The use of Augmented Reality (AR) for navigation purposes has shown beneficial in assisting physicians during the performance of surgical procedures. These applications commonly require knowing the pose of surgical tools and patients to provide visual information that surgeons can use during the performance of the task. Existing medical-grade tracking systems use infrared cameras placed inside the Operating Room (OR) to identify retro-reflective markers attached to objects of interest and compute their pose. Some commercially available AR Head-Mounted Displays (HMDs) use similar cameras for self-localization, hand tracking, and estimating the objects' depth. This work presents a framework that uses the built-in cameras of AR HMDs to enable accurate tracking of retro-reflective markers without the need to integrate any additional electronics into the HMD. The proposed framework can simultaneously track multiple tools without having previous knowledge of their geometry and only requires establishing a local network between the headset and a workstation. Our results show that the tracking and detection of the markers can be achieved with an accuracy of $0.09\pm 0.06\ mm$ on lateral translation, $0.42 \pm 0.32\ mm$ on longitudinal translation and $0.80 \pm 0.39^\circ$ for rotations around the vertical axis. Furthermore, to showcase the relevance of the proposed framework, we evaluate the system's performance in the context of surgical procedures. This use case was designed to replicate the scenarios of k-wire insertions in orthopedic procedures. For evaluation, seven surgeons were provided with visual navigation and asked to perform 24 injections using the proposed framework. A second study with ten participants served to investigate the capabilities of the framework in the context of more general scenarios. Results from these studies provided comparable accuracy to those reported in the literature for AR-based navigation procedures.

Current surgical interventions for the femoral neck fracture are using either cannulated screws (CCS) or a single large screw at a fixed angle with a side-plate (i.e., a sliding hip screw, AKA dynamic hip screw, DHS). Despite these interventions, the need for reoperation remains high (10.0–48.8%) and largely unchanged over the past 30 years. Femoral neck fracture is associated with substantial morbidity, mortality, and costs. In this study, our group designed a plate that combines the strength of both CCS and sliding hip screw, through providing three dynamic screws at a fixed angle with a side-plate, namely the slide compression anatomic place-femoral neck (SCAP-FN). Finite element analyses (FEA) were carried out to compare the outcomes of the combination of our SCAP-FN plate with DHS+DS (derotational screw) and to those of using cannulated screws alone. SCAP-FN produces more stable fixation with respect to the femur and the stress distributions, stress peaks, and rotational angles. The FEA encouraged us that in the following biomechanical experiment, SCAP-FN may remain the strengths of both CCS and DHS+DS and show a better performance in resisting shearing and rotational forces, therefore achieving the best stability in terms of smallest displacement and rotational angle.

Tendon and ligament ruptures are prevalent, and severe sports injuries require surgical repair. In clinical practice, monitoring of tissue strain is critical to alert severe postoperative complications such as graft reinjury and loosening. Here, we present a sensor system that integrates a strain sensor and communication coil onto surgical silk sutures, enabling in situ monitoring and wireless readout of tissue strains via surgical implantation. The flexible sensor shows excellent adaptability to soft tissues, providing a strain monitoring range of 0 to 10% with a minimum detection threshold of 0.25% and maintaining stability more than 300,000 stretching cycles. The wireless sensor could be integrated with complex structures in surgical scenarios involving lateral collateral ligament injury and anterior cruciate ligament reconstruction, enabling distinct responses to graft stretching, reinjury, and loosening. Animal experiments demonstrate that the sensor can acquire real-time, clinical-grade strain data while exhibiting high biocompatibility. The sensor system shows considerable potential in evaluating preclinical implant performance and monitoring implant-related surgical complications.

Augmented Reality (AR) has been proven beneficial to External Ventricular Drain (EVD) surgery by providing in-situ visual guidance during operations. During this procedure, the key challenge is estimating the spatial relationship between pre-operative images and actual patient anatomy accurately and efficiently. Previous works have revealed conflicts between tracking accuracy, workflow efficiency, and non-invasiveness in tracking pipelines. This research fully utilizes the capabilities of Time of Flight (ToF) depth sensors, including retro-reflective tool tracking and dense surface information, to construct a convenient and accurate EVD guiding pipeline. As previous studies have proven significant depth errors in ToF depth sensors, we first evaluated the feasibility of using ToF sensors in surgical guidance by estimating its accuracy under different conditions and corrected this error in our pipeline. Our results show $ \text{7.580}\pm \text{1.488}\,\text{mm}$ depth value errors on human skin under HoloLens 2 depth camera, indicating the significance of depth correction. This error was reduced by over 85% using proposed depth correction method on head phantoms in different materials. The corrected depth information can then be utilized to reconstruct the head surface with sub-millimeter accuracy, validated on a series of 3D-printed models and a sheep head. To demonstrate the effectiveness of the proposed framework, we conducted a case study simulating EVD surgery. Five surgeons were involved in this study, each performing nine k-wire insertions on a head phantom under virtual guidance without tracking for surgical tools. The results revealed $ \text{2.09} \pm \text{1.00}\,\text{mm}$ translational and $\text{2.97}\pm \text{1.95}^\circ$ orientational guidance accuracy, demonstrating competitive performance with previous research.

Chronic allodynia stemming from peripheral stump neuromas can persist for extended periods, significantly compromising patients’ quality of life. Conventional managements for nerve stumps have demonstrated limited effectiveness in ensuring their orderly termination. In this study, we present a spatially confined conduit strategy, designed to enhance the self-organization of regenerating nerves after truncation. This innovative approach elegantly enables the autonomous slowing of axonal outgrowth in response to the gradually constricting space, concurrently suppressing neuroinflammation through YAP-mediated mechanotransduction activation. Meanwhile, the decelerating axons exhibit excellent alignment and remyelination, thereby helping to prevent failure modes in nerve self-organization, such as axonal twisting in congested regions and overgrowth beyond the conduit’s capacity. Additionally, proteins associated with mechanical allodynia, including TRPA1 and CGRP, exhibit a gradual reduction in expression as spatial constraints tighten, a trend inversely validated by the administration of the YAP-targeted inhibitor Verteporfin. This spatially confined conduit strategy significantly alleviates allodynia, thus preventing autotomy behavior and reducing pain-induced gait alterations.

Intramedullary (IM) nail implantation is currently the standard treatment for femoral intertrochanteric fractures. However, individual differences in femur cavity bring a challenge in designing well-matched IM nails and cause difficulties in IM nail implantation. Therefore, there is an intense need to analyze femur cavities to predict difficulties in IM nail implantation to assist the design of IM nails. This study proposed a method to automatically identify subtypes of femur cavities that exhibit differences in potential difficulties in nail implantation by clustering the morphological features of femur models. The unsupervised subtype extraction method offers a scientific approach to stratify patients for designing and choosing well-matched IM nails. First, the quantitative morphological features of 422 femur cavities were extracted from computed tomography patient models. Second, 422 femur cavities were clustered into three distinct subtypes using a density peak-based k-means clustering method to provide a possible solution for the scientific design of IM nails. The effectiveness of the identified subtypes was validated by comparing subtype differences associated with IM nail implantation and the natural attributes of the patient. Quantitative evaluation of the mismatch degree and real clinical cases confirmed that the clustering results were clinically effective, with clear differences in the subtypes. Therefore, particular IM nails designed from the identified subtypes will potentially facilitate IM nail implantation and reduce complications. Compared with state-of-the-art methods, we used the largest-scale dataset and unsupervised clustering to achieve subtype identification of femur cavities with clinical significance.