spatial attention modules. The MRI studies and subsequent arthroscopic diagnosis of 259 knees were collected retrospectively. Models were trained based on multitask loss with coronal and sagittal sequences and fused using logistic regression (LR). We visualized the network's interpretability by the gradient-weighted class activation mapping method. The LR model achieved higher area under the curve and mean average precision of medial and lateral menisci than models trained on a single sagittal or coronal sequence. Our multitask model MCSNet_at outperformed the single-task model CNet and two clinicians in classification, with accuracy, precision, recall, F1-score of 0.980, 1.000, 0.952, 0.976 for medial and 0.920, 0.905, 0.905, 0.905 for the lateral, respectively. With the assistance of model results and visualized saliency maps, both clinicians showed improvement in their diagnostic performance. Compared to the baseline segmentation model, our model improved dice similarity coefficient and the 95% Hausdorff distance (HD₉₅) of the lateral meniscus for 2.3% and 0.860 mm in coronal images and 4.4% and 2.253 mm in sagittal images. Our multitask learning network quickly generated accurate clinicopathological classification and segmentation of knee MRI, demonstrating its potential to assist doctors in a clinical setting.

This study's objective was to investigate the extent to which two different levels of low-intensity vibration training (0.6 g or 1.0 g) affected musculoskeletal structure and function after a volumetric muscle loss (VML) injury in male C57BL/6J mice. All mice received a unilateral VML injury to the posterior plantar flexors. Mice were randomized into a control group (no vibration; VML-noTX), or one of two experimental groups. The two experimental groups received vibration training for 15-min/day, 5-days/week for 8 weeks at either 0.6 g (VML-0.6 g) or 1.0 g (VML-1.0 g) beginning 3-days after induction of VML. Muscles were analyzed for contractile and metabolic adaptations. Tibial bone mechanical properties and geometric structure were assessed by a three-point bending test and microcomputed tomography (µCT). Body mass-normalized peak isometric-torque was 18% less in VML-0.6 g mice compared with VML-noTx mice (p = 0.030). There were no statistically significant differences of vibration intervention on contractile power or muscle oxygen consumption (p ≥ 0.191). Bone ultimate load, but not stiffness, was ~16% greater in tibias of VML-1.0 g mice compared with those from VML-noTx mice (p = 0.048). Cortical bone volume was ~12% greater in tibias of both vibration groups compared with VML-noTx mice (p = 0.003). Importantly, cross-section moment of inertia, the primary determinant of bone ultimate load, was 44% larger in tibias of VML-0.6 g mice compared with VML-noTx mice (p = 0.006). These changes indicate that following VML, bones are more responsive to the selected vibration training parameters than muscle. Vibration training represents a possible adjuvant intervention to address bone deficits following VML.

The biological factors that affect healing after rotator cuff repair (RCR) are not well understood. Genetic variants in the extracellular matrix protein Tenascin C (TNC) are associated with impaired tendon healing and it is expressed in rotator cuff tendon tissue after injury, suggesting it may have a role in the repair process. The purpose of the current study was to determine the role of TNC on tendon healing after RCR in a murine model. The supraspinatus tendon was transected and repaired on the left shoulder of wild-type (WT-RCR), Tenascin C null (Tnc⁻-RCR) and Tnc heterozygous (Tnc^+/−-RCR) mice. Controls included the unoperated, contralateral shoulder of WT-RCR, Tnc^—RCR, Tnc^+/−-RCR mice and unoperated shoulders from age and genotype matched controls. We performed histologic, activity testing, bulk RNA-seq, and biomechanical analyses. At 8-weeks post-RCR, Tnc⁻ and Tnc^+/− mice had severe bone and tendon defects following RCR. Tnc⁻-RCR mice had reduced activity after RCR including reduced wheel rotations, wheel duration, and wheel episode average velocity compared with WT-RCR. Loss of Tnc following RCR altered gene expression in the shoulder, including upregulation of sex hormone and WNT pathways and a downregulation of inflammation and cell cycle pathways. Tnc⁻ mice had similar biomechanical properties after repair as WT. Further research is required to evaluate tissue specific alterations of Tnc, the interactions of Tnc and sex hormone and inflammation pathways as well as possible adjuvants to improve enthesis healing in the setting of reduced TNC function.

Helical plates used for proximal humeral shaft fracture fixation avoid the radial nerve distally as compared to straight plates. To investigate in a human cadaveric model the biomechanical competence of straight lateral plates versus 45° helical plates used for fixation of proximal comminuted humeral shaft fractures, eight pairs of human cadaveric humeri were instrumented using either a long straight PHILOS plate (Group 1) or a 45° helical plate (Group 2) for treatment of an unstable proximal humeral shaft fracture. All specimens were tested under non-destructive quasi-static loading in axial compression, internal and external rotation, and bending in four directions. Subsequently, progressively increasing cyclic loading in internal rotation was applied until failure and interfragmentary movements were monitored by motion tracking. Axial displacement (mm) was 3.13 ± 0.31 in Group 1 and 2.60 ± 0.42 in Group 2, p = 0.015. Flexion/extension deformation (°) in Group 1 and Group 2 was 0.56 ± 0.42 and 0.43 ± 0.23, p = 0.551. Varus/valgus deformation (°) was 6.39 ± 0.44 in Group 1 and 5.13 ± 0.87 in Group 2, p = 0.012. Shear (mm) and torsional (°) displacement were 5.36 ± 0.76 and 17.75 ± 1.06 in Group 1, and 5.03 ± 0.46 and 16.79 ± 1.36 in Group 2, p ≥ 0.090. Cycles to catastrophic failure were 10000 ± 1401 in Group 1 and 9082 ± 1933 in Group 2, p = 0.708. From a biomechanical perspective, 45° helical plating is associated with lower axial and varus/valgus displacement under axial loading and demonstrates comparable resistance to failure versus straight plating. Therefore, 45° helical plates can be considered as a valid alternative to straight plates for treatment of proximal humeral shaft fractures.

This study investigated joint kinematics and attachment tensile mechanics following resection of the medial meniscus anterior attachment. A secondary objective investigated the repair of the attachment. Yucatan minipigs underwent unilateral surgery for either Injury (en bloc) resection of the anterior attachment of the insertional ligament, (a portion of the cranial medial meniscotibial ligament) or Repair (immediate repair with a suture anchor), with the contralateral knee as Intact control. Evaluation at 6 weeks and 6 months included joint kinematics measured from MRI acquired under knee compression and tensile testing of the attachment. Injury resulted in large levels of meniscus extrusion, despite the development of a fibrovascular scar. At 6 weeks, the meniscus extruded 1.95 mm more than Intact; at 6 months, this extrusion was reduced to 0.77 mm. Under an applied 1× body weight load, the meniscus further extruded and was not different with treatment or time. During attachment tensile testing, elongation was 0.6 mm for Intact, following Injury, elongation was 2.7 mm at 6 weeks and was partially restored to 1.5 mm at 6 months. Despite this, the cartilage wear worsened over time. Repair was inadequate to avoid the extrusion or cartilage wear seen in the injury group at 6 weeks, so it was not continued for the 6-month group. This study demonstrates that while meniscus injury is useful to study cartilage degeneration, a holistic consideration of the role of the meniscus itself, including its changing material properties and its impact on joint mechanics during injury, repair, and rehabilitation, are key factors contributing to overall joint health.

This study emphasizes the importance of early detection of osteonecrosis of the femoral head (ONFH) in young patients on long-term glucocorticoid therapy, including those with acute lymphoblastic leukemia, lupus, and other diagnoses. While X-ray and magnetic resonance imaging (MRI) are standard imaging methods for staging ONFH, MRI can be costly and time-consuming. The research focuses on utilizing artificial intelligence (AI) to enhance the evaluation of radiographic images for ONFH detection. The study involved analyzing X-ray and MRI from 102 control hips and 104 ONFH-affected hips at Association Research Circulation Osseous (ARCO) Stage II and IIIa. We employed transfer learning with the YOLOv8 model for object detection, using 80% of the data for training and 20% for validation, then assessed detection accuracy through mean average precision (mAP) and a precision-recall curve. Additionally, AI generated synthetic MRI (sMRI) from X-ray images using a Generative Adversarial Network (GAN) and evaluated their similarity to original MRI. Results showed that the mAP for ONFH detection was 0.923 for the YOLOv8n model and 0.951 for YOLOv8x. The GAN-generated sMRI exhibited lower image quality compared with originals but maintained potential for lesion assessment. Intrarater reliability among evaluators was high. The findings indicate that AI techniques, particularly YOLOv8 for object detection and GAN for image generation, can effectively assist in ONFH screening, despite some limitations in the generated MRI quality.

During cementless total knee arthroplasty (TKA), an overlap between the resected tibia and the implant's geometry, termed interference fit, is introduced to facilitate primary stability and direct bone-implant contact. However, little is known about the actual interference achieved and the resulting mechanical response in the surrounding cancellous bone. The aim of this study was (1) to experimentally quantify the actual interference achieved for a commercially available cementless tibial implant and (2) to assess its effect on the post-impaction cancellous bone strain. Seven human cadaveric tibiae were micro-CT scanned intact (23 µm/pixel), once prepared for TKA (46 µm/pixel) and following implantation (46 µm/pixel). The actual interference across the entire bone-implant interface was quantified and, via digital volume correlation, the compressive strains of bone in contact with the implant and at increasing distance, were extracted. An inhomogeneous actual interference was found across the implant pegs and keel (median ± std dev: 0.70 ± 0.27 mm), which was lower than the intended. Limited interference (0.02 ± 0.12 mm) was found directly under the baseplate, with immediate bone-baseplate contact of 54%. The induced compressive strains were related to the actual interference within 3.14 mm from the bone-implant interface (R² = 0.269–0.450, p < 0.001), with higher compressive strains corresponding to higher interference, but not being related to the bone volume fraction. Clinical Significance: Insight is provided into the interaction between the variability of the resection and the resulting mechanical environment. A complex relationship is apparent, whereby the actual interference accounted for up to 45% of the variation in induced compressive strain magnitude.

Having osteoarthritis in one knee is reported as an independent risk factor for developing contralateral knee osteoarthritis (KOA). However, no study has been designed to predict the development of contralateral KOA (cKOA). The authors hypothesized that specific risk factors for cKOA development exist and that it could be accurately predicted with the assistance of machine learning. KOA was defined using the Kellgren-Lawrence grade (KLG) of 2 or higher. Data from 1353 unilateral KOA patients (900 from the Osteoarthritis Initiative [OAI] and 453 from the Multicenter Osteoarthritis Study [MOST]) over 4–5 years of follow-up were examined. The risk factors for cKOA development were analyzed, and a machine learning model was developed to predict cKOA using OAI as the development data set and MOST as the test data set. cKOA developed in 172 (19.1%) and 178 (39.3%) of the patients (OAI and MOST, respectively) over a period of 4–5 years. A machine learning model was developed using the Tree-based Pipeline Optimization Tool algorithm. This model utilized nine variables, including baseline lateral joint space narrowing grade of the contralateral knee (odds ratio 4.475). The area under the curve of the receiver operating characteristics curve, along with accuracy, precision, and F1-score, were recorded as 0.69, 0.60, 0.50, and 0.58, respectively, in the test data set. The development of cKOA could be effectively predicted using a limited number of variables through machine learning. Surgeons should consider the development of cKOA in patients with identified risk factors when managing KOA patients.