Computer Assisted Surgery最新文献

{"title":"HiEndo: harnessing large-scale data for generating high-resolution laparoscopy videos under a two-stage framework.","authors":"Zhao Wang, Yeqian Zhang, Jiayi Gu, Yueyao Chen, Yonghao Long, Xiang Xia, Puhua Zhang, Chunchao Zhu, Zerui Wang, Qi Dou, Zheng Wang, Zizhen Zhang","doi":"10.1080/24699322.2025.2536643","DOIUrl":"https://doi.org/10.1080/24699322.2025.2536643","url":null,"abstract":"<p><p>Recent success in generative AI has demonstrated great potential in various medical scenarios. However, how to generate realistic and high-fidelity gastrointestinal laparoscopy videos still lacks exploration. A recent work, Endora, proposes a basic generation model for a gastrointestinal laparoscopy scenario, producing low-resolution laparoscopy videos, which can not meet the real needs in robotic surgery. Regarding this issue, we propose an innovative two-stage video generation architecture HiEndo for generating high-resolution gastrointestinal laparoscopy videos with high fidelity. In the first stage, we build a diffusion transformer for generating a low-resolution laparoscopy video upon the basic capability of Endora as an initial start. In the second stage, we further design a super resolution module to improve the resolution of initial video and refine the fine-grained details. With these two stages, we could obtain high-resolution realistic laparoscopy videos with high fidelity, which can meet the real-world clinical usage. We also collect a large-scale gastrointestinal laparoscopy video dataset with 61,270 video clips for training and validation of our proposed method. Extensive experimental results have demonstrate the effectiveness of our proposed framework. For example, our model achieves 15.1% Fréchet Video Distance and 3.7% F1 score improvements compared with the previous state-of-the-art method.</p>","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":"30 1","pages":"2536643"},"PeriodicalIF":1.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144715233","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":"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":"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":"ArthroPhase: a novel dataset and method for phase recognition in arthroscopic video.","authors":"Ali Bahari Malayeri, Matthias Seibold, Nicola A Cavalcanti, Jonas Hein, Sascha Jecklin, Lazaros Vlachopoulos, Sandro Fucentese, Sandro Hodel, Philipp Fürnstahl","doi":"10.1080/24699322.2025.2508144","DOIUrl":"10.1080/24699322.2025.2508144","url":null,"abstract":"<p><p>This study advances surgical phase recognition in arthroscopic procedures, specifically Anterior Cruciate Ligament (ACL) reconstruction, by introducing the first arthroscopy dataset and a novel transformer-based model. We establish a benchmark for arthroscopic surgical phase recognition by leveraging spatio-temporal features to address challenges such as limited field of view, occlusions, and visual distortions. We developed the ACL27 dataset, comprising 27 videos of ACL surgeries, each labeled with surgical phases. Our model employs a transformer-based architecture, utilizing temporal-aware frame-wise feature extraction through ResNet-50 and transformer layers. This approach integrates spatio-temporal features and introduces a Surgical Progress Index (SPI) to quantify surgery progression. The model's performance was evaluated using accuracy, precision, recall, and Jaccard Index on the ACL27 and Cholec80 datasets. The proposed model achieved an overall accuracy of 72.9% on the ACL27 dataset. On the Cholec80 dataset, the model achieved performance comparable to state-of-the-art methods, with an accuracy of 92.4%. The SPI demonstrated an output error of 10.6% and 9.8% on ACL27 and Cholec80 datasets, respectively, indicating reliable surgery progression estimation. This study introduces a significant advancement in surgical phase recognition for arthroscopy, providing a comprehensive dataset and robust transformer-based model. The results validate the model's effectiveness and generalizability, highlighting its potential to improve surgical training, real-time assistance, and operational efficiency in orthopedic surgery. The publicly available dataset and code will facilitate future research in this critical field. Word Count: 6490.</p>","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":"30 1","pages":"2508144"},"PeriodicalIF":1.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144192536","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":"SurgPointTransformer: transformer-based vertebra shape completion using RGB-D imaging.","authors":"Aidana Massalimova, Florentin Liebmann, Sascha Jecklin, Fabio Carrillo, Mazda Farshad, Philipp Fürnstahl","doi":"10.1080/24699322.2025.2511126","DOIUrl":"10.1080/24699322.2025.2511126","url":null,"abstract":"<p><p>State-of-the-art computer- and robot-assisted surgery systems rely on intraoperative imaging technologies such as computed tomography and fluoroscopy to provide detailed 3D visualizations of patient anatomy. However, these methods expose both patients and clinicians to ionizing radiation. This study introduces a radiation-free approach for 3D spine reconstruction using RGB-D data. Inspired by the \"mental map\" surgeons form during procedures, we present SurgPointTransformer, a shape completion method that reconstructs unexposed spinal regions from sparse surface observations. The method begins with a vertebra segmentation step that extracts vertebra-level point clouds for subsequent shape completion. SurgPointTransformer then uses an attention mechanism to learn the relationship between visible surface features and the complete spine structure. The approach is evaluated on an <i>ex vivo</i> dataset comprising nine samples, with CT-derived data used as ground truth. SurgPointTransformer significantly outperforms state-of-the-art baselines, achieving a Chamfer distance of 5.39 mm, an F-score of 0.85, an Earth mover's distance of 11.00 and a signal-to-noise ratio of 22.90 dB. These results demonstrate the potential of our method to reconstruct 3D vertebral shapes without exposing patients to ionizing radiation. This work contributes to the advancement of computer-aided and robot-assisted surgery by enhancing system perception and intelligence.</p>","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":"30 1","pages":"2511126"},"PeriodicalIF":1.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12312754/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144217685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Retrospective case control study on the evaluation of the impact of augmented reality in gynecological laparoscopy on patients operated for myomectomy or adenomyomectomy.","authors":"Aurélie Comptour, Pauline Chauvet, Anne-Sophie Grémeau, Claire Figuier, Bruno Pereira, Matthieu Rouland, Prasad Samarakoon, Adrien Bartoli, Marie De Antonio, Nicolas Bourdel","doi":"10.1080/24699322.2025.2509686","DOIUrl":"10.1080/24699322.2025.2509686","url":null,"abstract":"<p><p>The objective of this study is to evaluate the safety of using augmented reality (AR) in laparoscopic (adeno)myomectomy, defined as an increase in operating time shorter than 15 min. A total of 17 AR cases underwent laparoscopic myomectomy or adenomyomectomy with the use of AR and 17 controls without AR for the resection of (adeno)myomas. The non-inferiority assumption was defined by an operative overtime not exceeding 15 min, representing 10% of the typical operative time. The 17 AR cases were matched to 17 controls. The criteria used in matching the two groups were the type of lesions, the size and the placement. The mean operative time was 135 ± 39 min for AR cases and 149 ± 62 min for controls. The margin of non-inferiority was expressed as a difference in operative time of 15 min between the case and control groups. The mean difference observed between AR cases and controls was -14 min with 90% CI [-38.3;11.3] and was significantly lower than the non-inferiority margin of 15 min (<i>p</i> = 0.03). This negative time difference means that the operative time is shorter for the AR cases group. Intraoperative data revealed a volume of bleeding ≤200 mL in 82.3% of AR cases and in 75% of controls (<i>p</i> = 0.62). No intra or postoperative complications were reported in the groups. The use of augmented reality in laparoscopic (adeno)myomectomy does not introduce additional constraints for the surgeon. It appears to be safe for the patients, with an absence of additional adverse events and of significantly prolonged operative time.</p>","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":"30 1","pages":"2509686"},"PeriodicalIF":1.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144135869","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":"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":"Three-dimensional image-guided navigation technique for femoral artery puncture.","authors":"Yunmeng Zhang, Shenglin Liu, Qiang Zhang, Qingmin Feng","doi":"10.1080/24699322.2025.2535967","DOIUrl":"10.1080/24699322.2025.2535967","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":"30 1","pages":"2535967"},"PeriodicalIF":1.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144735616","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":"MLP-UNet: an algorithm for segmenting lesions in breast and thyroid ultrasound images.","authors":"Tian-Feng Dong, Chang-Jiang Zhou, Zhen-Yi Huang, Hao Zhao, Xue-Long Wang, Shi-Ju Yan","doi":"10.1080/24699322.2025.2523266","DOIUrl":"10.1080/24699322.2025.2523266","url":null,"abstract":"<p><p>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 <b>MLP-UNet</b>, 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.</p>","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":"30 1","pages":"2523266"},"PeriodicalIF":1.5,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144531290","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}