Computer Assisted Surgery最新文献

{"title":"Three-dimensional (3D)-printed custom-made titanium ribs for chest wall reconstruction post-desmoid fibromatosis resection.","authors":"Chen Yang, Lei Chen, Xiangyu Xie, Changping Wu, Qianyun Wang","doi":"10.1080/24699322.2025.2456303","DOIUrl":"https://doi.org/10.1080/24699322.2025.2456303","url":null,"abstract":"<p><p>Desmoid fibromatosis (DF) is a rare low-grade benign myofibroblastic neoplasm that originates from fascia and muscle striae. For giant chest wall DF, surgical resection offer a radical form of treatment and the causing defects usually need repair and reconstruction, which can restore the structural integrity and rigidity of the thoracic cage. The past decade witnessed rapid advances in the application of various prosthetic material in thoracic surgery. However, three-dimensional (3D)-printed custom-made titanium ribs have never been reported for chest wall reconstruction post-DF resection. Here, we report a successful implantation of individualized 3D-printed titanium ribs to repair the chest wall defect in a patient with DF.</p>","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":"30 1","pages":"2456303"},"PeriodicalIF":1.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143017030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Imageless optical navigation system is clinically valid for total knee arthroplasty.","authors":"Taylor B Winberg, Sheila Wang, James L Howard","doi":"10.1080/24699322.2025.2466424","DOIUrl":"https://doi.org/10.1080/24699322.2025.2466424","url":null,"abstract":"<p><p>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 <i>via</i> 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.</p>","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":"30 1","pages":"2466424"},"PeriodicalIF":1.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143434411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning methods for clinical workflow phase-based prediction of procedure duration: a benchmark study.","authors":"Emanuele Frassini, Teddy S Vijfvinkel, Rick M Butler, Maarten van der Elst, Benno H W Hendriks, John J van den Dobbelsteen","doi":"10.1080/24699322.2025.2466426","DOIUrl":"10.1080/24699322.2025.2466426","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":"30 1","pages":"2466426"},"PeriodicalIF":1.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143484833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Risk prediction and analysis of gallbladder polyps with deep neural network.","authors":"Kerong Yuan, Xiaofeng Zhang, Qian Yang, Xuesong Deng, Zhe Deng, Xiangyun Liao, Weixin Si","doi":"10.1080/24699322.2024.2331774","DOIUrl":"10.1080/24699322.2024.2331774","url":null,"abstract":"&lt;p&gt;&lt;p&gt;The aim of this study is to analyze the risk factors associated with the development of adenomatous and malignant polyps in the gallbladder. Adenomatous polyps of the gallbladder are considered precancerous and have a high likelihood of progressing into malignancy. Preoperatively, distinguishing between benign gallbladder polyps, adenomatous polyps, and malignant polyps is challenging. Therefore, the objective is to develop a neural network model that utilizes these risk factors to accurately predict the nature of polyps. This predictive model can be employed to differentiate the nature of polyps before surgery, enhancing diagnostic accuracy. A retrospective study was done on patients who had cholecystectomy surgeries at the Department of Hepatobiliary Surgery of the Second People's Hospital of Shenzhen between January 2017 and December 2022. The patients' clinical characteristics, lab results, and ultrasonographic indices were examined. Using risk variables for the growth of adenomatous and malignant polyps in the gallbladder, a neural network model for predicting the kind of polyps will be created. A normalized confusion matrix, PR, and ROC curve were used to evaluate the performance of the model. In this comprehensive study, we meticulously analyzed a total of 287 cases of benign gallbladder polyps, 15 cases of adenomatous polyps, and 27 cases of malignant polyps. The data analysis revealed several significant findings. Specifically, hepatitis B core antibody (95% CI -0.237 to 0.061, &lt;i&gt;p&lt;/i&gt; &lt; 0.001), number of polyps (95% CI -0.214 to -0.052, &lt;i&gt;p&lt;/i&gt; = 0.001), polyp size (95% CI 0.038 to 0.051, &lt;i&gt;p&lt;/i&gt; &lt; 0.001), wall thickness (95% CI 0.042 to 0.081, &lt;i&gt;p&lt;/i&gt; &lt; 0.001), and gallbladder size (95% CI 0.185 to 0.367, &lt;i&gt;p&lt;/i&gt; &lt; 0.001) emerged as independent predictors for gallbladder adenomatous polyps and malignant polyps. Based on these significant findings, we developed a predictive classification model for gallbladder polyps, represented as follows, Predictive classification model for GBPs = -0.149 * core antibody - 0.033 * number of polyps + 0.045 * polyp size + 0.061 * wall thickness + 0.276 * gallbladder size - 4.313. To assess the predictive efficiency of the model, we employed precision-recall (PR) and receiver operating characteristic (ROC) curves. The area under the curve (AUC) for the prediction model was 0.945 and 0.930, respectively, indicating excellent predictive capability. We determined that a polyp size of 10 mm served as the optimal cutoff value for diagnosing gallbladder adenoma, with a sensitivity of 81.5% and specificity of 60.0%. For the diagnosis of gallbladder cancer, the sensitivity and specificity were 81.5% and 92.5%, respectively. These findings highlight the potential of our predictive model and provide valuable insights into accurate diagnosis and risk assessment for gallbladder polyps. We identified several risk factors associated with the development of adenomatous and malignant polyps in the gallbladder","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":"29 1","pages":"2331774"},"PeriodicalIF":2.1,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140195203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feasibility of proton dosimetry overriding planning CT with daily CBCT elaborated through generative artificial intelligence tools.","authors":"Matteo Rossi, Gabriele Belotti, Luca Mainardi, Guido Baroni, Pietro Cerveri","doi":"10.1080/24699322.2024.2327981","DOIUrl":"10.1080/24699322.2024.2327981","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":"29 1","pages":"2327981"},"PeriodicalIF":2.1,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140102858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A decade of progress: bringing mixed reality image-guided surgery systems in the operating room.","authors":"Zahra Asadi, Mehrdad Asadi, Negar Kazemipour, Étienne Léger, Marta Kersten-Oertel","doi":"10.1080/24699322.2024.2355897","DOIUrl":"https://doi.org/10.1080/24699322.2024.2355897","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":"29 1","pages":"2355897"},"PeriodicalIF":2.1,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141094751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SwinD-Net: a lightweight segmentation network for laparoscopic liver segmentation.","authors":"Shuiming Ouyang, Baochun He, Huoling Luo, Fucang Jia","doi":"10.1080/24699322.2024.2329675","DOIUrl":"10.1080/24699322.2024.2329675","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":"29 1","pages":"2329675"},"PeriodicalIF":2.1,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140177886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ultrasound-based 3D bone modelling in computer assisted orthopedic surgery - a review and future challenges.","authors":"Benjamin Hohlmann, Peter Broessner, Klaus Radermacher","doi":"10.1080/24699322.2023.2276055","DOIUrl":"10.1080/24699322.2023.2276055","url":null,"abstract":"<p><p>Computer-assisted orthopedic surgery requires precise representations of bone surfaces. To date, computed tomography constitutes the gold standard, but comes with a number of limitations, including costs, radiation and availability. Ultrasound has potential to become an alternative to computed tomography, yet suffers from low image quality and limited field-of-view. These shortcomings may be addressed by a fully automatic segmentation and model-based completion of 3D bone surfaces from ultrasound images. This survey summarizes the state-of-the-art in this field by introducing employed algorithms, and determining challenges and trends. For segmentation, a clear trend toward machine learning-based algorithms can be observed. For 3D bone model completion however, none of the published methods involve machine learning. Furthermore, data sets and metrics are identified as weak spots in current research, preventing development and evaluation of models that generalize well.</p>","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":"29 1","pages":"2276055"},"PeriodicalIF":2.1,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139543506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of additional hospital days in patients undergoing cervical spine surgery with machine learning methods.","authors":"Bin Zhang, Shengsheng Huang, Chenxing Zhou, Jichong Zhu, Tianyou Chen, Sitan Feng, Chengqian Huang, Zequn Wang, Shaofeng Wu, Chong Liu, Xinli Zhan","doi":"10.1080/24699322.2024.2345066","DOIUrl":"https://doi.org/10.1080/24699322.2024.2345066","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;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 &lt;8.64 days was categorized as the AHD-negative group (&lt;i&gt;n&lt;/i&gt; = 539), and a LOS &gt; 8.64 days comprised the AHD-positive group (&lt;i&gt;n&lt;/i&gt; = 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.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;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","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":"29 1","pages":"2345066"},"PeriodicalIF":2.1,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141302103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Augmented reality technology shortens aneurysm surgery learning curve for residents.","authors":"Xinman Liu, Weiping Xiao, Yibing Yang, Yan Yan, Feng Liang","doi":"10.1080/24699322.2024.2311940","DOIUrl":"10.1080/24699322.2024.2311940","url":null,"abstract":"<p><strong>Objectives: </strong>We aimed to prospectively investigate the benefit of using augmented reality (AR) for surgery residents learning aneurysm surgery.</p><p><strong>Materials and methods: </strong>Eight residents were included, and divided into an AR group and a control group (4 in each group). Both groups were asked to locate an aneurysm with a blue circle on the same screenshot after their viewing of surgery videos from both AR and non-AR tests. Only the AR group was allowed to inspect and manipulate an AR holographic representation of the aneurysm in AR tests. The actual location of the aneurysm was defined by a yellow circle by an attending physician after each test. Localization deviation was determined by the distance between the blue and yellow circle.</p><p><strong>Results: </strong>Localization deviation was lower in the AR group than in the control group in the last 2 tests (AR Test 2: 2.7 ± 1.0 mm vs. 5.8 ± 4.1 mm, <i>p</i> = 0.01, non-AR Test 2: 2.1 ± 0.8 mm vs. 5.9 ± 5.8 mm, <i>p</i> < 0.001). The mean deviation was lower in non-AR Test 2 as compared to non-AR Test 1 in both groups (AR: <i>p</i> < 0.001, control: <i>p</i> = 0.391). The localization deviation of the AR group decreased from 8.1 ± 3.8 mm in Test 2 to 2.7 ± 1.0 mm in AR Test 2 (<i>p</i> < 0.001).</p><p><strong>Conclusion: </strong>AR technology provides an effective and interactive way for neurosurgery training, and shortens the learning curve for residents in aneurysm surgery.</p>","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":"29 1","pages":"2311940"},"PeriodicalIF":2.1,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139693639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}