Artificial Intelligence in Medicine最新文献

{"title":"A prior knowledge-supervised fusion network predicts survival after radiotherapy in patients with advanced gastric cancer","authors":"Liang Sun ,&nbsp;Yongxin Lan ,&nbsp;Jian Sun ,&nbsp;Pengfei Ji ,&nbsp;Hongwei Ge ,&nbsp;Ming Cui ,&nbsp;Xin Yuan","doi":"10.1016/j.artmed.2025.103184","DOIUrl":"10.1016/j.artmed.2025.103184","url":null,"abstract":"<div><h3>Background and objective:</h3><div>Predicting overall survival (OS) for advanced gastric cancer patients after radiotherapy is critical for developing an individualized treatment plan. However, existing studies have focused on gastric cancer CT images with a large amount of redundant information, neglecting the role of physicians’ prior knowledge in guiding gastric cancer CT image information. We propose a multimodal fusion method based on prior knowledge to predict OS after radiotherapy in advanced gastric cancer patients to assist physicians in clinical diagnosis and treatment.</div></div><div><h3>Methods:</h3><div>A prior knowledge supervised fusion network (PKSFnet) is proposed. Firstly, PKSFnet uses a novel sampling strategy, which enables the input model data to obtain a complete feature space by analyzing the entire patient data space. Afterwards, under the guidance of the multi-domain feature fusion module (MdFF), multimodal information of patients is adaptively fused and mined to improve the prediction performance.</div></div><div><h3>Results:</h3><div>The results of the proposed model are superior to those of other unimodal and multimodal state-of-the-art methods. For the segmented survival time classification task, the AUC, specificity, sensitivity, precision of the proposed model are 0.8397, 0.875, 0.7556, and 0.875, respectively. For the survival risk regression task, the C-index and HR of the proposed model are 0.8574 and 4.658 respectively. Ablation experimental results further demonstrate the impact of each module of the proposed model. Finally, we apply the novel sampling strategy to other deep learning models and achieve significant improvement.</div></div><div><h3>Conclusion:</h3><div>The experimental results have demonstrated that the proposed model can effectively predict OS after radiotherapy in patients with advanced gastric cancer, which demonstrate that the proposed model can facilitate the development and application of robust clinical treatment strategies.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"167 ","pages":"Article 103184"},"PeriodicalIF":6.1,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144514072","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 anxiety screening framework integrating multimodal data and graph node correlation","authors":"Haimiao Mo ,&nbsp;Hongjia Wu ,&nbsp;Qian Rong ,&nbsp;Zhijian Hu ,&nbsp;Meng Yi ,&nbsp;Peipei Chen","doi":"10.1016/j.artmed.2025.103189","DOIUrl":"10.1016/j.artmed.2025.103189","url":null,"abstract":"<div><div>Anxiety disorders are a significant global health concern, profoundly impacting patients’ lives and social functioning while imposing considerable burdens on families and economies. However, current anxiety screening methods face limitations due to cost constraints and cognitive biases, particularly in their inability to deeply model correlations among multidimensional features. They often overlook crucial information inherent in their internal couplings, limiting their accuracy and applicability in clinical diagnostics. To address these challenges, we propose an advanced anxiety screening framework that integrates multimodal data, such as physiological, behavioral, audio, and textual, using a Graph Convolutional Network (GCN). While our framework draws upon existing technologies such as GCN, one-dimensional convolutional neural networks, and gated recurrent units, the uniqueness of our framework lies in how these components are combined to capture complex spatiotemporal relationships and correlations among multimodal features. Experimental results demonstrate the framework’s robust performance, achieving an accuracy of 93.48%, Area Under Curve of 94.58%, precision of 90.00%, sensitivity of 81.82%, specificity of 97.14%, F1 score of 85.71%. Notably, the method remains effective even when questionnaire data is unavailable, underscoring its practicality and reliability. This anxiety screening approach provides a new perspective for early identification and intervention of anxiety symptoms, offering a scientific basis for personalized treatment and prevention through the analysis of multimodal data and graph structures.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"167 ","pages":"Article 103189"},"PeriodicalIF":6.1,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144338472","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":"Label-independent framework for objective evaluation of cosmetic outcome in breast cancer","authors":"Sangjoon Park ,&nbsp;Yong Bae Kim ,&nbsp;Jee Suk Chang ,&nbsp;Seo Hee Choi ,&nbsp;Hyungjin Chung ,&nbsp;Ik Jae Lee ,&nbsp;Hwa Kyung Byun","doi":"10.1016/j.artmed.2025.103179","DOIUrl":"10.1016/j.artmed.2025.103179","url":null,"abstract":"<div><div>With advancements in the field of breast cancer treatment, the assessment of postsurgical cosmetic outcomes has gained increasing significance owing to its substantial impact on patients’ quality of life. However, evaluating breast cosmesis is challenging because of the inherently subjective nature of expert labeling. In this study, we present a novel automated approach, attention-guided denoising diffusion anomaly detection (AG-DDAD), designed to assess breast cosmesis following surgery. The model addresses the limitations of conventional supervised learning and existing anomaly detection models. Our approach leverages the attention mechanism of distillation with no labels and a self-supervised vision transformer, combined with a diffusion model, to achieve high-quality image reconstruction and precise transformation of discriminative regions. By training the diffusion model on unlabeled data, predominantly with normal cosmesis, we adopted an unsupervised anomaly detection perspective to automatically score the cosmesis. Real-world data experiments demonstrated the effectiveness of our method, providing visually appealing representations and quantifiable scores for cosmesis evaluation. Compared with commonly used rule-based programs, our fully automated approach eliminates the need for manual annotations and offers an objective evaluation. Moreover, our anomaly detection model exhibits state-of-the-art performance, surpassing existing models in terms of accuracy. Beyond the scope of breast cosmesis, our research represents a significant advancement in unsupervised anomaly detection within the medical domain, thereby paving the way for future investigations.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"167 ","pages":"Article 103179"},"PeriodicalIF":6.1,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144253336","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":"Probabilistic emotion and sentiment modelling of patient-reported experiences","authors":"Curtis Murray ,&nbsp;Lewis Mitchell ,&nbsp;Jonathan Tuke ,&nbsp;Mark Mackay","doi":"10.1016/j.artmed.2025.103178","DOIUrl":"10.1016/j.artmed.2025.103178","url":null,"abstract":"<div><div>Patient feedback is necessary to assess the extent to which healthcare delivery aligns with public needs and expectations. Surveys provide structured feedback that is readily analysed; however, they are costly, infrequent, and constrained by predefined questions, limiting a comprehensive understanding of patient experience. In contrast, the unstructured nature of online reviews and social-media posts can reveal unique insights into patient perspectives, yet that very lack of structure presents a challenge for analysis. In this study, we present a methodology for interpretable probabilistic modelling of patient emotions from patient-reported experiences. We employ metadata-network topic modelling to uncover key themes in 13,380 patient-reported experiences from Care Opinion (2012-2022) and reveal insightful relationships between these themes and labelled emotions. Our results show positivity and negativity relate most strongly to aspects of patient experience, such as patient-caregiver interactions, rather than clinical outcomes. Patient educational engagement exhibits strong positivity, whereas dismissal and rejection are linked to suicidality and depression. We develop a context-specific probabilistic emotion recommender system that predicts both multi-label emotions and binary sentiments with a Naïve Bayes classifier using topics as predictors. We assess performance with nDCG and Q-measure and achieve an F1 of 0.921, significantly outperforming standard sentiment lexicons. This methodology offers a cost-effective, timely, and transparent approach to harness unconstrained patient-reported feedback, with the potential to augment traditional patient-reported experience collection. Our R package and interactive dashboard make the approach readily accessible for future research and clinical practice applications, enabling hospitals to integrate emotional insights into surveys and tailor care to patient needs. Overall, this study provides a new avenue for understanding and improving patient experience and the quality of healthcare delivery.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"167 ","pages":"Article 103178"},"PeriodicalIF":6.1,"publicationDate":"2025-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144261671","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":"Position of artificial intelligence in healthcare and future perspective","authors":"Vedat Cicek,&nbsp;Ulas Bagci","doi":"10.1016/j.artmed.2025.103193","DOIUrl":"10.1016/j.artmed.2025.103193","url":null,"abstract":"<div><div>Artificial Intelligence (AI) has been used in healthcare with increasing momentum. According to a published report, 6.6 billion dollars were invested in AI healthcare in 2021, and this investment is expected to provide 150 billion dollars of benefit to the USA economy by 2026 (Duchateau and King, 2023 [1]). The future perspective on AI will undoubtedly open new horizons for the healthcare.</div><div>AI technology in the healthcare field is increasingly popular in the areas of diagnosis, prognosis, classification, therapy, and disease survival prediction. Now that AI has proven its worth, it's already time to re-ask the following three questions according to the fast pace of AI algorithms:<ul><li><span>1)</span><span><div>Where will AI be positioned in healthcare in the future?</div></span></li><li><span>2)</span><span><div>What kind of relationship will be defined between doctors, patients and AI?</div></span></li><li><span>3)</span><span><div>How can we direct AI studies according to the health problems in the world?</div></span></li></ul></div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"167 ","pages":"Article 103193"},"PeriodicalIF":6.1,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144270666","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 cell-interacting and multi-correcting method for automatic circulating tumor cells detection","authors":"Xuan Zhang ,&nbsp;Rensheng Lai ,&nbsp;Ling Bai ,&nbsp;Jianxin Ji ,&nbsp;Ruihao Qin ,&nbsp;Lihong Jiang ,&nbsp;Bin Meng ,&nbsp;Ying Zhang ,&nbsp;Xiaohan Zheng ,&nbsp;Yan Wang ,&nbsp;Xiang Kui ,&nbsp;Liuchao Zhang ,&nbsp;Dimin Ning ,&nbsp;Liuying Wang ,&nbsp;Yujiang Chen ,&nbsp;Xinling Wang ,&nbsp;Shuang Li ,&nbsp;Menglei Hua ,&nbsp;Junkai Wang ,&nbsp;Yong Cao ,&nbsp;Lei Cao","doi":"10.1016/j.artmed.2025.103164","DOIUrl":"10.1016/j.artmed.2025.103164","url":null,"abstract":"<div><div>Sensitive detection of circulating tumor cells (CTCs) from peripheral blood can serve as an effective tool in the early diagnosis and prognosis of cancer. Many methods based on modern object detectors were proposed in recent years for automatic abnormal cells detection in slide images. Although the modes of these methods can also be applied to the CTCs detection, several practical difficulties lead to suboptimal performance of them, such as accurate capture of CTCs in a large number of mixed cells and identification of CTCs and CTC-like cells with similar visual characteristics. Here, we develop a new cell-interacting and multi-correcting detector called CMD, and apply H&amp;E-stained slide images to detect CTCs automatically for the first time. Specifically, the proposed method incorporates two task-oriented novel modules: (1) a self-attention module for aggregating feature interactions between cells and allowing the model to pay more attention to key abnormal cells, (2) a hard sample mining sampler for progressively correcting predictions of cells with ambiguous classification boundaries. Experiments conducted on a multi-center dataset of 1247 annotated slide images confirm the superiority of our method over state-of-the-art cell detection methods. The results of ablation experiment part also prove the effectiveness of two modules. The source codes of this paper are available at <span><span>https://github.com/zx333445/CMD</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"167 ","pages":"Article 103164"},"PeriodicalIF":6.1,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144169170","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":"PhenoLinker: Phenotype-gene link prediction and explanation using heterogeneous graph neural networks","authors":"Jose L. Mellina Andreu ,&nbsp;Luis Bernal ,&nbsp;Antonio F. Skarmeta ,&nbsp;Mina Ryten ,&nbsp;Sara Álvarez ,&nbsp;Alejandro Cisterna García ,&nbsp;Juan A. Botía","doi":"10.1016/j.artmed.2025.103177","DOIUrl":"10.1016/j.artmed.2025.103177","url":null,"abstract":"<div><div>The association of a given human phenotype with a genetic variant remains a critical challenge in biomedical research. We present PhenoLinker, a novel graph-based system capable of associating a score to a phenotype-gene relationship by using heterogeneous information networks and a convolutional neural network-based model for graphs, which can provide an explanation for the predictions. Unlike previous approaches, PhenoLinker integrates gene and phenotype attributes, while maintaining explainability through Integrated Gradients. PhenoLinker consistently outperforms existing models in both retrospective and temporal validation tasks. This system can aid in the discovery of new associations and in understanding the consequences of human genetic variation.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"167 ","pages":"Article 103177"},"PeriodicalIF":6.1,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144169167","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 AI system for continuous knee osteoarthritis severity grading: An anomaly detection inspired approach with few labels","authors":"Niamh Belton ,&nbsp;Aonghus Lawlor ,&nbsp;Kathleen M. Curran","doi":"10.1016/j.artmed.2025.103138","DOIUrl":"10.1016/j.artmed.2025.103138","url":null,"abstract":"<div><div>The diagnostic accuracy and subjectivity of existing Knee Osteoarthritis (OA) ordinal grading systems has been a subject of on-going debate and concern. Existing automated solutions are trained to emulate these imperfect systems, whilst also being reliant on large annotated databases for fully-supervised training. This work proposes a three stage approach for automated continuous grading of knee OA that is built upon the principles of Anomaly Detection (AD); learning a robust representation of healthy knee X-rays and grading disease severity based on its distance to the centre of normality. In the first stage, SS-FewSOME is proposed, a self-supervised AD technique that learns the ‘normal’ representation, requiring only examples of healthy subjects and <span><math><mrow><mo>&lt;</mo><mn>3</mn><mtext>%</mtext></mrow></math></span> of the labels that existing methods require. In the second stage, this model is used to pseudo label a subset of unlabelled data as ‘normal’ or ‘anomalous’, followed by denoising of pseudo labels with CLIP. The final stage involves retraining on labelled and pseudo labelled data using the proposed Dual Centre Representation Learning (DCRL) which learns the centres of two representation spaces; normal and anomalous. Disease severity is then graded based on the distance to the learned centres. The proposed methodology outperforms existing techniques by margins of up to 24% in terms of OA detection and the disease severity scores correlate with the Kellgren-Lawrence grading system at the same level as human expert performance. Code available at <span><span>https://github.com/niamhbelton/SS-FewSOME_Disease_Severity_Knee_Osteoarthritis</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"167 ","pages":"Article 103138"},"PeriodicalIF":6.1,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144169169","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":"Large scale gene set ranking for survival-related gene sets","authors":"Martin Špendl ,&nbsp;Jaka Kokošar ,&nbsp;Ela Praznik ,&nbsp;Luka Ausec ,&nbsp;Miha Štajdohar ,&nbsp;Blaž Zupan","doi":"10.1016/j.artmed.2025.103149","DOIUrl":"10.1016/j.artmed.2025.103149","url":null,"abstract":"<div><div>Disease progression is closely linked to shifts in the expression levels of specific genes within molecular pathways. While gene set enrichment analysis is a widely employed method for identifying key disease markers, it has been underutilized in survival analysis. Here, we introduce a novel computational approach that adapts gene set enrichment analysis for survival analysis. The proposed approach considers a gene set, computes a single-sample gene set enrichment score, and, based on this score, splits the samples into cohorts. It then scores the gene sets by evaluating the differences in survival rates between the resulting cohorts. We aim to find gene sets that can lead to cohorts with significantly different survival probabilities. Utilizing gene expression data from The Cancer Genome Atlas and gene sets from the Molecular Signature Database, our results demonstrate that existing empirical research consistently supports the top gene sets our approach associates with survival prognosis. The proposed method broadens gene set enrichment analysis applications to include information on survival, bridging the gap between alterations in molecular pathways and their implications on survival.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"167 ","pages":"Article 103149"},"PeriodicalIF":6.1,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144169825","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":"Quantitative computed tomography imaging classification of cement dust-exposed patients-based Kolmogorov-Arnold networks","authors":"Ngan-Khanh Chau ,&nbsp;Woo Jin Kim ,&nbsp;Chang Hyun Lee ,&nbsp;Kum Ju Chae ,&nbsp;Gong Yong Jin ,&nbsp;Sanghun Choi","doi":"10.1016/j.artmed.2025.103166","DOIUrl":"10.1016/j.artmed.2025.103166","url":null,"abstract":"<div><h3>Background</h3><div>Occupational health assessment is critical for detecting respiratory issues caused by harmful exposures, such as cement dust. Quantitative computed tomography (QCT) imaging provides detailed insights into lung structure and function, enhancing the diagnosis of lung diseases. However, its high dimensionality poses challenges for traditional machine learning methods.</div></div><div><h3>Methods</h3><div>In this study, Kolmogorov-Arnold networks (KANs) were used for the binary classification of QCT imaging data to assess respiratory conditions associated with cement dust exposure. The dataset comprised QCT images from 609 individuals, including 311 subjects exposed to cement dust and 298 healthy controls. We derived 141 QCT-based variables and employed KANs with two hidden layers of 15 and 8 neurons. The network parameters, including grid intervals, polynomial order, learning rate, and penalty strengths, were carefully fine-tuned. The performance of the model was assessed through various metrics, including accuracy, precision, recall, F1 score, specificity, and the Matthews Correlation Coefficient (MCC). A five-fold cross-validation was employed to enhance the robustness of the evaluation. SHAP analysis was applied to interpret the sensitive QCT features.</div></div><div><h3>Results</h3><div>The KAN model demonstrated consistently high performance across all metrics, with an average accuracy of 98.03 %, precision of 97.35 %, recall of 98.70 %, F1 score of 98.01 %, and specificity of 97.40 %. The MCC value further confirmed the robustness of the model in managing imbalanced datasets. The comparative analysis demonstrated that the KAN model outperformed traditional methods and other deep learning approaches, such as TabPFN, ANN, FT-Transformer, VGG19, MobileNets, ResNet101, XGBoost, SVM, random forest, and decision tree. SHAP analysis highlighted structural and functional lung features, such as airway geometry, wall thickness, and lung volume, as key predictors.</div></div><div><h3>Conclusion</h3><div>KANs significantly improved the classification of QCT imaging data, enhancing early detection of cement dust-induced respiratory conditions. SHAP analysis supported model interpretability, enhancing its potential for clinical translation in occupational health assessments.</div></div>","PeriodicalId":55458,"journal":{"name":"Artificial Intelligence in Medicine","volume":"167 ","pages":"Article 103166"},"PeriodicalIF":6.1,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144185317","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}