Medical & Biological Engineering & Computing最新文献_第6页

Deep learning-based automatic cranial implant design through direct defect shape prediction and its comparison study. 基于深度学习的直接缺损形状预测颅种植体自动设计及其对比研究。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-09-01 Epub Date: 2025-05-02 DOI: 10.1007/s11517-025-03363-5

Afaque Rafique Memon, Haochen Shi, Tarique Rafique Memon, Jan Egger, Xiaojun Chen

{"title":"Deep learning-based automatic cranial implant design through direct defect shape prediction and its comparison study.","authors":"Afaque Rafique Memon, Haochen Shi, Tarique Rafique Memon, Jan Egger, Xiaojun Chen","doi":"10.1007/s11517-025-03363-5","DOIUrl":"10.1007/s11517-025-03363-5","url":null,"abstract":"Defects to human crania are one kind of head bone damages, and cranial implants can be used to repair the defected crania. The automation of the implant design process is crucial in reducing the corresponding therapy time. Taking the cranial implant design problem as a special kind of shape completion task, an automatic cranial implant design workflow is proposed, which consists of a deep neural network for the direct shape prediction of the missing part of the defective cranium and conventional post-processing steps to refine the automatically generated implant. To evaluate the proposed workflow, we employ cross-validation and report an average Dice Similarity Score and boundary Dice Similarity Score of 0.81 and 0.81, respectively. We also measure the surface distance error using the 95th quantile of the Hausdorff Distance, which yields an average of 3.01 mm. Comparison with the manual cranial implant design procedure also revealed the convenience of the proposed workflow. In addition, a plugin is developed for 3D Slicer, which implements the proposed automatic cranial implant design workflow and can facilitate the end-users.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"2815-2826"},"PeriodicalIF":2.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143992388","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}

引用次数: 0

Incorporating frequency domain features into radiomics for improved prognosis of esophageal cancer. 将频域特征纳入食管癌放射组学以改善预后。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-09-01 Epub Date: 2025-04-10 DOI: 10.1007/s11517-025-03356-4

Shu Chen, Shumin Zhou, Liyang Wu, Shuchao Chen, Shanshan Liu, Haojiang Li, Guangying Ruan, Lizhi Liu, Hongbo Chen

{"title":"Incorporating frequency domain features into radiomics for improved prognosis of esophageal cancer.","authors":"Shu Chen, Shumin Zhou, Liyang Wu, Shuchao Chen, Shanshan Liu, Haojiang Li, Guangying Ruan, Lizhi Liu, Hongbo Chen","doi":"10.1007/s11517-025-03356-4","DOIUrl":"10.1007/s11517-025-03356-4","url":null,"abstract":"Esophageal cancer is a highly aggressive gastrointestinal malignancy with a poor prognosis, making accurate prognostic assessment essential for patient care. The performance of the esophageal cancer prognosis model based on conventional radiomics is limited, as it mainly characterizes the spatial features such as texture of the tumor area, and cannot fully describe the complexity of esophageal cancer tumors. Therefore, we incorporate the frequency domain features into radiomics to improve the prognostic ability of esophageal cancer. Three hundred fifteen esophageal cancer patients participated in the death risk prediction experiment, with 80% being the training set and 20% being the testing set. We use fivefold cross validation for training, and fuse the 5 trained models through voting to obtain the final prognostic model for testing. The CatBoost achieved the best performance compared to machine learning methods such as random forests and decision tree. The experimental results showed that the combination of frequency domain and radiomics features achieved the highest performance in death predicting esophageal cancer (accuracy: 0.7423, precision: 0.7470, recall: 0.7375, specification: 0.8030, AUC: 0.8487), which was significantly better than the performance of frequency domain or radiomics features alone. The results of Kaplan-Meier survival analysis validated the performance of our method in death predicting esophageal cancer. The proposed method provides technical support for accurate prognosis of esophageal cancer.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"2753-2765"},"PeriodicalIF":2.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144006172","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}

引用次数: 0

Assessing detector stability and image quality of thermal cameras on smartphones for medical applications: a comparative study. 评估用于医疗应用的智能手机热像仪的探测器稳定性和图像质量：一项比较研究。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-09-01 Epub Date: 2025-04-04 DOI: 10.1007/s11517-025-03348-4

Vladan Bernard, Erik Staffa, Jana Pokorná, Adam Šimo

{"title":"Assessing detector stability and image quality of thermal cameras on smartphones for medical applications: a comparative study.","authors":"Vladan Bernard, Erik Staffa, Jana Pokorná, Adam Šimo","doi":"10.1007/s11517-025-03348-4","DOIUrl":"10.1007/s11517-025-03348-4","url":null,"abstract":"Introduction: Infrared thermography (IRT) has gained significant interest in medical applications for its potential in diagnosing various conditions. Smartphone-based IRT modules offer portability and affordability, leading to increased utilization in medical settings. However, differences in performance among these modules raise questions about their reliability for medical use.Materials and methods: This study compared three smartphone-based IRT modules (SmartIRT-Hikmicro, FLIR One Pro, and Seek Thermal CompactPRO-which, according to their datasheets, exhibit comparable quality and parameters. Temperature stability, surface temperature of the body, and spatial uniformity of provided images were assessed using calibrated black body measurements and surface temperature monitoring.Results: The Hikmicro module exhibited the most stable temperature readings, while FLIR One Pro showed the highest temperature increase over time. Seek Thermal CompactPRO demonstrated relatively better spatial uniformity. However, discrepancies in image resolution were noted, with FLIR One and Seek modules modifying image sizes through post-processing algorithms.Conclusion: While SmartIRT modules offer affordability and portability, their performance varies significantly. Temporal stability emerges as a critical factor, with the Hikmicro module demonstrating leadership in this aspect. Careful consideration and validation are necessary when selecting and utilizing SmartIRT modules for medical applications.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"2707-2715"},"PeriodicalIF":2.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12402008/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143781336","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}

引用次数: 0

Digital twin-enabled cardiovascular stent optimization: a virtual reality-driven approach to mitigate angioplasty-related deformations. 数字双启用心血管支架优化：虚拟现实驱动的方法，以减轻血管成形术相关的变形。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-09-01 Epub Date: 2025-03-26 DOI: 10.1007/s11517-025-03352-8

Juanyi Wang, Yi Zhang, Yang Yang, Xiao Wen, Xiaodong Xing, Zhiyong Zhang, Chen Dong

{"title":"Digital twin-enabled cardiovascular stent optimization: a virtual reality-driven approach to mitigate angioplasty-related deformations.","authors":"Juanyi Wang, Yi Zhang, Yang Yang, Xiao Wen, Xiaodong Xing, Zhiyong Zhang, Chen Dong","doi":"10.1007/s11517-025-03352-8","DOIUrl":"10.1007/s11517-025-03352-8","url":null,"abstract":"Percutaneous transluminal angioplasty with stenting is extensively applied for treatment of atherosclerosis. However, the effects of dogboning (db), foreshortening (fs), longitudinal recoil (lr) and radial recoil (Rr) usually occur to inflict damage to the artery and make the positioning difficult during the cardiovascular stent (CS) expansion to the maximum and after the inflated balloon removing. In the article, the design and manufacture of a CS were carried out based on digital twin (DT) technology rather than traditional expertise- and experience-based methods. The highly kinetic model of a CS was firstly derived from its upfront proposed geometric configuration, governing equations of solid mechanics and boundary conditions to construct its DT through virtual reality (VR). Then global sensitivity analysis (GSA) and dynamic response optimization (DRO) was implemented to optimize the material and processing parameters including Young's modulus (E), isotropic tangent modulus (Et), Poisson's ratio (ν), density (ρ) and initial yield stress (σ), in order to obtain a satisfied behavior requirements for effects of db, fs, lr and Rr. The prototype experiment result showed that the CS made of shape memory Nitinol with optimal material and processing parameters (ρ = 7050 kg m-3, ν = 0.27, E = 205 GPa, Et = 675.13 MPa and σ = 198.49 MPa) obtained from its digital twin through VR simulation could have desired behavior performance characteristics, such as weak effect of db and fs during the CS expansion to the maximum, and lr (-0.9%), distal Rr (0.4%) and central Rr (0.7%) after the inflated balloon removing.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"2665-2678"},"PeriodicalIF":2.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143712009","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}

引用次数: 0

A new parallel-path ConvMixer neural network for predicting neurodegenerative diseases from gait analysis. 从步态分析预测神经退行性疾病的新型并行路径 ConvMixer 神经网络。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-09-01 Epub Date: 2025-03-15 DOI: 10.1007/s11517-025-03334-w

Jihen Fourati, Mohamed Othmani, Khawla Ben Salah, Hela Ltifi

{"title":"A new parallel-path ConvMixer neural network for predicting neurodegenerative diseases from gait analysis.","authors":"Jihen Fourati, Mohamed Othmani, Khawla Ben Salah, Hela Ltifi","doi":"10.1007/s11517-025-03334-w","DOIUrl":"10.1007/s11517-025-03334-w","url":null,"abstract":"Neurodegenerative disorders (NDD) represent a broad spectrum of diseases that progressively impact neurological function, yet available therapeutics remain conspicuously limited. They lead to altered rhythms and dynamics of walking, which are evident in the sequential footfall contact times measured from one stride to the next. Early detection of aberrant walking patterns can prevent the progression of risks associated with neurodegenerative diseases, enabling timely intervention and management. In this study, we propose a new methodology based on a parallel-path ConvMixer neural network for neurodegenerative disease classification from gait analysis. Earlier research in this field depended on either gait parameter-derived features or the ground reaction force signal. This study has emerged to combine both ground reaction force signals and extracted features to improve gait pattern analysis. The study is being carried out on the gait dynamics in the NDD database, i.e., on the benchmark dataset Physionet gaitndd. Leave one out cross-validation is carried out. The proposed model achieved the best average rates of accuracy, precision, recall, and an F1-score of 97.77 <math><mo>%</mo></math> , 96.37 <math><mo>%</mo></math> , 96.5 <math><mo>%</mo></math> , and 96.25 <math><mo>%</mo></math> , respectively. The experimental findings demonstrate that our approach outperforms the best results achieved by other state-of-the-art methods.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"2601-2616"},"PeriodicalIF":2.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143634904","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}

引用次数: 0

Enhancing breast cancer diagnosis: transfer learning on DenseNet with neural hashing for histopathology fine-grained image classification. 增强乳腺癌诊断：基于DenseNet的迁移学习与神经散列的组织病理学细粒度图像分类。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-09-01 Epub Date: 2025-04-06 DOI: 10.1007/s11517-025-03346-6

Fatemeh Taheri, Kambiz Rahbar

{"title":"Enhancing breast cancer diagnosis: transfer learning on DenseNet with neural hashing for histopathology fine-grained image classification.","authors":"Fatemeh Taheri, Kambiz Rahbar","doi":"10.1007/s11517-025-03346-6","DOIUrl":"10.1007/s11517-025-03346-6","url":null,"abstract":"Breast cancer is one of the most common types of cancer worldwide. The number of breast cancer cases highlights the importance of disease management at various levels. One complementary method for breast cancer classification is microscopic imaging. Manual histopathological image analysis is time-consuming and prone to human errors. Computer-aided diagnosis (CAD) has emerged as a popular and feasible solution for analyzing medical images due to extensive advancements. Microscopic image analysis can assist physicians in more accurate diagnosis. However, the performance of CAD models needs improvement for practical purposes. In the proposed approach, a baseline model called DenseNet is considered for extracting features from histopathological images. The pre-trained DenseNet model alone is not sufficient for fine-grained feature discrimination between benign and malignant histopathological image samples. Therefore, two hash layers are incorporated at the end of the network to enhance feature separability of the two classes, benign and malignant. The performance of the proposed method is evaluated on the BreakHis histopathological image dataset, with magnifications of 40 × , 100 × , 200 × , and 400 × . The evaluation results confirm the effectiveness of the proposed approach compared to other existing approaches. Furthermore, the interpretability of the proposed approach is demonstrated using the LIME technique.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"2717-2731"},"PeriodicalIF":2.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143796846","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}

引用次数: 0

Attention-guided deep framework for polyp localization and subsequent classification via polyp local and Siamese feature fusion. 通过息肉局部特征和暹罗特征融合进行息肉定位和分类的注意引导深度框架。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-09-01 Epub Date: 2025-05-02 DOI: 10.1007/s11517-025-03369-z

Pradipta Sasmal, Susant Kumar Panigrahi, Swarna Laxmi Panda, M K Bhuyan

{"title":"Attention-guided deep framework for polyp localization and subsequent classification via polyp local and Siamese feature fusion.","authors":"Pradipta Sasmal, Susant Kumar Panigrahi, Swarna Laxmi Panda, M K Bhuyan","doi":"10.1007/s11517-025-03369-z","DOIUrl":"10.1007/s11517-025-03369-z","url":null,"abstract":"Colorectal cancer (CRC) is one of the leading causes of death worldwide. This paper proposes an automated diagnostic technique to detect, localize, and classify polyps in colonoscopy video frames. The proposed model adopts the deep YOLOv4 model that incorporates both spatial and contextual information in the form of spatial attention and channel attention blocks, respectively for better localization of polyps. Finally, leveraging a fusion of deep and handcrafted features, the detected polyps are classified as adenoma or non-adenoma. Polyp shape and texture are essential features in discriminating polyp types. Therefore, the proposed work utilizes a pyramid histogram of oriented gradient (PHOG) and embedding features learned via triplet Siamese architecture to extract these features. The PHOG extracts local shape information from each polyp class, whereas the Siamese network extracts intra-polyp discriminating features. The individual and cross-database performances on two databases suggest the robustness of our method in polyp localization. The competitive analysis based on significant clinical parameters with current state-of-the-art methods confirms that our method can be used for automated polyp localization in both real-time and offline colonoscopic video frames. Our method provides an average precision of 0.8971 and 0.9171 and an F1 score of 0.8869 and 0.8812 for the Kvasir-SEG and SUN databases. Similarly, the proposed classification framework for the detected polyps yields a classification accuracy of 96.66% on a publicly available UCI colonoscopy video dataset. Moreover, the classification framework provides an F1 score of 96.54% that validates the potential of the proposed framework in polyp localization and classification.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"2795-2814"},"PeriodicalIF":2.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144057224","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}

引用次数: 0

Automated detection of retinal artery occlusion in fundus photography via self-supervised deep learning and multimodal interpretability using a multimodal AI chatbot. 基于自监督深度学习和多模态可解释性的眼底摄影中视网膜动脉闭塞的自动检测

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-09-01 Epub Date: 2025-03-31 DOI: 10.1007/s11517-025-03353-7

Sun Young Ryu, Joon Yul Choi, Tae Keun Yoo

{"title":"Automated detection of retinal artery occlusion in fundus photography via self-supervised deep learning and multimodal interpretability using a multimodal AI chatbot.","authors":"Sun Young Ryu, Joon Yul Choi, Tae Keun Yoo","doi":"10.1007/s11517-025-03353-7","DOIUrl":"10.1007/s11517-025-03353-7","url":null,"abstract":"Retinal artery occlusion (RAO) is a sight-threatening condition that requires prompt diagnosis to prevent irreversible vision loss. This study presents an innovative AI-driven approach for RAO detection from fundus images, marking the first application of deep learning for this purpose. Using a self-supervised learning (SSL) framework with SimCLR, our model addresses the challenge of limited labeled RAO data. The ResNet50 model pretrained with SimCLR demonstrated high diagnostic accuracy, achieving areas under the receiver operating characteristic curve (AUC) of 0.924 and 0.988 on two external validation datasets, highlighting its robustness and generalizability in RAO detection. To enhance transparency in clinical AI, we incorporated a multimodal interpretability approach using a ChatGPT-4-based AI chatbot. This chatbot, combined with Grad-CAM visualizations, provides detailed clinical explanations of the model's predictions, emphasizing key RAO features such as retinal whitening and cherry-red spots. This multimodal interpretability framework improves clinicians' understanding of the model's decision-making process, facilitating clinical adoption and trust. By automating RAO detection, this AI model serves as a valuable tool for the early identification of ocular and systemic vascular risks, enabling timely intervention. These findings highlight the potential of fundus imaging for RAO detection and broader cardiovascular risk assessment, advancing AI's role in predictive healthcare.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"2679-2691"},"PeriodicalIF":2.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143755557","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}

引用次数: 0

Correction to: Hip implant performance prediction by acoustic emission techniques: a review. 更正：通过声发射技术预测髋关节植入物性能：综述。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-09-01 DOI: 10.1007/s11517-024-03164-2

Ampadi R Remya, B Vishwash, Christine Lee, P Srinivasa Pai, Alejandro A Espinoza Orías, Didem Ozevin, Mathew T Mathew

引用次数: 0

Advances in oxygen-releasing matrices for regenerative engineering applications. 再生工程中氧释放基质的研究进展。

IF 2.6 4区医学

Medical & Biological Engineering & Computing Pub Date : 2025-09-01 Epub Date: 2025-04-04 DOI: 10.1007/s11517-025-03354-6

Yihong Zeng, Can Yan, Guobao Chen, Zhongmin Chen, Fuping Wang

{"title":"Advances in oxygen-releasing matrices for regenerative engineering applications.","authors":"Yihong Zeng, Can Yan, Guobao Chen, Zhongmin Chen, Fuping Wang","doi":"10.1007/s11517-025-03354-6","DOIUrl":"10.1007/s11517-025-03354-6","url":null,"abstract":"In recent years, the effects of hypoxia on tissue repair have received wider attention with the deepening of tissue engineering research. Various oxygen supply strategies have wider applications in the field of tissue repair. Currently, commonly used methods of oxygen supply for defective tissues include hyperbaric oxygen (HBO) and oxygen-releasing materials. Between them, oxygen-releasing materials continuously and efficiently release oxygen from within the defective tissue. Compared with HBO, which may cause oxidative stress in healthy tissues, supplying oxygen via oxygen-releasing materials is safer because of their oxygen-releasing in situ and specific oxygen supply characteristics. However, there still exist some problems in the study of oxygen-releasing materials, such as cytotoxicity and the shortage of oxygen-releasing time. The current reviews on oxygen-releasing materials mostly elaborate on the principles of oxygen-releasing materials and lack a review of their preparation methods and applications. In this paper, different types of oxygen-releasing materials, such as hydrogels, microspheres, and layers, are reviewed concerning their applications, structures, current development status, and challenges.","PeriodicalId":49840,"journal":{"name":"Medical & Biological Engineering & Computing","volume":" ","pages":"2537-2552"},"PeriodicalIF":2.6,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143781330","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}

引用次数: 0