IEEE Journal of Biomedical and Health Informatics最新文献

{"title":"EEG-Based Emotion Recognition Using Multi-Axis Adapter Transformer","authors":"Zhongmin Wang;Puchun Liu;Jie Zhang;Lang He;Yan He","doi":"10.1109/JBHI.2026.3672612","DOIUrl":"10.1109/JBHI.2026.3672612","url":null,"abstract":"Electroencephalography (EEG) signals are vital physiological indicators widely used for emotion recognition due to their high temporal resolution and direct measurement of brain activity. However, due to considerable inter-individual variability in EEG signal patterns, traditional methods have showed limited effectiveness in cross-subject emotion recognition tasks. Existing Transformer models for EEG emotion recognition often employ hybrid architectures to capture feature dependencies across multiple dimensions, however, these composite structures may result in insufficient information interaction and increased model complexity. To address these limitations, this paper proposes a multi-axis adapter transformer (MAAT) network, which leverages a unified Transformer framework to extract dependencies across frequency, channel, and temporal dimensions without relying on additional model components. Firstly, a multi-axis module is designed to replace the traditional multi-head attention module in the Transformer. This module captures dependencies across frequency, channel, and temporal dimensions, effectively modeling the complex and multi-faceted relationships within EEG signals. Secondly, adapter layers are integrated into the Transformer's feed-forward layers, which facilitate cross-subject transfer learning through fine-tuning. This design enables the model to adapt to new subjects with minimal parameter adjustments, improving generalization in cross-subject emotion recognition tasks. Experimental validations were conducted on the SEED, SEED-IV, and DEAP datasets. The MAAT model achieved high accuracy in both subject-dependent and subject-independent EEG emotion recognition tasks. These results confirm the model's effectiveness, robustness, and generalizability across diverse EEG datasets, outperforming existing methods in cross-subject recognition scenarios.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8519-8530"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147432630","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 1D Snoring Waveform and 2D Composite Acoustic Feature Graph-Based Multi-Modal Fusion Network for Obstructive Sites Recognition","authors":"Xia Hu;Rui Fang;Huiping Luo;Jingchun Luo;Chen Chen;Wei Chen","doi":"10.1109/JBHI.2026.3670208","DOIUrl":"10.1109/JBHI.2026.3670208","url":null,"abstract":"As a critical factor in diagnostic work-up and treatment decision-making process of sleep-related breathing disorders, accurate localization of obstructive sites in the upper airway is in dire need. Snoring, as a dynamic acoustic signal, carries informative information relating to the sites and degree of obstruction in the upper airway, offering a non-invasive, cost-effective solution for obstructive sites recognition. However, most of existing snoring-based methods for recognizing obstructive sites only involve limited information (either mainly concentrated on traditional acoustic characteristics or spectrogram features), which may omit dynamic pathological information. Moreover, existing methods proceed from either a one-dimensional (1D) signal or two-dimensional (2D) image perspective, where complementary information from the other modality may be overlooked. In this paper, a multi-modal framework, which combines 1D snoring waveform and 2D Composite Acoustic Feature Graph (CAF-Graph), is proposed. 1D snoring waveform perceives fine time structure and local patterns, aiming at learning high-level discriminative representations by neural networks. 2D CAF-Graph is dedicated to emphasizing dynamic spatio-temporal and physiological-acoustic characteristic of snoring, which concatenates acoustic features related to Prosodic, Formant, Spectral, and Cepstral characteristics. Further, a multi-modal fusion network (BMFNet) effectively integrates independent and interactive information between single-modal features, which offers a more comprehensive perspective. The recognition task was formulated as a three-class classification problem, including upper (snoring caused by upper-level obstruction), lower (snoring caused by lower-level obstruction), and silence (obstruction without snoring). The proposed method was validated on a clinical dataset collected in the ENT institute and Department of Otorhinolaryngology, Eye & ENT Hospital, Fudan University, where it achieved 81.2% Accuracy, 86.8% Weighted Average Precision, 81.2% Weighted Average Recall, and 82.3% Weighted Average F1-Score. Results exhibit the effectiveness of multi-modal feature representations for snoring, providing a novel insight for obstructive sites recognition tasks.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8392-8403"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147369625","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":"Human Visual Transduction Mechanism-Inspired Adaptive Enhancement Network During Endoscopy","authors":"Xinzhen Ren;Wenju Zhou;Maoyu Jin;Wei Liang;Yiyou Gao;Shengqiang Cai","doi":"10.1109/JBHI.2025.3594213","DOIUrl":"10.1109/JBHI.2025.3594213","url":null,"abstract":"Disposable. endoscopes are increasingly popular due to the lower risk of cross-contamination. Clearer endoscopes have shown the ability to improve adenoma detection rate. However, due to cost constraints, disposable endoscopes are equipped with smaller sensor arrays than reusable ones, resulting in lower resolution and degraded performance in both subjective and automated diagnostics. In this paper, an adaptive enhancement network (AEN) is proposed to recover the details of the lesion region, thereby assisting endoscopists during endoscopy. It combines the physiological mechanism of the human eyes with artificial intelligence. The framework of the AEN is analogous to the human visual transduction mechanism, which contains photoreceptor outer segments & outer nuclear layer, outer plexiform layer, inner nuclear layer, inner plexiform layer, and ganglion cell layer. The first layer includes the rod cell module (RCM) and the cone cell module (CCM). Rod cells are shape-sensitive and fast, while cone cells are sensitive to color and detail. Therefore, the RCM is designed to segment lesion regions and the CCM is designed to perform enhancement on them. The outer plexiform layer transmits the information from RCM to CCM. The inner nuclear layer refines boundaries according to the poisson equation with the information from the inner plexiform layer. Ultimately, a clearer image of the lesion regions is generated in the ganglion cell layer and transmitted to the brain (display). Experiments demonstrate that the AEN can effectively enhance lesion regions with much sharper edges, finer texture details, fewer blurs and artifacts. Comprehensive evaluations on four datasets further illustrate its generalizability. The AEN offers advantages in real-time processing.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8369-8379"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144764851","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":"Sub-Band Embedding Based EEG Spatio-Temporal Activity Representation for Emotion Recognition","authors":"Jianye Shi;Panfeng An;Wenying Duan;Hong Rao;Shengbo Chen","doi":"10.1109/JBHI.2026.3673733","DOIUrl":"10.1109/JBHI.2026.3673733","url":null,"abstract":"EEG-based emotion recognition is a crucial task with significant implications for mental health monitoring, affective computing, and clinical decision support. While Graph Neural Networks (GNNs) have shown promise in modeling the complex spatio-temporal dynamics of EEG signals, existing methods fall short by only considering pairwise spatial dependencies and neglecting critical higher-order associations among functionally related brain regions. Moreover, these approaches often fail to achieve frequency-specific temporal learning, leading to suboptimal results. To overcome these limitations, we propose the Sub-band Embedded Spatio-Temporal Network (SESTN), a novel framework that jointly models multi-frequency spatial and temporal dependencies. In SESTN, EEG features are first projected into sub-band embedding spaces, enabling finer-grained frequency-specific representation. Our approach then uses neurophysiological priors to construct weighted hyperedges, capturing intra-regional spatial relationships more effectively. A multi-head, Mamba-based temporal module is subsequently utilized to extract frequency-specific temporal dynamics. Finally, a graph convolutional fusion strategy integrates these features across different frequency bands. Extensive experiments on two widely used EEG emotion recognition datasets, SEED and SEED-IV, demonstrate that SESTN significantly improves classification performance in both subject-dependent and subject-independent scenarios. These results underscore SESTN's potential as a robust and effective framework for real-world biomedical applications, particularly in affective state monitoring and mental health assessment","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8542-8556"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147456740","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":"Pseudo Anomalies and Hard Sample Mining for Ventricular Arrhythmia Anomaly Detection","authors":"Haoyi Fan;Shaochen Gao;Han Han;Mingzhe Han;Bing Zhou;Zongmin Wang","doi":"10.1109/JBHI.2026.3676678","DOIUrl":"10.1109/JBHI.2026.3676678","url":null,"abstract":"Ventricular arrhythmias (VA) are among the most prevalent and clinically significant cardiac arrhythmias. Conventional detection methodologies predominantly employ supervised learning approaches that depend on precisely annotated training datasets. However, the morphological similarity between VA waveforms and noise artifacts poses a significant challenge for traditional algorithms in discriminating these clinically distinct categories. In this paper, we propose a novel anomaly detection framework named PHVA (<bold>P</b>seudo-data and <bold>H</b>ard-sample mining for VA detection) based on one-class anomaly detection, where only normal ECGs are used for training, while pseudo-anomalies are generated through self-supervised modules. These modules, including pseudo-anomaly generation and hard-sample mining, provide supervisory signals without requiring abnormal labels. Specifically, we introduce a physiology-aware synthetic ECG generation method that captures the beat morphology and waveform characteristics of VA while incorporating realistic noise simulations based on conventional noise models. These pseudo-annotated signals are then used to refine the decision boundary of a time-frequency hypersphere, constructed from normal ECG features in both temporal and spectral domains. Additionally, we employ triplet-loss-based hard-sample mining to improve the model's discriminative power for ventricular fibrillation detection. Extensive experiments on three public ECG datasets demonstrate that our proposed method PHVA achieves superior overall performance, outperforming state-of-the-art anomaly detection baselines by up to 8.7% in AUC (the Area Under a Receiver Operating Characteristic Curve).","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8884-8896"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147503716","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":"Point-Supervised Coronary Semantic Segmentation in X-Ray Angiographic Images","authors":"Ying Chen;Danni Ai;Jianyu Du;Yuanyuan Wang;Tianyu Fu;Deqiang Xiao;Yucong Lin;Long Shao;Yang Yu;Jian Yang","doi":"10.1109/JBHI.2026.3670247","DOIUrl":"10.1109/JBHI.2026.3670247","url":null,"abstract":"Coronary semantic segmentation in X-ray angiography is essential for computer-aided diagnosis and treatment planning of coronary artery disease (CAD). Despite its importance, this task remains highlychallenging due to the complex and interconnected vascular topology, as well as the similar visual characteristics among different branches, making dense pixel-level manual annotation difficult and labor-intensive. To alleviate this burden, we propose a point-supervised coronary semantic segmentation framework that significantly reduces annotation effort without compromising segmentation accuracy. The primary challenge of point label based supervision lies in the model's tendency to overfit sparse point labels, leading to limited generalization to pixel-level predictions. To enrich the supervision signals and stabilize the training process with the sparse point labels, we propose an adaptive foreground mask generation module and a region regularization strategy to ensure accurate semantic guidance while maximizing meaningful coverage of the vascular structures. To enhance coronary topology perception and branch differentiation, we propose a multi-task learning framework that jointly performs keypoint detection and coronary semantic segmentation through a shared feature extraction encoder and two task-specific decoders. The experimental results demonstrate that our point-supervised model achieves performance comparable to fully supervised model, and outperforms the existing state-of-the-art point-supervised semantic segmentation methods.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8710-8722"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147354893","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":"RenSeg: Leveraging Unsupervised Segmentation Using Localization and Contour-Guided Quickshift for Renal Calculi and Carcinoma Segmentation and Classification","authors":"Farhan Faruk;H. M. Sarwer Alam;Rafeed Rahman;Md. Golam Rabiul Alam;Junho Jeong;Md. Kabir Hossain;Jia Uddin","doi":"10.1109/JBHI.2025.3629580","DOIUrl":"10.1109/JBHI.2025.3629580","url":null,"abstract":"Chronic kidney diseases present a major global health challenge, often leading to renal failure due to delayed diagnosis and the shortage of nephrologists, which limits timely treatment. Therefore, the development of AI-driven systems for early detection is essential. However, most existing automated renal disease detection methods rely on supervised learning frameworks that require large amounts of clinically annotated data, which are often scarce and difficult to obtain. To address these challenges, this study proposed kidney localization and an unsupervised contour-guided quickshift-based automated segmentation method namely RenSeg on a dataset of 8,737 axial and coronal CT-scan images. RenSeg focuses on two critical diseases: Renal Calculi, which are highly prevalent and associated with recurrent pain and long-term kidney dysfunction, and Renal Carcinoma, a life-threatening tumor if not detected early. The analysis shows that unsupervised segmented images outperform manually annotated datasets. RenSeg achieved Dice scores of 0.9458 for calculi and 0.9309 for carcinoma, a precision of 0.95 and a recall of 0.94. MobileNetV2 reached the highest classification accuracy of 0.98 on RenSeg compared to 0.92 on manual annotations. The proposed unsupervised RenSeg approach outperformed the manually annotated approach across all three models in terms of accuracy, F1-score, and ROC-AUC, demonstrating superior generalization. This research demonstrates that optimizing the region of interest (ROI) enhances predictive accuracy while reducing annotation effort, making RenSeg a scalable solution for timely renal disease detection.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8218-8231"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145714113","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":"AI-Driven Quantitative Analysis of Pathological Images for Membranous Nephropathy Across Macro and Micro Modalities","authors":"Guangze Shi;Ying Wang;Yongfei Wu;Xueyu Liu;Jia Shen;Hao Meng;Yexin Lai;Weixia Han;Yan Hu;Mingqiang Wei","doi":"10.1109/JBHI.2025.3624618","DOIUrl":"10.1109/JBHI.2025.3624618","url":null,"abstract":"The diagnosis of membranous nephropathy (MN) has been reliant on the identification of glomerular basement membrane (GBM) variations and lesions at both macro and micro levels. At the macro level, light microscopy (LM) has been used to reveal spike-like projections that indicate pathological changes, whereas at the micro level, transmission electron microscopy (TEM) has been employed to identify GBM thickening. However, qualitative diagnosis has been limited by inter-pathologist variability, creating the need for deep learning approaches capable of quantifying pathological changes and predicting MN progression. In this study, an AI-driven framework based on the Mamba model has been proposed, in which the area and proportion of spike-like projections are quantified at the macro level, and GBM thickness is segmented and measured at the micro level. Classical machine learning models are then applied to predict MN progression based on pathological indicators extracted through factor analysis. Unlike prior approaches, the framework has been designed to emulate the diagnostic workflow of pathologists by integrating LM and TEM images for joint analysis. Experiments on an external dataset of 109 cases demonstrated strong performance in glomeruli classification, GBM segmentation, and MN progression prediction. These findings highlight the potential of multi-scale integrated quantification to provide objective, reproducible, and clinically interpretable assessment of MN progression.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8290-8301"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145596319","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 Framework for Classifying Whole-Slide Multiplex Immunofluorescence Images to Predict Immunotherapy Response in Melanoma Patients","authors":"Priyanka Rana;Tuba N Gide;Nurudeen A Adegoke;Yizhe Mao;Shlomo Berkovsky;Enrico Coiera;James S Wilmott;Sidong Liu","doi":"10.1109/JBHI.2025.3623576","DOIUrl":"10.1109/JBHI.2025.3623576","url":null,"abstract":"Immunotherapy has emerged as a prominent approach in melanoma treatment, however a substantial number of patients do not respond effectively. This highlights the critical need to accurately predict immunotherapy responses for designing personalised treatment strategies. Current practices rely predominantly on clinical data and the expertise of oncologists; however, a deeper understanding of molecular interactions through tissue-based biomarkers offers a promising avenue for advancement. Whole-slide multiplex immunofluorescence (mIF) images enable detailed analysis of cells/tissues in their microenvironment, deepening insights into disease mechanisms. However, numerous channels, an extensive image size, and spatially dispersed information of mIF images pose analytical challenges, requiring advanced techniques to effectively learn these intricate features for optimal performance. In this study, we introduce a novel deep-learning framework, Channel Optimisation with Multi-Instance Learning (COMIL), specifically designed to classify whole-slide mIF images for predicting immunotherapy response in melanoma patients. The study demonstrates that a feature extraction method that models inter-channel relationships and captures complex interdependencies among multiple channels of mIF images enhances classification performance. Additionally, incorporating this method within an MIL framework, optimised at both the slide and instance levels, further improves the classification performance of whole-slide mIF images. We evaluated COMIL on mIF images from the Melanoma Institute Australia, where it outperformed baseline methods with substantial improvements on both the internal (AUC increase by 28%) and external test sets (AUC increase by 20%), underscoring its potential for predicting immunotherapy response.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8342-8355"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11265862","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145596309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SASG-DA: Sparse-Aware Semantic-Guided Diffusion Augmentation for Myoelectric Gesture Recognition","authors":"Chen Liu;Can Han;Weishi Xu;Yaqi Wang;Dahong Qian","doi":"10.1109/JBHI.2026.3670388","DOIUrl":"10.1109/JBHI.2026.3670388","url":null,"abstract":"Surface electromyography (sEMG)-based gesture recognition plays a critical role in human–machine interaction (HMI), particularly for rehabilitation and prosthetic control. However, sEMG-based systems often suffer from the scarcity of informative training data, leading to overfitting and poor generalization in deep learning models. Data augmentation offers a promising approach to increasing the size and diversity of training data, where faithfulness and diversity are two critical factors to effectiveness. However, promoting untargeted diversity can result in redundant samples with limited utility. To address these challenges, we propose a novel diffusion-based data augmentation approach, Sparse-Aware Semantic-Guided Diffusion Augmentation (SASG-DA). To enhance generation faithfulness, we introduce the Semantic Representation Guidance (SRG) mechanism by leveraging fine-grained, task-aware semantic representations as generation conditions. To enable flexible and diverse sample generation, we propose a Gaussian Modeling Semantic Sampling (GMSS) strategy, which models the semantic representation distribution and allows stochastic sampling to produce both faithful and diverse samples. To enhance targeted diversity, we further introduce a Sparse-Aware Semantic Sampling strategy to explicitly explore underrepresented regions, improving distribution coverage and sample utility. Extensive experiments on benchmark sEMG datasets, Ninapro DB2, DB4, and DB7, demonstrate that SASG-DA significantly outperforms existing augmentation methods. Overall, our proposed data augmentation approach effectively mitigates overfitting and improves recognition performance and generalization by offering both faithful and diverse samples.","PeriodicalId":13073,"journal":{"name":"IEEE Journal of Biomedical and Health Informatics","volume":"30 10","pages":"8930-8943"},"PeriodicalIF":7.7,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147354872","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}