Complex fracture reduction by exact identification of the fracture zone

Exploring Fracture Patterns: Assessing Representation Methods for Bone Fracture Simulation

Fracture pattern acquisition and representation in human bones play a crucial role in medical simulation, diagnostics, and treatment planning. This article presents a comprehensive review of methodologies employed in acquiring and representing bone fracture patterns. Several techniques, including segmentation algorithms, curvature analysis, and deep learning-based approaches, are reviewed to determine their effectiveness in accurately identifying fracture zones. Additionally, diverse methods for representing fracture patterns are evaluated. The challenges inherent in detecting accurate fracture zones from medical images, the complexities arising from multifragmentary fractures, and the need to automate fracture reduction processes are elucidated. A detailed analysis of the suitability of each representation method for specific medical applications, such as simulation systems, surgical interventions, and educational purposes, is provided. The study explores insights from a broad spectrum of research articles, encompassing diverse methodologies and perspectives. This review elucidates potential directions for future research and contributes to advancements in comprehending the acquisition and representation of fracture patterns in human bone.

Preoperative Virtual Reduction Planning Algorithm of Fractured Pelvis Based on Adaptive Templates

OBJECTIVE

The minimally invasive treatment of pelvic fractures is one of the most challenging trauma orthopedics surgeries, where preoperative planning is crucial for the performance and outcome of the surgery. However, planning the ideal position of fragments currently relies heavily on the experience of the surgeon.

METHODS

A pelvic fracture virtual reduction algorithm for target position is provided based on statistical shape models (SSM). First, according to sexual dimorphism, pelvic SSM based on point cloud curvature down-sampling are constructed as adaptive templates. Then, an optimization algorithm is designed to iteratively adjust the target pose of the fragments and the adaptive matching of the templates. Finally, the feasibility of the method is verified by simulating fractures and clinical data.

RESULTS

The pelvis has complex shape characteristics, which can be analyzed by SSM to clearly understand the pattern of change. Experiments showed that the SSM-based pelvic fracture reduction method had translation and rotation errors of 2.20±1.09 mm and 3.16±1.26° in simulated cases, and 2.78±0.95 mm and 3.10±0.53° in clinical cases, which has higher accuracy than methods based on mean shape models, and wider applicability than methods based on pelvic symmetry.

CONCLUSION

The pelvic digital model created by SSM has good generalization properties, and the SSM-based virtual reduction algorithm can effectively reconstruct the target position of the fractured pelvis in preoperative planning.

SIGNIFICANCE

The proposed reduction method has the characteristics of high precision and wide application range, which provides a powerful tool for the surgeon's virtual preoperative planning.

Two-Stage Structure-Focused Contrastive Learning for Automatic Identification and Localization of Complex Pelvic Fractures

Pelvic fracture is a severe trauma with a high rate of morbidity and mortality. Accurate and automatic diagnosis and surgical planning of pelvic fracture require effective identification and localization of the fracture zones. This is a challenging task due to the complexity of pelvic fractures, which often exhibit multiple fragments and sites, large fragment size differences, and irregular morphology. We have developed a novel two-stage method for the automatic identification and localization of complex pelvic fractures. Our method is unique in that it allows to combine the symmetry properties of the pelvic anatomy and capture the symmetric feature differences caused by the fracture on both the left and right sides, thereby overcoming the limitations of existing methods which consider only image or geometric features. It implements supervised contrastive learning with a novel Siamese deep neural network, which consists of two weight-shared branches with a structural attention mechanism, to minimize the confusion of local complex structures of the pelvic bones with the fracture zones. A structure-focused attention (SFA) module is designed to capture the spatial structural features and enhances the recognition ability of fracture zones. Comprehensive experiments on 103 clinical CT scans from the publicly available dataset CTPelvic1K show that our method achieves a mean accuracy and sensitivity of 0.92 and 0.93, which are superior to those reported with three SOTA contrastive learning methods and five advanced classification networks, demonstrating the effectiveness of identifying and localizing various types of complex pelvic fractures from clinical CT images.

Fracture pattern projection on 3D bone models as support for bone fracture simulations

BACKGROUND AND OBJECTIVE

Obtaining bone models that represent certain types of fractures is limited by the need for such fractures to occur in real life and to be processed from medical images. This work aims to propose a method that starts from the design of specific fracture patterns in order to be projected on 3D geometric bone models, being prepared for their subsequent geometric fracturing.

METHODS

The process of projecting expert-generated fracture patterns has been approached in such a way that they contain geometrical and topological information for the subsequent fracture of the triangle mesh representing the bone model, giving information about the validity of the fracture pattern due to the design process, the validation performed, and the relationships between the fracture lines.

RESULTS

Different 3D models of long bones have been used (femur, humerus, ulna and fibula). Also, different types of fracture patterns have been created. These patterns have been used to obtain their projection on three-dimensional bones. In this study, an expert validation of the fracture patterns projected on the bone models is performed. A forensic validation of the fracture patterns used as starting point for the projection is also performed for cases in which this fracture is produced by impact, for which there is scientific evidence based on forensic analysis. This validation also supports the experts, giving them the necessary feedback to complete or modify their fracture patterns according to criteria analyzed from a forensic point of view.

CONCLUSIONS

The patterns fit the bone models correctly, despite the irregularities of the bone models, and correspond to the expected projection. In addition, it provides us with a clear line of work, by using the topological information of the fracture pattern and the bone model, which allows us to establish a consistent basis for future guided fractures.

Taohong Siwu Decoction promotes the process of fracture healing by activating the VEGF-FAK signal pathway and systemically regulating the gut microbiota

AIMS

This study aimed to explore the effect of Taohong Siwu Decoction (THSWD) on Bone marrow mesenchymal stem cells (BMSCs) at the cellular level and the possible mechanism of systemic regulation of gut microbiota on fracture recovery.

METHODS AND RESULTS

Cell Counting Kit-8 (CCK-8) experiments show that THSWD effectively promotes the proliferation of BMSCs. Transwell and wound healing assays show that THSWD effectively promotes the invasion and migration of BMSCs. Alizarin red staining showed that the THSWD model enhanced the osteogenic differentiation of BMSCs. Moreover, the effect of THSWD on BMSCs is time- and concentration-dependent. RT-qPCR and Western blot results showed that THSWD treatment up-regulated the expression of vascular endothelial growth factor (VEGF) and focal adhesion kinase (FAK) at mRNA and protein levels, respectively. Hematoxylin-eosin and crocin O-quick green staining showed that rats with right femoral shaft fractures, after 14 days of THSWD treatment, the area of callus and cartilage regeneration at the fracture site increased significantly. Gut microbiota was changed in fractured rats, such as the abundance of Bacteroidetes and Firmicutes was increased. THSWD showed positive regulation of both to a certain extent.

CONCLUSION

THSWD up-regulates VEGF and activates the FAK signaling pathway to enhance the development and differentiation of BMSCs, and systematically regulates the gut microbiota to promote fracture healing.

SIGNIFICANCE AND IMPACT OF STUDY

This study provides new insights on the cellular and systemic level to understand the mechanism of THSWD in the treatment of fractures.