Artificial Intelligence in Medicine最新文献

{"title":"Improving prototypical parts abstraction for case-based reasoning explanations designed for the kidney stone type recognition","authors":"Daniel Flores-Araiza ,&nbsp;Francisco Lopez-Tiro ,&nbsp;Clément Larose ,&nbsp;Salvador Hinojosa ,&nbsp;Andres Mendez-Vazquez ,&nbsp;Miguel Gonzalez-Mendoza ,&nbsp;Gilberto Ochoa-Ruiz ,&nbsp;Christian Daul","doi":"10.1016/j.artmed.2025.103266","DOIUrl":"10.1016/j.artmed.2025.103266","url":null,"abstract":"<div><div>The in-vivo identification of the kidney stone types during an ureteroscopy would be a major medical advance in urology, as it could reduce the time of the tedious renal calculi extraction process, while diminishing infection risks. Furthermore, such an automated procedure would make possible to prescribe anti-recurrence treatments immediately. Nowadays, only few experienced urologists are able to recognize the kidney stone types in the images of the videos displayed on a screen during the endoscopy. This visual recognition by urologists is also highly operator dependent. Thus, several deep learning (DL) models have recently been proposed to automatically recognize the kidney stone types using ureteroscopic images. However, these DL models are of black box nature and do not establish the relationship of the visual features they used to take the decision with the color, texture and morphological features visually analyzed in biological laboratories to determine the type of extracted kidney stone fragments using the reference morphoconstitutional analysis (MCA) procedure. This contribution proposes a case-based reasoning DLmodel which uses prototypical parts (PPs) and generates local and global descriptors. The PPs encode for each class (i.e., kidney stone type) visual feature information (hue, saturation, intensity and textures) similar to that used by biologists during MCA. The PPs are optimally generated due a new loss function used during the model training. Moreover, the local and global descriptors of PPs allow to explain the decisions (“what” information, “where in the images”) in an understandable way for biologists and urologists. The proposed DL model has been tested on a database including images of the six most widespread kidney stone types in industrialized countries. The overall average classification accuracy was <span><math><mrow><mn>90</mn><mo>.</mo><mn>37</mn><mo>±</mo><mn>0</mn><mo>.</mo><mn>6</mn><mtext>%</mtext></mrow></math></span>. When comparing this results with that of the eight other DL models of the kidney stone state-of-the-art, it can be seen that the valuable gain in explanability was not reached at the expense of accuracy which was even slightly increased with respect to that (<span><math><mrow><mn>88</mn><mo>.</mo><mn>2</mn><mo>±</mo><mn>2</mn><mo>.</mo><mn>1</mn><mtext>%</mtext></mrow></math></span>) of the best method of the literature. These promising and interpretable results also encourage urologists to put their trust in AI-based solutions.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"170 ","pages":"Article 103266"},"PeriodicalIF":6.2,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145121256","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 dynamic grouping for adaptive clinical trials: Rethinking randomization in precision medicine","authors":"Madhur Mangalam","doi":"10.1016/j.artmed.2025.103272","DOIUrl":"10.1016/j.artmed.2025.103272","url":null,"abstract":"<div><div>Integrating artificial intelligence into biomedical human subjects research is transforming traditional experimental paradigms. This perspective introduces the concept of “dynamic grouping,” wherein artificial intelligence (AI) systems continuously reassign participants across experimental conditions based on real-time biomarker data and clinical response patterns. Unlike traditional biomedical research designs that rely on fixed treatment and control groups, dynamic grouping allows participant assignments to evolve throughout the study. We examine the ethical implications, methodological challenges, and research opportunities associated with this paradigm, particularly in clinical trials, precision medicine, and digital therapeutics. To support this analysis, we present three computational simulations that quantify its impact: <em>(i)</em> a heterogeneity simulation demonstrating how patient variability affects the advantage of dynamic grouping, <em>(ii)</em> a statistical power analysis showing potential sample size reductions in adaptive designs, and <em>(iii)</em> a clinical outcome distribution analysis highlighting how dynamic grouping reduces negative treatment outcomes and optimizes patient responses. Our findings suggest that dynamic grouping can improve treatment effectiveness, enhance resource allocation, and increase statistical efficiency, although it also raises new challenges for causal inference, informed consent, and regulatory oversight. As AI continues to reshape medical research, adapting ethical and methodological frameworks will be essential for its responsible implementation.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"170 ","pages":"Article 103272"},"PeriodicalIF":6.2,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145099518","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":"H-SynEx: Using synthetic images and ultra-high resolution ex vivo MRI for hypothalamus subregion segmentation","authors":"Livia Rodrigues ,&nbsp;Martina Bocchetta ,&nbsp;Oula Puonti ,&nbsp;Douglas Greve ,&nbsp;Ana Carolina Londe ,&nbsp;Marcondes França ,&nbsp;Simone Appenzeller ,&nbsp;Juan Eugenio Iglesias ,&nbsp;Leticia Rittner","doi":"10.1016/j.artmed.2025.103271","DOIUrl":"10.1016/j.artmed.2025.103271","url":null,"abstract":"<div><div>The hypothalamus is a small structure located in the center of the brain and is involved in significant functions such as sleeping, temperature, and appetite control. Various neurological disorders are also associated with hypothalamic abnormalities. Automated image analysis of this structure from brain MRI is thus highly desirable to study the hypothalamus <em>in vivo</em>. However, most of the automated segmentation tools currently available focus exclusively on T1w images. In this study, we introduce H-SynEx, a machine learning method for automated segmentation of hypothalamic subregions that generalizes across different MRI sequences and resolutions without retraining. H-synEx was trained with synthetic images built from label maps derived from ultra-high resolution <em>ex vivo</em> MRI scans, allowing finer-grained manual segmentation when compared with 1 mm isometric <em>in vivo</em> images. We validated our method using Dice Coefficient (DSC) and Average Hausdorff distance (AVD) across <em>in vivo</em> images from six different datasets with six different MRI sequences (T1, T2, proton density, quantitative T1, fractional anisotropy, and FLAIR). Statistical analysis compared hypothalamic subregion volumes in controls, Alzheimer’s disease (AD), and behavioral variant frontotemporal dementia (bvFTD) subjects using the Area Under the Receiver Operating Characteristic curve (AUROC) and the Wilcoxon rank sum test. Our results show that H-SynEx successfully leverages information from ultra-high resolution scans to segment <em>in vivo</em> from different MRI sequences. Our automated segmentation was able to discriminate controls versus patients with Alzheimer’s disease on FLAIR images with 5 mm spacing. H-SynEx is openly available at <span><span>https://github.com/liviamarodrigues/hsynex</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"170 ","pages":"Article 103271"},"PeriodicalIF":6.2,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158965","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":"Decoding the cortical responses to mechanical wrist perturbations: A two-step shared structure NARX method","authors":"Nan Zheng ,&nbsp;Yurong Li ,&nbsp;Wuxiang Shi ,&nbsp;Jiyu Tan","doi":"10.1016/j.artmed.2025.103273","DOIUrl":"10.1016/j.artmed.2025.103273","url":null,"abstract":"<div><div>Shared structure nonlinear autoregressive with exogenous input (NARX) model is a promising tool for exploring cortical responses mechanism to external stimuli, essential for advancing our understanding of brain function and developing methods for direct brain information encoding. In this paper, we proposed a two-step method to overcome limitations in existing method, which neglect data relationships and rely on a greedy search for regression terms, leading to less accurate models. In our approach, data from multiple trials are concatenated, and then the orthogonal forward regression (OFR) algorithm identifies model terms in first step, enhancing inter-trial connections and establishing a preliminary model for each subject. Shared model terms across subjects are then used to construct a general target model. Next, non-shared regression terms that best represent population-level information are identified, using adaptive multi-population genetic algorithms, and use to enhance the target models' descriptive power. Simulations results show significant competitiveness in terms of accuracy as compared to other state-of-the-art methods. When applied to real electroencephalography signals under mechanical disturbance, structural and parameter analysis revealed consistent neural response patterns across subjects, with subject-specific responses likely stemming from muscle feedback. Frequency response analysis further suggests that the brain may generate motor inhibition signals based on sensory inputs to maintain a pre-disturbance resting state. These findings provide valuable insights into cortical response mechanisms and have potential implications for future brain information encoding research.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"170 ","pages":"Article 103273"},"PeriodicalIF":6.2,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145099638","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":"The interpretable deep learning framework and validation for seizure detection in pediatric electroencephalography: An improved accuracy and performance analysis","authors":"Yu Zhou ,&nbsp;Yuxin Gao ,&nbsp;Qiang Li ,&nbsp;Ruiheng Wu ,&nbsp;Aiping Yang ,&nbsp;Ming-Lang Tseng","doi":"10.1016/j.artmed.2025.103276","DOIUrl":"10.1016/j.artmed.2025.103276","url":null,"abstract":"<div><div>This study proposes an interpretable deep learning framework and compares the two novel models. A fully convolutional network with squeeze-and-excitation modules (SE-FCN) is designed to enhance spatial sensitivity and retain temporal resolution. In addition, a transformer-based model (TransNet) is developed to capture temporal and channel-wise dependencies via self-attention. These two models output channel saliency weights to the EEG electrode space and generate heatmaps for inferring potential epileptogenic zones. Deep learning primarily adopts convolutional neural networks (CNNs) or sequence generation networks (SGNs) and faces the limitations. For instance, CNN-based models often lack hierarchical modeling and fail to quantify channel-wise contributions, hindering spatial localization. SGN-based models struggle to capture complex spatiotemporal dependencies and typically lack adaptive attention tailored to electroencephalography (EEG) characters. Epileptic seizure detection is vital for effective clinical intervention and existing methods operated as black boxes, limiting clinical interpretability. This study evaluates the models on the CHB-MIT pediatric EEG dataset using a subject-independent cross-validation protocol. SE-FCN achieves an AUC of 0.89 and accuracy of 86.7 %, while TransNet achieves an AUC of 0.92 and accuracy of 86.4 %. Saliency maps from both models demonstrate high consistency and enable categorization of 22 patients into five groups based on inferred seizure origins.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"170 ","pages":"Article 103276"},"PeriodicalIF":6.2,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145092832","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":"High-quality triage and diagnosis of gynecological diseases via artificial intelligence","authors":"Linru Fu ,&nbsp;Che Wang ,&nbsp;Zhaoyang Liu ,&nbsp;Changzai Pan ,&nbsp;Zhe Du ,&nbsp;Zhijing Sun ,&nbsp;Lan Zhu ,&nbsp;Ke Deng","doi":"10.1016/j.artmed.2025.103267","DOIUrl":"10.1016/j.artmed.2025.103267","url":null,"abstract":"<div><div>Timely detection and diagnosis of diseases are key elements of an efficient healthcare system. In recent years, artificial intelligence (AI) has played an increasingly important role in improving the accuracy and efficiency of disease diagnosis in clinical practice. However, most existing AI systems for disease diagnosis have focused on either classifying patients into broad disease categories or diagnosing a specific disease, leaving a gap in the development of a coherent AI system for both triage and diagnosis in a department of a general hospital. In this study, we fill this gap with SmartGyne, an advanced AI system that can achieve high-quality triage and diagnosis for a full spectrum of gynecological diseases. By extracting useful clinical evidence for diagnosis from a large amount of electronic medical records, SmartGyne establishes an effective framework to integrate real-world clinical evidence and knowledge into a coherent AI system that can effectively handle a full spectrum of complex diseases in a department of a general hospital. Validation experiments demonstrated that SmartGyne achieved an overall accuracy of 80.1 % in triage for gynecological diseases, and 99.4 % in diagnosis for a gynecological subspecialty. In comparison with human physicians, SmartGyne showed competitive triage and diagnostic performance, and improved consultation efficiency and accuracy for physicians with limited specialized experience. These results show that SmartGyne achieves high-quality triage and diagnosis, holding the potential to improve the efficiency of the healthcare system in China, as well as other countries lacking professional gynecologists.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"170 ","pages":"Article 103267"},"PeriodicalIF":6.2,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145121255","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":"Mining multi-electrode and multi-wave electroencephalogram based time-interval temporal patterns for improved classification capabilities and explainability","authors":"Ofir Landau,&nbsp;Nir Nissim","doi":"10.1016/j.artmed.2025.103269","DOIUrl":"10.1016/j.artmed.2025.103269","url":null,"abstract":"<div><div>Brain-computer interface (BCI) systems, and particularly electroencephalogram (EEG) based BCI systems, have become more widely used in recent years and are utilized in various applications and domains ranging from medicine and marketing to games and entertainment. While different algorithms have been used to analyze EEG data and enable its classification, existing algorithms have two main drawbacks; both their classification and explainability capabilities are limited. Lacking in explainability, they cannot indicate which electrodes and waves led to a classification decision or explain how areas and frequencies of the brain's activity correlate to a specific task.</div><div>In this study, we propose a novel extension for the time-interval temporal patterns mining algorithms aimed at enhancing the data mining process by enabling a richer set of patterns to be learned from the EEG data, thereby contributing to improved classification and explainability capabilities. The extended algorithm is designed to capture and leverage the unique nature of EEG data by decomposing it into different brain waves and modeling the relations among them and between different electrodes.</div><div>Our evaluation of the proposed extended algorithm on multiple learning tasks and three EEG datasets demonstrated the extended algorithm's ability to mine richer patterns that improve the classification performance by 4–11 % based on the Area-Under the receiver operating characteristic Curve (AUC) metric, compared to the original version of the algorithm. Moreover, the algorithm was shown to shed light on the areas and frequencies of the brain's activity that are correlated with specific tasks.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"170 ","pages":"Article 103269"},"PeriodicalIF":6.2,"publicationDate":"2025-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145099517","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":"Medical multimodal foundation models in clinical diagnosis and treatment: Applications, challenges, and future directions","authors":"Kai Sun ,&nbsp;Siyan Xue ,&nbsp;Fuchun Sun ,&nbsp;Haoran Sun ,&nbsp;Yu Luo ,&nbsp;Ling Wang ,&nbsp;Siyuan Wang ,&nbsp;Na Guo ,&nbsp;Lei Liu ,&nbsp;Tian Zhao ,&nbsp;Xinzhou Wang ,&nbsp;Lei Yang ,&nbsp;Shuo Jin ,&nbsp;Jun Yan ,&nbsp;Jiahong Dong","doi":"10.1016/j.artmed.2025.103265","DOIUrl":"10.1016/j.artmed.2025.103265","url":null,"abstract":"<div><div>Recent advancements in deep learning have significantly revolutionized the field of clinical diagnosis and treatment, offering novel approaches to improve diagnostic precision and treatment efficacy across diverse clinical domains, thus driving the pursuit of precision medicine. The growing availability of multi-organ and multimodal datasets has accelerated the development of large-scale Medical Multimodal Foundation Models (MMFMs). These models, known for their strong generalization capabilities and rich representational power, are increasingly being adapted to address a wide range of clinical tasks, from early diagnosis to personalized treatment strategies. This review offers a comprehensive analysis of recent developments in MMFMs, focusing on three key aspects: datasets, model architectures, and clinical applications. We also explore the challenges and opportunities in optimizing multimodal representations and discuss how these advancements are shaping the future of healthcare by enabling improved patient outcomes and more efficient clinical workflows.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"170 ","pages":"Article 103265"},"PeriodicalIF":6.2,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145092872","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":"An end-to-end solution for out-of-hospital emergency medical dispatch triage based on multimodal and continual deep learning","authors":"Pablo Ferri ,&nbsp;Carlos Sáez ,&nbsp;Antonio Félix-De Castro ,&nbsp;Purificación Sánchez-Cuesta ,&nbsp;Juan M. García-Gómez","doi":"10.1016/j.artmed.2025.103264","DOIUrl":"10.1016/j.artmed.2025.103264","url":null,"abstract":"<div><div>The objective of this study was to build a multimodal, multitask predictive model—named E2eDeepEMC²—to improve out-of-hospital emergency incident severity assessments while coping with shifts in data distributions over time. We drew on 2<!--> <!-->054<!--> <!-->694 independent incidents recorded by the Valencian emergency medical dispatch service between 2009 and 2019 (excluding 2013), combining demographic, temporal, clinical and free-text inputs. To handle temporal drift, our model integrates continual learning strategies and comprises three encoder modules (for context, clinical data and text), whose outputs are merged to predict the life-threatening level, admissible response delay and emergency system jurisdiction. Compared with the Valencian Region’s existing in-house triage protocol, E2eDeepEMC² achieved absolute F1-score gains of 18.46% for life-threatening level, 25.96% for response delay and 3.63% for jurisdiction. Compared to non-continual learning baselines, it also outperformed them by 3.04%, 9.66% and 0.58%, respectively. Deployment of E2eDeepEMC² is currently underway in the Valencian Region, underscoring its practical impact on real-world emergency dispatch decision-making.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"170 ","pages":"Article 103264"},"PeriodicalIF":6.2,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050144","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":"LoRA-PT: Low-rank adapting UNETR for hippocampus segmentation using principal tensor singular values and vectors","authors":"Guanghua He ,&nbsp;Wangang Cheng ,&nbsp;Hancan Zhu ,&nbsp;Gaohang Yu","doi":"10.1016/j.artmed.2025.103254","DOIUrl":"10.1016/j.artmed.2025.103254","url":null,"abstract":"<div><div>The hippocampus is an important brain structure involved in various psychiatric disorders, and its automatic and accurate segmentation is vital for studying these diseases. Recently, deep learning-based methods have made significant progress in hippocampus segmentation. However, training deep neural network models requires substantial computational resources, time, and a large amount of labeled training data, which is frequently scarce in medical image segmentation. To address these issues, we propose LoRA-PT, a novel parameter-efficient fine-tuning (PEFT) method that transfers the pre-trained UNETR model from the BraTS2021 dataset to the hippocampus segmentation task. Specifically, LoRA-PT divides the parameter matrix of the transformer structure into three distinct sizes, yielding three third-order tensors. These tensors are decomposed using tensor singular value decomposition to generate low-rank tensors consisting of the principal singular values and vectors, with the remaining singular values and vectors forming the residual tensor. During fine-tuning, only the low-rank tensors (i.e., the principal tensor singular values and vectors) are updated, while the residual tensors remain unchanged. We validated the proposed method on three public hippocampus datasets, and the experimental results show that LoRA-PT outperformed state-of-the-art PEFT methods in segmentation accuracy while significantly reducing the number of parameter updates. Our source code is available at <span><span>https://github.com/WangangCheng/LoRA-PT/tree/LoRA-PT</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"170 ","pages":"Article 103254"},"PeriodicalIF":6.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144997868","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}