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KNEE ARTHRITIS SEVERITY MEASUREMENT USING DEEP LEARNING: A publicLY AVAILABLE ALGORITHM WITH A MULTI-INSTITUTIONAL VALIDATION SHOWING RADIOLOGIST-LEVEL PERFORMANCE

arXiv

引用

arXiv 2022年

作者： Gu, Hanxue Li, Keyu Colglazier, Roy J. Yang, Jichen Lebhar, Michael O'Donnell, Jonathan Jiranek, William A. Mather, Richard C. French, Rob J. Said, Nicholas Zhang, Jikai Park, Christine Mazurowski, Maciej A. The Duke University of Medicine Durham27708 United States The Department of Radiology Radiology Musculoskeletal Imaging Radiology Durham27708 United States The Practice Transformation Unit The Department of Orthopaedic Surgery Duke University Durham27708 United States The Orthopaedic Surgery Orthopaedics Orthopaedics Duke University Durham27708 United States Population Health Sciences Population Health Sciences Basic Science Department Durham27708 United States Duke Clinical Research Institute Institutes and Centers Durham27708 United States Duke-Margolis Center for Health Policy Duke - Margolis Center For Health Policy Initiatives Durham27708 United States Radiology Radiology Clinical Science Department Durham27708 United States Biostatistics and Bioinformatics Biostatistics & Bioinformatics Basic Science Department Durham27708 United States The Department of Electrical and Computer Engineering Electrical and Computer Engineering Pratt School of Engineering Durham27708 United States Computer Science Computer Science Trinity College of Arts & Sciences Durham27708 United States Duke Cancer Institute Institutes and Centers in Duke University Durham27708 United States

The assessment of knee osteoarthritis (KOA) severity on knee X-rays is a central criteria for the use of total knee arthroplasty. However, this assessment suffers from imprecise standards and a remarkably high inter-reader variability. An algorithmic, automated assessment of KOA severity could improve overall outcomes of knee replacement procedures by increasing the appropriateness of its use. We propose a novel deep learning-based five-step algorithm to automatically grade KOA from posterior-anterior (PA) views of radiographs: (1) image preprocessing (2) localization of knees joints in the image using the YOLO v3-Tiny model, (3) initial assessment of the severity of osteoarthritis using a convolutional neural network-based classifier, (4) segmentation of the joints and calculation of the joint space narrowing (JSN), and (5), a combination of the JSN and the initial assessment to determine a final Kellgren-Lawrence (KL) score. Furthermore, by displaying the segmentation masks used to make the assessment, our algorithm demonstrates a higher degree of transparency compared to typical "black box" deep learning classifiers. We perform a comprehensive evaluation using two public datasets and one dataset from our institution, and show that our algorithm reaches state-of-the art performance. Moreover, we also collected ratings from multiple radiologists at our institution and showed that our algorithm performs at the radiologist level. © 2022, CC BY-NC-SA.

关键词： Image classification

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Gradient boosted tree based feature selection and parkinson's disease classification 5

Gradient boosted tree based feature selection and parkinson'...

引用

5th International Conference on Science and Technology, ICST 2019

作者： Wijaya, Adi Kharis Prastuti, Wini Universitas Gadjah Mada Department of Electrical and Information Engineering Yogyakarta Indonesia Universitas Gadjah Mada Department of Public Policy Management Yogyakarta Indonesia

ISBN: (纸本)9781728123691

Parkinson's Disease (PD) is one of the most common neurodegenerative disorder with increasing prevalence in the world. Many studies have been conducted to detect or diagnose PD. Recently, voice signal based PD detection has been conducted rapidly. However, the performance of the detection still not excellent, hence the need for excellent detection still an open issue in PD detection. In this study, PD detection based on voice signal was conducted using public dataset. Since the dataset consist of many features, the proposed method combines feature selection mechanism and classification based on Gradient Boosted Tree (GBT) algorithm. Feature selection was conducted by using feature weighting from GBT. The experimental results show that the proposed method outperforms prior research with the accuracy about 0.92 with the minimum number of features. This result proves that GBT able to perform feature selection and good classifier. This concludes that the proposed method is promising as PD detection based on voice signal analysis. © 2019 IEEE.

关键词： Signal analysis

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Design and Characterization of a Simple and Wideband Antenna for 5G mm-wave Applications

Design and Characterization of a Simple and Wideband Antenna...

引用

IEEE MTT-S Latin America Microwave Conference (LAMC)

作者： Syed Aftab Naqvi Wahaj Abbas Awan Mohammad Alibakhshikenari Francisco Falcone Ernesto Limiti Department of Electrical and Computer Eningeering COMSATS University Islamabad Sahiwal Pakistan Department of Integrated IT Eningeering Seoul National University of Science and Technology Seoul South Korea Electronic Engineering Department University of Rome &#x201C Tor Vergata&#x201D Rome Italy Electric Electronic and Communication Engineering Department Public University of Navarre Pamplona Spain

ISBN: (数字)9781728193588

ISBN: (纸本)9781728193595

An 8.5 GHz wideband and high Gain cross elliptical shape antenna is presented in this paper for the 5G mm-wave applications. A short-pin opposite to the feeding arm is deployed for the maximum current flow and consequently high bandwidth ranging from 26.7 to 35.2 GHz is achieved. The gain of the antenna varies from 6.1 dBi to 8.47 dBi in the entire frequency range with more than 76% efficiency. The compact electrical size of the antenna is 0.89λ0 × 0.89λ × 0.14λ0, where λ0 corresponds to the free space wavelength at the lowest resonance frequency. The results are compared with the state-of-the-artwork for the 5G mm-wave applications.

关键词： Microwave antennas 5G mobile communication Shape Resonant frequency Microwave theory and techniques Pins Broadband antennas

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Optimizing Global Genomic Surveillance for Early Detection of Emerging SARS-CoV-2 Variants

arXiv

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arXiv 2025年

作者： Gu, Haogao Li, Jifan Sun, Wanying Li, Mengting Leung, Kathy Wu, Joseph T. Yuan, Hsiang-Yu Wang, Maggie H. Yang, Bingyi McKay, Matthew R. Ning, Ning Poon, Leo L.M. School of Public Health LKS Faculty of Medicine The University of Hong Kong Hong Kong Department of Statistics Texas A&M University United States Hong Kong Science and Technology Park Hong Kong HKJC Global Health Institute LKS Faculty of Medicine The University of Hong Kong Hong Kong Department of Biomedical Sciences Jockey Club College of Veterinary Medicine and Life Sciences City University of Hong Kong Hong Kong Centre for Applied One Health Research and Policy Advice Jockey Club College of Veterinary Medicine and Life Sciences City University of Hong Kong Hong Kong JC School of Public Health and Primary Care Faculty of Medicine the Chinese University of Hong Kong Hong Kong Department of Electrical and Electronic Engineering University of Melbourne ParkvilleVIC3010 Australia Department of Microbiology and Immunology The Peter Doherty Institute for Infection and Immunity University of Melbourne MelbourneVIC3000 Australia Victorian Infectious Diseases Reference Laboratory Royal Melbourne Hospital the Peter Doherty Institute for Infection and Immunity Victoria Australia Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Centre for Immunology & Infection Hong Kong Science and Technology Park Hong Kong HKU-Pasteur Research Pole School of Public Health LKS Faculty of Medicine The University of Hong Kong Hong Kong

The global spread of viruses underscores the critical need for effective genomic surveillance to detect emerging variants and guide interventions. However, high costs and uneven resource distribution hinder its global implementation. This study demonstrates that optimizing genomic surveillance by targeting resources on international travelers in travel hubs, significantly improves the early detection of SARS-CoV-2 variants. Using a metapopulation multiple-strain model calibrated with extensive data, we show that targeting travelers effectively reduces detection delays without additional costs in retrospective analyses of the Omicron variant outbreak. Further, simulations indicate that focusing surveillance on key travel hubs outperform baseline practices in detecting future variants, even with reduced resources. This approach also remains effective in future pandemic scenarios with varying reproductive numbers (Reff) and vaccine effectiveness. These findings provide a sustainable framework for strengthening health security, offering actionable insights for optimizing genomic surveillance and improving preparedness for emerging infectious threats. Copyright © 2025, The Authors. All rights reserved.

关键词： Coronavirus

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Using Machine Learning to Identify Severe Dengue Cases in the Elderly and Non-Elderly in Southern Taiwan: A Step Toward International Collaboration on Dengue Syndromic Surveillance

SSRN

引用

SSRN 2024年

作者： Ho, Tzong-Shiann Chen, Wei Yang, Chin-Rur Yu, Chi-Hua Chen, Yu-Pei Chung, Chi-Huan Cheng, Ya-Yun Chang, Ko Hsu, Wei-Ting Liew, SingLek Chao, Yin-Chuan Huang, Wen-Xuan Wu, Chun-Ying Tsai, Thomas Claudio Guillermo Wu, Marie Yung-Chen Tsao, Ya-Fan Chang, Chia-Chun Lee, Ing-Kit King, Chwan-Chuen Hospital College of Medicine NCKU Tainan704302 Taiwan Taiwan Taiwan Taiwan Taiwan Taiwan Taiwan Taiwan Department of Medicine College of Medicine NCKU Tainan701401 Taiwan Taiwan Division of Infection Department of Internal Medicine Kuo General Hospital Tainan700002 Taiwan Taiwan Taiwan Taiwan Taiwan Department of Veterinary Medicine School of Veterinary Medicine NTU Taipei106319 Taiwan Department of Clinical Laboratory Sciences and Medical Biotechnology College of Medicine NTU Taipei100229 Taiwan Institute of Epidemiology and Preventive Medicine College of Public Health National Taiwan University Taipei100025 Taiwan Department of Emergency Medicine Kinmen Hospital MOHW Kinmen891002 Taiwan School of Medicine College of Medicine NTU Taipei100233 Taiwan Department of Public Health College of Public Health NTU Taipei100025 Taiwan Department of Computer Science and Information Engineering College of Electrical Engineering & Computer Science NTU Taipei106319 Taiwan Division of Infectious Diseases Department of Internal Medicine Kaohsiung Chang Gung Memorial Hospital Kaohsiung833401 Taiwan School of Medicine College of Medicine Chang Gung University Taoyuan333323 Taiwan

Background: High severity and case fatality rates among elderly dengue patients have become growing threats globally. Early recognition of severe dengue is crucial to saving ***: To evaluate the predictive factors leading to severe dengue in elderly patients, we enrolled 1,728 laboratory-confirmed dengue cases [including 267 severe (15.5%) and 1461 mild cases] from southern Taiwan: (1) two community hospitals and one tertiary medical center in 2015 outbreak in Tainan and (2) another medical center during outbreaks from 2002-2013 in Kaohsiung. Among these, 606 (35.1%) patients were elderly (≥ 65 years). The individual’s clinical symptoms/signs and laboratory findings of dengue were retrospectively collected from chart reviews. Data analyses were run using both epidemiological and machine learning approaches. We further applied the Synthetic Minority Oversampling Technique (SMOTE) to solve the imbalance between severe and non-severe cases. Then, we used a random forest model to select essential features to differentiate between the clinical manifestations of severe and mild ***: Our model using triage symptoms in the elderly and non-elderly achieved 97% accuracy in training and 95% accuracy in testing on predicting severe dengue. Hemorrhage ranked the highest symptom in predicting severe dengue. Fever was important for diagnosing severe dengue in the elderly, whereas GI-related symptoms were significant for predicting severe dengue in the non-elderly. Lastly, we developed a cloud service to help clinicians detect severe dengue cases ***: The machine learning model involving 15 triage symptoms, 43 accessory features, and four comorbidities predicted severe dengue cases in the elderly with > 95% ***: The study was sponsored by the National Health Research Institute (NHRI), National Science and Technology Council [NSTC, formerly the Ministry of Science and Technology (MOST) in Taiwan], and National Cheng Kung Universit

关键词： Hospitals

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A multimodal framework for extraction and fusion of satellite images and public health data

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Scientific Data 2024年第1期11卷 634页

作者： Moukheiber, Dana Restrepo, David Cajas, Sebastián Andrés Montoya, María Patricia Arbeláez Celi, Leo Anthony Kuo, Kuan-Ting López, Diego M. Moukheiber, Lama Moukheiber, Mira Moukheiber, Sulaiman Osorio-Valencia, Juan Sebastian Purkayastha, Saptarshi Paddo, Atika Rahman Wu, Chenwei Kuo, Po-Chih Laboratory for Computational Physiology Massachusetts Institute of Technology Cambridge MA United States Departamento de Telemática Universidad del Cauca Cauca Popayán Colombia John A. Paulson School of Engineering and Applied Sciences Harvard University Boston MA United States School of Computer Science University College Dublin Dublin Ireland Grupo de Epidemiología Facultad Nacional de Salud Pública Universidad de Antioquia Medellín Colombia Department of Biostatistics Harvard TH Chan School of Public Health Boston MA United States Department of Medicine Beth Israel Deaconess Medical Center Boston MA United States Department of Computer Science National Tsing Hua University Hsinchu Taiwan Department of Computer Science Worcester Polytechnic Institute Worcester MA United States ScienteLab Cundinamarca Bogota Colombia Department of BioHealth Informatics Indiana University Luddy School of Informatics Computing and Engineering Indianapolis IN United States Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI United States

In low- and middle-income countries, the substantial costs associated with traditional data collection pose an obstacle to facilitating decision-making in the field of public health. Satellite imagery offers a potential solution, but the image extraction and analysis can be costly and requires specialized expertise. We introduce SatelliteBench, a scalable framework for satellite image extraction and vector embeddings generation. We also propose a novel multimodal fusion pipeline that utilizes a series of satellite imagery and metadata. The framework was evaluated generating a dataset with a collection of 12,636 images and embeddings accompanied by comprehensive metadata, from 81 municipalities in Colombia between 2016 and 2018. The dataset was then evaluated in 3 tasks: including dengue case prediction, poverty assessment, and access to education. The performance showcases the versatility and practicality of SatelliteBench, offering a reproducible, accessible and open tool to enhance decision-making in public health. © The Author(s) 2024.

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Three Different Directions in Which the European Union Could Replace Russian Natural Gas

SSRN

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SSRN 2023年

作者： Nikas, Alexandros Frilingou, Natasha Heussaff, Conall Fragkos, Panagiotis Mittal, Shivika Sampedro, Jon Giarola, Sara Sasse, Jan-Philipp Rinaldi, Lorenzo Doukas, Haris Gambhir, Ajay Giannousakis, Anastasis Golinucci, Nicolò Koasidis, Konstantinos Rocco, Matteo Vincenzo Trutnevyte, Evelina Xexakis, Georgios Zachmann, Georg Zisarou, Eleftheria Colombo, Emanuela Hawkes, Adam Yarlagadda, Brinda Binsted, Matthew Iyer, Gokul Van de Ven, Dirk-Jan Energy Policy Unit School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Bruegel Brussels Belgium E3Modelling S.A. Athens Greece Grantham Institute for Climate Change and the Environment Imperial College London London United Kingdom Leioa Spain Department of Chemical Engineering Imperial College London London United Kingdom Section of Earth and Environmental Sciences University of Geneva Switzerland Department of Energy Politecnico di Milano Milan Italy HOLISTIC P.C. Athens Greece Joint Global Change Research Institute Pacific Northwest National Laboratory MD United States

Russia’s invasion of Ukraine fuelled an energy crisis, considerably impacting Europe given its heavy reliance on Russian natural gas. Based on four global integrated assessment models that are further soft-linked to two sectoral models, our study explores the trade-offs among three approaches to living without Russian gas in Europe: replacing with other gas imports, boosting domestic energy production, and accelerating energy efficiency. We find that substituting Russian gas from other trade partners would miss an opportunity to accelerate decarbonisation in end-use sectors while risking fossil-fuel lock-ins, despite featuring the lowest gas price spikes and reducing heating costs for end-users in the near term. Boosting domestic, primarily renewable, energy production would instead require considerable investments, potentially burdening consumers. Energy demand reductions, however, may offer more space for emissions cuts at the lowest power-sector investment costs;nonetheless, this strategy would also risk relocation of energy-intensive industries, an aspect of increasing policy relevance. © 2023, The Authors. All rights reserved.

关键词： Natural gas

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Brain computed tomography reading of stroke patients by resident doctors from different medical specialities: An eye-tracking study

引用

Journal of Clinical Neuroscience 2023年 117卷 173-180页

作者： Liu, Chi-Hung Chang, Chun-Wei Hung, June Lin, John J.H. Sung, Pi-Shan Lee, Li-Ang Hsiao, Cheng-Ting Chao, Yi-Ping Huang, Elaine Shinwei Wang, Shu-Ling Department of Neurology Chang Gung Memorial Hospital Linkou Medical Center Taoyuan Taiwan School of Medicine College of Medicine Chang Gung University Taoyuan Taiwan Division of Medical Education Graduate Institute of Clinical Medical Sciences College of Medicine Chang Gung University Taoyuan Taiwan Institute of Health Policy and Management College of Public Health National Taiwan University Taipei Taiwan Graduate Institute of Science Education National Taiwan Normal University Taipei City Taiwan Department of Neurology National Cheng Kung University Hospital College of Medicine National Cheng Kung University Tainan Taiwan Department of Otorhinolaryngology-Head and Neck Surgery Chang Gung Memorial Hospital Linkou Main Branch Taoyuan Taiwan Institute of Brain Science National Yang Ming Chiao Tung University Taipei Taiwan Department of Emergency Medicine Chang Gung Memorial Hospital Chiayi Taiwan Chang Gung Medical Education Research Centre Taoyuan Taiwan Department of Computer Science and Information Engineering Chang Gung University Taoyuan Taiwan Department of Biomedical Engineering Chang Gung University Taoyuan Taiwan Graduate Institute of Digital Learning and Education National Taiwan University of Science and Technology Taiwan

Background: Using the eye-tracking technique, our work aimed to examine whether difference in clinical background may affect the training outcome of resident doctors’ interpretation skills and reading behaviour related to brain computed tomography (CT). Methods: Twelve resident doctors in the neurology, radiology, and emergency departments were recruited. Each participant had to read CT images of the brain for two cases. We evaluated each participant's accuracy of lesion identification. We also used the eye-tracking technique to assess reading behaviour. We recorded dwell times, fixation counts, run counts, and first-run dwell times of target lesions to evaluate visual attention. Transition entropy was applied to assess the temporal relations and spatial dynamics of systematic image reading. Results: The eye-tracking results showed that the image reading sequence examined by transition entropy was comparable among resident doctors from different medical specialties (p = 0.82). However, the dwell time of the target lesions was shorter for the resident doctors from the neurology department (4828.63 ms, p = 0.01) than for those from the resident doctors from the radiology (6275.88 ms) and emergency (5305.00 ms) departments. The eye-tracking results in individual areas of interest only showed differences in the eye-tracking performance of the first-run dwell time (p = 0.05) in the anterior cerebral falx. Discussion: Our findings demonstrate that resident doctors from different medical specialties may achieve similar imaging reading patterns for brain CT. This may mitigate queries regarding the influence of different backgrounds on training outcomes. © 2023 Elsevier Ltd

关键词： Emergency department Eye-tracking Image reading behaviour Neurology Radiology

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Chiral-Dependent Redox Capacitive Biosensor Using Cu-Cys-GSH Nanoparticles for Ultrasensitive H2O2 Detection

Biosensors

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Biosensors 2025年第5期15卷 315-315页

作者： Yilmaz Aydin, Duygu Wu, Jie Jayne Chen, Jiangang Department of Electrical Engineering and Computer Science The University of Tennessee KnoxvilleTN37996 United States Department of Bioengineering Malatya Turgut Ozal University Malatya44210 Turkey Department of Public Health The University of Tennessee KnoxvilleTN37996 United States

Copper-thiolate nanostructures, formed through the self-assembly of cysteine (Cys) and glutathione (GSH) with copper ions, offer a versatile platform for redox-active applications due to their structural stability and chemical functionality. In this study, Cu-Cys-GSH nanoparticles were synthesized and employed to develop a capacitive biosensor for the ultralow concentration detection of hydrogen peroxide (H2O2). The detection mechanism leverages a Fenton-like reaction, where H2O2 interacts with Cu-Cys-GSH nanoparticles to generate hydroxyl radicals (·OH) through redox cycling between Cu2+ and Cu+ ions. These redox processes induce changes in the sensor’s surface charge and dielectric properties, enabling highly sensitive capacitive sensing at gold interdigitated electrodes (IDEs). The influence of chirality on sensing performance was investigated by synthesizing nanoparticles with both L- and D-cysteine enantiomers. Comparative analysis revealed that the stereochemistry of cysteine impacts the catalytic activity and sensor response, with Cu-L-Cys-GSH nanoparticles exhibiting superior performance. Specifically, the biosensor achieved a linear detection range from 1.0 fM to 1.0 pM and demonstrated an ultra-sensitive detection limit of 21.8 aM, outperforming many existing methods for H2O2 detection. The sensor’s practical performance was further validated using milk and saliva samples, yielding high recovery rates and confirming its robustness and accuracy for real-world applications. This study offers a disposable, low-cost sensing platform compatible with sustainable healthcare practices and facilitates easy integration into point-of-care diagnostic systems. © 2025 by the authors.

关键词： Redox reactions

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An analytical model for efficient indoor THz access point deployment

arXiv

引用

arXiv 2020年

作者： Singh, Rohit Sicker, Douglas Engineering and Public Policy Carnegie Mellon University Pittsburgh United States School of Computer Science Carnegie Mellon University Pittsburgh United States

Ultra-densification of user equipment (UE) and access points (APs) are anticipated to take a toll on the future spectrum needs. Higher frequency bands, such as mmWave (30-300GHz) and THz spectrum (0.3-10THz), can be used to cater to the high-throughput needs of ultra-dense networks. These high-frequency bands have a tremendous amount of green-filed contiguous spectrum, ranging in hundreds of GHz. However, these bands, especially the THz bands, face numerous challenges, such as high spreading, absorption, and penetration losses. To combat these challenges, the THz-APs need to be either equipped with high transmit power, high antenna gains (i.e., narrow antenna beams), or limit the communication to short-ranges. All of these factors are bounded due to technical or economic challenges, which will result in a "distance-power dilemma" while deciding on the deployment strategy of THz-APs. In this paper, we present an analytical model to deploy THz-APs in an indoor setting efficiently. We further show through extensive numerical analysis, the optimal number of APs and optimal room length for different blocks of the THz spectrum. Furthermore, these THz-APs need to be efficiently packed to avoid outages due to handoffs, which can add more complexity to the dilemma. To mitigate the packing problem, we propose two solutions over the optimal solution: (a) Radius Increase, and (b) Repeater Assistance, and present an analytical model for each. Copyright © 2020, The Authors. All rights reserved.

关键词： Energy efficiency

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