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

引用

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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Power Efficient Deployment of VLC-enabled UAVs

Power Efficient Deployment of VLC-enabled UAVs

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IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Zhiyu Zhu Yang Yang Caili Guo Mingzhe Chen Shuguang Cui H. Vincent Poor Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Department of Electrical Engineering Princeton University Princeton NJ USA The Future Network of Intelligence Institute The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China

ISBN: (数字)9781728144900

ISBN: (纸本)9781728144917

In this paper, a power efficient deployment for visible light communication (VLC)-enabled unmanned aerial vehicles (UAVs) is studied. In the studied model, each UAV can provide communication service for ground users and illumination builds the VLC links between UAVs and users. Hence, each UAV's signal transmission and illumination will affect other UAVs' signal transmission and illumination. Therefore, to deploy VLC-enabled UAVs so as to efficiently service the ground users, the interference caused by the signal transmission and illumination of UAVs must be considered. This problem is formulated as an optimization problem whose goal is to optimize the deployment of UAVs so as to minimize the power consumption for signal transmission and illumination. An iterative algorithm is first proposed to transform the optimization problem into a series of interdependent subproblems, and the transformed problems are then solved by the Lagrangian dual method. In addition, convergence and complexity of the algorithm are also analyzed. Numerical results show that the proposed scheme can reduce at least 53.7% power consumption when compared to the baselines with UAVs at the center of each sub-area.

关键词： Lighting Optimization Light emitting diodes Unmanned aerial vehicles Power demand Optical transmitters Convergence

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Supplementary material for CDC submission no. 1461

arXiv

引用

arXiv 2020年

作者： Ju, Yue Chen, Tianshi Mu, Biqiang Ljung, Lennart Chinese University of Hong Kong Shenzhen518172 Hong Kong School of Science and Engineering Shenzhen Research Institute of Big Data Chinese University of Hong Kong Shenzhen518172 Hong Kong Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and System Science Chinese Academy of Sciences Beijing100190 China Division of Automatic Control Department of Electrical Engineering Linköping University LinköpingSE-58183 Sweden

In this paper, we study the influence of ill-conditioned regression matrix on two hyper-parameter estimation methods for the kernel-based regularization method: the empirical Bayes (EB) and the Stein’s unbiased risk estimator (SURE). First, we consider the convergence rate of the cost functions of EB and SURE, and we find that they have the same convergence rate but the influence of the ill-conditioned regression matrix on the scale factor are different: the scale factor for SURE contains one more factor cond(ΦTΦ) than that of EB, where Φ is the regression matrix and cond(·) denotes the condition number of a matrix. This finding indicates that when Φ is ill-conditioned, i.e., cond(ΦTΦ) is large, the cost function of SURE converges slower than that of EB. Then we consider the convergence rate of the optimal hyper-parameters of EB and SURE, and we find that they are both asymptotically normally distributed and have the same convergence rate, but the influence of the ill-conditioned regression matrix on the scale factor are different. In particular, for the ridge regression case, we show that the optimal hyper-parameter of SURE converges slower than that of EB with a factor of 1/n2, as cond(ΦTΦ) goes to ∞, where n is the FIR model order. Copyright © 2020, The Authors. All rights reserved.

关键词： Cost functions

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Handling of uncertainty in medical data using machine learning and probability theory techniques: A review of 30 years (1991-2020)

arXiv

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arXiv 2020年

作者： Alizadehsani, Roohallah Roshanzamir, Mohamad Hussain, Sadiq Khosravi, Abbas Koohestani, Afsaneh Zangooei, Mohammad Hossein Abdar, Moloud Beykikhoshk, Adham Shoeibi, Afshin Zare, Assef Panahiazar, Maryam Nahavandi, Saeid Srinivasan, Dipti Atiya, Amir F. Acharya, U. Rajendra Deakin University Geelong Australia Department of Engineering Fasa Branch Islamic Azad University Post Box No 364 Fasa Fars*** Iran System Administrator Dibrugarh University Assam786004 India University of Texas Dallas United States Applied Artificial Intelligence Institute Deakin University Geelong Australia Computer Engineering Department Ferdowsi University of Mashhad Mashhad Iran Faculty of Electrical and Computer Engineering Biomedical Data Acquisition Lab K. N. Toosi University of Technology Tehran Iran Faculty of Electrical Engineering Gonabad Branch Islamic Azad University Gonabad Iran Institute for Computational Health Sciences University of California San Francisco United States Dept. of Electrical and Computer Engineering National University of Singapore Singapore117576 Singapore Department of Computer Engineering Faculty of Engineering Cairo University Cairo12613 Egypt Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore Singapore Department of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore Department of Bioinformatics and Medical Engineering Asia University Taiwan

Understanding data and reaching valid conclusions are of paramount importance in the present era of big data. Machine learning and probability theory methods have widespread application for this purpose in different fields. One critically important yet less explored aspect is how data and model uncertainties are captured and analyzed. Proper quantification of uncertainty provides valuable information for optimal decision making. This paper reviewed related studies conducted in the last 30 years (from 1991 to 2020) in handling uncertainties in medical data using probability theory and machine learning techniques. Medical data is more prone to uncertainty due to the presence of noise in the data. So, it is very important to have clean medical data without any noise to get accurate diagnosis. The sources of noise in the medical data need to be known to address this issue. Based on the medical data obtained by the physician, diagnosis of disease, and treatment plan are prescribed. Hence, the uncertainty is growing in healthcare and there is limited knowledge to address these problems. We have little knowledge about the optimal treatment methods as there are many sources of uncertainty in medical science. Our findings indicate that there are few challenges to be addressed in handling the uncertainty in medical raw data and new models. In this work, we have summarized various methods employed to overcome this problem. Nowadays, application of novel deep learning techniques to deal such uncertainties have significantly increased. Copyright © 2020, The Authors. All rights reserved.

关键词： Monte Carlo methods

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CeliacNet: Celiac disease severity diagnosis on duodenal histopathological images using deep residual networks

arXiv

引用

arXiv 2019年

作者： Sali, Rasoul Ehsan, Lubaina Kowsari, Kamran Khan, Marium Moskaluk, Christopher A. Syed, Sana Brown, Donald E. Department of System and Information Engineering University of Virginia CharlottesvilleVA United States Department of Pediatrics School of Medicine University of Virginia CharlottesvilleVA United States School of Data Science University of Virginia CharlottesvilleVA United States

Celiac Disease (CD) is a chronic autoimmune disease that affects the small intestine in genetically predisposed children and adults. Gluten exposure triggers an inflammatory cascade which leads to compromised intestinal barrier function. If this enteropathy is unrecognized, this can lead to anemia, decreased bone density, and, in longstanding cases, intestinal cancer. The prevalence of the disorder is 1% in the United States. An intestinal (duodenal) biopsy is considered the "gold standard" for diagnosis. The mild CD might go unnoticed due to non-specific clinical symptoms or mild histologic features. In our current work, we trained a model based on deep residual networks to diagnose CD severity using a histological scoring system called the modified Marsh score. The proposed model was evaluated using an independent set of 120 whole slide images from 15 CD patients and achieved an AUC greater than 0.96 in all classes. These results demonstrate the diagnostic power of the proposed model for CD severity classification using histological images. Copyright © 2019, The Authors. All rights reserved.

关键词： Medical imaging

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Quantifying material stocks in long-lived products: Challenges and improvements for informing sustainable resource use strategies

引用

Resources, Conservation and Recycling 2025年 221卷

作者： Jan Streeck André Baumgart Helmut Haberl Fridolin Krausmann Bowen Cai Tomer Fishman Maud Lanau Peter Berrill Zhi Cao Sebastiaan Deetman David Frantz Volker Krey Alessio Mastrucci Alessio Miatto Stefan Pauliuk Lola Sylvie Annie Rousseau Shoshanna Saxe Danielle Densley Tingley Gamze Ünlü Dominik Wiedenhofer BOKU University Institute of Social Ecology Department of Economics and Social Sciences Schottenfeldgasse 29 1070 Vienna Austria Wuhan University School of Remote Sensing and Information Engineering Wuhan Hubei Province 430079 China Leiden University Institute of Environmental Sciences (CML) 2333 CC Leiden Netherlands Chalmers University of Technology Department of Architecture and Civil Engineering Research Area Sustainable Built Environments Gothenburg 412 96 Sweden Universität Berlin Sustainability Economics of Human Settlements Berlin 10623 Germany College of Environmental Science and Engineering Nankai University Tianjin 300350 China School of Earth System Science Tianjin University Tianjin 300072 China Trier University Geoinformatics -Spatial Data Science Trier Rhineland-Palatinate 54296 Germany International Institute for Applied Systems Analysis (IIASA) Energy Climate and Environment (ECE) Program Schlossplatz 1 2361 Laxenburg Austria Commonwealth Scientific and Industrial Research Organisation (CSIRO) Canberra 2601 ACT Australia University of Freiburg Faculty of Environment and Natural Resources Freiburg Baden-Wuerttemberg D-79102 Germany Norwegian University of Science and Technology (NTNU) Industrial Ecology Programme Department of Energy and Process Engineering Trondheim 7034 Norway University of Toronto Department of Civil and Mineral Engineering Toronto Ontario M5S 1A1 Canada University of Sheffield School of Mechanical Aerospace and Civil Engineering Mappin Street Sheffield S1 3JD United Kingdom

Material stocks in long-lived products require over half of the annual global resource extraction for their construction and maintenance, and lock in energy use through their technical and geospatial characteristics. A thorough understanding of material stocks is therefore essential to inform sustainable resource use strategies. However, despite substantial advances in material stock research in recent decades, their robust quantification remains challenging and bears considerable uncertainties. We assess the (dis)agreement of material stock estimates from 32 recent studies across global, national, and urban scales, and propose recommendations for future work. Overall, we observe medium to high divergences between studies estimating the same material stocks. For end-use categories that aggregate multiple material stocks (e.g., buildings), most global-level estimates show divergences within 140 %. At the national level, most estimates for the USA diverge by <210 %, while those for China by <550 %. At the urban level, most estimates for Beijing fall within 90 %, and for Vienna, within 70 %. For low-income countries, non-residential buildings, and individual materials, the differences are often substantially higher, highlighting the need for an improved scientific basis for policy and planning. These disparities arise from differences in system boundaries, methodology, data sources, definitions, and lack of data to capture the diversity of material stock types. To robustly inform sustainable resource use strategies, the scientific community and practitioners should systematically assess and report sensitivity and uncertainty, and reduce the latter through transparent documentation, model intercomparisons, consensus and open-access databases, enhanced data collection, and comprehensive quantification of material stocks.

关键词： Material flow analysis Sustainable resource management Uncertainty analysis Socioeconomic metabolism Material stocks

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Correction to: Non-extensitivity and criticality of atomic hydropathicity around a voltage-gated sodium channel's pore: a modeling study

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Journal of biological physics 2022年第4期48卷 477-478页

作者： Markos N Xenakis Dimos Kapetis Yang Yang Jordi Heijman Stephen G Waxman Giuseppe Lauria Catharina G Faber Hubert J Smeets Patrick J Lindsey Ronald L Westra Department of Toxicogenomics Section Clinical Genomics Maastricht University PO Box 616 6200 MD Maastricht The Netherlands. mrksxenakis@***. Research School for Mental Health and Neuroscience (MHeNS) Maastricht University PO Box 616 6200 MD Maastricht The Netherlands. mrksxenakis@***. Neuroalgology Unit Fondazione IRCCS Istituto Neurologico "Carlo Besta" Via Celoria 11 20133 Milan Italy. Department of Medicinal Chemistry and Molecular Pharmacology Purdue University College of Pharmacy IN 47907 West Lafayette USA. Purdue Institute for Integrative Neuroscience IN 47907 West Lafayette USA. Department of Cardiology CARIM School for Cardiovascular Diseases Maastricht University PO Box 616 6200 MD Maastricht The Netherlands. Department of Neurology and Center for Neuroscience and Regeneration Research Yale University School of Medicine New Haven CT 06510 USA. Rehabilitation Research Center Veterans Affairs Connecticut Healthcare System West Haven CT 06516 USA. Department of Biomedical and Clinical Sciences "Luigi Sacco" University of Milan Via G.B. Grassi 74 20157 Milan Italy. Department of Neurology Maastricht University Medical Center PO Box 5800 6202 AZ Maastricht The Netherlands. Department of Toxicogenomics Section Clinical Genomics Maastricht University PO Box 616 6200 MD Maastricht The Netherlands. Research School for Mental Health and Neuroscience (MHeNS) Maastricht University PO Box 616 6200 MD Maastricht The Netherlands. Research School for Oncology and Developmental Biology (GROW) Maastricht University PO Box 616 6200 MD Maastricht The Netherlands. Department of Data Science and Knowledge Engineering Maastricht University PO Box 616 6200 MD Maastricht The Netherlands.

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Protein Identification Strategies: Towards a Hybrid Approach to Control FDR

Protein Identification Strategies: Towards a Hybrid Approach...

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2018 International Symposium on Advanced Electrical and Communication Technologies, ISAECT 2018

作者： Jadid, Sara El Touahni, Raja Moussa, Ahmed Department of Physics Faculty of Science Ibn Tofail University Laboratory of Telecommunication Systems and Decision Engineering Kenitra Morocco System and Data Engineering Team Abdelmalek Essaadi University National School of Applied Science Tangier Morocco

ISBN: (纸本)9781538673287

Tandem Mass Spectrometry has made a massive advance as a main technology for peptide sequencing. It generates a flood of data that represents significant challenges for their interpretation. With the emergence of tandem mass spectrometry data (MS/MS), a lot of computational methods have been developed to infer peptide sequences from them. However, the correct identification of all proteins present in a sample is of utmost importance to provide a suitable biological interpretation. Mostly, there are two strategies, which are widely used for peptide/protein identification: (i) database searching strategy consists of comparing the experimental spectra acquired through the mass spectrometer, with the identified spectra belonging to peptide libraries. (ii) De novo sequencing strategy predicts, without the assistance of peptide database, amino acids sequence from its tandem mass spectrum. These two strategies differ in term of their ability and effectiveness to control the False Discovery Rate (FDR). In this paper, we introduce a review study of database searching and de novo sequencing for peptide identification. Some background information of mass spectrometry and peptide/protein identification are first outlined. Afterward, we discuss the process and tools of both database searching and de novo strategies. Aiming to control the False Discovery Rate, an effective and hybrid approach, which combines database searching and de novo strategies, is proposed in this work. © 2018 IEEE.

关键词： Mass spectrometry

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The Single Polybag Loading Problem

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Electronic Notes in Discrete Mathematics 2018年 69卷 69-76页

作者： Che, Yuxin Liu, Ying Gao, Qiang Lim, Andrew Hu, Qian Department of Computer Science Xiamen University Xiamen China School of Management and Engineering Nanjing University Nanjing China Big Data Center Suning Commerce Group Co. Ltd. Nanjing China Department of Industrial System Engineering and Management National University of Singapore Singapore

In the logistics industry, not only cartons but also polybags are used to pack goods for delivery. We study a single polybag loading problem, which is faced by many e-retailers and courier companies. In the problem, a set of rotatable boxes and a two-dimensional rectangular polybag are given. Because the polybag is flexible, the three-dimensional space inside the polybag can be variable when the boxes are loaded. The problem is to choose a subset of the boxes and pack them orthogonally into the polybag so as to maximize the space utilization. We introduce a mixed integer programming formulation and propose a tree search heuristic for the problem. Methods are evaluated by computational experiments on randomly generated data. © 2018 Elsevier B.V.

关键词： heuristic mixed integer programming model Packing polybag packing single polybag loading problem

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Association of Risk Factors For Venous Thromboembolism and Overall Survival in Lung Cancer

Blood Vessels, Thrombosis & Hemostasis

引用

Blood Vessels, Thrombosis & Hemostasis 2025年

作者： Thomas E. Plate Asaad Trabolsi Rachel S. Kronenfeld Leticia E. Campoverde Dan Morgenstern-Kaplan Alyssa J. Mercadel Michael Caballero Wei Zhao Gerald A. Soff Sylvester Comprehensive Cancer Center University of Miami Miller School of Medicine Miami Fl USA Hematology Oncology Fellowship Program Jackson Memorial Health System/ University of Miami Miami Fl USA Division of Pediatric Hematology/Oncology University of Miami/Jackson Memorial Hospital Miami FL Internal Medicine Residency program division of internal medicine Department of Medicine University of Miami/ Jackson Memorial Hospital Miami Division of Gynecologic Oncology Department of Obstetrics Gynecology and Reproductive Sciences University of Miami Leonard M. Miller School of Medicine Miami Data Science and Engineering UHealth Information Technology University of Miami Miami FL Biostatistics and Bioinformatics Shared Resource Sylvester Comprehensive Cancer Center University of Miami Miami FL Division of Hematology Department of Medicine University of Miami Miller School of Medicine Miami Fl USA

Venous thromboembolism (VTE) is a frequent complication in patients with lung cancer, but the risk factors and incidence in different lung cancer subtypes have not been fully characterized. Despite multiple studies supporting the use of VTE prophylaxis in patients with cancer at increased risk of VTE by the Khorana score (KS), routine use of VTE prophylaxis is uncommon in clinical practice for these patients. This study further characterizes the risk factors and incidence of VTE and in patients with lung cancer at a university cancer center. Further, we assessed the association of KS and its individual components with overall survival in this same group of patients with lung cancer. Utilizing natural language processing and human review to detect thrombotic events in the electronic medical record, a 12-month incidence of 10.1% was identified in the 632 lung cancer patients analyzed. Significant risk factors included age <60 years and white blood cell count (WBC) ≥ 11× 10 9 /L, but KS itself was not significantly associated with VTE. The median overall survival was 12 months with VTE. The KS, age ≥ 60 years, stage III-IV, WBC ≥ 11× 10 9 /L, Hgb <10 gm/dL, BMI, surgery and VTE were identified as significant predictors of death. These findings warrant further validation, as the KS and two of its individual components in this study of lung cancer were significantly associated with reduced overall survival.

关键词： Lung cancer thrombosis Khorana Score cancer associated thrombosis

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