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Intelligent non-invasive elderly fall monitoring by designing software defined radio frequency sensing system

Digital Communications and Networks 2024年

作者： Adeel Akram Muhammad Bilal Khan Najah Abed Abu Ali Qixing Zhang Awais Ahmad Muhammad Shahid Iqbal Syed Atif Moqurrab Laboratory for Intelligent Sensing and Computing Institute of Advanced Technology University of Science and Technology of China Hefei 230031 Anhui China State Key Laboratory of Fire Science University of Science and Technology of China Hefei 230026 Anhui China College of IT United Arab Emirates University Al-Ain 15551 United Arab Emirates Department of Electrical and Computer Engineering COMSATS University Islamabad Attock 43600 Pakistan College of Computer and Information Sciences Imam Muhammad Ibn Saud Islamic University (IMSIU) Riyadh 11432 Kingdom of Saudi Arabia Department of Computer Science and Information Technology Women University of AJ&K Bagh Pakistan School of Computing Gachon University 1342 Seongnam-daero Sujeong-gu Seongnam-si 13120 Korea

The global increase in life expectancy poses challenges related to the safety and well-being of the elderly population, especially in relation to falls. While falls can lead to significant cognitive impairments, timely intervention can mitigate their adverse effects. In this context, the need for non-invasive, efficient monitoring systems becomes paramount. Although wearable sensors have gained traction for monitoring health activities, they may cause discomfort during prolonged use, especially for the elderly. To address this issue, we present an intelligent, non-invasive Software-Defined Radio Frequency (SDRF) sensing system, tailored red for monitoring elderly people's falls during routine activities. Harnessing the power of deep learning and machine learning, our system processes the Wireless Channel State Information (WCSI) generated during regular and fall activities. By employing sophisticated signal processing techniques, the system captures unique patterns that distinguish falls from normal activities. In addition, we use statistical features to streamline data processing, thereby optimizing the computational efficiency of the system. Our experiments, conducted for a typical home environment while using treadmill, demonstrate the robustness of the system. The results show high classification accuracies of 92.5%, 95.1%, and 99.8% for three Artificial Intelligence (AI) algorithms. Notably, the SDRF-based approach offers flexibility, cost-effectiveness, and adaptability through software modifications, circumventing the need for hardware overhaul. This research attempts to bridge the gap in RF-based sensing for elderly fall monitoring, providing a solution that combines the benefits of non-invasiveness with the precision of deep learning and machine learning.

关键词： AI Elderly falls Intelligent learning SDRF WCSI

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Biodegradability of Partially Reduced Nanographene Oxide by Human, Plant and Microbial Enzymes: Impact of Magnetic Nanoparticles

SSRN

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SSRN 2024年

作者： Kurapati, Rajendra Swetha, K. Sudeshna, Samantaray Silva, Filipa A.L.S. Silva, Filipa C. Freitas, Bruno Incorvia, Jean Anne C. Banerjee, Sourav Afonso Fernandes, José Ramiro Jayaraj, Arya Singh, N. Sadananda Magalhães, Fernão D. Pinto, Artur M. School of Chemistry Indian Institute of Science Education and Research Thiruvananthapuram Maruthimala PO Thiruvananthapuram India LEPABE - Laboratory for Process Engineering Environment Biotechnology and Energy Faculdade de Engenharia Universidade do Porto Rua Dr. Roberto Frias s/n Porto4200-180 Portugal ALiCE - Associate Laboratory in Chemical Engineering Faculdade de Engenharia Universidade do Porto Rua Dr. Roberto Frias s/n Porto4200-180 Portugal i3S - Instituto de Investigação e Inovação em Saúde Universidade do Porto Rua Alfredo Allen 208 Porto4200-180 Portugal INEB - Instituto de Engenharia Biomédica Universidade do Porto Rua Alfredo Allen 208 Porto4200-180 Portugal IPATIMUP - Instituto de Patologia e Imunologia Molecular da Universidade do Porto Universidade do Porto Porto4200-180 Portugal Department of Electrical and Computer Engineering University of Texas at Austin. AustinTX78712 United States CQVR – Centro de Química Vila Real Universidade de Trás-os-Montes e Alto Douro Portugal Physical Department University of Trás-os-Montes and Alto Douro Quinta dos Prados Vila Real5000-801 Portugal School of Biology Indian Institute of Science Education and Research Thiruvananthapuram Maruthimala PO Thiruvananthapuram India

Although graphene-based materials continuously expand their range of industrial and biomedical applications, understanding their long-term fate in the organisms and environment is still in its infancy. Herein we examine the biodegradation of partially reduced graphene oxide (p-rGOn) and magnetic nanoparticles (IONPs) embedded rGOn (@rGOn) by incubating with the human myeloperoxidase (hMPO) isolated from the neutrophils, including with plant enzyme, horseradish peroxidase and treating with microbial laccase in the presence of its redox mediator ABTS [2,2'-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid)]. Further, the biodegradation of p-rGOn and @rGOn was investigated by incubating with the laccase-secreting fungi, Aspergillus sp, present in the compost soil. The biodegradation of both rGO samples was analyzed for morphological and structural changes using electron microscopy and Raman spectroscopy. The results confirmed the efficient degradation of p-rGOn and that the presence of IONPs enhanced the biodegradability of @rGOn (magnetic) over p-rGOn, which could be attributed to the Fenton-type reaction because of IONPs. These results confirmed that the presence of IONPs facilitates the magnetic manipulation of @rGOn and increases their biodegradability. The current work could be crucial for better designing the potential biomedical applications of @rGOn (magnetic) for near-infrared photothermal (PTT) and externally controlled magnetotherapy applications useful in cancer treatment. © 2024, The Authors. All rights reserved.

关键词： Enzymes

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Multi-stage Speaker Extraction with Utterance and Frame-Level Reference Signals

arXiv

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arXiv 2020年

作者： Ge, Meng Xu, Chenglin Wang, Longbiao Chng, Eng Siong Dang, Jianwu Li, Haizhou Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China School of Computer Science and Engineering Nanyang Technological University Singapore Japan Advanced Institute of Science and Technology Ishikawa Japan Department of Electrical and Computer Engineering National University of Singapore Singapore Machine Listening Lab University of Bremen Germany

Speaker extraction requires a sample speech from the target speaker as the reference. However, enrolling a speaker with a long speech is not practical. We propose a speaker extraction technique, that performs in multiple stages to take full advantage of short reference speech sample. The extracted speech in early stages is used as the reference speech for late stages. For the first time, we use frame-level sequential speech embedding as the reference for target speaker. This is a departure from the traditional utterance-based speaker embedding reference. In addition, a signal fusion scheme is proposed to combine the decoded signals in multiple scales with automatically learned weights. Experiments on WSJ0-2mix and its noisy versions (WHAM! and WHAMR!) show that SpEx++ consistently outperforms other state-of-the-art baselines. Copyright © 2020, The Authors. All rights reserved.

关键词： Embeddings

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Data and Physics Driven Learning Models for Fast MRI - Fundamentals and Methodologies from CNN, GAN to Attention and Transformers

arXiv

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arXiv 2022年

作者： Huang, Jiahao Fang, Yingying Nan, Yang Wu, Huanjun Wu, Yinzhe Gao, Zhifan Li, Yang Wang, Zidong Lio, Pietro Rueckert, Daniel Eldar, Yonina C. Yang, Guang The National Heart and Lung Institute Imperial College London Guy Scadding Building Dovehouse St LondonSW3 6LY United Kingdom The Department of Bioengineering Imperial College London South Kensington LondonSW7 2AZ United Kingdom The School of Biomedical Engineering Sun Yat-Sen University Guangdong Guangzhou510006 China The School of Automation Sciences and Electrical Engineering Beihang University Beijing China The Department of Computer Science Brunel University London LondonUB8 3PH United Kingdom The Computer Laboratory University of Cambridge CambridgeCB3 0FD United Kingdom The Department of Computing Imperial College London South Kensington Campus LondonSW7 2AZ United Kingdom Technical University of Munich Munich80333 Germany Faculty of Mathematics and Computer Science Weizmann Institute of Science Rehovot7610001 Israel

Research studies have shown no qualms about using data driven deep learning models for downstream tasks in medical image analysis, e.g., anatomy segmentation and lesion detection, disease diagnosis and prognosis, and treatment planning. However, deep learning models are not the sovereign remedy for medical image analysis when the upstream imaging is not being conducted properly (with artefacts). This has been manifested in MRI studies, where the scanning is typically slow, prone to motion artefacts, with a relatively low signal to noise ratio, and poor spatial and/or temporal resolution. Recent studies have witnessed substantial growth in the development of deep learning techniques for propelling fast MRI. This article aims to (1) introduce the deep learning based data driven techniques for fast MRI including convolutional neural network and generative adversarial network based methods, (2) survey the attention and transformer based models for speeding up MRI reconstruction, and (3) detail the research in coupling physics and data driven models for MRI acceleration. Finally, we will demonstrate through a few clinical applications, explain the importance of data harmonisation and explainable models for such fast MRI techniques in multicentre and multi-scanner studies, and discuss common pitfalls in current research and recommendations for future research directions. © 2022, CC BY-NC-ND.

关键词： Signal to noise ratio

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BRIDGE: Benchmarking Large Language Models for Understanding real-world Clinical Practice Text

arXiv

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arXiv 2025年

作者： Wu, Jiageng Gu, Bowen Zhou, Ren Xie, Kevin Snyder, Doug Jiang, Yixing Carducci, Valentina Wyss, Richard Desai, Rishi J. Alsentzer, Emily Celi, Leo Anthony Rodman, Adam Schneeweiss, Sebastian Chen, Jonathan H. Romero-Brufau, Santiago Lin, Kueiyu Joshua Yang, Jie Division of Pharmacoepidemiology and Pharmacoeconomics Department of Medicine Brigham and Women’s Hospital Harvard Medical School BostonMA United States Siebel School of Computing and Data Science The Grainger College of Engineering University of Illinois Urbana-Champaign UrbanaIL United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Department of Otorhinolaryngology – Head & Neck Surgery Mayo Clinic RochesterMN United States Department of Biostatistics Harvard T.H. Chan School of Public Health Harvard University BostonMA United States Department of Biomedical Data Science Stanford University Palo AltoCA United States Laboratory for Computational Physiology Massachusetts Institute of Technology CambridgeMA United States Division of Pulmonary Critical Care and Sleep Medicine Beth Israel Deaconess Medical Center BostonMA United States Division of General Internal Medicine Department of Medicine Beth Israel Deaconess Medical Center BostonMA United States Stanford Center for Biomedical Informatics Research Stanford University StanfordCA United States Division of Hospital Medicine Stanford University StanfordCA United States Stanford Clinical Excellence Research Center Stanford University StanfordCA United States Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University MA United States Broad Institute of MIT and Harvard CambridgeMA United States Harvard Data Science Initiative Harvard University CambridgeMA United States

Large language models (LLMs) hold great promise for medical applications and are evolving rapidly, with new models being released at an accelerated pace. However, current evaluations of LLMs in clinical contexts remain limited. Most existing benchmarks rely on medical exam-style questions or PubMed-derived text, failing to capture the complexity of real-world electronic health record (EHR) data. Others focus narrowly on specific application scenarios, limiting their generalizability across broader clinical use. To address this gap, we present BRIDGE, a comprehensive multilingual benchmark comprising 87 tasks sourced from real-world clinical data sources across nine languages. We systematically evaluated 52 state-of-the-art LLMs (including DeepSeek-R1, GPT-4o, Gemini, and Llama 4) under various inference strategies. With a total of 13,572 experiments, our results reveal substantial performance variation across model sizes, languages, natural language processing tasks, and clinical specialties. Notably, we demonstrate that open-source LLMs can achieve performance comparable to proprietary models, while medically fine-tuned LLMs based on older architectures often underperform versus updated general-purpose models. The BRIDGE and its corresponding leaderboard serve as a foundational resource and a unique reference for the development and evaluation of new LLMs in real-world clinical text understanding. © 2025, CC BY-NC-SA.

关键词： Electronic health record

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FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

arXiv

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arXiv 2023年

作者： Lekadir, Karim Feragen, Aasa Fofanah, Abdul Joseph Frangi, Alejandro F. Buyx, Alena Emelie, Anais Lara, Andrea Porras, Antonio R. Chan, An-Wen Navarro, Arcadi Glocker, Ben Botwe, Benard O. Khanal, Bishesh Beger, Brigit Wu, Carol C. Cintas, Celia Langlotz, Curtis P. Rueckert, Daniel Mzurikwao, Deogratias Fotiadis, Dimitrios I. Zhussupov, Doszhan Ferrante, Enzo Meijering, Erik Weicken, Eva González, Fabio A. Asselbergs, Folkert W. Prior, Fred Krestin, Gabriel P. Collins, Gary S. Tegenaw, Geletaw S. Kaissis, Georgios Misuraca, Gianluca Tsakou, Gianna Dwivedi, Girish Kondylakis, Haridimos Jayakody, Harsha Woodruf, Henry C. Mayer, Horst Joachim Aerts, Hugo JWL Walsh, Ian Chouvarda, Ioanna Buvat, Irène Tributsch, Isabell Rekik, Islem Duncan, James Kalpathy-Cramer, Jayashree Zahir, Jihad Park, Jinah Mongan, John Gichoya, Judy W. Schnabel, Julia A. Kushibar, Kaisar Riklund, Katrine Mori, Kensaku Marias, Kostas Amugongo, Lameck M. Fromont, Lauren A. Maier-Hein, Lena Alberich, Leonor Cerdá Rittner, Leticia Phiri, Lighton Marrakchi-Kacem, Linda Donoso-Bach, Lluís Martí-Bonmatí, Luis Cardoso, M. Jorge Bobowicz, Maciej Shabani, Mahsa Tsiknakis, Manolis Zuluaga, Maria A. Bielikova, Maria Fritzsche, Marie-Christine Camacho, Marina Linguraru, Marius George Wenzel, Markus De Bruijne, Marleen Tolsgaard, Martin G. Ghassemi, Marzyeh Ashrafuzzaman, Md Goisauf, Melanie Yaqub, Mohammad Abadía, Mónica Cano Mahmoud, Mukhtar M.E. Elattar, Mustafa Rieke, Nicola Papanikolaou, Nikolaos Lazrak, Noussair Díaz, Oliver Salvado, Olivier Pujol, Oriol Sall, Ousmane Guevara, Pamela Gordebeke, Peter Lambin, Philippe Brown, Pieta Abolmaesumi, Purang Dou, Qi Lu, Qinghua Osuala, Richard Nakasi, Rose Zhou, S. Kevin Napel, Sandy Colantonio, Sara Albarqouni, Shadi Joshi, Smriti Carter, Stacy Klein, Stefan Petersen, Steffen E. Aussó, Susanna Awate, Suyash Raviv, Tammy Riklin Cook, Tessa Mutsvangwa, Tinashe E.M. Rogers, Wendy A. Niessen, Wiro J. Puig-Bosch, Xènia Zeng, Yi Mohammed, Yunusa G. Aquino, Yves Saint James Salahuddin, Zohaib Starmans, Martijn P.A. Department de Matemàtiques i Informàtica Universitat de Barcelona Barcelona Spain Barcelona Spain DTU Compute Technical University of Denmark Kgs Lyngby Denmark Department of Mathematics and Computer Science Faculty of Science and Technology Milton Margai Technical University Freetown Sierra Leone Center for Computational Imaging & Simulation Technologies in Biomedicine Schools of Computing and Medicine University of Leeds Leeds United Kingdom Cardiovascular Science and Electronic Engineering Departments KU Leuven Leuven Belgium Institute of History and Ethics in Medicine Technical University of Munich Munich Germany Faculty of Engineering of Systems Informatics and Sciences of Computing Galileo University Guatemala City Guatemala Department of Biostatistics and Informatics Colorado School of Public Health University of Colorado Anschutz Medical Campus AuroraCO United States Department of Medicine Women’s College Research Institute University of Toronto Toronto Canada Universitat Pompeu Fabra BarcelonaBeta Brain Research Center Barcelona Spain Department of Computing Imperial College London London United Kingdom School of Biomedical & Allied Health Sciences University of Ghana Accra Ghana Department of Midwifery & Radiography School of Health & Psychological Sciences City University of London United Kingdom Kathmandu Nepal European Heart Network Brussels Belgium Department of Thoracic Imaging University of Texas MD Anderson Cancer Center Houston United States IBM Research Africa Nairobi Kenya Departments of Radiology Medicine and Biomedical Data Science Stanford University School of Medicine Stanford United States Institute for AI and Informatics in Medicine Klinikum rechts der Isar Technical University Munich Munich Germany Department of Computing Imperial College London London United Kingdom Muhimbili University of Health and Allied Sciences Dar es Salaam Tanzania United Republic of Ioannina Greece Almaty AI Lab Almaty Kazakhstan

Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raised about the clinical, technical, ethical and legal risks associated with healthcare AI. To increase adoption in the real world, it is essential that AI technologies are trusted and accepted by patients, clinicians, health organisations and authorities. This paper describes the FUTURE-AI framework as the first international consensus guideline for trustworthy AI in healthcare. Methods: The FUTURE-AI consortium was founded in 2021 and now comprises 117 interdisciplinary experts from 50 countries representing all continents, including AI scientists, clinical researchers, biomedical ethicists, and social scientists. Over a two-year period, the consortium established guiding principles and best practices for trustworthy and deployable AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. Findings: The FUTURE-AI framework was established based on six guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 30 best practices were defined, addressing technical, clinical, socio-ethical and legal dimensions of trustworthy AI. The recommendations cover the entire lifecycle of healthcare AI, from design, development and validation to regulation, deployment, and monitoring. Interpretation: FUTURE-AI is a structured, risk-informed framework which provides guidance for constructing healthcare AI tools that will be trusted, deployed and adopted in real-world clinical practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of healthcare AI. Copyright © 2023, The Authors. All rights

关键词： Clinical research

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Lightweight Task-Oriented Semantic Communication Empowered by Large-Scale AI Models

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IEEE Transactions on Vehicular Technology 2025年

作者： Liu, Chuanhong Guo, Caili Yang, Yang Chen, Mingzhe Quek, Tony Q. S. Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China University of Miami Department of Electrical and Computer Engineering Institute for Data Science and Computing Coral GablesFL United States Singapore University of Technology and Design Dept. of Information Systems Technology and Design 487372 Singapore

Recent studies have focused on leveraging large-scale artificial intelligence (LAI) models to improve semantic representation and compression capabilities. However, the substantial computational demands of LAI models pose significant challenges for real-time communication scenarios. To address this, this paper proposes utilizing knowledge distillation (KD) techniques to extract and condense knowledge from LAI models, effectively reducing model complexity and computation latency. Nevertheless, the inherent complexity of LAI models leads to prolonged inference times during distillation, while their lack of channel awareness compromises the distillation performance. These limitations make standard KD methods unsuitable for task-oriented semantic communication scenarios. To address these issues, we propose a fast distillation method featuring a pre-stored compression mechanism that eliminates the need for repetitive inference, significantly improving efficiency. Furthermore, a channel adaptive module is incorporated to dynamically adjust the transmitted semantic information based on varying channel conditions, enhancing communication reliability and adaptability. In addition, an information bottleneck-based loss function is derived to guide the fast distillation process. Simulation results verify that the proposed scheme outperform baselines in term of task accuracy, model size, computation latency, and training data requirements. © 1967-2012 IEEE.

关键词： NP-hard

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MONAI: An open-source framework for deep learning in healthcare

arXiv

引用

arXiv 2022年

作者： Cardoso, M. Jorge Li, Wenqi Brown, Richard Ma, Nic Kerfoot, Eric Wang, Yiheng Murrey, Benjamin Myronenko, Andriy Zhao, Can Yang, Dong Nath, Vishwesh He, Yufan Xu, Ziyue Hatamizadeh, Ali Zhu, Wentao Liu, Yun Zheng, Mingxin Tang, Yucheng Yang, Isaac Zephyr, Michael Hashemian, Behrooz Alle, Sachidanand Darestani, Mohammad Zalbagi Budd, Charlie Modat, Marc Vercauteren, Tom Wang, Guotai Li, Yiwen Hu, Yipeng Fu, Yunguan Gorman, Benjamin Johnson, Hans Genereaux, Brad Erdal, Barbaros S. Gupta, Vikash Diaz-Pinto, Andres Dourson, Andre Maier-Hein, Lena Jaeger, Paul F. Baumgartner, Michael Kalpathy-Cramer, Jayashree Flores, Mona Kirby, Justin Cooper, Lee A.D. Roth, Holger R. Xu, Daguang Bericat, David Floca, Ralf Zhou, S. Kevin Shuaib, Haris Farahani, Keyvan Maier-Hein, Klaus H. Aylward, Stephen Dogra, Prerna Ourselin, Sebastien Feng, Andrew School of Biomedical Engineering & Imaging Sciences King’s College London London United Kingdom NVIDIA Corporation Santa Clara and Bethesda United States School of Electrical and Computer Engineering Rice University Houston United States Department of Engineering Science University of Oxford Oxford United Kingdom Department of Medical Physics and Biomedical Engineering University College London London United Kingdom Department of BioAI InstaDeep Ltd London United Kingdom Department of Electrical & Computer Engineering University of Iowa Iowa City United States Mayo Clinic Jacksonville United States Mars Incorporated United States Div. Intelligent Medical Systems German Cancer Research Center Heidelberg Germany Division of Medical Image Computing German Cancer Research Center Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany School of Biomedical Engineering Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China Department of Medical Physics Guy’s & St Thomas’ NHS Foundation Trust London United Kingdom National Cancer Institute Bethesda United States Kitware Inc. Clifton Park United States Department of Ophthalmology University of Colorado Aurora United States Frederick National Laboratory for Cancer Research Frederick United States Department of Pathology Northwestern University Chicago United States

Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and intervene on human disease. For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e.g. geometry, physiology, physics) of medical data being processed. This work introduces MONAI, a freely available, community-supported, and consortium-led PyTorch-based framework for deep learning in healthcare. MONAI extends PyTorch to support medical data, with a particular focus on imaging, and provide purpose-specific AI model architectures, transformations and utilities that streamline the development and deployment of medical AI models. MONAI follows best practices for software-development, providing an easy-to-use, robust, well-documented, and well-tested software framework. MONAI preserves the simple, additive, and compositional approach of its underlying PyTorch libraries. MONAI is being used by and receiving contributions from research, clinical and industrial teams from around the world, who are pursuing applications spanning nearly every aspect of healthcare. © 2022, CC BY.

关键词： Clinical research

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Calibration of Flux Crosstalk in Large-Scale Flux-Tunable Superconducting Quantum Circuits

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PRX Quantum 2021年第4期2卷 040313-040313页

作者： X. Dai D.M. Tennant R. Trappen A.J. Martinez D. Melanson M.A. Yurtalan Y. Tang S. Novikov J.A. Grover S.M. Disseler J.I. Basham R. Das D.K. Kim A.J. Melville B.M. Niedzielski S.J. Weber J.L. Yoder D.A. Lidar A. Lupascu Institute for Quantum Computing and Department of Physics and Astronomy University of Waterloo Waterloo Ontario N2L 3G1 Canada Department of Electrical and Computer Engineering University of Waterloo Waterloo Ontario N2L 3G1 Canada Northrop Grumman Corporation Linthicum Maryland 21090 USA MIT Lincoln Laboratory 244 Wood Street Lexington Massachusetts 02421 USA Departments of Electrical & Computer Engineering Chemistry and Physics and Center for Quantum Information Science & Technology University of Southern California Los Angeles California 90089 USA Waterloo Institute for Nanotechnology University of Waterloo Waterloo Ontario N2L 3G1 Canada

Magnetic flux tunability is an essential feature in most approaches to quantum computing based on superconducting qubits. Independent control of the fluxes in multiple loops is hampered by crosstalk. Calibrating flux crosstalk becomes a challenging task when the circuit elements interact strongly. We present a novel approach to flux crosstalk calibration, which is circuit model independent and relies on an iterative process to gradually improve calibration accuracy. This method allows us to reduce errors due to the inductive coupling between loops. The calibration procedure is automated and implemented on devices consisting of tunable flux qubits and couplers with up to 27 control loops. We devise a method to characterize the calibration error, which is used to show that the errors of the measured crosstalk coefficients are all below 0.17%.

关键词： Adiabatic quantum optimization Quantum information architectures & platforms Quantum information processing Superconducting qubits

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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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