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":"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":1.9,"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":"Efficient computer vision pipeline for automated anesthetic injection documentation.","authors":"Amit Nissan, Fadi Mahameed, Sapir Gershov, Aeyal Raz, Shlomi Laufer","doi":"10.1080/24699322.2025.2582020","DOIUrl":"10.1080/24699322.2025.2582020","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":"30 1","pages":"2582020"},"PeriodicalIF":1.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145402900","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":"Patient-specific functional liver segments based on centerline classification of the hepatic and portal veins.","authors":"Gabriella d'Albenzio, Ruoyan Meng, Davit Aghayan, Egidijus Pelanis, Tomas Sakinis, Ole Vegard Solberg, Geir Arne Tangen, Rahul P Kumar, Ole Jakob Elle, Bjørn Edwin, Rafael Palomar","doi":"10.1080/24699322.2025.2580307","DOIUrl":"10.1080/24699322.2025.2580307","url":null,"abstract":"<p><strong>Purpose: </strong>Couinaud's liver segment classification has been widely adopted for liver surgery planning, yet its rigid anatomical boundaries often fail to align precisely with individual patient anatomy. This study proposes a novel patient-specific liver segmentation method based on detailed classification of hepatic and portal veins to improve anatomical adherence and clinical relevance.</p><p><strong>Methods: </strong>Our proposed method involves two key stages: (1) surgeons annotate vascular endpoints on 3D models of hepatic and portal veins, from which vessel centerlines are computed; and (2) liver segments are calculated by assigning voxel labels based on proximity to these vascular centerlines. The accuracy and clinical applicability of our Hepatic and Portal Vein-based Classification (HPVC) were compared with conventional Plane-Based Classification (PBC), Portal Vein-Based Classification (PVC), and an automated deep learning method (nnU-Net) using volumetric measurements, Dice similarity scores, and expert evaluations.</p><p><strong>Results: </strong>HPVC demonstrated superior anatomical conformity compared to traditional methods, especially in complex segments like 5 and 8, providing segmentations more reflective of actual vascular territories. Volumetric analysis revealed significant discrepancies among the methods, particularly with nnU-Net generally producing larger segment volumes. HPVC consistently achieved higher surgeon-rated scores in patient-specific anatomical adherence, perfusion region assessment, and accuracy in surgical planning compared to PBC, PVC, and nnU-Net.</p><p><strong>Conclusion: </strong>The presented HPVC method offers substantial improvements in liver segmentation precision, especially relevant for surgical planning in anatomically complex cases. Its integration into clinical workflows <i>via</i> the open-source platform 3D Slicer significantly enhances its accessibility and usability.</p>","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":"30 1","pages":"2580307"},"PeriodicalIF":1.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145423537","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":"Exploring the survival benefits of surgical treatment for pancreatic adenocarcinoma using the DeepSurv neural network model.","authors":"Xin Wang, Wenmao Yan, Jingdong Shi, Shi Cheng, Wei Yu, Hongyi Zhang","doi":"10.1080/24699322.2025.2556334","DOIUrl":"10.1080/24699322.2025.2556334","url":null,"abstract":"<p><p>To develop a DeepSurv model for predicting survival in pancreatic adenocarcinoma patients, evaluating the benefit of surgical versus non-surgical treatment across different stages, including stage IV subcategories. Clinical data were extracted from the SEER database (2000-2020). Patients were randomly divided into a model-building group and an experimental group. The DeepSurv model was trained and hyperparameter-optimized. Simulated paired data were created by switching treatment status. Predicted survival rates were compared using generalized estimating equations. SHAP values analyzed variable importance.The study included 16,068 patients. The final model achieved a C-index of 0.85. Surgical treatment yielded higher survival rates than non-surgical across all stages (p<0.001), though the benefit diminished in advanced stages. For stage IV, surgery improved survival in T1-3 and N0 stages (p<0.001) but not in T4 and N1. SHAP analysis ranked M stage as the most significant predictor of mortality, followed by T stage, overall stage, and surgical status. M1 metastasis was associated with a 14% increased mortality risk, while surgery reduced risk by 11%.Surgery reduces mortality across stages, with declining efficacy in advanced disease. For stage IV patients, surgery is beneficial except for those with T4 or N1 disease. Combining DeepSurv with SHAP analysis facilitates individualized prediction of surgical survival benefits.</p>","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":"30 1","pages":"2556334"},"PeriodicalIF":1.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145014488","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":"Application of 5E teaching model combined with virtual endoscopic surgery simulation system in surgical teaching.","authors":"Xingtao Zhao, Zhinan Jing, Ruilin Zhang, Liang Chang, Feng Gao, Zhenxing Wang, Xuewei Hao","doi":"10.1080/24699322.2025.2576882","DOIUrl":"10.1080/24699322.2025.2576882","url":null,"abstract":"<p><p>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 <i>t</i>-test was performed on two sets of measurement data using SPSS 26.0 software. The theoretical test scores (<i>t</i> = 17.240, <i>p</i> = 0.000) and skill test scores (<i>t</i> = 21.335, <i>p</i> = 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 (<i>t</i> = 3.557, <i>p</i> = 0.001), knowledge application (<i>t</i> = 4.936, <i>p</i> = 0.000), and overall performance (<i>t</i> = 2.999, <i>p</i> = 0.003) after training. The attitudes of students in the experimental group toward ability training (<i>t</i> = 3.818, <i>p</i> = 0.000), class order (<i>t</i> = 3.189, <i>p</i> = 0.002), teaching mode (<i>t</i> = 2.955, <i>p</i> = 0.004), and teaching level evaluation (<i>t</i> = 6.238, <i>p</i> = 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.</p>","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":"30 1","pages":"2576882"},"PeriodicalIF":1.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145369274","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":"Robotic-assisted complex primary total hip arthroplasty with customized metal acetabular augment - a technical case report.","authors":"Elvis Chun Sing Chui, Rex Wang-Fung Mak, Tsz Lung Choi, Gloria Yan Ting Lam, Dennis Cham Kit Wong, Pengfei Lei, Kevin Ho, Jonathan Patrick Ng, Patrick Shu Hang Yung, Michael Tim-Yun Ong","doi":"10.1080/24699322.2025.2588565","DOIUrl":"10.1080/24699322.2025.2588565","url":null,"abstract":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":"30 1","pages":"2588565"},"PeriodicalIF":1.9,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145552009","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}