检索结果-内蒙古大学图书馆

Morphological and metabolic asymmetries of the thalamic subregions in temporal lobe epilepsy predict cognitive functions

引用

Scientific Reports 2023年第1期13卷 1-9页

作者： Tung, Hsin Tsai, Shih-Chuan Huang, Pu-Rong Hsieh, Peiyuan F. Lin, Yi-Ching Peng, Syu-Jyun Department of Post-Baccalaureate Medicine College of Medicine National Chung Hsing University Taichung Taiwan Center of Faculty Development Taichung Veterans General Hospital Taichung Taiwan Neurological Institute Taichung Veterans General Hospital Taichung Taiwan Institute of Clinical Medicine National Yang Ming Chiao Tung University Taipei Taiwan Department of Nuclear Medicine Taichung Veterans General Hospital Taichung Taiwan Department of Medical Imaging and Radiological Technology Institute of Radiological Science Central Taiwan University of Science and Technology Taichung Taiwan Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University No.250 Wuxing St. Xinyi Dist. Taipei City 110 Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Medical University Taipei Taiwan

Both morphological and metabolic imaging were used to determine how asymmetrical changes of thalamic subregions are involved in cognition in temporal lobe epilepsy (TLE). We retrospectively recruited 24 left-TLE and 15 right-TLE patients. Six thalamic subnuclei were segmented by magnetic resonance imaging, and then co-registered onto Positron emission tomography images. We calculated the asymmetrical indexes of the volumes and normalized standard uptake value ratio (SUVR) of the entire and individual thalamic subnuclei. The SUVR of ipsilateral subnuclei were extensively and prominently decreased compared with the volume loss. The posterior and medial subnuclei had persistently lower SUVR in both TLE cases. Processing speed is the cognitive function most related to the metabolic asymmetry. It negatively correlated with the metabolic asymmetrical indexes of subregions in left-TLE, while positively correlated with the subnuclei volume asymmetrical indexes in right-TLE. Epilepsy duration negatively correlated with the volume asymmetry of most thalamic subregions in left-TLE and the SUVR asymmetry of ventral and intralaminar subnuclei in right-TLE. Preserved metabolic activity of contralateral thalamic subregions is the key to maintain the processing speed in both TLEs. R-TLE had relatively preserved volume of the ipsilateral thalamic volume, while L-TLE had relatively decline of volume and metabolism in posterior subnucleus. © 2023, The Author(s).

关键词：

来源：评论

学校读者我要写书评

暂无评论

TrialBench: Multi-Modal artificial intelligence-Ready Clinical Trial datasets

arXiv

引用

arXiv 2024年

作者： Chen, Jintai Hu, Yaojun Lu, Yingzhou Wang, Yue Huang, Kexin Cao, Xu Lin, Miao Xu, Hongxia Wu, Jian Xiao, Cao Sun, Jimeng Glass, Lucas Zitnik, Marinka Fu, Tianfan Computer Science Department University of Illinois at Urbana-Champaign Urbana United States College of Computer Science and Technology Zhejiang University Hangzhou China Computer Science Department Stanford University StanfordCA United States School of Medicine Stanford University StanfordCA United States Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University College of Pharmaceutical Sciences Zhejiang University Hangzhou China Medical Big Data Center Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Southern Medical University Guangzhou China Department of Computational Science Rensselaer Polytechnic Institute NY United States The Second Affiliated Hospital Zhejiang University School of Medicine Hangzhou China GE HealthCare Chicago United States IQVIA Boston United States Informatics Harvard Medical School Harvard University United States

Clinical trials are pivotal for developing new medical treatments but typically carry risks such as patient mortality and enrollment failure that waste immense efforts spanning over a decade. Applying artificial intelligence (AI) to predict key events in clinical trials holds great potential for providing insights to guide trial designs. However, complex data collection and question definition requiring medical expertise have hindered the involvement of AI thus far. This paper tackles these challenges by presenting a comprehensive suite of 23 meticulously curated AI-ready datasets covering multi-modal input features and 8 crucial prediction challenges in clinical trial design, encompassing prediction of trial duration, patient dropout rate, serious adverse event, mortality rate, trial approval outcome, trial failure reason, drug dose finding, design of eligibility criteria. Furthermore, we provide basic validation methods for each task to ensure the datasets' usability and reliability. We anticipate that the availability of such open-access datasets will catalyze the development of advanced AI approaches for clinical trial design, ultimately advancing clinical trial research and accelerating medical solution development. The curated dataset are available at https://***/datasets/ML2Healthcare/ClinicalTrialdataset and the validation frameworks are available at https://***/ML2Health/ML2ClinicalTrials/tree/main/AI4Trial. Copyright © 2024, The Authors. All rights reserved.

关键词： Clinical research

来源：评论

学校读者我要写书评

暂无评论

Predictive modeling and interpretative analysis of risks of instability in patients with Myasthenia Gravis requiring intensive care unit admission

引用

Heliyon 2024年第24期10卷 e41084页

作者： Kuo, Chao-Yang Su, Emily Chia-Yu Yeh, Hsu-Ling Yeh, Jiann-Horng Chiu, Hou-Chang Chung, Chen-Chih Graduate Institute of Artificial Intelligence and Big Data in Healthcare Smart Healthcare Interdisciplinary College National Taipei University of Nursing and Health Sciences Taipei 112 Taiwan Institute of Biomedical Informatics College of Medicine National Yang Ming Chiao Tung University Taipei 112 Taiwan Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei 110 Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei 110 Taiwan Institute of Public Health National Yang Ming Chiao Tung University Taipei 112 Taiwan Department of Neurology Shin Kong Wu Ho-Su Memorial Hospital Taipei 111 Taiwan School of Medicine Fu Jen Catholic University New Taipei City 242 Taiwan Department of Neurology Kaohsiung Medical University Kaohsiung 807 Taiwan Department of Neurology Taipei Medical University – Shuang Ho Hospital New Taipei City 235 Taiwan Taipei Neuroscience Institute Taipei Medical University – Shuang Ho Hospital New Taipei City 235 Taiwan Department of Neurology School of Medicine College of Medicine Taipei Medical University Taipei 110 Taiwan

Objective: Myasthenia gravis (MG), a low-prevalence autoimmune disorder characterized by clinical heterogeneity and unpredictable disease fluctuations, presents significant risks of acute exacerbations requiring intensive care. These crises contribute substantially to patient morbidity and mortality. This study aimed to develop and validate machine-learning models for predicting intensive care unit (ICU) admission risk among patients with MG-related disease instability. Methods: In this retrospective analysis of 314 MG patients hospitalized between 2015 and 2018, we implemented four machine learning algorithms, including logistic regression, support vector machine, extreme gradient boosting (XGBoost), and random forest, to predict ICU admission risk. The models incorporated fourteen clinical parameters as predictive features. The SHapley Additive exPlanations method was utilized to assess the importance of factors associated with ICU admission. Results: Through 10-fold cross-validation, the XGBoost model demonstrated superior predictive performance (area under the receiver operating characteristic curve: 0.8943, accuracy: 0.8603, sensitivity: 0.7222, and specificity: 0.9125). Among the analyzed features, MG severity, as classified by the Myasthenia Gravis Foundation of America clinical classification, was identified as the most significant factor influencing ICU admission. Additionally, disease duration, a key continuous variable, was inversely correlated with the risk of ICU admission. Conclusion: MG severity is the primary determinant of ICU admission, with shorter disease duration increasing the risk, possibly due to greater susceptibility to exacerbations. The XGBoost model exhibited excellent performance and accuracy, effectively identifying critical clinical factors for predicting ICU admission risk in MG patients. This novel, personalized approach to risk stratification elucidates crucial risk factors and has the potential to enhance clinical decision-making,

关键词： artificial intelligence Explainable machine-learning Extreme gradient boosting Intensive care unit Myasthenia gravis Prediction

来源：评论

学校读者我要写书评

暂无评论

Improved Metaheuristic Algorithms for Metabolic Network Optimization

引用

IOP Conference Series: Materials Science and Engineering 2019年第1期551卷

作者： K Mohd Daud Z Zakaria R Hassan M S Mohamad Z Ali Shah Artificial Intelligence and Bioinformatics Research Group School of Computing Faculty of Engineering Universiti Teknologi Malaysia 81310 Johor Bahru Malaysia Software Engineering Department School of Computing Faculty of Engineering Universiti Teknologi Malaysia 81310 Johor Bahru Malaysia Institute for Artificial Intelligence and Big Data Universiti Malaysia Kelantan City Campus Pengkalan Chepa 16100 Kota Bharu Kelantan Malaysia Faculty of Bioengineering and Technology Universiti Malaysia Kelantan Jeli Campus Lock Bag 100 17600 Jeli Kelantan Malaysia

Metaheuristic algorithms have been used in various domains to solve the optimization problem. In metabolic engineering, the problem of identifying near-optimal reactions knockout that can optimize the production rate of desired metabolites are hindered by the complexity of the metabolic networks. Through Flux Balance Analysis, different metaheuristics algorithms have been improved to optimize the desired phenotypes. In this paper, a comparative study of four metaheuristic algorithms have been proposed. Differential Search Algorithm (DSA), Particle Swarm Optimization (PSO), artificial Bee Colony (ABC) and Genetic Algorithm (GA) are considered. These algorithms are tested on succinic acid production in Escherichia coli. The comparative performances are measured based on production rate, growth rate, and computational time. Hence, from the results, the best metaheuristic algorithms to solve the metabolic network optimization is identified.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Image reconstruction from photoacoustic projections

引用

Photonics Insights 2024年第3期3卷 145-209页

作者： Chao Tian Kang Shen Wende Dong Fei Gao Kun Wang Jiao Li Songde Liu Ting Feng Chengbo Liu Changhui Li Meng Yang Sheng Wang Jie Tian Institute of Artificial Intelligence Hefei Comprehensive National Science CenterHefeiChina School of Engineering Science University of Science and Technology of ChinaHefeiChina Department of Anesthesiology the First Affiliated Hospital of USTCDivision of Life Sciences and MedicineUniversity of Science and Technology of ChinaHefeiChina Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing HefeiChina College of Automation Engineering Nanjing University of Aeronautics and AstronauticsNanjingChina School of Information Science and Technology ShanghaiTech UniversityShanghaiChina Shanghai Clinical Research and Trial Center ShanghaiChina CAS Key Laboratory of Molecular Imaging Institute of AutomationChinese Academy of SciencesBeijingChina School of Precision Instruments and Optoelectronics Engineering Tianjin UniversityTianjinChina Academy for Engineering and Technology Fudan UniversityShanghaiChina Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhenChina Department of Biomedical Engineering College of Future TechnologyPeking UniversityBeijingChina National Biomedical Imaging Center Peking UniversityBeijingChina Departments of Ultrasound State Key Laboratory of Complex Severe and Rare DiseasesPeking Union Medical College HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijingChina School of Engineering Medicine Beihang UniversityBeijingChina Key Laboratory of Big Data-Based Precision Medicine Beihang UniversityMinistry of Industry and Information TechnologyBeijingChina

Photoacoustic computed tomography(PACT)is a rapidly developing biomedical imaging modality and has attracted substantial attention in recent *** reconstruction from photoacoustic projections plays a critical role in image formation in *** we review six major classes of image reconstruction approaches developed in the past three decades,including delay and sum,filtered back projection,series expansion,time reversal,iterative reconstruction,and deep-learning-based *** principal ideas and implementations of the algorithms are summarized,and their reconstruction performances under different imaging scenarios are *** challenges,future directions,and perspectives for the development of image reconstruction algorithms in PACT are also *** review provides a self-contained reference guide for beginners and specialists in the photoacoustic community,to facilitate the development and application of novel photoacoustic image reconstruction algorithms.

关键词： optical imaging photoacoustic tomography image reconstruction deep learning

来源：评论

学校读者我要写书评

暂无评论

DTSD: A Dual Teacher-Student-Based Discrimination Model for Anomaly Detection

引用

ACM Transactions on Multimedia Computing, Communications, and applications 1000年

作者： Weizhi Xian Junyi Wang Xuekai Wei Jielu Yan Yueting Huang Kunyin Guo Weijia Jia Mingliang Zhou Chongqing Research Institute of Harbin Institute of Technology Harbin Institute of Technology China and School of Computer Science Chongqing University China Chengdu Institute of Computer Application Chinese Academy of Sciences China and University of Chinese Academy of Sciences China School of Computer Science Chongqing University China National Elite Institute of Engineering Chongqing University China Institute of Artificial Intelligence and Future Networks Beijing Normal University China and Guangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College China

The rapid development of computer vision technology for detecting anomalies in industrial products has received unprecedented attention. In this paper, we propose a dual teacher–student-based discrimination model (DTSD) for anomaly detection, which combines the advantages of both embedding-based and reconstruction-based methods. First, the DTSD builds a dual teacher-student architecture consisting of a pretrained teacher encoder with frozen parameters, a student encoder and a student decoder. By distillation of knowledge from the teacher encoder, the two teacher-student modules acquire the ability to capture both local and global anomaly patterns. Second, to address the issue of poor reconstruction quality faced by previous reconstruction-based approaches in some challenging cases, the model employs a feature bank that stores encoded features of normal samples. By incorporating template features from the feature bank, the student decoder receives explicit guidance to enhance the quality of reconstruction. Finally, a segmentation network is utilized to adaptively integrate multiscale anomaly information from the two teacher–student modules, thereby improving segmentation accuracy. Extensive experiments demonstrate that our method outperforms existing state-of-the-art approaches. The code of DTSD is publicly available on https://***/Math-Computer/DTSD.

关键词： Unsupervised learning anomaly detection teacher-student difference discrimination

来源：评论

学校读者我要写书评

暂无评论

Analysis characterisation of coolant monitoring system for nuclear research reactor digital instrumentation

引用

AIP Conference Proceedings 2022年第1期2676卷

作者： Nurfaizatul Akmal Mat Fadil Murizah Kassim Ahmad Azhari Mohamad Nor Mohd Sabri Minhat 1School of Electrical Engineering College of Engineering Universiti Teknologi MARA 40450 Shah Alam Selangor. MALAYSIA 2Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Kompleks Al-Khawarizmi Universiti Teknologi MARA 40450 Shah Alam Selangor Malaysia 3Reactor Technology Centre Malaysian Nuclear Agency Kajang Selangor Malaysia

Nuclear energy is now one of the most impactful forms of energy generation today, and it is still widely utilized. This research presents a pre-analysis on the Characterization of Coolant Monitoring System for Nuclear Research Reactor Digital Instrumentation. The process passes from the primary coolant system to the Nuclear Research Reactor Digital Instrumentation monitoring system. Less study was conducted on variable modelling to determine the lifespan of PUSPATI TRIGA Reactor (RTP) operations in Malaysia. The principal coolant deprived the heat exchanger’s performance and temperature regulation for prediction analysis data. This paper presents an analysis for pre-studied modelling for preventive maintenance on cooling monitoring systems for plant reactor at RTP. data collection on the flow, temperature, and pressure for the cooling monitoring system was identified for both primary and secondary conditions. A day data was collected based on the interarrival time that is counted about 1000k+ data in time. Basic statistics based on the water flow, temperature, and pressure were analyzed. The result presents a normal condition status for the coolant system at RTP. The characterization of data is important in modelling future analysis for digital preventive maintenance. The significance of this study is to ensure that RTP’s primary coolant system is functioning well and to raise awareness of the possibility of an accident occurring without proper monitoring.

关键词：

来源：评论

学校读者我要写书评

暂无评论

COVID-19 in Malaysia and the trusted elements of mobile health records management issues

引用

AIP Conference Proceedings 2023年第1期2827卷

作者： Fatin Nur Zulkipli Azman Ismail Nurussobah Hussain Mohd Fakhrul Fahmi Sarwani Saiful Farik Mat Yatin Information Science Studies College of Computing Informatics and Media Universiti Teknologi MARA Puncak Perdana Selangor Malaysia Information Science Studies College of Computing Informatics and Media Universiti Teknologi MARA (UiTM) Kelantan Branch Kelantan Malaysia Universiti Kuala Lumpur Malaysian Institute of Marine Engineering Technology Lumut Perak Malaysia Meridian Project Management Sdn Bhd Cyberjaya Selangor Malaysia Institute for Big Data Analytics and Artificial Intelligence Universiti Teknologi MARA Selangor Malaysia

The novel Coronavirus (COVID-19) global pandemic has become a challenge for the healthcare industry and the digitalization era. Thus, it is high time for government hospitals to implement one of the Eleventh Malaysia Planning (11MP) strategies to integrate the transformation of the mobile application systems with future-ready interfaces. The reliability of a healthcare records system that includes vital information about patients like demographics and medical history requires high-security measures. The COVID-19 outbreak highlights the importance of the trustworthiness of the healthcare system through a stronger partnership between security and information technology. However, the lack of standards concerning the safety and storage of records and legal compliance are crucial issues for mobile health records management (MHRM). Since the outbreak, the Malaysian government has launched the “MySejahtera” mobile platform to educate citizens on effective information dissemination. However, the increased COVID-19 testing highlights the shortfall of “MySejahtera,” suggesting that enhancements to the existing system are much needed. Thus, this study aims to analyze mobile health records’ trustworthiness in Malaysian hospitals by creating a reliable framework. A qualitative approach using descriptive and comparative data will be employed. The study’s outcome is expected to be a guide for Malaysian healthcare in providing conducive environments for all.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Designing preamplifier for sensing atmospheric electrostatic field strength via supercapacitive sensor

引用

Journal of Physics: Conference Series 2020年第1期1607卷

作者： Changhao Wu Yi Xiong Wei Zeng National Engineering Research Center for Agro-Ecological Big Data Analysis and Application School of Electronics and Information Engineering Anhui University Hefei 230601 People's Republic of China Science and Technology Institute Hubei Key Laboratory of Biomass Fibers and Eco-Dyeing and Finishing Wuhan Textile University Wuhan 430073 People's Republic of China School of Physics and Technology MOE Key Laboratory of Artificial Micro- and Nano-Structures and Center for Electron Microscopy Wuhan University Wuhan 430072 People's Republic of China

In order to effectively obtain the signal from sensor, the analogy signal needs to be amplified and then converted into a digital signal for matching to the sensor characteristics. With a supercapacitive electric field sensor based on graphene aerogel, the response current signal from the electric field sensor is weak and unstable. Herein, a high gain and low noise preamplifier is developed, and an amplifier circuit with double T-type feedback network is proposed to reduce the Johnson noise for the amplifier. This design can reduce the thermal noise of resistance by using the smaller resistance under the same gain, and it can effectively reduce the interference of peak noise by adding the feedback capacitance, so as to improve the detection accuracy. The simulation results show that under the same gain condition, the Johnson noise can be reduced by 46% and the detection accuracy can be improved by 12% compared with the traditional T-type feedback network.

关键词：

来源：评论

学校读者我要写书评

暂无评论

One for Multiple: Physics-informed Synthetic data Boosts Generalizable Deep Learning for Fast MRI Reconstruction

arXiv

引用

arXiv 2023年

作者： Wang, Zi Yu, Xiaotong Wang, Chengyan Chen, Weibo Wang, Jiazheng Chu, Ying-Hua Sun, Hongwei Li, Rushuai Li, Peiyong Yang, Fan Han, Haiwei Kang, Taishan Lin, Jianzhong Yang, Chen Chang, Shufu Shi, Zhang Hua, Sha Li, Yan Hu, Juan Zhu, Liuhong Zhou, Jianjun Lin, Meijing Guo, Jiefeng Cai, Congbo Chen, Zhong Guo, Di Yang, Guang Qu, Xiaobo Department of Electronic Science Intelligent Medical Imaging R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance National Institute for Data Science in Health and Medicine Xiamen University China Human Phenome Institute Fudan University China Philips Healthcare China Siemens Healthineers Ltd. China United Imaging Research Institute of Intelligent Imaging China Department of Nuclear Medicine Nanjing First Hospital China Shandong Aoxin Medical Technology Company China Department of Radiology The First Affiliated Hospital of Xiamen University China Department of Radiology Zhongshan Hospital Affiliated to Xiamen University China China Department of Cardiology Shanghai Institute of Cardiovascular Diseases Zhongshan Hospital Fudan University China Department of Radiology Zhongshan Hospital Fudan University China Department of Cardiovascular Medicine Heart Failure Center Ruijin Hospital Lu Wan Branch Shanghai Jiaotong University School of Medicine China Department of Radiology Ruijin Hospital Shanghai Jiaotong University School of Medicine China Medical Imaging Department The First Affiliated Hospital of Kunming Medical University China Xiamen Key Laboratory of Clinical Transformation of Imaging Big Data and Artificial Intelligence China Department of Applied Marine Physics and Engineering Xiamen University China Department of Microelectronics and Integrated Circuit Xiamen University China School of Computer and Information Engineering Xiamen University of Technology China Department of Bioengineering Imperial College London United Kingdom

Magnetic resonance imaging (MRI) is a widely used radiological modality renowned for its radiation-free, comprehensive insights into the human body, facilitating medical diagnoses. However, the drawback of prolonged scan times hinders its accessibility. The k-space undersampling offers a solution, yet the resultant artifacts necessitate meticulous removal during image reconstruction. Although Deep Learning (DL) has proven effective for fast MRI image reconstruction, its broader applicability across various imaging scenarios has been constrained. Challenges include the high cost and privacy restrictions associated with acquiring large-scale, diverse training data, coupled with the inherent difficulty of addressing mismatches between training and target data in existing DL methodologies. Here, we present a novel Physics-Informed Synthetic data learning framework for Fast MRI, called PISF. PISF marks a breakthrough by enabling generalized DL for multi-scenario MRI reconstruction through a single trained model. Our approach separates the reconstruction of a 2D image into many 1D basic problems, commencing with 1D data synthesis to facilitate generalization. We demonstrate that training DL models on synthetic data, coupled with enhanced learning techniques, yields in vivo MRI reconstructions comparable to or surpassing those of models trained on matched realistic datasets, reducing the reliance on real-world MRI data by up to 96%. Additionally, PISF exhibits remarkable generalizability across multiple vendors and imaging centers. Its adaptability to diverse patient populations has been validated through evaluations by ten experienced medical professionals. PISF presents a feasible and cost-effective way to significantly boost the widespread adoption of DL in various fast MRI applications. Importantly, it alleviates the ethical and practical challenges associated with in vivo human data acquisitions, opening new horizons for advancing fast MRI technologies. Copyright © 20

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：