Computerized Medical Imaging and Graphics最新文献

{"title":"Twin-ViMReg: DXR driven synthetic dynamic Standing-CBCTs through Twin Vision Mamba-based 2D/3D registration.","authors":"Jiashun Wang, Hao Tang, Zhan Wu, Yikun Zhang, Yan Xi, Yang Chen, Chunfeng Yang, Yixin Zhou, Hui Tang","doi":"10.1016/j.compmedimag.2025.102648","DOIUrl":"https://doi.org/10.1016/j.compmedimag.2025.102648","url":null,"abstract":"<p><p>Medical imaging of the knee joint under physiological weight bearing is crucial for diagnosing and analyzing knee lesions. Existing modalities have limitations: Standing Cone-Beam Computed Tomography (Standing-CBCT) provides high-resolution 3D data but with long acquisition time and only a single static view, while Dynamic X-ray Imaging (DXR) captures continuous motion but lacks 3D structural information. These limitations motivate the need for dynamic 3D knee generation through 2D/3D registration of Standing-CBCT and DXR. Anatomically, although the femur, patella, and tibia-fibula undergo rigid motion, the joint as a whole exhibits non-rigid behavior. Consequently, existing rigid or non-rigid 2D/3D registration methods fail to fully address this scenario. We propose Twin-ViMReg, a twin-stream 2D/3D registration framework for multiple correlated objects in the knee joint. It extends conventional 2D/3D registration paradigm by establishing a pair of twined sub-tasks. By introducing a Multi-Objective Spatial Transformation (MOST) module, it models inter-object correlations and enhances registration robustness. The Vision Mamba-based encoder also strengthens the representation capacity of the method. We used 1,500 simulated data pairs from 10 patients for training and 56 real data pairs from 3 patients for testing. Quantitative evaluation shows that the mean TRE reached 3.36 mm, the RSR was 8.93% higher than the SOTA methods. With an average computation time of 1.22 s per X-ray image, Twin-ViMReg enables efficient 2D/3D knee joint registration within seconds, making it a practical and promising solution.</p>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"125 ","pages":"102648"},"PeriodicalIF":4.9,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145214148","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning for automatic vertebra analysis: A methodological survey of recent advances.","authors":"Zhuofan Xie, Zishan Lin, Enlong Sun, Fengyi Ding, Jie Qi, Shen Zhao","doi":"10.1016/j.compmedimag.2025.102652","DOIUrl":"https://doi.org/10.1016/j.compmedimag.2025.102652","url":null,"abstract":"<p><p>Automated vertebra analysis (AVA), encompassing vertebra detection and segmentation, plays a critical role in computer-aided diagnosis, surgical planning, and postoperative evaluation in spine-related clinical workflows. Despite notable progress, AVA continues to face key challenges, including variations in the field of view (FOV), complex vertebral morphology, limited availability of high-quality annotated data, and performance degradation under domain shifts. Over the past decade, numerous studies have employed deep learning (DL) to tackle these issues, introducing advanced network architectures and innovative learning paradigms. However, the rapid evolution of these methods has not been comprehensively captured by existing surveys, resulting in a knowledge gap regarding the current state of the field. To address this, this paper presents an up-to-date review that systematically summarizes recent advances. The review begins by consolidating publicly available datasets and evaluation metrics to support standardized benchmarking. Recent DL-based AVA approaches are then analyzed from two methodological perspectives: network architecture improvement and learning strategies design. Finally, an examination of persistent technical barriers and emerging clinical needs that are shaping future research directions is provided. These include multimodal learning, domain generalization, and the integration of foundation models. As the most current survey in the field, this review provides a comprehensive and structured synthesis aimed at guiding future research toward the development of robust, generalizable, and clinically deployable AVA systems in the era of intelligent medical imaging.</p>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"125 ","pages":"102652"},"PeriodicalIF":4.9,"publicationDate":"2025-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145208514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Collect vascular specimens in one cabinet: A hierarchical prompt-guided universal model for 3D vascular segmentation.","authors":"Yinuo Wang, Cai Meng, Zhe Xu","doi":"10.1016/j.compmedimag.2025.102650","DOIUrl":"https://doi.org/10.1016/j.compmedimag.2025.102650","url":null,"abstract":"<p><p>Accurate segmentation of vascular structures in volumetric medical images is critical for disease diagnosis and surgical planning. While deep neural networks have shown remarkable effectiveness, existing methods often rely on separate models tailored to specific modalities and anatomical regions, resulting in redundant parameters and limited generalization. Recent universal models address broader segmentation tasks but struggle with the unique challenges of vascular structures. To overcome these limitations, we first present VasBench, a new comprehensive vascular segmentation benchmark comprising nine sub-datasets spanning diverse modalities and anatomical regions. Building on this foundation, we introduce VasCab, a novel prompt-guided universal model for volumetric vascular segmentation, designed to \"collect vascular specimens in one cabinet\". Specifically, VasCab is equipped with learnable domain and topology prompts to capture shared and unique vascular characteristics across diverse data domains, complemented by morphology perceptual loss to address complex morphological variations. Experimental results demonstrate that VasCab surpasses individual models and state-of-the-art medical foundation models across all test datasets, showcasing exceptional cross-domain integration and precise modeling of vascular morphological variations. Moreover, VasCab exhibits robust performance in downstream tasks, underscoring its versatility and potential for unified vascular analysis. This study marks a significant step toward universal vascular segmentation, offering a promising solution for unified vascular analysis across heterogeneous datasets. Code and dataset are available at https://github.com/mileswyn/VasCab.</p>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"125 ","pages":"102650"},"PeriodicalIF":4.9,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145201977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SA²Net: Scale-adaptive structure-affinity transformation for spine segmentation from ultrasound volume projection imaging.","authors":"Hao Xie, Zixun Huang, Yushen Zuo, Yakun Ju, Frank H F Leung, N F Law, Kin-Man Lam, Yong-Ping Zheng, Sai Ho Ling","doi":"10.1016/j.compmedimag.2025.102649","DOIUrl":"https://doi.org/10.1016/j.compmedimag.2025.102649","url":null,"abstract":"<p><p>Spine segmentation, based on ultrasound volume projection imaging (VPI), plays a vital role for intelligent scoliosis diagnosis in clinical applications. However, this task faces several significant challenges. Firstly, the global contextual knowledge of spines may not be well-learned if we neglect the high spatial correlation of different bone features. Secondly, the spine bones contain rich structural knowledge regarding their shapes and positions, which deserves to be encoded into the segmentation process. To address these challenges, we propose a novel scale-adaptive structure-aware network (SA²Net) for effective spine segmentation. First, we propose a scale-adaptive complementary strategy to learn the cross-dimensional long-distance correlation features for spinal images. Second, motivated by the consistency between multi-head self-attention in Transformers and semantic level affinity, we propose structure-affinity transformation to transform semantic features with class-specific affinity and combine it with a Transformer decoder for structure-aware reasoning. In addition, we adopt a feature mixing loss aggregation method to enhance model training. This method improves the robustness and accuracy of the segmentation process. The experimental results demonstrate that our SA²Net achieves superior segmentation performance compared to other state-of-the-art methods. Moreover, the adaptability of SA²Net to various backbones enhances its potential as a promising tool for advanced scoliosis diagnosis using intelligent spinal image analysis.</p>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"125 ","pages":"102649"},"PeriodicalIF":4.9,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145193971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SGRRG: Leveraging radiology scene graphs for improved and abnormality-aware radiology report generation","authors":"Jun Wang ,&nbsp;Lixing Zhu ,&nbsp;Abhir Bhalerao ,&nbsp;Yulan He","doi":"10.1016/j.compmedimag.2025.102644","DOIUrl":"10.1016/j.compmedimag.2025.102644","url":null,"abstract":"<div><div>Radiology report generation (RRG) methods often lack sufficient medical knowledge to produce clinically accurate reports. A scene graph provides comprehensive information for describing objects within an image. However, automatically generated radiology scene graphs (RSG) may contain noise annotations and highly overlapping regions, posing challenges in utilizing RSG to enhance RRG. To this end, we propose Scene Graph aided RRG (SGRRG), a framework that leverages an automatically generated RSG and copes with noisy supervision problems in the RSG with a transformer-based module, effectively distilling medical knowledge in an end-to-end manner. SGRRG is composed of a dedicated scene graph encoder responsible for translating the radiography into a RSG, and a scene graph-aided decoder that takes advantage of both patch-level and region-level visual information and mitigates the noisy annotation problem in the RSG. The incorporation of both patch-level and region-level features, alongside the integration of the essential RSG construction modules, enhances our framework’s flexibility and robustness, enabling it to readily exploit prior advanced RRG techniques. A fine-grained, sentence-level attention method is designed to better distill the RSG information. Additionally, we introduce two proxy tasks to enhance the model’s ability to produce clinically accurate reports. Extensive experiments demonstrate that SGRRG outperforms previous state-of-the-art methods in report generation and can better capture abnormal findings. Code is available at <span><span>https://github.com/Markin-Wang/SGRRG</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"125 ","pages":"Article 102644"},"PeriodicalIF":4.9,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145103172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unveiling hidden risks: A Holistically-Driven Weak Supervision framework for ultra-short-term ACS prediction using CCTA","authors":"Zhen Liu ,&nbsp;Bangkang Fu ,&nbsp;Jiahui Mao ,&nbsp;Junjie He ,&nbsp;Jiangyue Xiang ,&nbsp;Hongjin Li ,&nbsp;Yunsong Peng ,&nbsp;Bangguo Li ,&nbsp;Rongpin Wang","doi":"10.1016/j.compmedimag.2025.102636","DOIUrl":"10.1016/j.compmedimag.2025.102636","url":null,"abstract":"<div><div>This paper proposes MH-STR, a novel end-to-end framework for predicting the three-month risk of Acute Coronary Syndrome (ACS) from Coronary CT Angiography (CCTA) images. The model combines hybrid attention mechanisms with convolutional networks to capture subtle and irregular lesion patterns that are difficult to detect visually. A stage-wise transfer learning strategy helps distill general features and transfer vascular-specific knowledge. To reconcile feature scale mismatches in the dual-branch architecture, we introduce a wavelet-based multi-scale fusion module for effective integration across scales. Experiments show that MH-STR achieves an AUC of 0.834, an F1 score of 0.82, and a precision of 0.92, outperforming existing methods and highlighting its potential for improving ACS risk prediction.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"125 ","pages":"Article 102636"},"PeriodicalIF":4.9,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145088019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A segmentation-based hierarchical feature interaction attention model for gene mutation status identification in colorectal cancer","authors":"Yu Miao ,&nbsp;Sijie Song ,&nbsp;Lin Zhao ,&nbsp;Jun Zhao ,&nbsp;Yingsen Wang ,&nbsp;Ran Gong ,&nbsp;Yan Qiang ,&nbsp;Hua Zhang ,&nbsp;Juanjuan Zhao","doi":"10.1016/j.compmedimag.2025.102646","DOIUrl":"10.1016/j.compmedimag.2025.102646","url":null,"abstract":"<div><div>Precise identification of Kirsten Rat Sarcoma (KRAS) gene mutation status is critical for both qualitative analysis of colorectal cancer and formulation of personalized therapeutic regimens. In this paper, we propose a Segmentation-based Hierarchical feature Interaction Attention Model (SHIAM) that synergizes multi-task learning with hierarchical feature integration, aiming to achieve accurate prediction of the KRAS gene mutation status. Specifically, we integrate segmentation and classification tasks, sharing feature representations between them. To fully focus on the lesion areas at different levels and their potential associations, we design a multi-level synergistic attention block that enables adaptive fusion of lesion characteristics of varying granularity with their contextual associations. To transcend the constraints of conventional methodologies in modeling long-range relationships, we design a global collaborative interaction attention module, an efficient improved long-range perception Transformer. As the core component of module, the long-range perception block provides robust support for mining feature integrity with its excellent perception ability. Furthermore, we introduce a hybrid feature engineering strategy that integrates hand-crafted features encoded as statistical information entropy with automatically learned deep representations, thereby establishing a complementary feature space. Our SHIAM has been rigorously trained and verified on the colorectal cancer dataset provided by Shanxi Cancer Hospital. The results show that it achieves an accuracy of 89.42% and an AUC value of 95.89% in KRAS gene mutation status prediction, with comprehensive performance superior to all current non-invasive assays. In clinical practice, our model possesses the capability to enable computer-aided diagnosis, effectively assisting physicians in formulating suitable personalized treatment plans for patients.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"125 ","pages":"Article 102646"},"PeriodicalIF":4.9,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145103150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A self-attention model for robust rigid slice-to-volume registration of functional MRI","authors":"Samah Khawaled ,&nbsp;Onur Afacan ,&nbsp;Simon K. Warfield ,&nbsp;Moti Freiman","doi":"10.1016/j.compmedimag.2025.102643","DOIUrl":"10.1016/j.compmedimag.2025.102643","url":null,"abstract":"<div><div>Functional Magnetic Resonance Imaging (fMRI) is vital in neuroscience, enabling investigations into brain disorders, treatment monitoring, and brain function mapping. However, head motion during fMRI scans, occurring between shots of slice acquisition, can result in distortion, biased analyses, and increased costs due to the need for scan repetitions. Therefore, retrospective slice-level motion correction through slice-to-volume registration (SVR) is crucial. Previous studies have utilized deep learning (DL) based models to address the SVR task; however, they overlooked the uncertainty stemming from the input stack of slices and did not assign weighting or scoring to each slice. Treating all slices equally ignores the variability in their relevance, leading to suboptimal predictions. In this work, we introduce an end-to-end SVR model for aligning 2D fMRI slices with a 3D reference volume, incorporating a self-attention mechanism to enhance robustness against input data variations and uncertainties. Our SVR model utilizes independent slice and volume encoders and a self-attention module to assign pixel-wise scores for each slice. We used the publicly available Healthy Brain Network (HBN) dataset. We split the volumes into training (64%), validation (16%), and test (20%) sets. To conduct the simulated motion study, we synthesized rigid transformations across a wide range of parameters and applied them to the reference volumes. Slices were then sampled according to the acquisition protocol to generate 2,000, 500, and 200 3D volume–2D slice pairs for the training, validation, and test sets, respectively. Our experimental results demonstrate that our model achieves competitive performance in terms of alignment accuracy compared to state-of-the-art deep learning-based methods (Euclidean distance of 0.93 [mm] vs. 1.86 [mm], a paired t-test with a <span><math><mi>p</mi></math></span>-value of <span><math><mrow><mi>p</mi><mo>&lt;</mo><mn>0</mn><mo>.</mo><mn>03</mn></mrow></math></span>). Furthermore, our approach exhibits faster registration speed compared to conventional iterative methods (0.096 s vs. 1.17 s). Our end-to-end SVR model facilitates real-time head motion tracking during fMRI acquisition, ensuring reliability and robustness against uncertainties in the inputs.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"125 ","pages":"Article 102643"},"PeriodicalIF":4.9,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145151729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mamba-based context-aware local feature network for vessel detail enhancement","authors":"Keyi Han ,&nbsp;Anqi Xiao ,&nbsp;Jie Tian ,&nbsp;Zhenhua Hu","doi":"10.1016/j.compmedimag.2025.102645","DOIUrl":"10.1016/j.compmedimag.2025.102645","url":null,"abstract":"<div><h3>Objective</h3><div>Blood vessel analysis is essential in various clinical fields. Detailed vascular imaging enables clinicians to assess abnormalities and make timely, effective interventions. Near-infrared-II (NIR-II, 1000–1700 nm) fluorescence imaging offers superior resolution, sensitivity, and deeper tissue visualization, making it highly promising for vascular imaging. However, deep vessels exhibit relatively low contrast, making differentiation challenging, and accurate vessel segmentation remains a difficult task.</div></div><div><h3>Methods</h3><div>We propose CALFNet, a context-aware local feature network based on the Mamba module, which can segment more vascular details in low-contrast regions. CALFNet overall follows a UNet-like architectures, with a ResNet-based encoder for extracting local features and a Mamba-based context-aware module in the latent space for the awareness of the global context. By incorporating the global vessel contextual information, the network can enhance segmentation performance in locally low-contrast areas, capturing finer vessel structures more effectively. Furthermore, a feature-enhance module between the encoder and decoder is designed to preserve critical historical local features from the encoder and use them to further refine the vascular details in the decoder's feature representations.</div></div><div><h3>Results</h3><div>We conducted experiments on two types of clinical datasets, including an NIR-II fluorescent vascular imaging dataset and retinal vessel datasets captured under visible light. The results show that CALFNet outperforms the comparison methods, demonstrating superior robustness and achieving more accurate vessel segmentation, particularly in low-contrast regions.</div></div><div><h3>Conclusion and Significance</h3><div>CALFNet is an effective vessel segmentation network showing better performance in accurately segmenting vessels within low-contrast regions. It can enhance the capability of NIR-II fluorescence imaging for vascular analysis, providing valuable support for clinical diagnosis and medical intervention.</div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"125 ","pages":"Article 102645"},"PeriodicalIF":4.9,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145118689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accurate and fast monocular endoscopic depth estimation of structure-content integrated diffusion","authors":"Min Tan ,&nbsp;Yushun Tao ,&nbsp;Boyun Zheng ,&nbsp;Gaosheng Xie ,&nbsp;Zeyang Xia ,&nbsp;Jing Xiong","doi":"10.1016/j.compmedimag.2025.102640","DOIUrl":"10.1016/j.compmedimag.2025.102640","url":null,"abstract":"<div><div>Endoscopic depth estimation is crucial for video understanding, robotic navigation, and 3D reconstruction in minimally invasive surgeries. However, existing methods for monocular depth estimation often struggle with the challenging conditions of endoscopic imagery, such as complex illumination, narrow luminal spaces, and low-contrast surfaces, resulting in inaccurate depth predictions. To address these challenges, we propose the Structure-Content Integrated Diffusion Estimation (SCIDE) for accurate and fast endoscopic depth estimation. Specifically, we introduce the Structure Content Extractor (SC-Extractor), a module specifically designed to extract structure and content priors to guide the depth estimation process in endoscopic environments. Additionally, we propose the Fast Optimized Diffusion Sampler (FODS) to meet the real-time needs in endoscopic surgery scenarios. FODS is a general sampling mechanism that optimizes selection of time steps in diffusion models. Our method (SCIDE) shows remarkable performance with an RMSE value of 0.0875 and a reduction of 74.2% in inference time when using FODS. These results demonstrate that our SCIDE framework achieves state-of-the-art accuracy of endoscopic depth estimation, and making real-time application feasible in endoscopic surgeries. <span><span>https://misrobotx.github.io/scide/</span><svg><path></path></svg></span></div></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":"125 ","pages":"Article 102640"},"PeriodicalIF":4.9,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145026858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}