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Non-invasive biopsy diagnosis of diabetic kidney disease via deep learning applied to retinal images: a population-based study

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The Lancet Digital Health 2025年第5期7卷 100868页

作者： Meng, Ziyao Guan, Zhouyu Yu, Shujie Wu, Yilan Zhao, Yaoning Shen, Jie Lim, Cynthia Ciwei Chen, Tingli Yang, Dawei Ran, An Ran He, Feng Hamzah, Haslina Singh, Sarkaaj Abd Raof, Anis Syazwani Lee-Boey, Jian Wen Samuel Lim, Soo-Kun Sun, Xufang Ge, Shuwang Xu, Gang Su, Hua Cheng, Yang Lu, Feng Liao, Xiaofei Jin, Hai Deng, Chenxin Ruan, Lei Zhang, Cuntai Wu, Chan Dai, Rongping Jin, Yixiao Wang, Wenxiao Li, Tingyao Liu, Ruhan Li, Jiajia Shu, Jia Lu, Yuwei Wang, Xiangning Wu, Qiang Qin, Yiming Tang, Jin Sheng, Xiaohua Jiao, Qiong Yang, Xiaokang Guo, Minyi McKay, Gareth J Hogg, Ruth E Liew, Gerald Chee, Evelyn Yi Lyn Hsu, Wynne Lee, Mong Li Szeto, Simon Luk, Andrea O Y Chan, Juliana C N Cheung, Carol Y Tan, Gavin Siew Wei Tham, Yih-Chung Cheng, Ching-Yu Sabanayagam, Charumathi Lim, Lee-Ling Jia, Weiping Li, Huating Sheng, Bin Wong, Tien Yin Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Institute for Proactive Healthcare Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Centre for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Beijing Tsinghua Changgung Hospital Eye Center School of Clinical Medicine Tsinghua Medicine Tsinghua University Beijing China Medical Records and Statistics Office Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Renal Medicine Singapore General Hospital SingHealth-Duke Academic Medical Centre Singapore Singapore Department of Ophthalmology Shanghai Health and Medical Center Wuxi China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong Special Administrative Region China Singapore Eye Research Institute Singapore National Eye Centre Singapore Department of Medicine Faculty of Medicine Universiti Malaya Kuala Lumpur Malaysia Department of Ophthalmology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Hubei Wuhan China Department of Nephrology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Nephrology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Ophthalmology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Centre for Big Dat

Background: Improving the accessibility of screening diabetic kidney disease (DKD) and differentiating isolated diabetic nephropathy from non-diabetic kidney disease (NDKD) are two major challenges in the field of diabetes care. We aimed to develop and validate an artificial intelligence (AI) deep learning system to detect DKD and isolated diabetic nephropathy from retinal fundus images. Methods: In this population-based study, we developed a retinal image-based AI-deep learning system, DeepDKD, pretrained using 734 084 retinal fundus images. First, for DKD detection, we used 486 312 retinal images from 121 578 participants in the Shanghai Integrated Diabetes Prevention and Care System for development and internal validation, and ten multi-ethnic datasets from China, Singapore, Malaysia, Australia, and the UK (65 406 participants) for external validation. Second, to differentiate isolated diabetic nephropathy from NDKD, we used 1068 retinal images from 267 participants for development and internal validation, and three multi-ethnic datasets from China, Malaysia, and the UK (244 participants) for external validation. Finally, we conducted two proof-of-concept studies: a prospective real-world study with 3 months' follow-up to evaluate the effectiveness of DeepDKD in screening DKD;and a longitudinal analysis of the effectiveness of DeepDKD in differentiating isolated diabetic nephropathy from NDKD on renal function changes with 4·6 years' follow-up. Findings: For detecting DKD, DeepDKD achieved an area under the receiver operating characteristic curve (AUC) of 0·842 (95% CI 0·838–0·846) on the internal validation dataset and AUCs of 0·791–0·826 across external validation datasets. For differentiating isolated diabetic nephropathy from NDKD, DeepDKD achieved an AUC of 0·906 (0·825–0·966) on the internal validation dataset and AUCs of 0·733–0·844 across external validation datasets. In the prospective study, compared with the metadata model, DeepDKD could detect DKD wit

关键词： Deep learning

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data and Physics driven Deep Learning Models for Fast MRI Reconstruction: Fundamentals and Methodologies

arXiv

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arXiv 2024年

作者： Huang, Jiahao Wu, Yinzhe Wang, Fanwen Fang, Yingying Nan, Yang Alkan, Cagan Abraham, Daniel Liao, Congyu Xu, Lei Gao, Zhifan Wu, Weiwen Zhu, Lei Chen, Zhaolin Lally, Peter Bangerter, Neal Setsompop, Kawin Guo, Yike Rueckert, Daniel Wang, Ge Yang, Guang Department of Bioengineering Faculty of Engineering Imperial College London Exhibition Road LondonSW7 2AZ United Kingdom Imperial-X and School of Biomedical Engineering & Imaging Sciences King's College London United Kingdom Department of Electrical Engineering Stanford University CA94305 United States Department of Radiology Beijing Anzhen Hospital Capital Medical University 2nd Anzhen Road Chaoyang District Beijing100029 China School of Biomedical Engineering Sun Yat-sen University Guangdong Guangzhou510006 China Hong Kong University of Science and Technology Hongkong Hong Kong Department of Data Science and AI Faculty of Information Technology Monash University Wellington Rd ClaytonVIC3800 Australia Electrical and Computer Engineering Department Boise State University ID83725 United States Department of Radiology Stanford University CA94305 United States Department of Computing Imperial College London South Kensington Campus LondonSW7 2AZ United Kingdom Technical University of Munich Munich80333 Germany New York United States

Magnetic Resonance Imaging (MRI) is a pivotal clinical diagnostic tool, yet its extended scanning times often compromise patient comfort and image quality, especially in volumetric, temporal and quantitative scans. This review elucidates recent advances in MRI acceleration via data and physics-driven models, leveraging techniques from algorithm unrolling models, enhancement-based methods, and plug-and-play models to the emerging full spectrum of generative model-based methods. We also explore the synergistic integration of data models with physics-based insights, encompassing the advancements in multi-coil hardware accelerations like parallel imaging and simultaneous multi-slice imaging, and the optimization of sampling patterns. We then focus on domain-specific challenges and opportunities, including image redundancy exploitation, image integrity, evaluation metrics, data heterogeneity, and model generalization. This work also discusses potential solutions and future research directions, with an emphasis on the role of data harmonization and federated learning for further improving the general applicability and performance of these methods in MRI reconstruction. © 2024, CC BY.

关键词： Magnetic resonance imaging

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The association between bone density of lumbar spines and different daily protein intake in different renal function

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Renal Failure 2024年第1期46卷 2298080页

作者： Lee, Chia-Lin Chen, Kun-Hui Liu, Wei‑Ju Chen, Ching-Hsien Tsai, Shang-Feng Department of Post-Baccalaureate Medicine College of Medicine National Chung Hsing University Taichung Taiwan School of Medicine National Yang-Ming University Taipei Taiwan Division of Endocrinology and Metabolism Department of Internal Medicine Taichung Veterans General Hospital Taichung Taiwan Intelligent data mining laboratory Department of Medical Research Taichung Veterans General Hospital Taichung Taiwan Department of Public Health College of Public Health China Medical University Taichung Taiwan Department of Orthopedic Surgery Taichung Veterans General Hospital Taichung Taiwan Department of Computer Science & Information Engineering College of Computing and Informatics Providence University Taichung Taiwan Divisions of Nephrology and Pulmonary Critical Care and Sleep Medicine Department of Internal Medicine University of California at Davis Davis CA United States Division of Nephrology Department of Internal Medicine Taichung Veterans General Hospital Taichung Taiwan Department of Life Science Tunghai University Taichung Taiwan

Background: Low protein intake (LPI) has been suggested as a treatment for chronic kidney disease (CKD). However, protein intake is essential for bone health. Methods: We studied the database of the National Health and Nutrition Examination Survey, 2005–2010. Basic variables, metabolic diseases, and bone density of different femoral areas were stratified into four subgroups according to different protein intake (DPI) (that is, <0.8, 0.8–1.0, 1.0–1.2, and >1.2 g/kg/day). Results: Significant differences were found among all lumbar area bone mineral density (BMD) and T-scores (p < 0.0001). There was an apparent trend between a decreasing BMD in the CKD groups with increasing DPI in all single lumbar spines (L1, L2, L3, and L4) and all L spines (L1-L4). Compared with DPI (0.8–1.0 g/day/kg), higher risks of osteoporosis were noticed in the subgroup of >1.2 g/day/kg over L2 (relative risk (RR)=1.326, 95% confidence interval (CI)=1.062–1.656), subgroup >1.2 g/day/kg over L3 (RR = 1.31, 95%CI = 1.057–1.622), subgroup <0.8 g/day/kg over L4 (RR = 1.276, 95%CI = 1.015–1.605), subgroup <0.8 g/day/kg over all L spines (RR = 11.275, 95%CI = 1.051–1.548), and subgroup >1.2 g/day/kg over all L spines (RR = 0.333, 95%CI = 1.098–1.618). However, a higher risk of osteoporosis was observed only in the non-CKD group. There was an apparent trend of higher DPI coexisting with lower BMD and T scores in patients with CKD. For osteoporosis (reference:0.8–1.0 g/day/kg), lower (<0.8 g/day/kg) or higher DPI (>1.2 g/day/kg) was associated with higher risks in the non-CKD group, but not in the CKD group. Conclusions: In the CKD group, LPI for renal protection was safe without threatening L spine bone density and without causing a higher risk of osteoporosis. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Bone density chronic kidney disease lumbar spine osteoporosis protein diet

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Reverse Perspective Network for Perspective-Aware Object Counting

Reverse Perspective Network for Perspective-Aware Object Cou...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Yifan Yang Guorong Li Zhe Wu Li Su Qingming Huang Nicu Sebe School of Computer Science and Technology UCAS Beijing China Key Lab of Big Data Mining and Knowledge Management UCAS Beijing China Key Lab of Intelligent Information Processing Institute of Computing Technology CAS Beijing China University of Trento Trento Italy

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

One of the critical challenges of object counting is the dramatic scale variations, which is introduced by arbitrary perspectives. We propose a reverse perspective network to solve the scale variations of input images, instead of generating perspective maps to smooth final outputs. The reverse perspective network explicitly evaluates the perspective distortions, and efficiently corrects the distortions by uniformly warping the input images. Then the proposed network delivers images with similar instance scales to the regressor. Thus the regression network doesn't need multi-scale receptive fields to match the various scales. Besides, to further solve the scale problem of more congested areas, we enhance the corresponding regions of ground-truth with the evaluation errors. Then we force the regressor to learn from the augmented ground-truth via an adversarial process. Furthermore, to verify the proposed model, we collected a vehicle counting dataset based on Unmanned Aerial Vehicles (UAVs). The proposed dataset has fierce scale variations. Extensive experimental results on four benchmark datasets show the improvements of our method against the state-of-the-arts.

关键词： Distortion Training Feature extraction Estimation Convolution Kernel Adaptation models

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Interactive Two-Stream Decoder for Accurate and Fast Saliency Detection

Interactive Two-Stream Decoder for Accurate and Fast Salienc...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Huajun Zhou Xiaohua Xie Jian-Huang Lai Zixuan Chen Lingxiao Yang School of Data and Computer Science Sun Yat-sen University China Guangdong Province Key Laboratory of Information Security Technology China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Sun Yat-sen University Guangzhou China

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Recently, contour information largely improves the performance of saliency detection. However, the discussion on the correlation between saliency and contour remains scarce. In this paper, we first analyze such correlation and then propose an interactive two-stream decoder to explore multiple cues, including saliency, contour and their correlation. Specifically, our decoder consists of two branches, a saliency branch and a contour branch. Each branch is assigned to learn distinctive features for predicting the corresponding map. Meanwhile, the intermediate connections are forced to learn the correlation by interactively transmitting the features from each branch to the other one. In addition, we develop an adaptive contour loss to automatically discriminate hard examples during learning process. Extensive experiments on six benchmarks well demonstrate that our network achieves competitive performance with a fast speed around 50 FPS. Moreover, our VGG-based model only contains 17.08 million parameters, which is significantly smaller than other VGG-based approaches. Code has been made available at: https://***/moothes/ITSD-pytorch.

关键词： Correlation Saliency detection Task analysis Decoding Linear programming Silicon Benchmark testing

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Correction to: development of a cloud-assisted classification technique for the preservation of secure data storage in smart cities

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Journal of Cloud computing 2023年第1期12卷 1-1页

作者： Kumar, Ankit Khan, Surbhi Bhatia Pandey, Saroj Kumar Shankar, Achyut Maple, Carsten Mashat, Arwa Malibari, Areej A. Department of Computer Engineering & Applications GLA University Mathura India Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Department of Data Science School of Science Engineering and Environment University of Salford Manchester Byblos UK WMG University of Warwick Coventry UK Secure Cyber Systems Research Group (SCSRG) WMG University of Warwick Coventry UK Faculty of Computing and Information Science King Abdulaziz University Rabigh Saudi Arabia Department of Industrial and Systems Engineering College of Engineering Princess Nourah Bint Abdulrahman University Riyadh Saudi Arabia

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Field-free spin-orbit torque-induced switching of perpendicular magnetization in a ferrimagnetic layer with vertical composition gradient

arXiv

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arXiv 2021年

作者： Zheng, Zhenyi Zhang, Yue Lopez-Dominguez, Victor Sánchez-Tejerina, Luis Shi, Jiacheng Feng, Xueqiang Chen, Lei Wang, Zilu Zhang, Zhizhong Zhang, Kun Hong, Bin Xu, Yong Zhang, Youguang Carpentieri, Mario Fert, Albert Finocchio, Giovanni Zhao, Weisheng Amiri, Pedram Khalili Fert Beijing Research Institute School of Integrated Circuit Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China Department of Electrical and Computer Engineering Northwestern University EvanstonIL United States School of Electronics and Information Engineering Beihang University Beijing China Department of Mathematical and Computer Sciences Physical Sciences and Earth Sciences University of Messina Messina Italy Dipartimento di Ingegneria Elettrica e dell’Informazione Politecnico di Bari Bari Italy Unité Mixte de Physique CNRS Thales Univ. Paris-Sud University of Paris-Saclay Palaiseau France

Current-induced spin-orbit torques (SOTs) are of interest for fast and energy-efficient manipulation of magnetic order in spintronic devices. To be deterministic, however, switching of perpendicularly magnetized materials by SOT requires a mechanism for in-plane symmetry breaking. Existing methods to do so involve the application of an in-plane bias magnetic field, or incorporation of in-plane structural asymmetry in the device, both of which can be difficult to implement in practical applications. Here, we report bias-field-free SOT switching in a single perpendicular CoTb layer with an engineered vertical composition gradient. The vertical structural inversion asymmetry induces strong intrinsic SOTs and a gradient-driven Dzyaloshinskii–Moriya interaction (g-DMI), which breaks the in-plane symmetry during the switching process. Micromagnetic simulations are in agreement with experimental results, and elucidate the role of g-DMI in the deterministic switching. This bias-field-free switching scheme for perpendicular ferrimagnets with g-DMI provides a strategy for efficient and compact SOT device design. © 2021, CC BY.

关键词： Switching

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Addressing research software sustainability via institutes

arXiv

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arXiv 2021年

作者： Katz, Daniel S. Carver, Jeffrey C. Chue Hong, Neil P. Gesing, Sandra Hettrick, Simon Honeyman, Tom Ram, Karthik Weber, Nicholas NCSA & CS & ECE & iSchool University of Illinois UrbanaIL United States Department of Computer Science University of Alabama TuscaloosaAL United States Software Sustainability Institute EPCC University of Edinburgh Edinburgh United Kingdom Center for Research Computing University of Notre Dame Notre DameIN United States Software Sustainability Institute ECS University of Southampton Southampton United Kingdom Software Program Australian Research Data Commons Univ. of Technology Sydney Australia Berkeley Institute for Data Science University of California BerkeleyCA United States Information School University of Washington SeattleWA United States

Research software is essential to modern research, but it requires ongoing human effort to sustain: to continually adapt to changes in dependencies, to fix bugs, and to add new features. Software sustainability institutes, amongst others, develop, maintain, and disseminate best practices for research software sustainability, and build community around them. These practices can both reduce the amount of effort that is needed and create an environment where the effort is appreciated and rewarded. The UK SSI is such an institute, and the US URSSI and the Australian AuSSI are planning to become institutes, and this extended abstract discusses them and the strengths and weaknesses of this approach. Copyright © 2021, The Authors. All rights reserved.

关键词： Sustainable development

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Retraction Note: Advanced artificial intelligence in heart rate and blood pressure monitoring for stress management

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Journal of Ambient Intelligence and Humanized computing 2024年第1期15卷 255-255页

作者： Lin, Qiang Li, Tongtong Shakeel, P. Mohamed Samuel, R. Dinesh Jackson School of Mathematics and Computer Science Northwest Minzu University LanzhouGansu China Key Laboratory of Streaming Data Computing Technologies and Application Northwest Minzu University LanzhouGansu China Key Laboratory of China’s Ethnic Languages and Information Technology of Ministry of Education Northwest Minzu University LanzhouGansu China Faculty of Information and Communication Technology Universiti Teknikal Malaysia Melaka (UTeM) Durian Tunggal Malaysia Faculty of Technology Design and Environment Visual Artificial Intelligence Lab Oxford Brookes University Oxford UK

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Explainable AI: Definition and attributes of a good explanation for health AI

arXiv

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arXiv 2024年

作者： Kyrimi, Evangelia McLachlan, Scott Wohlgemut, Jared M. Perkins, Zane B. Lagnado, David A. Marsh, William Gimson, Alexander Shafti, Ali Ercole, Ari Banerjee, Amitava Glocker, Ben Schafer, Burkhard Gatsonis, Constantine Grosan, Crina Sent, Danielle Berman, David S. Glass, David O'Regan, Declan P. Letsios, Dimitrios Morrissey, Dylan Pisirir, Erhan Leofante, Francesco Soyel, Hamit Williamson, Jon Grieman, Keri Dube, Kudakwashe Mardsen, Max Nagendran, Myura Tai, Nigel Kostopoulou, Olga Jones, Owain Curzon, Paul Stoner, Rebecca S. Tandle, Sankalp Joshi, Shalmali Mossadegh, Somayyeh Buijsman, Stefan Miller, Tim Madai, Vince Istvan School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom School of Nursing Midwifery and Palliative Care King's College London London United Kingdom Centre for Trauma Sciences Blizard Institute Queen Mary University of London London United Kingdom Royal London Hospital Barts Health NHS Trust London United Kingdom Department of Experiment Psychology University College London London United Kingdom Cambridge Liver Unit Addenbrooke's Hospital Cambridge University Hospitals Hills Road Box 210 Cambridge United Kingdom University of Cambridge United Kingdom Cambridge Consultants Ltd. United Kingdom Division of Anaesthesia Cambridge Centre for AI in Medicine University of Cambridge Cambridge United Kingdom Magdelene College University of Cambridge United Kingdom Institute of Health Informatics University College London London United Kingdom Department of Computing Imperial College London London United Kingdom SCRIPT Centre for IT and IP Law School of Law University of Edinburgh United Kingdom Center for Statistical Sciences School of Public Health Brown University Providence United States Research Group Artificial Intelligence HU University of Applied Sciences Utrecht Netherlands Jheronimus Academy of Data Science Tilburg University Eindhoven University of Technology s-Hertogenbosch Netherlands Centre for Theoretical Physics Queen Mary University of London London United Kingdom School of Computing Ulster University Belfast United Kingdom MRC Laboratory of Medical Sciences Imperial College London London United Kingdom Department of Informatics King's College London United Kingdom Physiotherapy Department Barts Health NHS Trust London United Kingdom Sport and Exercise Medicine Queen Mary University of London London United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Department of Computing Imperial College London United Kingdom Departmen

Proposals of artificial intelligence (AI) solutions based on increasingly complex and accurate predictive models are becoming ubiquitous across many disciplines. As the complexity of these models grows, transparency and users' understanding often diminish. This suggests that accurate prediction alone is insufficient for making an AI-based solution truly useful. In the development of healthcare systems, this introduces new issues related to accountability and safety. Understanding how and why an AI system makes a recommendation may require complex explanations of its inner workings and reasoning processes. Although research on explainable AI (XAI) has significantly increased in recent years and there is high demand for XAI in medicine, defining what constitutes a good explanation remains ad hoc, and providing adequate explanations continues to be challenging. To fully realize the potential of AI, it is critical to address two fundamental questions about explanations for safety-critical AI applications, such as health-AI: (1) What is an explanation in health-AI? and (2) What are the attributes of a good explanation in health-AI? In this study, we examined published literature and gathered expert opinions through a two-round Delphi study. The research outputs include (1) a definition of what constitutes an explanation in health-AI and (2) a comprehensive list of attributes that characterize a good explanation in health-AI. © 2024, CC BY.

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