Optimizing glenoid component positioning is a key determinant of stability, function and implant survival in total shoulder arthroplasty. Postoperative computed tomography (CT) is considered the reference method for assessing guide-pin or implant alignment, but it remains a retrospective evaluation and is often difficult to obtain in cadaveric research settings. Optical navigation systems such as the NDI Polaris provide immediate intraoperative geometric measurements and may represent a pragmatic alternative. This cadaveric validation study aimed to evaluate the agreement between NDI Polaris optical-navigation measurements and postoperative CT analysis for glenoid guide-pin positioning. Eight cadaveric shoulders underwent augmented-reality-guided placement of a glenoid guide-pin, followed by postoperative CT acquisition. Intraoperative optical-navigation measurements were recorded using an NDI Polaris tracking station. Two accuracy domains were analyzed relative to CT: (1) linear entry-point deviation (mm) and (2) angular deviation in version and inclination (degrees). Agreement between both measurement modalities was assessed using Bland-Altman analysis, complemented by descriptive accuracy statistics. Sixteen paired measurements were available for comparison. The mean absolute NDI-CT difference was 0.98 mm (SD 0.55; range 0.15-2.17 mm) for entry-point localization and 1.58° (SD 0.97; range 0.03-3.28°) for trajectory orientation. Bland-Altman analysis demonstrated a small systematic bias for both linear (-0.15 mm) and angular (-0.75°) parameters, with most discrepancies contained within approximately ±2 mm and ±4°, respectively. These results suggest that, under standardized acquisition conditions, optical navigation provides geometric measurements that closely approximate CT-based reference values and may serve as a practical surrogate in cadaveric research when CT access is limited. NDI Polaris optical tracking demonstrated good agreement with postoperative CT for evaluating glenoid pin positioning and may represent a practical alternative for accuracy assessment in cadaveric studies. These preliminary findings require confirmation in larger validation studies.
Meniscus informatics is a growing subject of study in the healthcare industry. One of the major hindrances to the healthcare system’s transformation is obtaining knowledge and meaningful information from complicated, high-dimensional and diverse sources. Modern biomedical research, for instance, has seen an increase in the use of complex, dissimilar, poorly documented, and generally unstructured electronic health records, imaging, sensor data and text, even after many current techniques have been used to extract more robust and useful elements from the data for analysis. New efficient standards for building end-to-end learning models from complex data are therefore needed. Therefore, the current study aims to examine the most recent research on the use of deep learning techniques for diagnosing meniscus tears and recommend creating comprehensive and meaningful interpretable structures that might benefit the healthcare industry. We also draw attention to shortcomings and the need for better technique development, and we provide new perspectives about this exciting new development in the field.
Robot-assisted surgery (RAS) is increasingly utilized in arthroplasty, with emerging evidence suggesting potential benefits, however, the overall evidence base remains immature. Given the heterogeneity of available systems, device-specific evaluation is required. The VELYS Robotic-Assisted Solution (VRAS; Johnson & Johnson MedTech) is an imageless, semi-active robotic platform for knee arthroplasty. This study provides a structured strategic evaluation of VRAS aligned with the National Institute for Health and Care Excellence Early Value Assessment (EVA) and proposes a roadmap for future evidence generation. 16 relevant studies were identified in a systematic literature search, predominantly retrospective and lacking randomized controlled trials. Findings were synthesized using a strengths, weaknesses, opportunities, and threats framework, with subsequent analysis to inform strategic recommendations. Key strengths of VRAS include avoidance of pre-operative computed tomography imaging, integration with established implant systems, a relatively short learning curve, and early signals of improved short-term outcomes. Weaknesses center on the limited quality and maturity of the evidence base, absence of long-term outcome data, and system-specific design considerations such as lack of haptic feedback boundaries. Opportunities include reduced resource burden with imageless workflows and potential optimization of patient outcomes, while threats involve high capital costs, competition from more established RAS platforms, and the risk of unmet evidence requirements within defined timelines. Across EVA domains, evidence for patient-reported outcomes, cost-effectiveness, and long-term clinical benefit remains insufficient. High-quality, adequately powered studies, comprehensive health-economic analyses, and registry-linked longitudinal studies are essential to define the clinical and economic value of VRAS in light of the EVA.
2D X-ray imaging is widely employed in image-guided interventions to provide real-time visualization of patient anatomy and interventional devices. However, lacking depth information and soft-tissue contrast, intraoperative X-rays are often complemented with high-resolution preoperative 3D computed tomography (CT) scans. Clinicians must mentally register the 3D preoperative information onto the 2D visualization, increasing cognitive workload and motivating the need for automated solutions. Traditionally, this registration task is formulated as estimating the position of the X-ray source relatively to the CT scan. State-of-the-art 3D/2D registration approaches are trained using synthetic CT-to-X-ray projections, but these methods still require manual annotations and employ a time-consuming optimization that limits their deployment for live image guidance. In this paper, we propose LXPose (live X-ray pose estimation), a fast multi-stage 3D/2D registration framework for real-time image guidance. Specifically, we introduce an efficient two-stage CNN that effectively bypasses slow optimization for fast inference. Critically, we also eliminate the need for any manual annotations by introducing an automated strategy for landmark extraction. Finally, we train LXPose using a projection loss for high accuracy, and apply extensive data augmentation in a first attempt to reduce the domain gap between the synthetic training X-rays and real testing data. We demonstrate LXPose on two datasets from different anatomical regions, where it yields results comparable to the state-of-the-art, while reducing inference time by two orders of magnitude, from several seconds to 20 ms. Overall, our results demonstrate the potential of LXPose for real-time clinical deployment. Our code is available at https://github.com/fedefacente/LXPose.
Lumbar plexus block (LPB) is a regional anesthesia technique widely used for hip and knee surgeries. However, despite the assistance of ultrasound guidance, the complex anatomical structure of the lumbar plexus poses significant challenges for anesthesiologists during the procedure. To accurately identify the lumbar plexus located in the posterior third of the psoas major in the Shamrock view, a deep learning-based segmentation model named DMRNet was proposed. This model is designed to precisely delineate muscles, nerves, and bony structures in Shamrock view ultrasound images. DMRNet integrates several innovative modules, including Adaptive Multi-Scale Dilated (AMD) Module that enhances the model's ability to capture multi-scale features; Dense Attention Residual (DAR) Module that adaptively selects salient feature regions; Attention-Enhanced Hybrid (AEH) Module that emphasizes critical features while suppressing irrelevant ones; and two attention mechanisms, Boundary-Aware Spatial Attention Mechanism (BASA) and the Enhanced Residual Multi-Head Attention Mechanism (ER-MHA), that improve the model's capacity to recognize complex contextual patterns. Experimental results demonstrated that DMRNet achieved a mean Intersection over Union of 0.863 and a mean Dice coefficient of 0.926 across all target structures, outperforming other state-of-the-art models. These findings suggest that DMRNet may serve as a useful assistive tool for sonoanatomical recognition in Shamrock view ultrasound images and may provide educational support for ultrasound-guided LPB training.
Postoperative nausea and vomiting (PONV) is a common complication after laparoscopic surgery and may impair recovery. This study investigated the independent association between preoperative serum albumin (ALB) levels and PONV, and assessed the value of ALB as a preoperative predictor. In this single-center retrospective cohort study, 263 adult patients undergoing elective laparoscopic surgery at Ganzhou People’s Hospital between June 2022 and May 2025 were consecutively enrolled. Patients were stratified into tertiles according to preoperative ALB levels. ROC analysis was used to determine the optimal ALB cutoff for predicting PONV, and multivariable logistic regression was performed after adjustment for potential confounders. The incidence of PONV was 45.5% in the low-ALB group, 17.2% in the middle-ALB group, and 13.6% in the high-ALB group, showing a significant inverse trend (P<0.001). Compared with the low-ALB group, the adjusted odds ratios for PONV were 0.24 (95% CI: 0.11-0.53) in the middle-ALB group and 0.21 (95% CI: 0.09-0.48) in the high-ALB group (P<0.001). ROC analysis showed that preoperative ALB had moderate predictive ability for PONV (AUC=0.792, 95% CI: 0.732-0.852), with an optimal cutoff of 37.2 g/L (sensitivity 74.6%, specificity 70.4%). Subgroup analyses showed consistent associations across sex, ASA physical status, and surgical type. Among patients with low ALB levels, female sex and total intraoperative remifentanil dose were independently associated with PONV. Preoperative hypoalbuminemia (ALB <37.2 g/L) was independently associated with increased PONV risk after laparoscopic surgery. Serum albumin may be a simple and useful marker for preoperative risk stratification and individualized prophylaxis.
Spread through air spaces (STAS) is recognized as an aggressive pattern of invasion in lung cancer and has been associated with poorer survival outcomes. However, STAS is frequently overlooked or misdiagnosed during routine pathological diagnosis. We analyzed 129 pathological slides from 91 STAS-positive patients with non-small cell lung cancer (NSCLC). An artificial intelligence framework consisting of a tumor region segmentation algorithm and an object detection algorithm was developed. The segmentation algorithm was first used to isolate the main tumor region, followed by STAS detection using an improved object detection model. The segmentation module achieved a Jaccard similarity of 0.846. The proposed object detection algorithm demonstrated superior performance with a precision of 0.738, recall of 0.747 and average precision (AP) of 0.784 and F1 score of 0.742, outperforming other object detection methods. Both segmentation and detection results met diagnostic requirements, with higher accuracy observed in lung adenocarcinoma (LUAD) than in lung squamous cell carcinoma (LUSC). In external validation, the model achieved a precision of 0.670, recall of 0.705 and AP of 0.732 and F1 score of 0.687. Additionally, the AI-derived STAS counts showed substantial agreement with pathologists (interclass correlation coefficient = 0.703). Kaplan-Meier survival analysis revealed that the number of STAS events was significantly associated with disease-free survival (DFS) in stage I LUAD (p < .05). We propose a deep learning-based artificial intelligence framework for automated STAS detection and quantification in digital whole-slide images of NSCLC. This model holds promising potential to assist pathologists in achieving more comprehensive and accurate diagnosis of STAS.
Right Vertical Infra-Axillary Thoracotomy (RVIAT) offers superior cosmetic outcomes but presents challenges due to restricted access. In pediatric patients, the ‘crowded thorax’ necessitates simultaneous visualization of intracardiac defects and central cannulation sites. Therefore, the choice of the intercostal incision can significantly affect the surgical field. To address this, we propose a patient-specific, geometry-driven framework to objectively optimize surgical corridors. A web-based surgical planner was developed to simulate incision strategies using patient-specific computed tomography data. The system utilizes ray-casting algorithms to compute a quantitative ‘Visibility Score’ for multiple anatomical targets. The framework was validated through a complex dual-pathology case and a multi-parametric sensitivity analysis involving varying chest wall thicknesses and instrument constraints. The system was successfully implemented as a platform-independent web application capable of real-time, client-side processing. In the dual-pathology validation, the simulation revealed that the standard 4th intercostal space (ICS) provided limited visibility for the secondary target (Patent Ductus Arteriosus, PDA: 72%). The optimizer identified the 3rd ICS as the superior vector, increasing PDA visibility to 86% without compromising primary Ventricular septal defect exposure (100%). Sensitivity analysis further indicated that while deep intracardiac targets maintained robust visibility across varying anatomical conditions, the exposure of cannulation sites was reduced. The proposed framework provides a deterministic method to evaluate surgical corridors preoperatively. By objectively quantifying exposure for both intracardiac defects and obligatory cannulation sites, the system assists surgeons in selecting the optimal incision level to ensure comprehensive procedural safety.
The accurate cup placement is still crucial for total hip arthroplasty (THA). However, achieving adequate registration accuracy is difficult in the initial cases, which leads surgeons to discontinue the use of CT-based navigation. The objective of this study was to evaluate its learning curve using a cumulative summation (CUSUM) analysis of the absolute registration error for cup orientation. A retrospective review was performed on 75 consecutive patients who underwent minimally invasive THA, allowing the calculation of the difference in cup anteversion and inclination between the intraoperative values shown on the navigation system and postoperative values measured by postoperative CT. We reviewed those absolute registration errors and the patient reported outcomes (PROMs) one-year postoperatively. We plotted the CUSUM values of the absolute registration error in chronological order and added subgroup analysis regarding body mass index (BMI). CUSUM analysis revealed that performing THA using CT-based navigation was associated with a learning curve in 7 cases for cup inclination and anteversion. There were no significant differences in the absolute registration error of cup orientation between high and low BMI groups, or in the mean PROMs at one-year postoperatively, regardless of time sequence or BMI. In conclusion, CT-based navigation assisted acetabular cup placement was associated with a learning curve of 7 cases for achieving suitable registration accuracy. These findings are important, as orthopedic surgeons often discontinue CT-based navigation surgery early in the initial cases, despite its benefits in severe cases. Therefore, careful use of CT-based navigation is critical particularly within the first 7 cases.
Complex pelvic fractures are infamously challenging to fix surgically because of their fine anatomy and proximity to vital neurovascular structures. Traditional open reduction and internal fixation (ORIF) improves stability but is complicated by excessive blood loss, longer operative time, and morbidity. Robotic-assisted surgical methods, i.e. Robot-Assisted Fracture Reduction (RAFR) and the TiRobot platform, provide a paradigm shift toward precise, minimally invasive fracture reduction and fixation. The RAFR system blends preoperative high-definition 3D CT imaging with intraoperative cone-beam CT and real-time navigation for dynamic visualization and accurate fragment control to eliminate guesswork and minimize the risk of malposition. Its cutting-edge robotic arm, electrically actuated holding devices, and elastic counterforces of traction ensure controlled and safe fracture reduction with soft tissue and neurovascular integrity preservation. Robot-assisted support is assisted by extensive clinical evidence to enhance the accuracy of surgery with sub-millimeter positioning discrepancies, reduce intraoperative blood loss, reduce exposure to radiation, reduce operative and hospital stay times, and enhance functional restoration according to scores demonstrated. Robot over conventional techniques reduces postoperative infection, implant loosening, nonunion, and nerve or vessel injury. TiRobot enhances fixation using artificial intelligence-assisted screw path planning and navigation. Albeit promising, it has limitations in adoption, such as being costly, having no feedback, and a high learning curve. More multicenter randomized clinical trials are required to estimate long-term efficacy, safety, and cost-effectiveness. Robot-assisted pelvic fracture surgery is a leading-edge development that has the ability to improve patient outcomes and the delivery of trauma care.
Background Three-dimensional (3D) simulation and virtual reality (VR) technologies are increasingly used in aesthetic surgery consultations to enhance decision-making and expectation management. However, their impact on surgical decision-making and postoperative satisfaction across different procedures remains unclear.Objectives This study aimed to evaluate the influence of 3D simulation and VR technology in patients undergoing rhinoplasty, breast augmentation, mastopexy, augmentation-mastopexy and breast reduction.Methods A retrospective study was conducted with 75 female patients who underwent primary aesthetic surgery. Preoperative 3D simulations and VR visualizations were generated using the Crisalix Virtual Esthetics system (Crisalix S.A., Switzerland). Patients were assessed postoperatively at one year using structured surveys to evaluate the influence of 3D simulation and VR technology on their decision-making and satisfaction. Statistical analyses included the Kruskal-Wallis H test, the Chi-Square test, and Spearman’s correlation.Results 3D simulation had the greatest influence on breast augmentation (8.4/10), rhinoplasty (7.6/10), and augmentation-mastopexy (7.1/10) patients but was less impactful for mastopexy (6.6/10) and breast reduction (3.8/10) patients (p < 0.001). The most decisive factors were previous patient photos (30.7%) and communication with the surgeon (29.3%), with simulation ranking third (18.7%). Postoperative similarity ratings were highest in breast augmentation (7.9/10) and rhinoplasty (7.5/10) patients. While 70.7% of patients would recommend 3D simulation, VR headset use did not influence decisions (p < 0.001).Conclusions 3D simulation enhances patient engagement and expectation management across various aesthetic procedures. While its influence is more significant in surgeries primarily focused on aesthetic outcomes, it serves as a complementary tool rather than a definitive factor in decision-making.
Cancer resection surgery is unsuccessful if tumor tissue is left behind in the surgical cavity. Identifying the residual cancer requires additional imaging or postoperative histological analysis. Photoacoustic imaging can be used to image both the surface and depths of the resection cavity; however, its performance hinges on consistent probe placement and stable acoustic and optical coupling. As intra-cavity deployment of photoacoustic imaging is largely uncharted, several potential embodiments warrant rigorous investigation. We address this need with an open-source robotic testbed for intraoperative tumor-bed inspection using photoacoustic imaging. The platform integrates the da Vinci Research Kit, depth imaging, and electromagnetic tracking to automate cavity scanning and maintain repeatable probe trajectories. Using tissue-mimicking phantoms, we (i) demonstrate a novel imaging embodiment for photoacoustic tumor-bed inspection and (ii) show how this testbed can be used to investigate and optimize tumor bed inspection strategies and configurations. This study establishes the feasibility of detecting and mapping residual cancer within a simulated surgical cavity. The primary contribution is the testbed itself, designed for integration with existing surgical navigation workflows and rapid prototyping. This testbed serves as an essential foundation for systematic evaluation of photoacoustic, robot-assisted strategies for improving intraoperative margin assessment.
PURPOSE:To develop a deep learning model based on CT bone window images to enhance the accuracy of early diagnosis of spinal tuberculosis. METHODS:This study adopted multicenter retrospective data (n = 1027). Firstly, the vertebral body region of the spine was extracted through the U-Net segmentation model. Then, the segmented images were input into the improved ResNet50 network. Combined with the CT bone window gradient attention mechanism, an end-to-end deep learning diagnostic model was constructed. RESULTS:In the internal validation datasets, the model achieved an AUC of 0.920, accuracy of 0.874 and sensitivity of 0.876. For External test datasets 1, the AUC was 0.867, accuracy 0.801 and sensitivity 0.794; for External test datasets 2, the AUC was 0.866, accuracy 0.769, and sensitivity 0.883; and for External test datasets 3, the AUC was 0.941, accuracy 0.843 and sensitivity 0.790. CONCLUSION:The multi-center study built up a deep learning model for spinal tuberculosis diagnosis with the assist of the CT bone window gradient attention mechanism. The model achieved a good internal verification ability (AUC = 0.920, accuracy rate = 0.874) and external verification ability (AUC = 0.866-0.941, accuracy rate = 0.769-0.843) which showed the wide applicability of the model to different medical institutions. The main developments of this work are the good performances for features that extract relevant information about trabecular micro-fractures and calcification contours' gradients.
Colorectal cancer represents a major global health concern and obesity adds complicates its surgical management. This meta-analysis aimed to evaluate the comparative effectiveness and safety of robotic-assisted surgery and standard laparoscopic surgery in obese colorectal cancer patients. A comprehensive literature search performed across databases from inception to April 2024. Pooled estimates included hospital stay duration, drainage tube removal time, first ventilation time, complication rates, re-admission rates and re-operative rates. Six studies involving 4215 patients were included. Robotic-assisted surgery was associated with a statistically significant but modest reduction in hospital stay time compared to laparoscopic surgery (p = 0.02). No significant differences were found for drainage tube removal time (p = 0.42) and first ventilation time (p = 0.27). Complication rates (OR [odds ratio] = 0.92, 95% confidence interval [CI]: 0.74 to 1.13, p = 0.41), re-admission rates (OR = 0.81, 95% CI: 0.31 to 2.13, p = 0.67) and re-operative rates (OR = 1.20, 95% CI: 0.77 to 1.86, p = 0.41) did not significantly differ between surgical approaches. Robotic-assisted surgery significantly provides a modest reduction in hospital stay duration without compromising patient safety for obese colorectal cancer patients. These findings should be interpreted with caution. Future randomized controlled trials are required to confirm these results.
Reinforcement learning (RL) has emerged as a powerful artificial intelligence paradigm in medical image analysis, excelling in complex decision-making tasks. This systematic review synthesizes the applications of RL across diverse imaging domains—including landmark detection, image segmentation, lesion identification, disease diagnosis, and image registration—by analyzing 20 peer-reviewed studies published between 2019 and 2023. RL methods are categorized into classical and deep reinforcement learning (DRL) approaches, focusing on their performance, integration with other machine learning models, and clinical utility. Deep Q-Networks (DQN) demonstrated strong performance in anatomical landmark detection and cardiovascular risk estimation, while Proximal Policy Optimization (PPO) and Advantage Actor-Critic (A2C) achieved optimal policy learning for vessel tracking. Policy gradient methods such as REINFORCE, Twin-Delayed Deep Deterministic Policy Gradient (TD3), and Soft Actor-Critic (SAC) were successfully applied to breast lesion detection, white-matter connectivity analysis, and vertebral segmentation.Monte Carlo learning, meta-RL, and A3C methods proved effective for adaptive questioning, image quality evaluation, and multimodal image registration. To consolidate these findings, we propose a unified Reinforcement Learning Medical Imaging (RLMI) framework encompassing four core components: state representation, policy optimization, reward formulation, and environment modeling. This framework enhances sequential agent learning, stabilizes navigation, and generalizes across imaging modalities and tasks. Key challenges remain, including optimizing task-specific policies, integrating anatomical contexts, addressing data scarcity, and improving interpretability. This review highlights RL’s potential to enhance accuracy, adaptability, and efficiency in medical image analysis, providing valuable guidance for researchers and clinicians applying RL in real-world healthcare settings.
Plate and screw fixation is a widely used method in the surgical treatment of femoral shaft fractures; however, mechanical performance may vary depending on implant material, fracture gap size and loading conditions. This study aimed to investigate the biomechanical behavior of femoral shaft fractures stabilized with plate and screw fixation by applying finite element analysis (FEA) and to evaluate the predictive performance of machine learning (ML) algorithms based on numerical results. Three different fracture gap sizes (1, 2,and 3 mm) were modeled on a femur geometry, and axial loads ranging from 400 N to 1200 N (in 100 N increments) were applied. Two implant materials, Ti-6Al-4V and 316 L stainless steel (SS), were assessed. The stress distribution on the plate and first screw and the displacements at the femoral head and fracture site were analyzed using two different mesh densities. Subsequently, ML algorithms including Decision Tree (DT), Multilayer Perceptron (MLP) and Support Vector Machine (SVM) were used to predict the stress and displacement values based on the numerical dataset. The finer mesh provided more accurate results. Ti-6Al-4V showed lower von Mises stress values and displacement magnitudes compared to 316 L SS. Among the ML methods, MLP and SVM demonstrated better prediction accuracy than DT. The integration of FEA and ML techniques enables efficient prediction of implant biomechanics, offering a promising approach for preclinical evaluation and optimization of orthopedic fixation systems.
This retrospective observational study aimed to explore the associations of AI-assisted CT-quantified C4/C5 skeletal muscle and adipose tissue indices with postoperative cervical kyphosis and long-term functional outcomes in patients undergoing laminoplasty. Postoperative cervical kyphosis is a prevalent complication of laminoplasty with incompletely elucidated pathogenesis, and the role of neck muscle and adipose tissue in this complication lacks validation via standardized 3D quantification. We enrolled 114 patients with cervical spondylosis who underwent laminoplasty at Wuhan Union Hospital between 2018 and 2022, excluding those with severe comorbidities. Preoperative CT scans (obtained within 3 months before surgery) were processed using a ResU-Net model to quantify C4/C5 tissue indices. Statistical analyses (SPSS 27.0) included multivariate logistic regression, and receiver operating characteristic (ROC) curves with the Youden index were used to determine predictive thresholds. Postoperative kyphosis was diagnosed based on routine follow-up cervical X-rays. Multivariate logistic regression revealed that C4 and C5 subcutaneous fat volume (SFV) were independently associated with postoperative kyphosis (p<0.05). The combined model integrating tissue indices and clinical variables achieved area under the curve (AUC) values of 0.706 (C4 SFV) and 0.717 (C5 SFV) (p<0.05). AI-assisted CT-quantified C4/C5 SFV is correlated with postoperative cervical kyphosis. Integration of CT-derived tissue metrics and clinical indicators improves the prediction of laminoplasty outcomes, providing a data-driven foundation for optimizing surgical planning and postoperative rehabilitation in cervical spondylosis patients.
Background Severe acetabular bone loss in complex primary total hip arthroplasty (THA) with dysplasia and post-traumatic defects poses a formidable challenge. We describe a novel technique integrating a custom-designed, 3D-printed titanium augment with open-platform robotic assistance to reconstruct a Paprosky type IIIB acetabular defect.Methods An elderly patient with severe dysplasia and chronic Paprosky IIIB acetabular defect underwent complex primary THA utilizing a patient-specific titanium augment and a cementless cup. Preoperative planning employed CT imaging and 3D modeling to ensure a precise defect fit and optimal cup support. An open-platform robotic system facilitated accurate reaming and component impaction according to the surgical plan.Results Intraoperatively, the augment was anchored to the host bone with screws, enabling placement of the cementless cup in an optimal orientation under robotic guidance. The construct restored the hip center of rotation and provided primary stability. The procedure proceeded without intraoperative complications. Estimated blood loss and operative time were recorded. Postoperative imaging demonstrated well-fixed augment and cup with anatomically restored hip center. Operative time: 361 minutes; blood loss: 3200 mL. Early rehabilitation proceeded without incident.Conclusion This case demonstrates the feasibility of combining patient-specific implants with robotic-assisted techniques in complex primary THA. The approach supported stable reconstruction of a substantial acetabular defect and suggested potential for enhanced precision in implant positioning and favorable early postoperative trajectories. Nevertheless, due to the single-case nature and limited follow-up, findings should be interpreted cautiously, and longer-term outcomes in larger, diverse cohorts are needed to determine broader applicability and durability.
This study introduces a novel computer vision approach to automate documentation of anesthetic injection events in the operating room. The objective is to enhance documentation accuracy and reliability by providing precise identification of injection events and anesthetic amounts administered, while addressing stopcock placement variability. We developed a computer vision pipeline tailored for automated anesthetic injection documentation in surgical environments. The pipeline leverages the Segment Anything Model (SAM) for robust syringe segmentation, combined with vector similarity matching for generalization across different syringe sizes and occlusions. This few-shot segmentation strategy ensures generalization while minimizing annotation effort. The pipeline also integrates lightweight methods for motion detection, syringe classification, and volume estimation to ensure quasi-real-time performance. The system was tested on 304 injection events performed by 19 anesthesiologists using syringes of four sizes (3, 5, 10 and 20 ml). The pipeline achieved 100% injection-event detection sensitivity and an overall 86.3% documentation success rate. Volume estimation accuracy varied across syringe sizes, with mean absolute error (MAE) values of 0.10, 0.22, 0.37, and 0.61 ml for 3, 5, 10, and 20 ml syringes, respectively. Results compare favorably to manual measurements, which can have mean percentage errors of 1.4%-18.6%. Runtime optimization ensured quasi-real-time operation, processing each event within 10-12 s, supporting clinical workflow integration. This work presents a solution to significantly improve anesthetic injection documentation while enhancing patient safety, standardizing procedures, and reducing anesthesiologists' workload, representing a fully automated, camera-only pipeline validated on clinicians in quasi-real-time.
Exploring the application of 5E teaching model combined with virtual endoscopic surgical simulation system in surgical teaching. Eighty-six students who received standardized residency training in the Department of General Surgery at the Second Hospital of Shanxi Medical University from September 2022 to June 2023 were selected as the research subjects. They were randomly divided into experimental and control groups, with 43 students in each group. The experimental group adopts the 5E teaching mode combined with a virtual endoscopic surgery simulation system for teaching. In contrast, the control group was taught using traditional teaching and a simple endoscopic simulation training box. A comparison was made between the evaluation results, self-evaluation, and teaching mode evaluation of the two groups. A t-test was performed on two sets of measurement data using SPSS 26.0 software. The theoretical test scores (t = 17.240, p = 0.000) and skill test scores (t = 21.335, p = 0.000) of students in the experimental group were higher than those in the control group. Compared to the control group, the experimental group showed significant improvement in operational skills (t = 3.557, p = 0.001), knowledge application (t = 4.936, p = 0.000), and overall performance (t = 2.999, p = 0.003) after training. The attitudes of students in the experimental group toward ability training (t = 3.818, p = 0.000), class order (t = 3.189, p = 0.002), teaching mode (t = 2.955, p = 0.004), and teaching level evaluation (t = 6.238, p = 0.000) were significantly higher than those in the control group. The virtual endoscopic surgery simulation system combined with the 5E teaching mode can significantly improve the theoretical knowledge and clinical practice skills of resident physicians in standardized training. Suggest applying it to clinical teaching.
