International Journal of Computer Assisted Radiology and Surgery最新文献

{"title":"Automated thermographic detection of blood vessels for DIEP flap reconstructive surgery.","authors":"Edgar Cardenas De La Hoz, Jan Verstockt, Simon Verspeek, Warre Clarys, Filip E F Thiessen, Thierry Tondu, Wiebren A A Tjalma, Gunther Steenackers, Steve Vanlanduit","doi":"10.1007/s11548-024-03199-8","DOIUrl":"10.1007/s11548-024-03199-8","url":null,"abstract":"<p><strong>Purpose: </strong>Inadequate perfusion is the most common cause of partial flap loss in tissue transfer for post-mastectomy breast reconstruction. The current state-of-the-art uses computed tomography angiography (CTA) to locate the best perforators. Unfortunately, these techniques are expensive and time-consuming and not performed during surgery. Dynamic infrared thermography (DIRT) can offer a solution for these disadvantages.</p><p><strong>Methods: </strong>The research presented couples thermographic examination during DIEP flap breast reconstruction with automatic segmentation approach using a convolutional neural network. Traditional segmentation techniques and annotations by surgeons are used to create automatic labels for the training.</p><p><strong>Results: </strong>The network used for image annotation is able to label in real-time on minimal hardware and the labels created can be used to locate and quantify perforator candidates for selection with a dice score accuracy of 0.8 after 2 min and 0.9 after 4 min.</p><p><strong>Conclusions: </strong>These results allow for a computational system that can be used in place during surgery to improve surgical success. The ability to track and measure perforators and their perfused area allows for less subjective results and helps the surgeon to select the most suitable perforator for DIEP flap breast reconstruction.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":"1733-1741"},"PeriodicalIF":2.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141629314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bone marrow edema detection for diagnostic support of axial spondyloarthritis using MRI.","authors":"Akira Kojima, Tetsuya Tomita, Shigeyoshi Tsuji, Yuho Kadono, Kurisu Tada, Taiki Nozaki, Masashi Tamaki, Yoshinobu Koyama, Hiroaki Dobashi, Tadashi Okano, Satoshi Kawaai, Tatsuya Atsumi, Naoto Tamura, Yoshifuji Matsumoto, Hitoshi Goto, Yoshinori Taniguchi, Yukitaka Ueki, Michiaki Takagi, Kiyoshi Matsui, Kohei Hagimori, Akinobu Shimizu","doi":"10.1007/s11548-024-03228-6","DOIUrl":"10.1007/s11548-024-03228-6","url":null,"abstract":"<p><strong>Purpose: </strong>This study proposes a process for detecting slices with bone marrow edema (BME), a typical finding of axSpA, using MRI scans as the input. This process does not require manual input of ROIs and provides the results of the judgment of the presence or absence of BME on a slice and the location of edema as the rationale for the judgment.</p><p><strong>Methods: </strong>First, the signal intensity of the MRI scans of the sacroiliac joint was normalized to reduce the variation in signal values between scans. Next, slices containing synovial joints were extracted using a slice selection network. Finally, the BME slice detection network determines the presence or absence of the BME in each slice and outputs the location of the BME.</p><p><strong>Results: </strong>The proposed method was applied to 86 MRI scans collected from 15 hospitals in Japan. The results showed that the average absolute error of the slice selection process was 1.49 slices for the misalignment between the upper and lower slices of the synovial joint range. The accuracy, sensitivity, and specificity of the BME slice detection network were 0.905, 0.532, and 0.974, respectively.</p><p><strong>Conclusion: </strong>This paper proposes a process to detect the slice with BME and its location as the rationale of the judgment from an MRI scan and shows its effectiveness using 86 MRI scans. In the future, we plan to develop a process for detecting other findings such as bone erosion from MR scans, followed by the development of a diagnostic support system.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":"1699-1711"},"PeriodicalIF":2.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141861620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Self-supervised learning for classifying paranasal anomalies in the maxillary sinus.","authors":"Debayan Bhattacharya, Finn Behrendt, Benjamin Tobias Becker, Lennart Maack, Dirk Beyersdorff, Elina Petersen, Marvin Petersen, Bastian Cheng, Dennis Eggert, Christian Betz, Anna Sophie Hoffmann, Alexander Schlaefer","doi":"10.1007/s11548-024-03172-5","DOIUrl":"10.1007/s11548-024-03172-5","url":null,"abstract":"<p><strong>Purpose: </strong>Paranasal anomalies, frequently identified in routine radiological screenings, exhibit diverse morphological characteristics. Due to the diversity of anomalies, supervised learning methods require large labelled dataset exhibiting diverse anomaly morphology. Self-supervised learning (SSL) can be used to learn representations from unlabelled data. However, there are no SSL methods designed for the downstream task of classifying paranasal anomalies in the maxillary sinus (MS).</p><p><strong>Methods: </strong>Our approach uses a 3D convolutional autoencoder (CAE) trained in an unsupervised anomaly detection (UAD) framework. Initially, we train the 3D CAE to reduce reconstruction errors when reconstructing normal maxillary sinus (MS) image. Then, this CAE is applied to an unlabelled dataset to generate coarse anomaly locations by creating residual MS images. Following this, a 3D convolutional neural network (CNN) reconstructs these residual images, which forms our SSL task. Lastly, we fine-tune the encoder part of the 3D CNN on a labelled dataset of normal and anomalous MS images.</p><p><strong>Results: </strong>The proposed SSL technique exhibits superior performance compared to existing generic self-supervised methods, especially in scenarios with limited annotated data. When trained on just 10% of the annotated dataset, our method achieves an area under the precision-recall curve (AUPRC) of 0.79 for the downstream classification task. This performance surpasses other methods, with BYOL attaining an AUPRC of 0.75, SimSiam at 0.74, SimCLR at 0.73 and masked autoencoding using SparK at 0.75.</p><p><strong>Conclusion: </strong>A self-supervised learning approach that inherently focuses on localizing paranasal anomalies proves to be advantageous, particularly when the subsequent task involves differentiating normal from anomalous maxillary sinuses. Access our code at https://github.com/mtec-tuhh/self-supervised-paranasal-anomaly .</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":"1713-1721"},"PeriodicalIF":2.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11365849/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141293929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Periacetabular osteotomy using computed tomography-based navigation: preoperative planning and accuracy evaluation.","authors":"Yutaka Inaba, Taro Tezuka, Masatoshi Oba, Hyonmin Choe, Hiroyuki Ike","doi":"10.1007/s11548-024-03210-2","DOIUrl":"10.1007/s11548-024-03210-2","url":null,"abstract":"<p><strong>Purpose: </strong>Since 2011, we have used computed tomography (CT)-based navigation to perform safe and accurate rotational acetabular osteotomy (RAO) for treating developmental dysplasia of the hip. We developed a new method with four fiducial points to improve the accuracy of a published technique. In this study, we introduced a new method to achieve reorientation in accordance with planning and evaluated its accuracy.</p><p><strong>Methods: </strong>This study included 40 joints, which underwent RAO used CT-based navigation. In 20 joints, reorientation was confirmed by touching the lateral aspect of the rotated fragment with navigation and checking whether it matched the preoperative plan. A new fiducial point method was adopted for the remaining 20 joints. To assess the accuracy of the position of the rotated fragment in each group, postoperative radial reformatted CT images were obtained around the acetabulum and three-dimensional evaluation was performed. The accuracy of acetabular fragment repositioning was evaluated using the acetabular sector angle (ASA).</p><p><strong>Results: </strong>The absolute value of ΔASA, which represents the error between preoperative planning and the actual postoperative position, was significantly smaller in the new fiducial method group than the previous method group in the area from 11:30 to 13:30 (p < 0.05). The Harris Hip Score at 1 year after surgery did not differ significantly between the previous and new fiducial point methods.</p><p><strong>Conclusion: </strong>The new fiducial point method significantly reduced reorientation error in the superior-lateral area of the acetabulum: significantly fewer errors and fewer cases of under-correction of lateral acetabular coverage were recorded. The four-reference fiducial method facilitates reorientation of the acetabulum as planned, with fewer errors. The effect of the improved accuracy of the fiducial point method on clinical outcomes will be investigated in the future work.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":"1833-1842"},"PeriodicalIF":2.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11365835/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141421764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Virtual reconstruction of orbital defects using Gaussian process morphable models.","authors":"Pieter Vanslambrouck, Jeroen Van Dessel, Constantinus Politis, Robin Willaert, Michel Bila, Yi Sun, Peter Claes","doi":"10.1007/s11548-024-03200-4","DOIUrl":"10.1007/s11548-024-03200-4","url":null,"abstract":"<p><strong>Purpose: </strong>The conventional method to reconstruct the bone level for orbital defects, which is based on mirroring and manual adaptation, is time-consuming and the accuracy highly depends on the expertise of the clinical engineer. The aim of this study is to propose and evaluate an automated reconstruction method utilizing a Gaussian process morphable model (GPMM).</p><p><strong>Methods: </strong>Sixty-five Computed Tomography (CT) scans of healthy midfaces were used to create a GPMM that can model shape variations of the orbital region. Parameter optimization was performed by evaluating several quantitative metrics inspired on the shape modeling literature, e.g. generalization and specificity. The reconstruction error was estimated by reconstructing artificial defects created in orbits from fifteen CT scans that were not included in the GPMM. The developed algorithms utilize the existing framework of Gaussian process morphable models, as implemented in the Scalismo software.</p><p><strong>Results: </strong>By evaluating the proposed quality metrics, adequate parameters are chosen for non-rigid registration and reconstruction. The resulting median reconstruction error using the GPMM was lower (0.35 ± 0.16 mm) compared to the mirroring method (0.52 ± 0.18 mm). In addition, the GPMM-based reconstruction is automated and can be applied to large bilateral defects with a median reconstruction error of 0.39 ± 0.11 mm.</p><p><strong>Conclusion: </strong>The GPMM-based reconstruction proves to be less time-consuming and more accurate than reconstruction by mirroring. Further validation through clinical studies on patients with orbital defects is warranted. Nevertheless, the results underscore the potential of GPMM-based reconstruction as a promising alternative for designing patient-specific implants.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":"1909-1917"},"PeriodicalIF":2.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141421765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An intensity-based self-supervised domain adaptation method for intervertebral disc segmentation in magnetic resonance imaging.","authors":"Maria Chiara Fiorentino, Francesca Pia Villani, Rafael Benito Herce, Miguel Angel González Ballester, Adriano Mancini, Karen López-Linares Román","doi":"10.1007/s11548-024-03219-7","DOIUrl":"10.1007/s11548-024-03219-7","url":null,"abstract":"<p><strong>Background and objective: </strong>Accurate IVD segmentation is crucial for diagnosing and treating spinal conditions. Traditional deep learning methods depend on extensive, annotated datasets, which are hard to acquire. This research proposes an intensity-based self-supervised domain adaptation, using unlabeled multi-domain data to reduce reliance on large annotated datasets.</p><p><strong>Methods: </strong>The study introduces an innovative method using intensity-based self-supervised learning for IVD segmentation in MRI scans. This approach is particularly suited for IVD segmentations due to its ability to effectively capture the subtle intensity variations that are characteristic of spinal structures. The model, a dual-task system, simultaneously segments IVDs and predicts intensity transformations. This intensity-focused method has the advantages of being easy to train and computationally light, making it highly practical in diverse clinical settings. Trained on unlabeled data from multiple domains, the model learns domain-invariant features, adeptly handling intensity variations across different MRI devices and protocols.</p><p><strong>Results: </strong>Testing on three public datasets showed that this model outperforms baseline models trained on single-domain data. It handles domain shifts and achieves higher accuracy in IVD segmentation.</p><p><strong>Conclusions: </strong>This study demonstrates the potential of intensity-based self-supervised domain adaptation for IVD segmentation. It suggests new directions for research in enhancing generalizability across datasets with domain shifts, which can be applied to other medical imaging fields.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":"1753-1761"},"PeriodicalIF":2.3,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11365836/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141555903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning to predict risk of lateral skull base cerebrospinal fluid leak or encephalocele.","authors":"Steven D Curry, Kieran S Boochoon, Geoffrey C Casazza, Daniel L Surdell, Justin A Cramer","doi":"10.1007/s11548-024-03259-z","DOIUrl":"https://doi.org/10.1007/s11548-024-03259-z","url":null,"abstract":"<p><strong>Purpose: </strong>Skull base features, including increased foramen ovale (FO) cross-sectional area, are associated with lateral skull base spontaneous cerebrospinal fluid (sCSF) leak and encephalocele. Manual measurement requires skill in interpreting imaging studies and is time consuming. The goal of this study was to develop a fully automated deep learning method for FO segmentation and to determine the predictive value in identifying patients with sCSF leak or encephalocele.</p><p><strong>Methods: </strong>A retrospective cohort study at a tertiary care academic hospital of 34 adults with lateral skull base sCSF leak or encephalocele were compared with 815 control patients from 2013-2021. A convolutional neural network (CNN) was constructed for image segmentation of axial computed tomography (CT) studies. Predicted FO segmentations were compared to manual segmentations, and receiver operating characteristic (ROC) curves were constructed.</p><p><strong>Results: </strong>295 CTs were used for training and validation of the CNN. A separate dataset of 554 control CTs was matched 5:1 on age and sex with the sCSF leak/encephalocele group. The mean Dice score was 0.81. The sCSF leak/encephalocele group had greater mean (SD) FO cross-sectional area compared to the control group, 29.0 (7.7) mm² versus 24.3 (7.6) mm² (P = .002, 95% confidence interval 0.02-0.08). The area under the ROC curve was 0.69.</p><p><strong>Conclusion: </strong>CNNs can be used to segment the cross-sectional area of the FO accurately and efficiently. Used together with other predictors, this method could be used as part of a clinical tool to predict the risk of sCSF leak or encephalocele.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142114556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to: Noctopus: a novel device and method for patient registration and navigation in image-guided cranial surgery.","authors":"Yusuf Özbek, Zoltán Bárdosi, Wolfgang Freysinger","doi":"10.1007/s11548-024-03251-7","DOIUrl":"https://doi.org/10.1007/s11548-024-03251-7","url":null,"abstract":"","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142037747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HybGrip: a synergistic hybrid gripper for enhanced robotic surgical instrument grasping.","authors":"Jorge Badilla-Solórzano, Sontje Ihler, Thomas Seel","doi":"10.1007/s11548-024-03245-5","DOIUrl":"https://doi.org/10.1007/s11548-024-03245-5","url":null,"abstract":"<p><strong>Purpose: </strong>A fundamental task of a robotic scrub nurse is handling surgical instruments. Thus, a gripper capable of consistently grasping a wide variety of tools is essential. We introduce a novel gripper that combines granular jamming and pinching technologies to achieve a synergistic improvement in surgical instrument grasping.</p><p><strong>Methods: </strong>A reliable hybrid gripper is constructed by integrating a pinching mechanism and a standard granular jamming gripper, achieving enhanced granular interlocking. For our experiments, our prototype is affixed to the end-effector of a collaborative robot. A novel grasping strategy is proposed and utilized to evaluate the robustness and performance of our prototype on 18 different surgical tools with diverse geometries.</p><p><strong>Results: </strong>It is demonstrated that the integration of the pinching mechanism significantly enhances grasping performance compared with standard granular jamming grippers, with a success rate above 98%. It is shown that with the combined use of our gripper with an underlying grid, i.e., a complementary device placed beneath the instruments, robustness and performance are further enhanced.</p><p><strong>Conclusion: </strong>Our prototype's performance in surgical instrument grasping stands on par with, if not surpasses, that of comparable contemporary studies, ensuring its competitiveness. Our gripper proves to be robust, cost-effective, and simple, requiring no instrument-specific grasping strategies. Future research will focus on addressing the sterilizability of our prototype and assessing the viability of the introduced grid for intra-operative use.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142019487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence facilitates the potential of simulator training: An innovative laparoscopic surgical skill validation system using artificial intelligence technology.","authors":"Atsuhisa Fukuta, Shogo Yamashita, Junnosuke Maniwa, Akihiko Tamaki, Takuya Kondo, Naonori Kawakubo, Kouji Nagata, Toshiharu Matsuura, Tatsuro Tajiri","doi":"10.1007/s11548-024-03253-5","DOIUrl":"https://doi.org/10.1007/s11548-024-03253-5","url":null,"abstract":"<p><strong>Purpose: </strong>The development of innovative solutions, such as simulator training and artificial intelligence (AI)-powered tutoring systems, has significantly changed surgical trainees' environments to receive the intraoperative instruction necessary for skill acquisition. In this study, we developed a new objective assessment system using AI for forceps manipulation in a surgical training simulator.</p><p><strong>Methods: </strong>Laparoscopic exercises were recorded using an iPad®, which provided top and side views. Top-view movies were used for AI learning of forceps trajectory. Side-view movies were used as supplementary information to assess the situation. We used an AI-based posture estimation method, DeepLabCut (DLC), to recognize and positionally measure the forceps in the operating field. Tracking accuracy was quantitatively evaluated by calculating the pixel differences between the annotation points and the points predicted by the AI model. Tracking stability at specified key points was verified to assess the AI model.</p><p><strong>Results: </strong>We selected a random sample to evaluate tracking accuracy quantitatively. This sample comprised 5% of the frames not used for AI training from the complete set of video frames. We compared the AI detection positions and correct positions and found an average pixel discrepancy of 9.2. The qualitative evaluation of the tracking stability was good at the forceps hinge; however, forceps tip tracking was unstable during rotation.</p><p><strong>Conclusion: </strong>The AI-based forceps tracking system can visualize and evaluate laparoscopic surgical skills. Improvements in the proposed system and AI self-learning are expected to enable it to distinguish the techniques of expert and novice surgeons accurately. This system is a useful tool for surgeon training and assessment.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142005803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}