Comparison of re-revision rate and radiological outcomes between Kerboull-type plate and metal mesh with impaction bone grafting for revision total hip arthroplasty.

BACKGROUND: This study compared the re-revision rate and radiographic outcomes of revision total hip arthroplasty (THA) using a Kerboull-type acetabular reinforcement device (KT plate) with bulk structural allograft and metal mesh with impaction bone grafting (IBG). METHODS: Ninety-one hips of 81 patients underwent revision THA for American Academy of Orthopedic Surgeons (AAOS) classification type III defects from 2008 to 2018. Of these, seven hips of five patients and 15 hips of 13 patients were excluded due to insufficient follow-up information (< 24 months) and large bone defects with a vertical defect height ≥ 60 mm, respectively. The current study compared the survival and radiographic parameters of 45 hips of 41 patients using a KT plate (KT group) and 24 hips of 24 patients using a metal mesh with IBG (mesh group). RESULTS: Eleven hips (24.4%) in the KT group and 1 hip (4.2%) in the mesh group exhibited radiological failure. Moreover, 8 hips in the KT group (17.0%) required a re-revision THA, while none of the patients in the mesh group required a re-revision. The survival rate with radiographic failure as the endpoint in the mesh group was significantly higher than that in the KT group (100% vs 86.7% at 1-year and 95.8% vs 80.0% at 5-years, respectively; p = 0.032). On multivariable analysis evaluating factors associated with radiographic failure, there were no significant associations with any radiographic measurement. Of the 11 hips with radiographic failure, 1 (11.1%), 3 (12.5%), and 7 (58.3%) hips were of Kawanabe classification stages 2, 3, and 4, respectively. CONCLUSIONS: The findings of this study suggest that revision THA using KT plates with bulk structure allografts could provide poorer clinical outcomes than revision THA using a metal mesh with IBG. Although revision THA using KT plates with bulk structural allografts could set the true hip center, there is no association between a high hip center and clinical outcomes. The relationship between the position of the KT plate and the host bone might be considered more carefully.

Siam-U-Net: encoder-decoder siamese network for knee cartilage tracking in ultrasound images.

The tracking of the knee femoral condyle cartilage during ultrasound-guided minimally invasive procedures is important to avoid damaging this structure during such interventions. In this study, we propose a new deep learning method to track, accurately and efficiently, the femoral condyle cartilage in ultrasound sequences, which were acquired under several clinical conditions, mimicking realistic surgical setups. Our solution, that we name Siam-U-Net, requires minimal user initialization and combines a deep learning segmentation method with a siamese framework for tracking the cartilage in temporal and spatio-temporal sequences of 2D ultrasound images. Through extensive performance validation given by the Dice Similarity Coefficient, we demonstrate that our algorithm is able to track the femoral condyle cartilage with an accuracy which is comparable to experienced surgeons. It is additionally shown that the proposed method outperforms state-of-the-art segmentation models and trackers in the localization of the cartilage. We claim that the proposed solution has the potential for ultrasound guidance in minimally invasive knee procedures.

Deep Learning for US Image Quality Assessment Based on Femoral Cartilage Boundary Detection in Autonomous Knee Arthroscopy.

Knee arthroscopy is a complex minimally invasive surgery that can cause unintended injuries to femoral cartilage or postoperative complications, or both. Autonomous robotic systems using real-time volumetric ultrasound (US) imaging guidance hold potential for reducing significantly these issues and for improving patient outcomes. To enable the robotic system to navigate autonomously in the knee joint, the imaging system should provide the robot with a real-time comprehensive map of the surgical site. To this end, the first step is automatic image quality assessment, to ensure that the boundaries of the relevant knee structures are defined well enough to be detected, outlined, and then tracked. In this article, a recently developed one-class classifier deep learning algorithm was used to discriminate among the US images acquired in a simulated surgical scenario on which the femoral cartilage either could or could not be outlined. A total of 38 656 2-D US images were extracted from 151 3-D US volumes, collected from six volunteers, and were labeled as "1" or as "0" when an expert was or was not able to outline the cartilage on the image, respectively. The algorithm was evaluated using the expert labels as ground truth with a fivefold cross validation, where each fold was trained and tested on average with 15 640 and 6246 labeled images, respectively. The algorithm reached a mean accuracy of 78.4% ± 5.0, mean specificity of 72.5% ± 9.4, mean sensitivity of 82.8% ± 5.8, and mean area under the curve of 85% ± 4.4. In addition, interobserver and intraobserver tests involving two experts were performed on an image subset of 1536 2-D US images. Percent agreement values of 0.89 and 0.93 were achieved between two experts (i.e., interobserver) and by each expert (i.e., intraobserver), respectively. These results show the feasibility of the first essential step in the development of automatic US image acquisition and interpretation systems for autonomous robotic knee arthroscopy.

Ultrasound guidance in minimally invasive robotic procedures.

In the past decade, medical robotics has gained significant traction within the surgical field. While the introduction of fully autonomous robotic systems for surgical procedures still remains a challenge, robotic assisted interventions have become increasingly more interesting for the scientific and clinical community. This happens especially when difficulties associated with complex surgical manoeuvres under reduced field of view are involved, as encountered in minimally invasive surgeries. Various imaging modalities can be used to support these procedures, by re-creating a virtual, enhanced view of the intervention site. Among them, ultrasound imaging showed several advantages, such as cost effectiveness, non-invasiveness and real-time volumetric imaging. In this review we comprehensively report about the interventional applications where ultrasound imaging has been used to provide guidance for the intervention tools, allowing the surgeon to visualize intra-operatively the soft tissue configuration in real-time and to compensate for possible anatomical changes. Future directions are also discussed, in particular how the recent developments in 3D/4D ultrasound imaging and the introduction of advanced imaging capabilities (such as elastography) in commercially available systems may fulfil the unmet needs towards fully autonomous robotic interventions.

Segmentation of Femoral Cartilage from Knee Ultrasound Images Using Mask R-CNN.

Segmentation of knee cartilage from Ultrasound (US) images is essential for various clinical tasks in diagnosis and treatment planning of Osteoarthritis. Moreover, the potential use of US imaging for guidance in robotic knee arthroscopy is presently being investigated. The femoral cartilage being the main organ at risk during the operation, it is paramount to be able to segment this structure, to make US guidance feasible. In this paper, we set forth a deep learning network, Mask R-CNN, based femoral cartilage segmentation in 2D US images for these types of applications. While the traditional imaging approaches showed promising results, they are mostly not real-time and involve human interaction. This being the case, in recent years, deep learning has paved its way into medical imaging showing commendable results. However, deep learning-based segmentation in US images remains unexplored. In the present study we employ Mask R-CNN on US images of the knee cartilage. The performance of the method is analyzed in various scenarios, with and without Gaussian filter preprocessing and pretraining the network with different datasets. The best results are observed when the images are preprocessed and the network is pretrained with COCO 2016 image dataset. A maximum Dice Similarity Coefficient (DSC) of 0.88 and an average DSC of 0.80 is achieved when tested on 55 images indicating that the proposed method has a potential for clinical applications.

Depletion of Gangliosides Enhances Articular Cartilage Repair in Mice.

Elucidation of the healing mechanisms in damaged tissues is a critical step for establishing breakthroughs in tissue engineering. Articular cartilage is clinically one of the most successful tissues to be repaired with regenerative medicine because of its homogeneous extracellular matrix and few cell types. However, we only poorly understand cartilage repair mechanisms, and hence, regenerated cartilage remains inferior to the native tissues. Here, we show that glycosylation is an important process for hypertrophic differentiation during articular cartilage repair. GM3, which is a precursor molecule for most gangliosides, was transiently expressed in surrounding damaged tissue, and depletion of GM3 synthase enhanced cartilage repair. Gangliosides also regulated chondrocyte hypertrophy via the Indian hedgehog pathway. These results identify a novel mechanism of cartilage healing through chondrocyte hypertrophy that is regulated by glycosylation. Manipulation of gangliosides and their synthases may have beneficial effects on articular cartilage repair.

An Articular Cartilage Repair Model in Common C57Bl/6 Mice.

To analyze the genetic and biomolecular mechanisms underlying cartilage repair, an optimized mouse model of osteochondral repair is required. Although several models of articular cartilage injury in mice have recently been established, the articular surface in adult C57Bl/6 mice heals poorly. Since C57Bl/6 mice are the most popular strain of genetically manipulated mice, an articular cartilage repair model using C57Bl/6 mice would be helpful for analysis of the mechanisms of cartilage repair. The purpose of this study was to establish a cartilage repair model in C57Bl/6 mice using immature animals. To achieve this goal, full-thickness injuries were generated in 3-week-old (young), 4-week-old (juvenile), and 8-week-old (adult) C57Bl/6 mice. To investigate the reproducibility and consistency of full-thickness injuries, mice were sacrificed immediately after operation, and cartilage thickness at the patellar groove, depth of the cartilage injury, cross-sectional width, and cross-sectional area were compared among the three age groups. The depth of cartilage injury/cartilage thickness ratio (%depth) and the coefficient of variation (CV) for each parameter were also calculated. At 8 weeks postoperatively, articular cartilage repair was assessed using a histological scoring system. With respect to the reproducibility and consistency of full-thickness injuries, cartilage thickness, depth of cartilage injury, and cross-sectional area were significantly larger in young and juvenile mice than in adult mice, whereas cross-sectional width and %depth were almost equal among the three age groups. CVs of %depths were less than 10% in all groups. With respect to articular cartilage repair, young and juvenile mice showed superior results. In conclusion, we established a novel cartilage repair model in C57Bl/6 mice. This model will be valuable in achieving mechanistic insights into the healing process of the joint surface, as it will facilitate the use of genetically modified mice, which are most commonly developed on a C57Bl/6 background.