Artificial Intelligence in Medicine最新文献

{"title":"ECG synthesis for cardiac arrhythmias: Integrating self-supervised learning and generative adversarial networks","authors":"Lorenzo Simone,&nbsp;Davide Bacciu,&nbsp;Vincenzo Gervasi","doi":"10.1016/j.artmed.2025.103162","DOIUrl":"10.1016/j.artmed.2025.103162","url":null,"abstract":"<div><div>Arrhythmia classifiers relying on supervised deep learning models usually require a substantial amount of labeled clinical data. The distribution of these labels is strictly related to the statistics of cardiovascular diseases among the population, which inherently narrows models’ performance for classification tasks. Furthermore, during acquisition and data retrieval from electronic health records, concerns arise regarding patient anonymization due to stringent clinical policies. We introduce a conditional generative architecture for electrocardiography time series, which integrates self-supervision and generative adversarial principles. Empirical validation confirms the enhancement of morphological plausibility in synthetic data, showcasing its effectiveness in generating realistic signals. We propose a novel model (ECGAN), proving its capability of conditioning the probability distribution of ECG recordings. The proposed methodology is assessed upon various rhythm abnormalities including severe congestive heart failure, myocardial infarction, sinus rhythm, and premature ventricular contractions. Our proposed workflow for synthetic time series assessment demonstrates competitive performance compared to state-of-the-art models, achieving an average improvement of 2.4% in arrhythmia classification accuracy across MIT-BIH, BIDMC, and PTB datasets, while ensuring realistic synthetic data and improving training stability.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"167 ","pages":"Article 103162"},"PeriodicalIF":6.1,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144190477","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":"Symbolic and hybrid AI for brain tissue segmentation using spatial model checking","authors":"Gina Belmonte ,&nbsp;Vincenzo Ciancia ,&nbsp;Mieke Massink","doi":"10.1016/j.artmed.2025.103154","DOIUrl":"10.1016/j.artmed.2025.103154","url":null,"abstract":"<div><div>Segmentation of 3D medical images, and brain segmentation in particular, is an important topic in neuroimaging and in radiotherapy. Overcoming the current, time consuming, practise of manual delineation of brain tumours and providing an accurate, explainable, and replicable method of segmentation of the tumour area and related tissues is therefore an open research challenge.</div><div>In this paper, we first propose a novel symbolic approach to brain segmentation and delineation of brain lesions based on <em>spatial model checking</em>. This method has its foundations in the theory of closure spaces, a generalisation of topological spaces, and spatial logics. At its core is a high-level declarative logic language for image analysis, ImgQL, and an efficient spatial model checker, VoxLogicA, exploiting state-of-the-art image analysis libraries in its model checking algorithm. We then illustrate how this technique can be combined with Machine Learning techniques leading to a hybrid AI approach that provides accurate and explainable segmentation results.</div><div>We show the results of the application of the symbolic approach on several public datasets with 3D magnetic resonance (MR) images. Three datasets are provided by the 2017, 2019 and 2020 international MICCAI BraTS Challenges with 210, 259 and 293 MR images, respectively, and the fourth is the BrainWeb dataset with 20 (synthetic) 3D patient images of the normal brain. We then apply the hybrid AI method to the BraTS 2020 training set. Our segmentation results are shown to be in line with the state-of-the-art with respect to other recent approaches, both from the accuracy point of view as well as from the view of computational efficiency, but with the advantage of them being explainable.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"167 ","pages":"Article 103154"},"PeriodicalIF":6.1,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144169827","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":"Integrated analysis of differential intra-chromosomal community interactions: A study of breast cancer","authors":"Zhihao Yao ,&nbsp;Kun Fang ,&nbsp;Gege Liu ,&nbsp;Magnar Bjørås ,&nbsp;Victor X. Jin ,&nbsp;Junbai Wang","doi":"10.1016/j.artmed.2025.103180","DOIUrl":"10.1016/j.artmed.2025.103180","url":null,"abstract":"<div><div>It is challenging to analyze the dynamics of intra-chromosomal interactions when considering multiple high-dimensional epigenetic datasets. A computational approach, differential network analysis in intra-chromosomal community interaction (DNAICI), was proposed here to elucidate these dynamics by integrating Hi-C data with other epigenetic data. DNAICI utilized a novel hyperparameter tuning method, for optimizing the network clustering, to identify valid intra-chromosomal community interactions at different resolutions. The approach was first trained on Hi-C data and other epigenetic data in an untreated and one hour estrogen (E2)-treated breast cancer cell line, MCF7, and uncovered two major types of valid intra-chromosomal community interactions (active/repressive) that resembles the properties of A/B compartments (or open/closed chromatin domains). It was further tested on the breast cancer cell line MCF7 and its corresponding tamoxifen-resistant (TR) derivative, MCF7TR, and identified 515 differentially interacting and expressed genes (DIEGs) within intra-chromosomal community interactions. In silico analysis of these DIEGs revealed that endocrine resistance is among the top biological pathways, suggesting an interacting/looping-mediated mechanism in regulating breast cancer tamoxifen resistance. This novel integrated network analysis approach offers a broad application in diverse biological systems for identifying a biological-context-specific differential community interaction.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"167 ","pages":"Article 103180"},"PeriodicalIF":6.1,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144169168","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":"From black box to clarity: Strategies for effective AI informed consent in healthcare","authors":"M. Chau ,&nbsp;M.G. Rahman ,&nbsp;T. Debnath","doi":"10.1016/j.artmed.2025.103169","DOIUrl":"10.1016/j.artmed.2025.103169","url":null,"abstract":"<div><h3>Background</h3><div>Informed consent is fundamental to ethical medical practice, ensuring that patients understand the procedures they undergo, the associated risks, and available alternatives. The advent of artificial intelligence (AI) in healthcare, particularly in diagnostics, introduces complexities that traditional informed consent forms do not adequately address. AI technologies, such as image analysis and decision-support systems, offer significant benefits but also raise ethical, legal, and practical concerns regarding patient information and autonomy.</div></div><div><h3>Main body</h3><div>The integration of AI in healthcare diagnostics necessitates a re-evaluation of current informed consent practices to ensure that patients are fully aware of AI's role, capabilities, and limitations in their care. Existing standards, such as those in the UK's National Health Service and the US, highlight the need for transparency and patient understanding but often fall short when applied to AI. The “black box” phenomenon, where the inner workings of AI systems are not transparent, poses a significant challenge. This lack of transparency can lead to over-reliance or distrust in AI tools by clinicians and patients alike. Additionally, the current informed consent process often fails to provide detailed explanations about AI algorithms, the data they use, and inherent biases. There is also a notable gap in the training and education of healthcare professionals on AI technologies, which impacts their ability to communicate effectively with patients. Ethical and legal considerations, including data privacy and algorithmic fairness, are frequently inadequately addressed in consent forms. Furthermore, integrating AI into clinical workflows presents practical challenges that require careful planning and robust support systems.</div></div><div><h3>Conclusion</h3><div>This review proposes strategies for redesigning informed consent forms. These include using plain language, visual aids, and personalised information to improve patient understanding and trust. Implementing continuous monitoring and feedback mechanisms can ensure the ongoing effectiveness of these forms. Future research should focus on developing comprehensive regulatory frameworks and enhancing communication techniques to convey complex AI concepts to patients. By improving informed consent practices, we can uphold ethical standards, foster patient trust, and support the responsible integration of AI in healthcare, ultimately benefiting both patients and healthcare providers.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"167 ","pages":"Article 103169"},"PeriodicalIF":6.1,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144169829","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":"Creating, anonymizing and evaluating the first medical language model pre-trained on Dutch Electronic Health Records: MedRoBERTa.nl","authors":"Stella Verkijk,&nbsp;Piek Vossen","doi":"10.1016/j.artmed.2025.103148","DOIUrl":"10.1016/j.artmed.2025.103148","url":null,"abstract":"<div><div>Electronic Health Records (EHRs) contain written notes by all kinds of medical professionals about all aspects of well-being of a patient. When adequately processed with a Large Language Model (LLM), this enormous source of information can be analyzed quantitatively, which can lead to new insights, for example in treatment development or in patterns of patient recovery. However, the language used in clinical notes is very idiosyncratic, which available generic LLMs have not encountered in their pre-training. They therefore have not internalized an adequate representation of the semantics of this data, which is essential for building reliable Natural Language Processing (NLP) software. This article describes the development of the first domain-specific LLM for Dutch EHRs: MedRoBERTa.nl. We discuss in detail why and how we built our model, pre-training it on the notes in EHRs using different strategies, and how we were able to publish it publicly by thoroughly anonymizing it. We evaluate our model extensively, comparing it to various other LLMs. We also illustrate how our model can be used, discussing various studies that built medical text mining technology on top of our model.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"167 ","pages":"Article 103148"},"PeriodicalIF":6.1,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144205559","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":"Mitigating patient harm risks: A proposal of requirements for AI in healthcare","authors":"Juan M. Garcia-Gomez ,&nbsp;Vicent Blanes-Selva ,&nbsp;Celia Alvarez Romero ,&nbsp;José Carlos de Bartolomé Cenzano ,&nbsp;Felipe Pereira Mesquita ,&nbsp;Alejandro Pazos ,&nbsp;Ascensión Doñate-Martínez","doi":"10.1016/j.artmed.2025.103168","DOIUrl":"10.1016/j.artmed.2025.103168","url":null,"abstract":"<div><div>With the rise Artificial Intelligence (AI), mitigation strategies may be needed to integrate AI-enabled medical software responsibly, ensuring ethical alignment and patient safety. This study examines how to mitigate the key risks identified by the European Parliamentary Research Service (EPRS). For that, we discuss how complementary risk-mitigation requirements may ensure the main aspects of AI in Healthcare: Reliability - <em>Continuous performance evaluation, Continuous usability test, Encryption and use of field-tested libraries, Semantic interoperability</em> -, Transparency - <em>AI passport, eXplainable AI, Data quality assessment, Bias Check</em> -, Traceability - <em>User management, Audit trail, Review of cases</em>-, and Responsibility - <em>Regulation check, Academic use only disclaimer, Clinicians double check</em> -. A survey conducted among 216 Medical ICT professionals (medical doctors, ICT staff and complementary profiles) between March and June 2024 revealed these requirements were perceived positive by all profiles. Responders deemed <em>explainable AI</em> and <em>data quality assessment</em> essential for transparency; <em>audit trail</em> for traceability; and <em>regulatory compliance</em> and <em>clinician double check</em> for responsibility. Clinicians rated the following requirements more relevant (<em>p</em> &lt; 0.05) than technicians: continuous performance assessment, usability testing, encryption, AI passport, retrospective case review, and academic use check. Additionally, users found the AI passport more relevant for transparency than decision-makers (<em>p</em> &lt; 0.05). We trust that this proposal can serve as a starting point to endow the future AI systems in medical practice with requirements to ensure their ethical deployment.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"167 ","pages":"Article 103168"},"PeriodicalIF":6.1,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144169828","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":"Enhancing colorectal polyp segmentation with TCFMA-Net: A transformer-based cross feature and multi-attention network","authors":"Malik Abdul Manan ,&nbsp;Jinchao Feng ,&nbsp;Shahzad Ahmed ,&nbsp;Abdul Raheem","doi":"10.1016/j.artmed.2025.103167","DOIUrl":"10.1016/j.artmed.2025.103167","url":null,"abstract":"<div><div>To enhance polyp segmentation in colonoscopy images for early detection and diagnosis of colorectal cancer. The study proposed the Transformer-based cross feature multi-attention network (TCFMA-Net) for polyp segmentation by addressing challenges such as varying polyp sizes and the problem of accurate boundaries. TCFMA-Net utilizes swin transformer-based encoders, a cross-feature enhancer network with multiple cross-feature enhancer blocks, and multi-attention modules integrated within and outside the decoder blocks. This enables comprehensive cross-feature fusion, preserving image clarity and facilitating the flow of information, allowing efficient processing of both low-level and high-level features. TCFMA-Net effectively captures the complexities of polyp size variations and boundaries issues and consistently outperforms existing methods on six benchmark datasets with confidence interval (CI), achieving a Dice score of 92.74 ± 0.10, (CI: 91.92, 94.04), 91.46 ± 0.14 (CI: 91.12, 92.72), and 87.34 ± 0.13, (CI: 86.19, 88.10) on the CVC-ClinicDB, Kvasir-SEG and BKAI-IGH datasets respectively, demonstrating its robustness in diverse polyp segmentation tasks. Generalizability tests also yielded Dice scores of 89.51 ± 0.10, (CI: 88.67, 89.71), 72.91 ± 0.09, (CI: 71.39, 74.14), and 65.83 ± 0.22, (CI: 65.47, 66.52) on the CVC-300, CVC-ColonDB, and Polypgen databases respectively. TCFMA-Net demonstrates superior performance in segmenting polyps across datasets, effectively handling variations in polyp characteristics and demonstrating robust generalization capabilities. This study presents a significant advancement in polyp segmentation methods, offering an accurate and reliable tool for colorectal cancer diagnosis.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"167 ","pages":"Article 103167"},"PeriodicalIF":6.1,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144178215","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":"Causal inference model for accurate medical diagnosis in Coronary Artery Bypass Graft operation","authors":"Qiyi Zhang ,&nbsp;Wei Zhang ,&nbsp;Qiang Li ,&nbsp;Yunpeng Bai ,&nbsp;Weizhi Nie ,&nbsp;Keliang Xie","doi":"10.1016/j.artmed.2025.103150","DOIUrl":"10.1016/j.artmed.2025.103150","url":null,"abstract":"<div><div>Coronary Artery Bypass Grafting (CABG) is the most commonly performed cardiac surgery. Predicting postoperative complication risks for patients undergoing CABG is crucial for medical professionals. Considering the susceptibility of traditional models to confounding factors and the scarcity of medical data, it is necessary to design a model that can truly capture the cause-and-effect relationship between the disease and its underlying causes and achieve high accuracy even with limited data. In this paper, a novel Causal Inference Operation Risk Predictor (CIORP) is proposed. We construct a Structural Causal Model (SCM) that demonstrates how two confounders influence the model’s predictions. Then we utilize the backdoor adjustment strategy to control potential confounders from pre-operative information and non-causal intraoperative data. In parallel, capitalizing on few-shot learning techniques, we initiate pre-training using categories with ample samples to extract essential features. Subsequently, we fine-tuned our model on sparse sets of labeled data, facilitating accurate predictions in scenarios with limited annotated samples. The experimental outcomes demonstrate that our model surpasses most existing methods in the internal Electronic Health Record (EHR) of CABG patients, effectively predicting low cardiac output, new-onset atrial fibrillation, perioperative myocardial infarction, and cardiac arrest or ventricular fibrillation post-operation. Our work effectively mitigates the impact of confounding factors, allowing the model to make accurate predictions with minimal medical data.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"167 ","pages":"Article 103150"},"PeriodicalIF":6.1,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144116883","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":"Speech translation for multilingual medical education leveraged by large language models","authors":"Jorge Iranzo-Sánchez,&nbsp;Jaume Santamaría-Jordà,&nbsp;Gerard Mas-Mollà,&nbsp;Gonçal V. Garcés Díaz-Munío,&nbsp;Javier Iranzo-Sánchez,&nbsp;Javier Jorge,&nbsp;Joan Albert Silvestre-Cerdà,&nbsp;Adrià Giménez,&nbsp;Jorge Civera,&nbsp;Albert Sanchis,&nbsp;Alfons Juan","doi":"10.1016/j.artmed.2025.103147","DOIUrl":"10.1016/j.artmed.2025.103147","url":null,"abstract":"<div><div>The application of large language models (LLMs) to speech translation (ST) or, in general, to machine translation (MT) has recently provided excellent results, superseding conventional encoder–decoder MT systems in the general domain. However, this is not clearly the case when LLMs as MT systems are translating medical-related materials. In this respect, the provision of multilingual training materials for oncology professionals is a goal of the EU project Interact-Europe in which this work was framed. To this end, cross-language technology adapted to the oncology domain was developed, evaluated and deployed for multilingual interspecialty medical education. More precisely, automatic speech recognition (ASR) and MT models were adapted to the oncology domain to translate English pre-recorded training videos, kindly provided by the European School of Oncology (ESO), into French, Spanish, German and Slovene. In this work, three categories of MT models adapted to the medical domain were assessed: bilingual encoder–decoder MT models trained from scratch, pre-trained large multilingual encoder–decoder MT models, and multilingual decoder-only LLMs. The experimental results underline the competitiveness in translation quality of LLMs compared to encoder–decoder MT models. Finally, the ESO speech dataset, comprising roughly 1000 videos and 745 h for the training and evaluation of ASR, MT and ST models, was publicly released for the scientific community.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"166 ","pages":"Article 103147"},"PeriodicalIF":6.1,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144069762","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":"Surgery scheduling based on large language models","authors":"Fang Wan ,&nbsp;Tao Wang ,&nbsp;Kezhi Wang ,&nbsp;Yuanhang Si ,&nbsp;Julien Fondrevelle ,&nbsp;Shuimiao Du ,&nbsp;Antoine Duclos","doi":"10.1016/j.artmed.2025.103151","DOIUrl":"10.1016/j.artmed.2025.103151","url":null,"abstract":"<div><div>Large Language Models (LLMs) have shown remarkable potential in various fields. This study explores their application in solving multi-objective combinatorial optimization problems–surgery scheduling problem. Traditional multi-objective optimization algorithms, such as the Non-dominated Sorting Genetic Algorithm II (NSGA-II), often require domain expertise for designing precise operators. Here, we propose LLM-NSGA, where LLMs act as evolutionary optimizers, performing selection, crossover, and mutation operations. Results show that for 40 cases, LLMs can independently generate high-quality solutions from prompts. As problem size increases, LLM-NSGA outperformed traditional approaches like NSGA-II and MOEA/D, achieving average improvements of 5.39 %, 80 %, and 0.42 % in three objectives. While LLM-NSGA provided similar results to EoH, another LLM-based method, it outperformed EoH in overall resource allocation. Additionally, we applied LLMs for hyperparameter optimization, comparing them with Bayesian Optimization and Ant Colony Optimization (ACO). LLMs reduced runtime by an average of 23.68 %, and their generated parameters, validated with NSGA-II, produced better surgery scheduling solutions. This demonstrates that LLMs can not only help traditional algorithms find better solutions but also optimize their parameters efficiently.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"166 ","pages":"Article 103151"},"PeriodicalIF":6.1,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143929017","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}