Computer Assisted Surgery最新文献

Percutaneous femoral arterial access is a fundamental procedure in minimally invasive vascular interventions. However, inadequate visualization of the femoral artery may lead to inaccurate puncture and complications, with reported incidence rates of 3 to 18%. This study proposes a three-dimensional (3D) image-guided navigation system designed to enhance real-time visualization of the target vessel and puncture site during femoral artery access. This system employed an Iterative Closest Point (ICP)-based point cloud algorithm to achieve spatial registration between image space and patient space. An improved ICP method is implemented to optimize surface point cloud alignment, providing higher efficiency and accuracy compared to conventional approaches. Validation experiments were conducted using a standard model and a human phantom. Registration and navigation accuracy were quantified using fiducial registration error (FRE) for spatial alignment, target registration error (TRE) for navigation accuracy, and distance error for puncture precision. The system achieved a FRE of 0.944 mm. On the standard model, the average distance error was 0.885 mm, and the TRE was 0.915 mm. On the human phantom, the average distance error is 0.967 mm, and the average TRE is 0.981 mm. These results confirm the feasibility and effectiveness of the proposed 3D navigation system in guiding femoral artery puncture. All error metrics were within clinically acceptable thresholds, suggesting potential for improved procedural safety and precision in percutaneous vascular interventions.

Achieving optimal implant position and orientation during total knee arthroplasty (TKA) is a pivotal factor in long-term survival. Computer-assisted navigation (CAN) has been recognized as a trusted technology that improves the accuracy and consistency of femoral and tibial bone cuts. Imageless CAN offers advantages over image-based CAN by reducing cost, radiation exposure, and time. The purpose of this study was to evaluate the accuracy of an imageless optical navigation system for TKA in a clinical setting. Forty-two consecutive patients who underwent primary TKA with CAN were retrospectively reviewed. Femoral and tibial component coronal alignment was assessed via post-operative radiographs by two independent reviewers and compared against coronal alignment angles from the CAN. The primary outcome was the mean absolute difference of femoral and tibial varus/valgus angles between radiograph and intra-operative device measurements. Bland-Altman plots were used to assess agreement between the methods and statistically analyze potential systematic bias. The mean absolute differences between navigation-guided cut measurements and post-operative radiographs were 1.16 ± 1.03° and 1.76 ± 1.38° for femoral and tibial alignment respectively. About 88% of coronal measurements were within ±3°, while 99% were within ±5°. Bland-Altman analysis demonstrated a bias between CAN and radiographic measurements with CAN values averaging 0.52° (95% CI: 0.11°-0.93°) less than their paired radiographic measurements. This study demonstrated the ability of an optical imageless navigation system to measure, on average, femoral and tibial coronal cuts to within 2.0° of post-operative radiographic measurements in a clinical setting.

Hyperspectral imaging (HSI) is a technique that captures and processes information across a wide spectrum of wavelengths, providing detailed spectral data for each pixel in an image to identify and analyze materials or objects. In the surgical domain, it can provide quantitative and qualitative tissue information without the need of any contrast agent, thereby making it possible to distinguish between different tissue types objectively. In this article, we review the applications of hyperspectral imaging in surgery, focusing on: (1) hardware components and scanning mechanisms of HSI devices, (2) image preprocessing and processing/analysis methods, including classification, segmentation, tissue characterization, and perfusion analysis, and (3) the feasibility of HSI in various surgical procedures, based on human and animal studies. A systematic review of hyperspectral imaging based on PRISMA guideline was conducted using specific keywords: allintitle: hyperspectral AND intraoperative OR intervention OR surgery. After applying predefined inclusion and exclusion criteria, 85 papers from the literature were selected for analysis. Our systematic review shows that HSI has demonstrated significant potential as an intraoperative guidance tool, assisting surgeons during tumor resection by generating detailed tissue density maps. Additionally, HSI can play a role in hemodynamic monitoring, providing perfusion maps to assess blood flow during surgery and detect postoperative complications. Despite its promise, challenges, such as hardware limitations, real-time processing, and clinical integration remain, highlighting the need for further research and development to advance HSI in surgical applications.

This study evaluates the performance of deep learning models in the prediction of the end time of procedures performed in the cardiac catheterization laboratory (cath lab). We employed only the clinical phases derived from video analysis as input to the algorithms. Our results show that InceptionTime and LSTM-FCN yielded the most accurate predictions. InceptionTime achieves Mean Absolute Error (MAE) values below 5 min and Symmetric Mean Absolute Percentage Error (SMAPE) under 6% at 60-s sampling intervals. In contrast, LSTM with attention mechanism and standard LSTM models have higher error rates, indicating challenges in handling both long-term and short-term dependencies. CNN-based models, especially InceptionTime, excel at feature extraction across different scales, making them effective for time-series predictions. We also analyzed training and testing times. CNN models, despite higher computational costs, significantly reduce prediction errors. The Transformer model has the fastest inference time, making it ideal for real-time applications. An ensemble model derived by averaging the two best performing algorithms reported low MAE and SMAPE, although needing longer training. Future research should validate these findings across different procedural contexts and explore ways to optimize training times without losing accuracy. Integrating these models into clinical scheduling systems could improve efficiency in cath labs. Our research demonstrates that the models we implemented can form the basis of an automated tool, which predicts the optimal time to call the next patient with an average error of approximately 30 s. These findings show the effectiveness of deep learning models, especially CNN-based architectures, in accurately predicting procedure end times.

Breast and thyroid cancers are among the most prevalent and fastest growing malignancies worldwide with ultrasound imaging serving as the primary modality for screening and surgical navigation of these lesions. Accurate and real-time lesion segmentation in ultrasound images is crucial for guiding precise needle placement during biopsies and surgeries. To address this clinical need, we propose MLP-UNet, a deep learning model for automatic segmentation of breast tumors and thyroid nodules in ultrasound images. MLP-UNet adopts an encoder-decoder architecture with a U-shaped structure and integrates a MLP-based module(MAP) module within the encoder stage. Attention module is a lightweight employed during the skip connections to enhance feature representation. Using only using 33.75 M parameters, MLP-UNet achieves state-of-the-art segmentation performance. On the BUSI, it attains Dice, IoU, and Recall of 80.61%, 67.93%, and 80.48%, respectively. And on the DDTI, it attains Dice, IoU, and Recall of 81.67% for Dice, 71.72%. These results outperform several classical and state-of-the-art segmentation networks while maintaining low computational complexity, highlighting its significant potential for clinical application in ultrasound-guided surgical navigation systems.

Advancements in mixed reality (MR) have led to innovative approaches in image-guided surgery (IGS). In this paper, we provide a comprehensive analysis of the current state of MR in image-guided procedures across various surgical domains. Using the Data Visualization View (DVV) Taxonomy, we analyze the progress made since a 2013 literature review paper on MR IGS systems. In addition to examining the current surgical domains using MR systems, we explore trends in types of MR hardware used, type of data visualized, visualizations of virtual elements, and interaction methods in use. Our analysis also covers the metrics used to evaluate these systems in the operating room (OR), both qualitative and quantitative assessments, and clinical studies that have demonstrated the potential of MR technologies to enhance surgical workflows and outcomes. We also address current challenges and future directions that would further establish the use of MR in IGS.

Radiotherapy commonly utilizes cone beam computed tomography (CBCT) for patient positioning and treatment monitoring. CBCT is deemed to be secure for patients, making it suitable for the delivery of fractional doses. However, limitations such as a narrow field of view, beam hardening, scattered radiation artifacts, and variability in pixel intensity hinder the direct use of raw CBCT for dose recalculation during treatment. To address this issue, reliable correction techniques are necessary to remove artifacts and remap pixel intensity into Hounsfield Units (HU) values. This study proposes a deep-learning framework for calibrating CBCT images acquired with narrow field of view (FOV) systems and demonstrates its potential use in proton treatment planning updates. Cycle-consistent generative adversarial networks (cGAN) processes raw CBCT to reduce scatter and remap HU. Monte Carlo simulation is used to generate CBCT scans, enabling the possibility to focus solely on the algorithm's ability to reduce artifacts and cupping effects without considering intra-patient longitudinal variability and producing a fair comparison between planning CT (pCT) and calibrated CBCT dosimetry. To showcase the viability of the approach using real-world data, experiments were also conducted using real CBCT. Tests were performed on a publicly available dataset of 40 patients who received ablative radiation therapy for pancreatic cancer. The simulated CBCT calibration led to a difference in proton dosimetry of less than 2%, compared to the planning CT. The potential toxicity effect on the organs at risk decreased from about 50% (uncalibrated) up the 2% (calibrated). The gamma pass rate at 3%/2 mm produced an improvement of about 37% in replicating the prescribed dose before and after calibration (53.78% vs 90.26%). Real data also confirmed this with slightly inferior performances for the same criteria (65.36% vs 87.20%). These results may confirm that generative artificial intelligence brings the use of narrow FOV CBCT scans incrementally closer to clinical translation in proton therapy planning updates.

Background: Machine learning (ML), a subset of artificial intelligence (AI), uses algorithms to analyze data and predict outcomes without extensive human intervention. In healthcare, ML is gaining attention for enhancing patient outcomes. This study focuses on predicting additional hospital days (AHD) for patients with cervical spondylosis (CS), a condition affecting the cervical spine. The research aims to develop an ML-based nomogram model analyzing clinical and demographic factors to estimate hospital length of stay (LOS). Accurate AHD predictions enable efficient resource allocation, improved patient care, and potential cost reduction in healthcare.

Methods: The study selected CS patients undergoing cervical spine surgery and investigated their medical data. A total of 945 patients were recruited, with 570 males and 375 females. The mean number of LOS calculated for the total sample was 8.64 ± 3.7 days. A LOS equal to or <8.64 days was categorized as the AHD-negative group (n = 539), and a LOS > 8.64 days comprised the AHD-positive group (n = 406). The collected data was randomly divided into training and validation cohorts using a 7:3 ratio. The parameters included their general conditions, chronic diseases, preoperative clinical scores, and preoperative radiographic data including ossification of the anterior longitudinal ligament (OALL), ossification of the posterior longitudinal ligament (OPLL), cervical instability and magnetic resonance imaging T2-weighted imaging high signal (MRI T2WIHS), operative indicators and complications. ML-based models like Lasso regression, random forest (RF), and support vector machine (SVM) recursive feature elimination (SVM-RFE) were developed for predicting AHD-related risk factors. The intersections of the variables screened by the aforementioned algorithms were utilized to construct a nomogram model for predicting AHD in patients. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve and C-index were used to evaluate the performance of the nomogram. Calibration curve and decision curve analysis (DCA) were performed to test the calibration performance and clinical utility.

Results: For these participants, 25 statistically significant parameters were identified as risk factors for AHD. Among these, nine factors were obtained as the intersection factors of these three ML algorithms and were used to develop a nomogram model. These factors were gender, age, body mass index (BMI), American Spinal Injury Association (ASIA) scores, magnetic resonance imaging T2-weighted imaging high signal (MRI T2WIHS), operated segment, intraoperative bleeding volume, the volume of drainage, and diabetes. After model validation, the AUC was 0.753 in the training cohort and 0.777 in the validation cohort. The calibration curve exhibited a satisfactory agreement between the nomogram predictions and actual probabilities. T

The real-time requirement for image segmentation in laparoscopic surgical assistance systems is extremely high. Although traditional deep learning models can ensure high segmentation accuracy, they suffer from a large computational burden. In the practical setting of most hospitals, where powerful computing resources are lacking, these models cannot meet the real-time computational demands. We propose a novel network SwinD-Net based on Skip connections, incorporating Depthwise separable convolutions and Swin Transformer Blocks. To reduce computational overhead, we eliminate the skip connection in the first layer and reduce the number of channels in shallow feature maps. Additionally, we introduce Swin Transformer Blocks, which have a larger computational and parameter footprint, to extract global information and capture high-level semantic features. Through these modifications, our network achieves desirable performance while maintaining a lightweight design. We conduct experiments on the CholecSeg8k dataset to validate the effectiveness of our approach. Compared to other models, our approach achieves high accuracy while significantly reducing computational and parameter overhead. Specifically, our model requires only 98.82 M floating-point operations (FLOPs) and 0.52 M parameters, with an inference time of 47.49 ms per image on a CPU. Compared to the recently proposed lightweight segmentation network UNeXt, our model not only outperforms it in terms of the Dice metric but also has only 1/3 of the parameters and 1/22 of the FLOPs. In addition, our model achieves a 2.4 times faster inference speed than UNeXt, demonstrating comprehensive improvements in both accuracy and speed. Our model effectively reduces parameter count and computational complexity, improving the inference speed while maintaining comparable accuracy. The source code will be available at https://github.com/ouyangshuiming/SwinDNet.