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arXiv 2024年

作者： Barnes, Edwin Bennett, Michael B. Boltasseva, Alexandra Borish, Victoria Brown, Bennett Carr, Lincoln D. Ceballos, Russell R. Dukes, Faith Easton, Emily W. Economou, Sophia E. Edwards, E.E. Finkelstein, Noah D. Fracchiolla, C. Franklin, Diana Freericks, J.K. Goss, Valerie Hannum, Mark Holincheck, Nancy Kelly, Angela M. Lanes, Olivia Lewandowski, H.J. Matsler, Karen Jo Mercurio, Emily Montaño, Inès Murdock, Maajida Peltz, Kiera Perron, Justin K. Richardson, Christopher J.K. Rosenberg, Jessica L. Ross, Richard S. Ryu, Minjung Samuel, Raymond E. Schrode, Nicole Schwamberger, Susan Searles, Thomas A. Singh, Chandralekha Tingle, Alexandra Zwickl, Benjamin M. Department of Physics Virginia Tech Center for Quantum Information Science and Engineering Virginia Tech 850 West Campus Drive BlacksburgVA24061 United States Q-SEnSE NSF QLCI University of Colorado 440 UCB BoulderCO80301 United States Elmore Family School of Electrical and Computer Engineering Birck Nanotechnology Center Purdue Quantum Science and Engineering Institute Purdue University 610 Purdue Mall West LafayetteIN47907 United States Quantum Science Center A National Quantum Information Science Research Center of The U.S. Department of Energy Oak Ridge National Laboratory 1 Bethel Valley Road Oak RidgeTN37830 United States JILA National Institute of Standards and Technology the University of Colorado BoulderCO80309 United States Department of Physics University of Colorado 390 UCB BoulderCO80309 United States QuSTEAM 510 Ronalds St. Iowa CityIA52245 United States Quantum Engineering Program Department of Physics Colorado School of Mines 1523 Illinois St. GoldenCO80401 United States QuSTEAM Non-Profit Wilbur Wright College City Colleges of Chicago 4300 N Narragansett Ave ChicagoIL60634 United States Government and Community Relations Office - K-12 Programs Lawrence Berkeley National Laboratory 1 Cyclotron Road BerkeleyCA United States Chicago Quantum Exchange 5235 S. Harper Court ChicagoIL60615 United States Department of Physics Virginia Tech 850 West Campus Drive BlacksburgVA24060 United States Department of Electrical and Computer Engineering Duke Quantum Center Duke University Duke University 130 Hudson Hall DurhamNC27708 United States American Physical Society 555 12th St. NW WashingtonDC20004 United States Computer Science University of Chicago 5730 S. Ellis Ave. ChicagoIL60637 United States Department of Physics Georgetown University 552 Reiss Science Building WashingtonDC20057 United States Chemistry Physics and Engineering Studies Chicago State University 9501 S. King Drive WSC 309 ChicagoIL606

In response to numerous programs seeking to advance quantum education and workforce development in the United States, experts from academia, industry, government, and professional societies convened for a National science Foundation-sponsored workshop in February 2024 to explore the benefits and challenges of establishing a national center for quantum education. Broadly, such a center would foster collaboration and build the infrastructure required to develop a diverse and quantum-ready workforce. The workshop discussions centered around how a center could uniquely address gaps in public, K-12, and undergraduate quantum information science and engineering (QISE) education. Specifically, the community identified activities that, through a center, could lead to an increase in student awareness of quantum careers, boost the number of educators trained in quantum-related subjects, strengthen pathways into quantum careers, enhance the understanding of the U.S. quantum workforce, and elevate public engagement with QISE. Core proposed activities for the center include professional development for educators, coordinated curriculum development and curation, expanded access to educational laboratory equipment, robust evaluation and assessment practices, network building, and enhanced public engagement with quantum science. © 2024, CC BY.

关键词： Students

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Biphenylene monolayer as a two-dimensional nonbenzenoid carbon allotrope: A first-principles study

arXiv

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arXiv 2021年

作者： Bafekry, Asadolah Faraji, M. Fadlallah, M.M. Jappor, H.R. Karbasizadeh, S. Ghergherehchi, M. Gogova, D. Department of Radiation Application Shahid Beheshti University Tehran*** Iran Micro and Nanotechnology Graduate Program TOBB University of Economics and Technology Sogutozu Caddesi No 43 Sogutozu Ankara06560 Turkey Department of Physics Faculty of Science Benha University Benha13518 Egypt Department of Physics College of Education for Pure Sciences University of Babylon Hilla Iraq Department of Physics Isfahan University of Technology Isfahan84156-83111 Iran Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of Department of Physics University of Oslo P.O. Box 1048 Blindern Oslo Norway

In a very recent accomplishment, the two-dimensional form of Biphenylene network (BPN) has been successfully fabricated [Fan et al., science, 372, 852-856 (2021)]. Motivated by this exciting experimental result on 2D layered BPN structure, herein we perform detailed density functional theory-based first-principles calculations, for the first time, in order to gain insight into the structural, electronic and optical properties of this promising nanomaterial. Our theoretical results reveal the BPN structure is constructed from three rings of tetragon, hexagon and octagon, meanwhile the electron localization function shows very strong bonds between the C atoms in the structure. The dynamical stability of BPN is verified via the phonon band dispersion calculations. The mechanical properties reveal the brittle behavior of BPN monolayer. The Young's modulus has been computed as 0.1 TPa, which is smaller than the corresponding value of graphene, while the Poisson's ratio determined to be 0.26 is larger than that of graphene. The band structure is evaluated to show the electronic features of the material;determining the BPN monolayer as metallic with a band gap of zero. The optical properties (real and imaginary parts of the dielectric function, and the absorption spectrum) uncover BPN as an insulator along the zz direction, while owning metallic properties in xx and yy directions. We anticipate that our discoveries will pave the way to the successful implementation of this new 2D allotrope of carbon in advanced nanoelectronics. © 2021, CC BY.

关键词： Monolayers

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An Open-Source Knowledge Graph Ecosystem for the Life sciences

arXiv

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arXiv 2023年

作者： Callahan, Tiffany J. Tripodi, Ignacio J. Stefanski, Adrianne L. Cappelletti, Luca Taneja, Sanya B. Wyrwa, Jordan M. Casiraghi, Elena Matentzoglu, Nicolas A. Reese, Justin Silverstein, Jonathan C. Hoyt, Charles Tapley Boyce, Richard D. Malec, Scott A. Unni, Deepak R. Joachimiak, Marcin P. Robinson, Peter N. Mungall, Christopher J. Cavalleri, Emanuele Fontana, Tommaso Valentini, Giorgio Mesiti, Marco Gillenwater, Lucas A. Santangelo, Brook Vasilevsky, Nicole A. Hoehndorf, Robert Bennett, Tellen D. Ryan, Patrick B. Hripcsak, George Kahn, Michael G. Bada, Michael Baumgartner, William A. Hunter, Lawrence E. Computational Bioscience Program University of Colorado Anschutz Medical Campus AuroraCO80045 United States Department of Biomedical Informatics Columbia University Irving Medical Center New YorkNY10032 United States Computer Science Department Interdisciplinary Quantitative Biology University of Colorado Boulder BoulderCO80301 United States AnacletoLab Computer Science Department University of Milan 20122 Italy Intelligent Systems Program University of Pittsburgh PittsburghPA15260 United States Department of Physical Medicine and Rehabilitation School of Medicine University of Colorado Anschutz Medical Campus AuroraCO80045 United States Division of Environmental Genomics and Systems Biology Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Semanticly Ltd Athens Greece Department of Biomedical Informatics University of Pittsburgh School of Medicine PittsburghPA15206 United States Laboratory of Systems Pharmacology Harvard Medical School BostonMA02115 United States Division of Translational Informatics University of New Mexico School of Medicine AlbuquerqueNM87131 United States SIB Swiss Institute of Bioinformatics Basel Switzerland Berlin Institute of Health at Charité-Universitatsmedizin Berlin10117 Germany ELLIS European Laboratory for Learning and Intelligent Systems Germany Department of Biomedical Informatics University of Colorado School of Medicine AuroraCO80045 United States Data Collaboration Center Critical Path Institute 1840 E River Rd. Suite 100 TucsonAZ85718 United States Computer Electrical and Mathematical Sciences & Engineering Division Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia Department of Pediatrics University of Colorado School of Medicine AuroraCO80045 United States Janssen Research and Development RaritanNJ08869 United States Division of General Internal Medicine University of Colorado School of Medicine A

Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowledge graphs (KGs) are used to model complex phenomena, and methods exist to construct them automatically. However, tackling complex biomedical integration problems requires flexibility in the way knowledge is modeled. Moreover, existing KG construction methods provide robust tooling at the cost of fixed or limited choices among knowledge representation models. PheKnowLator (Phenotype Knowledge Translator) is a semantic ecosystem for automating the FAIR (Findable, Accessible, Interoperable, and Reusable) construction of ontologically grounded KGs with fully customizable knowledge representation. The ecosystem includes KG construction resources (e.g., data preparation APIs), analysis tools (e.g., SPARQL endpoints and abstraction algorithms), and benchmarks (e.g., prebuilt KGs and embeddings). We evaluated the ecosystem by systematically comparing it to existing open-source KG construction methods and by analyzing its computational performance when used to construct 12 large-scale KGs. With flexible knowledge representation, PheKnowLator enables fully customizable KGs without compromising performance or usability. © 2023, CC BY.

关键词： Ecosystems

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Pseudo-linear summation explains neural geometry of multi-finger movements in human premotor cortex

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Nature Communications 2025年第1期16卷 1-15页

作者： Shah, Nishal P. Avansino, Donald Kamdar, Foram Nicolas, Claire Kapitonava, Anastasia Vargas-Irwin, Carlos Hochberg, Leigh R. Pandarinath, Chethan Shenoy, Krishna V. Willett, Francis R. Henderson, Jaimie M. Department of Neurosurgery Stanford University Stanford CA United States Department of Electrical and Computer Engineering Rice University Houston TX United States Department of Electrical and Computer Engineering Rice University Houston United States Neuroengineering Initiative Rice University Houston TX United States Howard Hughes Medical Institute at Stanford University Stanford CA United States Center for Neurotechnology and Neurorecovery Department of Neurology Massachusetts General Hospital Harvard Medical School Boston MA United States VA RR & D Center for Neurorestoration and Neurotechnology Rehabilitation R & D Service Providence VA Medical Center Providence RI United States Department of Neuroscience Brown University Providence RI United States Robert J. and Nancy D. Carney Institute for Brain Science Brown University Providence RI United States School of Engineering Brown University Providence RI United States Wallace H. Coulter Department of Biomedical Engineering Emory University and Georgia Institute of Technology Atlanta GA United States Department of Neurosurgery Emory University Atlanta GA United States Wu Tsai Neurosciences Institute Stanford University Stanford CA United States Bio-X Institute Stanford University Stanford CA United States Department of Electrical Engineering Stanford University Stanford CA United States Department of Bioengineering Stanford University Stanford CA United States Department of Neurobiology Stanford University Stanford CA United States

How does the motor cortex combine simple movements (such as single finger flexion/extension) into complex movements (such as hand gestures, or playing the piano)? To address this question, motor cortical activity was recorded using intracortical multi-electrode arrays in two male people with tetraplegia as they attempted single, pairwise and higher-order finger movements. Neural activity for simultaneous movements was largely aligned with linear summation of corresponding single finger movement activities, with two violations. First, the neural activity exhibited normalization, preventing a large magnitude with an increasing number of moving fingers. Second, the neural tuning direction of weakly represented fingers changed significantly as a result of the movement of more strongly represented fingers. These deviations from linearity resulted in non-linear methods outperforming linear methods for neural decoding. Simultaneous finger movements are thus represented by the combination of individual finger movements by pseudo-linear summation. © The Author(s) 2025.

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Digital synthesis of histological stains using micro-structured and multiplexed virtual staining of label-free tissue

arXiv

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arXiv 2020年

作者： Zhang, Yijie de Haan, Kevin Rivenson, Yair Li, Jingxi Delis, Apostolos Ozcan, Aydogan Electrical and Computer Engineering Department University of California Los AngelesCA90095 United States Bioengineering Department University of California Los AngelesCA90095 United States University of California Los AngelesCA90095 United States Department of Computer Science University of California Los AngelesCA90095 United States Department of Surgery David Geffen School of Medicine University of California Los AngelesCA90095 United States

Histological staining is a vital step used to diagnose various diseases and has been used for more than a century to provide contrast to tissue sections, rendering the tissue constituents visible for microscopic analysis by medical experts. However, this process is time-consuming, labor-intensive, expensive and destructive to the specimen. Recently, the ability to virtually-stain unlabeled tissue sections, entirely avoiding the histochemical staining step, has been demonstrated using tissue-stain specific deep neural networks. Here, we present a new deep learning-based framework which generates virtually-stained images using label-free tissue, where different stains are merged following a micro-structure map defined by the user. This approach uses a single deep neural network that receives two different sources of information at its input: (1) autofluorescence images of the label-free tissue sample, and (2) a "digital staining matrix" which represents the desired microscopic map of different stains to be virtually generated at the same tissue section. This digital staining matrix is also used to virtually blend existing stains, digitally synthesizing new histological stains. We trained and blindly tested this virtual-staining network using unlabeled kidney tissue sections to generate micro-structured combinations of Hematoxylin and Eosin (H&E), Jones silver stain, and Masson's Trichrome stain. Using a single network, this approach multiplexes virtual staining of label-free tissue with multiple types of stains and paves the way for synthesizing new digital histological stains that can be created on the same tissue cross-section, which is currently not feasible with standard histochemical staining *** Codes 68T01, 68T05, 68U10, 62M45, 78M32, 92C50, 92C55, 94A08 Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks

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Synthesizing MR Image Contrast Enhancement Using 3D High-resolution ConvNets

arXiv

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arXiv 2021年

作者： Chen, Chao Raymond, Catalina Speier, William Jin, Xinyu Cloughesy, Timothy F. Enzmann, Dieter Ellingson, Benjamin M. Arnold, Corey W. Information Science and Electrical Engineering Department Zhejiang Univeristy Hangzhou310027 China Department of Radiological Sciences David Geffen School of Medicine The University of California Los Angeles United States Department of Neurology David Geffen School of Medicine The University of California Los Angeles United States UCLA Brain Tumor Imaging Laboratory Center for Computer Vision and Imaging Biomarkers Department of Radiological Sciences David Geffen School of Medicine University of California Los Angeles Los Angeles United States Computational Diagnostics Lab The Department of Radiological Sciences The Department of Pathology and Laboratory Medicine The Department of Bioengineering The University of California Los Angeles 924 Westwood Blvd Suite 600 CA90024 United States

Objective: Gadolinium-based contrast agents (GBCAs) have been widely used to better visualize disease in brain magnetic resonance imaging (MRI). However, gadolinium deposition within the brain and body has raised safety concerns about the use of GBCAs. Therefore, the development of novel approaches that can decrease or even eliminate GBCA exposure while providing similar contrast information would be of significant use clinically. Methods: In this work, we present a deep learning based approach for contrast-enhanced T1 synthesis on brain tumor patients. A 3D high-resolution fully convolutional network (FCN), which maintains high resolution information through processing and aggregates multi-scale information in parallel, is designed to map pre-contrast MRI sequences to contrast-enhanced MRI sequences. Specifically, three pre-contrast MRI sequences, T1, T2 and apparent diffusion coefficient map (ADC), are utilized as inputs and the post-contrast T1 sequences are utilized as target output. To alleviate the data imbalance problem between normal tissues and the tumor regions, we introduce a local loss to improve the contribution of the tumor regions, which leads to better enhancement results on tumors. Results: Extensive quantitative and visual assessments are performed, with our proposed model achieving a PSNR of 28.24dB in the brain and 21.2dB in tumor regions. Conclusion and Significance: Our results suggest the potential of substituting GBCAs with synthetic contrast images generated via deep learning. Code is available at https://***/chenchao666/ Contrast-enhanced-MRI-Synthesis Copyright © 2021, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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iPhantom: a framework for automated creation of individualized computational phantoms and its application to CT organ dosimetry

arXiv

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arXiv 2020年

作者： Fu, Wanyi Sharma, Shobhit Abadi, Ehsan Iliopoulos, Alexandros-Stavros Wang, Qi Lo, Joseph Y. Sun, Xiaobai Segars, William P. Samei, Ehsan Department of Electrical and Computer Engineering Carl E. Ravin Advanced Imaging Laboratories Duke University DurhamNC27705 United States Department of Physics Carl E. Ravin Advanced Imaging Laboratories Duke University DurhamNC27705 United States Department of Computer Science Duke University DurhamNC27708 United States Department of Radiology Fourth Clinical Hospital of Hebei Medical University Heibei050011 China Departments of Electrical and Computer Engineering Biomedical Engineering Medical Physics Graduate Program Carl E. Ravin Advanced Imaging Laboratories Duke University DurhamNC27705 United States Departments of Biomedical Engineering Medical Physics Graduate Program and Radiology Carl E. Ravin Advanced Imaging Laboratories Duke University DurhamNC27705 United States Carl E. Ravin Advanced Imaging Laboratories Medical Physics Graduate Program Departments of Radiology Electrical and Computer Engineering Biomedical Engineering and Physics Duke University DurhamNC27705 United States

Objective: This study aims to develop and validate a novel framework, iPhantom, for automated creation of patient-specific phantoms or "digital-twins (DT)" using patient medical images. The framework is applied to assess radiation dose to radiosensitive organs in CT imaging of individual patients. Method: From patient CT images, iPhantom segments selected anchor organs (e.g. liver, bones, pancreas) using a learning-based model developed for multi-organ CT segmentation. Organs challenging to segment (e.g. intestines) are incorporated from a matched phantom template, using a diffeomorphic registration model developed for multi-organ phantom-voxels. The resulting full-patient phantoms are used to assess organ doses during routine CT exams. Result: iPhantom was validated on both the XCAT (n=50) and an independent clinical (n=10) dataset with similar accuracy. iPhantom precisely predicted all organ locations with good accuracy of Dice Similarity Coefficients (DSC) >0.6 for anchor organs and DSC of 0.3-0.9 for all other organs. iPhantom showed Copyright © 2020, The Authors. All rights reserved.

关键词： Phantoms

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Chiral Phonons with Giant Magnetic Moments in a Topological Crystalline Insulator

arXiv

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arXiv 2022年

作者： Hernandez, Felix G.G. Baydin, Andrey Chaudhary, Swati Tay, Fuyang Katayama, Ikufumi Takeda, Jun Nojiri, Hiroyuki Okazaki, Anderson K. Rappl, Paulo H.O. Abramof, Eduardo Rodriguez-Vega, Martin Fiete, Gregory A. Kono, Junichiro Instituto de Física Universidade de São Paulo SP São Paulo05508-090 Brazil Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Physics The University of Texas at Austin AustinTX78712 United States Department of Physics Northeastern University BostonMA02115 United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Physics Graduate School of Engineering Science Yokohama National University Yokohama240-8501 Japan Institute for Materials Research Tohoku University Sendai980-8577 Japan Instituto Nacional de Pesquisas Espaciais SP São José dos Campos12201-970 Brazil Department of Physics and Astronomy Rice University HoustonTX77005 United States Department of Material Science and NanoEngineering Rice University HoustonTX77005 United States

We have studied the magnetic response of transverse optical phonons in Pb1−xSnxTe films. Polarization-dependent terahertz magnetospectroscopy measurements revealed Zeeman splittings and diamagnetic shifts, demonstrating that these phonon modes become chiral in magnetic fields. Films in the topological crystalline insulator phase (x > 0.32) exhibited magnetic moment values that are larger than those for topologically trivial films (x Copyright © 2022, The Authors. All rights reserved.

关键词： Phonons

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Author Correction: Boosting hydrogel conductivity via water-dispersible conducting polymers for injectable bioelectronics

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Nature communications 2025年第1期16卷 5308页

作者： Hossein Montazerian Elham Davoodi Canran Wang Farnaz Lorestani Jiahong Li Reihaneh Haghniaz Rohan R Sampath Neda Mohaghegh Safoora Khosravi Fatemeh Zehtabi Yichao Zhao Negar Hosseinzadeh Tianhan Liu Tzung K Hsiai Alireza Hassani Najafabadi Robert Langer Daniel G Anderson Paul S Weiss Ali Khademhosseini Wei Gao David H. Koch Institute for Integrative Cancer Research Massachusetts Institute of Technology Cambridge Massachusetts USA. Department of Bioengineering University of California Los Angeles Los Angeles California USA. Mechanical Engineering Department University of Utah Salt Lake City Utah USA. Terasaki Institute for Biomedical Innovation Los Angeles California USA. Andrew and Peggy Cherng Department of Medical Engineering Division of Engineering and Applied Science California Institute of Technology Pasadena California USA. Department of Engineering Science and Mechanics Pennsylvania State University University Park Pennsylvania USA. Department of Chemistry and Biochemistry University of California Los Angeles Los Angeles California USA. Department of Electrical and Computer Engineering University of British Columbia Vancouver British Columbia Canada. Terasaki Institute for Biomedical Innovation Los Angeles California USA. hassania@***. Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA. Department of Anesthesiology Boston Children's Hospital Boston Massachusetts USA. Institute for Medical Engineering and Science Massachusetts Institute of Technology Cambridge Massachusetts USA. Harvard-Massachusetts Institute of Technology Division of Health Sciences and Technology Massachusetts Institute of Technology Cambridge Massachusetts USA. Department of Bioengineering University of California Los Angeles Los Angeles California USA. psw@cnsi.ucla.edu. Department of Chemistry and Biochemistry University of California Los Angeles Los Angeles California USA. psw@cnsi.ucla.edu. Department of Materials Science and Engineering University of California Los Angeles Los Angeles California USA. psw@cnsi.ucla.edu. Terasaki Institute for Biomedical Innovation Los Angeles California USA. khademh@***. Andrew and Peggy Cherng Department of Medical Engineering Division of Engineering and Applied Science California In

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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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