Computer-assisted preoperative planning improves the learning curve of PFNA-II in the treatment of intertrochanteric femoral fractures

The effect of digital orthopedic 3D printing technology on the surgical treatment of intertrochanteric fractures of the femur: A meta-analysis

BACKGROUND

The effect of preoperative use of 3D printing technology on the overall outcome of surgical treatment of patients with intertrochanteric femoral fracture is unclear. The purpose of this study was to systematically evaluate the effect of 3D printing technology on the surgical treatment of patients with intertrochanteric femoral fractures.

METHODS

This study used a meta-analysis design. We searched PubMed, The Cochrane Library, CNKI, Embase, and the Web of Science for randomized controlled trials and cohort studies up to April 26, 2023. Mean differences with 95% confidence intervals were used to assess continuous variables.

RESULTS

A total of 11 studies involving 660 participants were included in this meta-analysis.The results showed that 3D printing improved operation time (MD = -16.18, 95% CI = -20.64 to -11.73, P < .00001), intraoperative blood loss (MD = -39.54, 95% CI = -52.10 to -26.98, P < .00001), postoperative drainage volume (MD = -12.36, 95% CI = -14.27 to -10.45, P < .00001), length of hospital stay (MD = -0.61, 95% CI = -1.07 to -0.14, P = .01), intraoperative fluoroscopy (MD = -4.29, 95% CI = -6.65 to -1.94, P = .0004), one-time nail placement success rate (MD = 7.12, 95% CI = 2.87-17.70, P < .0001), fracture healing time (MD = -2.91, 95% CI = -5.49 to -0.34, P = .03), hip function score (MD = 1.60, 95% CI = 0.29-2.92, P = .02).

CONCLUSION

As an important technical tool in the construction of digital orthopedics, the use of 3D printing technology can effectively improve the operation time, intraoperative bleeding loss, postoperative drainage volume, length of hospital stay, intraoperative fluoroscopy, one-time nail placement success rate, fracture healing time, and hip function score.

Application of Virtual Reality Systems in Bone Trauma Procedures

Background and Objectives: Bone fractures contribute significantly to the global disease and disability burden and are associated with a high and escalating incidence and tremendous economic consequences. The increasingly challenging climate of orthopaedic training and practice re-echoes the established potential of leveraging computer-based reality technologies to support patient-specific simulations for procedural teaching and surgical precision. Unfortunately, despite the recognised potential of virtual reality technologies in orthopaedic surgery, its adoption and integration, particularly in fracture procedures, have lagged behind other surgical specialities. We aimed to review the available virtual reality systems adapted for orthopaedic trauma procedures. Materials and Methods: We performed an extensive literature search in Medline (PubMed), Science Direct, SpringerLink, and Google Scholar and presented a narrative synthesis of the state of the art on virtual reality systems for bone trauma procedures. Results: We categorised existing simulation modalities into those for fracture fixation techniques, drilling procedures, and prosthetic design and implantation and described the important technical features, as well as their clinical validity and applications. Conclusions: Over the past decade, an increasing number of high- and low-fidelity virtual reality systems for bone trauma procedures have been introduced, demonstrating important benefits with regard to improving procedural teaching and learning, preoperative planning and rehearsal, intraoperative precision and efficiency, and postoperative outcomes. However, further technical developments in line with industry benchmarks and metrics are needed in addition to more standardised and rigorous clinical validation.

3D reconstruction of proximal femoral fracture from biplanar radiographs with fractural representative learning

A femoral fracture is a severe injury occurring in traumatic and pathologic causes. Diagnosis and Preoperative planning are indispensable procedures relying on preoperative radiographs such as X-ray and CT images. Nevertheless, CT imaging has a higher cost, radiation dose, and longer acquisition time than X-ray imaging. Thus, the fracture 3D reconstruction from X-ray images had been needed and remains a challenging problem, as well as a lack of dataset. This paper proposes a 3D proximal femoral fracture reconstruction from biplanar radiographs to improve the 3D visualization of bone fragments during preoperative planning. A novel Fracture Reconstruction Network (FracReconNet) is proposed to retrieve the femoral bone shape with fracture details, including the 3D Reconstruction Network (3DReconNet), novel Auxiliary class (AC), and Fractural augmentation (FA). The 3D reconstruction network applies a deep learning-based, fully Convolutional Network with Feature Pyramid Network architecture. Specifically, the auxiliary class is proposed, which refers to fracture representation. It encourages network learning to reconstruct the fracture. Since the samples are scarce to acquire, the fractural augmentation is invented to enlarge the fracture training samples and improve reconstruction accuracy. The evaluation of FracReconNet achieved a mIoU of 0.851 and mASSD of 0.906 mm. The proposed FracReconNet's results show fracture detail similar to the real fracture, while the 3DReconNet cannot offer.

Preoperative templating in orthopaedic fracture surgery: The past, present and future

Preoperative planning in orthopaedic fracture surgery corroborates with the goal of establishing the best possible surgical result and ensuring a functioning limb for the patient. From placing sketches on overhead projector paper and measuring lengths from anatomical landmarks, ways of preoperative planning have evolved rapidly over the last 100 years. Today, preoperative planning includes methods such as advanced 3-Dimensional (3D) printed models and software programs incorporating entire libraries of osteosynthesis materials that can be shaped and rotated to fit a patient's specific anatomy. Relevant literature was evaluated to review the development of preoperative templating from the past and present, in order to assess its impact on the future of osteosynthesis.We identified studies on 3D-imaging, computer-assisted systems, and 3D-printed fractured bones and drill guides. The use of some of these systems resulted in a reduction in operation time, blood loss, perioperative fluoroscopy and hospital stay, as well as better placement of osteosynthesis material. Only few studies have identified differences in patient morbidity and mortality. Future techniques of preoperative templating are on the rise and the potential is vast. The cost-effectiveness and usefulness of certain methods need to be evaluated further, but the benefit of preoperative templating has the potential of being revolutionary, with the possibility of radical advances within orthopaedic surgery.

Computer-assisted preoperative planning of bone fracture fixation surgery: A state-of-the-art review

Background: Bone fracture fixation surgery is one of the most commonly performed surgical procedures in the orthopedic field. However, fracture healing complications occur frequently, and the choice of the most optimal surgical approach often remains challenging. In the last years, computational tools have been developed with the aim to assist preoperative planning procedures of bone fracture fixation surgery. Objectives: The aims of this review are 1) to provide a comprehensive overview of the state-of-the-art in computer-assisted preoperative planning of bone fracture fixation surgery, 2) to assess the clinical feasibility of the existing virtual planning approaches, and 3) to assess their clinical efficacy in terms of clinical outcomes as compared to conventional planning methods. Methods: A literature search was performed in the MEDLINE-PubMed, Ovid-EMBASE, Ovid-EMCARE, Web of Science, and Cochrane libraries to identify articles reporting on the clinical use of computer-assisted preoperative planning of bone fracture fixation. Results: 79 articles were included to provide an overview of the state-of-the art in virtual planning. While patient-specific geometrical model construction, virtual bone fracture reduction, and virtual fixation planning are routinely applied in virtual planning, biomechanical analysis is rarely included in the planning framework. 21 of the included studies were used to assess the feasibility and efficacy of computer-assisted planning methods. The reported total mean planning duration ranged from 22 to 258 min in different studies. Computer-assisted planning resulted in reduced operation time (Standardized Mean Difference (SMD): -2.19; 95% Confidence Interval (CI): -2.87, -1.50), less blood loss (SMD: -1.99; 95% CI: -2.75, -1.24), decreased frequency of fluoroscopy (SMD: -2.18; 95% CI: -2.74, -1.61), shortened fracture healing times (SMD: -0.51; 95% CI: -0.97, -0.05) and less postoperative complications (Risk Ratio (RR): 0.64, 95% CI: 0.46, 0.90). No significant differences were found in hospitalization duration. Some studies reported improvements in reduction quality and functional outcomes but these results were not pooled for meta-analysis, since the reported outcome measures were too heterogeneous. Conclusion: Current computer-assisted planning approaches are feasible to be used in clinical practice and have been shown to improve clinical outcomes. Including biomechanical analysis into the framework has the potential to further improve clinical outcome.

Accuracy and reliability analysis of a machine learning based segmentation tool for intertrochanteric femoral fracture CT

Introduction

Three-dimensional (3D) reconstruction of fracture fragments on hip Computed tomography (CT) may benefit the injury detail evaluation and preoperative planning of the intertrochanteric femoral fracture (IFF). Manually segmentation of bony structures was tedious and time-consuming. The purpose of this study was to propose an artificial intelligence (AI) segmentation tool to achieve semantic segmentation and precise reconstruction of fracture fragments of IFF on hip CTs.

Materials and Methods

A total of 50 labeled CT cases were manually segmented with Slicer 4.11.0. The ratio of training, validation and testing of the 50 labeled dataset was 33:10:7. A simplified V-Net architecture was adopted to build the AI tool named as IFFCT for automatic segmentation of fracture fragments. The Dice score, precision and sensitivity were computed to assess the segmentation performance of IFFCT. The 2D masks of 80 unlabeled CTs segmented by AI tool and human was further assessed to validate the segmentation accuracy. The femoral head diameter (FHD) was measured on 3D models to validate the reliability of 3D reconstruction.

Results

The average Dice score of IFFCT in the local test dataset for “proximal femur”, “fragment” and “distal femur” were 91.62%, 80.42% and 87.05%, respectively. IFFCT showed similar segmentation performance in cross-dataset, and was comparable to that of human expert in human-computer competition with significantly reduced segmentation time (p < 0.01). Significant differences were observed between 2D masks generated from semantic segmentation and conventional threshold-based segmentation (p < 0.01). The average FHD in the automatic segmentation group was 47.5 ± 4.1 mm (41.29∼56.59 mm), and the average FHD in the manual segmentation group was 45.9 ± 6.1 mm (40.34∼64.93 mm). The mean absolute error of FHDs in the two groups were 3.38 mm and 3.52 mm, respectively. No significant differences of FHD measurements were observed between the two groups (p > 0.05). All ICCs were greater than 0.8.

Conclusion

The proposed AI segmentation tool could effectively segment the bony structures from IFF CTs with comparable performance of human experts. The 2D masks and 3D models generated from automatic segmentation were effective and reliable, which could benefit the injury detail evaluation and preoperative planning of IFFs.

Advanced CT visualization improves the accuracy of orthopaedic trauma surgeons and residents in classifying proximal humeral fractures: a feasibility study

PURPOSE

Osteosynthesis of proximal humeral fractures remains challenging with high reported failure rates. Understanding the fracture type is mandatory in surgical treatment to achieve an optimal anatomical reduction. Therefore, a better classification ability resulting in improved understanding of the fracture pattern is important for preoperative planning. The purpose was to investigate the feasibility and added value of advanced visualization of segmented 3D computed tomography (CT) images in fracture classification.

METHODS

Seventeen patients treated with either plate-screw-osteosynthesis or shoulder hemi-prosthesis between 2015 and 2019 were included. All preoperative CT scans were segmented to indicate every fracture fragment in a different color. Classification ability was tested in 21 orthopaedic residents and 12 shoulder surgeons. Both groups were asked to classify fractures using three different modalities (standard CT scan, 3D reconstruction model, and 3D segmented model) into three different classification systems (Neer, AO/OTA and LEGO).

RESULTS

All participants were able to classify the fractures more accurately into all three classification systems after evaluating the segmented three-dimensional (3D) models compared to both 2D slice-wise evaluation and 3D reconstruction model. This finding was significant (p < 0.005) with an average success rate of 94%. The participants experienced significantly more difficulties classifying fractures according to the LEGO system than the other two classifications.

CONCLUSION

Segmentation of CT scans added value to the proximal humeral fracture classification, since orthopaedic surgeons were able to classify fractures significantly better into the AO/OTA, Neer, and LEGO classification systems compared to both standard 2D slice-wise evaluation and 3D reconstruction model.