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Tuberculosis screening among children and adolescents in China:insights from a mathematical model

Intelligent Medicine 2023年第3期3卷 157-163页

作者： Janne Estill Yangqin Xu Shouyuan Wu Lidong Hu Nan Yang Shu Yang Yaolong Chen Guobao Li Evidence-Based Medicine Center School of Basic Medical SciencesLanzhou UniversityLanzhouGansu 730000China Institute of Global Health University of GenevaGenevaSwitzerland School of Public Health Lanzhou UniversityLanzhouGansu 730000China Department of Rheumatology and Immunology The First Medical CenterChinese PLA General HospitalBejing 100853China College of Medical Information Engineering Chengdu University of Traditional Chinese MedicineChengduSichuan 610000China Research Unit of Evidence-Based Evaluation and Guidelines Chinese Academy of Medical Sciences(2021RU017)School of Basic Medical SciencesLanzhou UniversityLanzhouGansu 730000China Lanzhou University Institute of Health Data Science LanzhouGansu 730000China WHO Collaborating Centre for Guideline Implementation and Knowledge Translation LanzhouGansu 730000China Department of Lung Disease Shenzhen Third People's HospitalThe Second Affiliated HospitalSouthern University of Science and TechnologyShenzhen Guangdong 518112China

Background Tuberculosis(TB)continues to be prevalent in China also among children and adolescents in *** built a dynamic mathematical model for TB transmission in China,and applied it to compare the epidemic trends 2021–2030 under a range of screening interventions focusing on children and *** We developed a dynamic mathematical model with a flexible *** model can be applied either stochastically or deterministically,and can encompass arbitrary age structure and resistance *** the present version,we used the deterministic version excluding resistance but including age structure with six groups:0–5,6–11,12–14,15–17,18–64,and 65 years and *** parameterized the model by literature data and fitting it to case and death estimates provided by the World Health *** compared the new TB cases and TB-related deaths in each age group over the period 2021–2030 in 10 scenarios that involved intensified screening of particular age groups of children,adolescents,or young adults,or decreased or increased diagnostic accuracy of the *** Screening the entire age class of 18-year-old persons would prevent 517,000 TB cases and 14,600 TB-related deaths between years 2021 and 2030,corresponding to 6.6%and 5.5%decrease from the standard of care projection,*** screening of children aged 6–11 and,to a lesser extent,0–5 years,also reduced TB incidence and mortality,particularly among children of the respective ages but also in other age *** contrast,intensified screening of adolescents did not have a major *** with a simpler and less accurate method resulted in worsened outcomes,which could not be offset by more intensive *** accurate screening and better sensitivity to detect latent TB could prevent 2.3 million TB cases and 68,500 TB deaths in the coming 10 *** Routine screening in schools can efficiently reduce the burden of TB in *** should be intensifie

关键词： Tuberculosis China Children and adolescents Mathematical model Screening

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Forest fire area estimation using support vector machine as an approximator 10th

Forest fire area estimation using support vector machine as ...

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10th International Joint Conference on Computational Intelligence, IJCCI 2018

作者： Kerdprasop, Nittaya Poomka, Pumrapee Chuaybamroong, Paradee Kerdprasop, Kittisak Data and Knowledge Engineering Research Unit School of Computer Engineering Suranaree University of Technology Nakhon Ratchasima Thailand Department of Environmental Science Thammasat University Thailand

ISBN: (纸本)9789897583278

Forest fire is critical environmental issue that can cause severe damage. Fast detection and accurate estimation of forest fire burned area can help firefighters to effectively control damage. Thus, the purpose of this paper is to apply state of the art data modeling method to estimate the area of forest fire burning using support vector machine (SVM) algorithm as a tool for area approximation. The dataset is real forest fires data from the Montesinho natural park in the northeast region of Portugal. The original dataset comprises of 517 records with 13 attributes. We randomly sample the data 10 times to obtain 10 data-subsets for building estimation models using two kinds of SVM kernel: radial basis function and polynomial function. The obtained models are compared against other proposed techniques to assess performances based on the two measurement metrics: mean absolute error (MAE) and root mean square error (RMSE). The experimental results show that our SVM predictor using polynomial kernel function can precisely estimate forest fire damage area with the MAE and RMSE as low as 6.48 and 7.65, respectively. These errors are less than other techniques reported in the literature. Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

关键词： Fires

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Modeling method for temperature anomaly analysis 10th

Modeling method for temperature anomaly analysis

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10th International Joint Conference on Computational Intelligence, IJCCI 2018

作者： Kerdprasop, Kittisak Chuaybamroong, Paradee Kerdprasop, Nittaya Data and Knowledge Engineering Research Unit School of Computer Engineering Suranaree University of Technology Nakhon Ratchasima Thailand Department of Environmental Science Thammasat University Thailand

ISBN: (纸本)9789897583278

This study applies intelligent analytical methods to analyze temperature anomaly events during the past seven centuries of countries in the Southeast Asia including Thailand, Malaysia, Myanmar, and Cambodia. The temperature reconstruction during the years 1300 to 1999 were used as data source for anomaly analysis. In the analytical process, correlation analysis was applied to initially investigate the temperature variability concordance among the Southeast Asian countries. The results are that temperature variability patterns in Thailand, Myanmar, and Cambodia are moderately correlated to each other. On the contrary, the temperature variation patterns of Malaysia do not correlate to other countries in the same region. The further in-depth analysis focuses on the temperature anomaly of Thailand that shows high variability from the 14th to 16th centuries. Several machine learning algorithms had been applied to estimate the temperature anomaly of Thailand based on the anomaly events among the neighbors. The learned models reveal that Myanmar temperature anomaly most associate to the Thailand’s temperature variation. The performance of each model had been assessed and the results reveal that the chi-squared automatic interaction detection, or CHAID, is the best one with 0.624 correlation coefficient and relative error around 0.611. Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

关键词： Machine learning

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On Training Classifiers for Linking Event Templates 27

On Training Classifiers for Linking Event Templates

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2018 Events and Stories in the News Workshop, EventStory 2018, colocated with the 27th International Conference on Computational Linguistics, COLING 2018

作者： Piskorski, Jakub Šarić, Fredi Zavarella, Vanni Atkinson, Martin Text and Data Mining Unit Joint Research Centre of the European Ispra Italy Text Analysis and Knowledge Engineering Lab University of Zagreb Croatia

ISBN: (纸本)9781948087599

The paper reports on exploring various machine learning techniques and a range of textual and meta-data features to train classifiers for linking related event templates automatically extracted from online news. With the best model using textual features only we achieved 94.7% (92.9%) F1 score on GOLD (SILVER) dataset. These figures were further improved to 98.6% (GOLD) and 97% (SILVER) F1 score by adding meta-data features, mainly thanks to the strong discriminatory power of automatically extracted geographical information related to events. © 2018 EventStory 2018 - Events and Stories in the News, Proceedings of the Workshop. All rights reserved.

关键词： Gold

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Deep learning - A first meta-survey of selected reviews across scientific disciplines, their commonalities, challenges and research impact

arXiv

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arXiv 2020年

作者： Egger, Jan Pepe, Antonio Gsaxner, Christina Jin, Yuan Li, Jianning Kern, Roman Institute of Computer Graphics and Vision Faculty of Computer Science and Biomedical Engineering Graz University of Technology Graz Austria Computer Algorithms for Medicine Laboratory Graz Austria Department of Oral and Maxillofacial Surgery Medical University of Graz Graz Austria University Medicine Essen Essen Germany Research Center for Connected Healthcare Big Data Zhejiang Lab Zhejiang Hangzhou China Research Unit Experimental Neurotraumatology Department of Neurosurgery Medical University of Graz Graz Austria Knowledge Discovery Know-Center Graz Austria Institute of Interactive Systems and Data Science Graz University of Technology Graz Austria

Deep learning belongs to the field of artificial intelligence, where machines perform tasks that typically require some kind of human intelligence. Deep learning tries to achieve this by drawing inspiration from the learning of a human brain. Similar to the basic structure of a brain, which consists of (billions of) neurons and connections between them, a deep learning algorithm consists of an artificial neural network, which resembles the biological brain structure. Mimicking the learning process of humans with their senses, deep learning networks are fed with (sensory) data, like texts, images, videos or sounds. These networks outperform the state-of-the-art methods in different tasks and, because of this, the whole field saw an exponential growth during the last years. This growth resulted in way over 10,000 publications per year in the last years. For example, the search engine PubMed alone, which covers only a sub-set of all publications in the medical field, provides already over 11,000 results in Q3 2020 for the search term 'deep learning', and around 90% of these results are from the last three years. Consequently, a complete overview over the field of deep learning is already impossible to obtain and, in the near future, it will potentially become difficult to obtain an overview over a subfield. However, there are several review articles about deep learning, which are focused on specific scientific fields or applications, for example deep learning advances in computer vision or in specific tasks like object detection. With these surveys as a foundation, the aim of this contribution is to provide a first high-level, categorized meta-survey of selected reviews on deep learning across different scientific disciplines and outline the research impact that they already have during a short period of time. The categories (computer vision, language processing, medical informatics and additional works) have been chosen according to the underlying data sources (image,

关键词： Surveys

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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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Publisher Correction: Global estimates on the number of people blind or visually impaired by cataract: a meta-analysis from 2000 to 2020

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Eye (London, England) 2024年第11期38卷 2229-2231页

作者： Konrad Pesudovs Hugh R. Taylor Tabassom Sedighi Rupert R. A. Bourne Shahina Pardhan Van Charles Lansingh John H. Kempen Ian Tapply Arthur G. Fernandes Maria Vittoria Cicinelli Alessandro Arrigo Nicolas Leveziel Serge Resnikoff Seth Flaxman Mukkharram M. Bikbov Tasanee Braithwaite Alain Bron Ching-Yu Cheng Monte A. Del Monte Joshua R. Ehrlich Leon B. Ellwein David Friedman João M. Furtado Gus Gazzard Ronnie George M. Elizabeth Hartnett Jost B. Jonas Rim Kahloun Moncef Khairallah Rohit C. Khanna Janet Leasher Julie-Anne Little Vinay Nangia Michal Nowak Tunde Peto Pradeep Ramulu Fotis Topouzis Mitiadis Tsilimbaris Ya Xing Wang Ningli Wang Paul Svitil Briant Theo Vos Katrin Burkart Kaleb Coberly Xiaochen Dai Stephen S. Lim Tomislav Mestrovic Ali H. Mokdad Christopher J. L. Murray Jaimie D. Steinmetz Awoke Misganaw Yohannes Habtegiorgis Abate Mohammad Abdollahi Mozhan Abdollahi Ayele Mamo Abebe Olumide Abiodun Richard Gyan Aboagye Woldu Aberhe Abrha Hasan Abualruz Hiwa Abubaker Ali Eman Abu-Gharbieh Salahdein Aburuz Tadele Girum Girum Adal Mesafint Molla Adane Isaac Yeboah Addo Qorinah Estiningtyas Sakilah Adnani Muhammad Sohail Afzal Muhammad Umair Shahin Aghamiri Bright Opoku Ahinkorah Aqeel Ahmad Sajjad Ahmad Ali Ahmadi Siamak Sabour Ayman Ahmed Haroon Ahmed Ahmad Samir Alfaar Abid Ali Syed Shujait Shujait Ali Awais Altaf Hubert Amu Emmanuel Manu Sofia Androudi Rodrigo Anguita Abhishek Anil Rimple Jeet Kaur Muhammad Aaqib Shamim Saeid Anvari Anayochukwu Edward Anyasodor Francis Appiah Jalal Arabloo Haleh Ayatollahi Mosab Arafat Damelash Areda Reza Arefnezhad Brhane Berhe Aregawi Akeza Awealom Asgedom Tahira Ashraf Seyyed Shamsadin Athari Bantalem Tilaye Tilaye Atinafu Birhan Tsegaw Taye Maha Moh’d Wahbi Atout Alok Atreya Ahmed Y. Azzam Hassan Babamohamadi Sara Bagherieh Yogesh Bahurupi Atif Amin Baig Shumaila Nargus Biswajit Banik Mainak Bardhan Saurav Basu Kavita Batra Nebiyou Simegnew Bayileyegn Fatemeh Bazvand Addisu Shunu Beyene Awoke Masrie Asrat Derese Devidas S. Bhagat Akshaya Srikanth Bhagavathula Pankaj Bh Medicine & Health University of New South Wales Sydney NSW Australia School of Optometry and Vision Science University of New South Wales Sydney NSW Australia School of Population and Global Health University of Melbourne Carlton VIC Australia Vision and Eye Research Institute Anglia Ruskin University Cambridge UK HelpMeSee Instituto Mexicano de Oftalmologia Santiago de Querétaro Mexico University of Miami Gables FL USA University of Utah Salt Lake City UT USA Chief Medical Office HelpMeSee New York NY USA Mexican Institute of Ophthalmology Queretaro Mexico Department of Ophthalmology Massachusetts Eye and Ear/Shepens Eye Research Institute/Harvard Medical School Boston MA USA Eye Unit MyungSung Christian Medical Center (MCM) Comprehensive Specialized Hospital and MyungSun Medical College Addis Ababa Ethiopia Department of Ophthalmology Addis Ababa University Addis Ababa Ethiopia Sight for Souls Bellevue WA USA Department of Ophthalmology Harvard University Boston MA USA Eye Unit MyungSung Medical College Addis Ababa Ethiopia Addenbrooke’s Hospital Cambridge UK Department of Ophthalmology Cambridge University Hospitals Cambridge UK Federal University of Sao Paolo Sao Paolo SP Brazil University of Calgary Calgary AB Canada Department of Ophthalmology and Visual Sciences Federal University of São Paulo Sao Paulo Brazil School of Medicine Vita-Salute San Raffaele University Milan Italy Department of Ophthalmology IRCCS San Raffaele Scientific Institute Milan Italy Department of Ophthalmology San Raffaele Scientific Institute Milano Italy IRCCS San Raffaele Scientific Institute Vita-Salute University Milan Italy Scientific Institute San Raffaele Hospital Vita-Salute University Milan Italy University of Poitiers Poitiers France CHU de Poitiers Poitiers France Ophthalmology Department CHU de Poitiers (Poitiers University Hospital) Poitiers France Unité 1084 National Institute of Health and Medical Research (INSERM) Poitiers France

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Global, regional, and national burden of neck pain, 1990–2020, and projections to 2050: a systematic analysis of the Global Burden of Disease Study 2021

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The Lancet Rheumatology 2024年第3期6卷 e142-e155页

作者： Wu, Ai-Min Cross, Marita Elliott, James M. Culbreth, Garland T. Cousin, Ewerton Haile, Lydia M. Steinmetz, Jaimie D. Hagins, Hailey Kopec, Jacek A. Brooks, Peter M. Woolf, Anthony D. Kopansky-Giles, Deborah R. Walton, David M. Treleaven, Julia M. Dreinhoefer, Karsten E. Betteridge, Neil Abbasifard, Mitra Abbasi-Kangevari, Zeinab Addo, Isaac Yeboah Adesina, Miracle Ayomikun Adnani, Qorinah Estiningtyas Sakilah Aithala, Janardhana P. Alhalaiqa, Fadwa Alhalaiqa Naji Alimohamadi, Yousef Amiri, Sohrab Amu, Hubert Antony, Benny Arabloo, Jalal Aravkin, Aleksandr Y. Asghari-Jafarabadi, Mohammad Atomsa, Gamechu Hunde Azadnajafabad, Sina Azzam, Ahmed Y. Baghdadi, Soroush Balogun, Saliu A. Balta, Asaminew Birhanu Banach, Maciej Banakar, Morteza Barrow, Amadou Bashiri, Azadeh Bekele, Alehegn Bensenor, Isabela M. Bhardwaj, Pankaj Bhat, Ajay Nagesh Bilchut, Awraris Hailu Briggs, Andrew M. Buchbinder, Rachelle Cao, Chao Chaurasia, Akhilanand Chirinos-Caceres, Jesus Lorenzo Christensen, Steffan Wittrup McPhee Coberly, Kaleb Dadras, Omid Dai, Xiaochen de Luca, Katie Dehghan, Azizallah Dong, Huan-Ji Ekholuenetale, Michael Elhadi, Muhammed Eshetu, Habitu Birhan Eskandarieh, Sharareh Etaee, Farshid Fagbamigbe, Adeniyi Francis Fares, Jawad Fatehizadeh, Ali Feizkhah, Alireza Ferreira, Manuela L. Ferreira, Nuno Fischer, Florian Franklin, Richard Charles Ganesan, Balasankar Gebremichael, Mathewos Alemu Gerema, Urge Gholami, Ali Ghozy, Sherief Gill, Tiffany K. Golechha, Mahaveer Goleij, Pouya Golinelli, Davide Graham, Simon Matthew Haj-Mirzaian, Arvin Harlianto, Netanja I. Hartvigsen, Jan Hasanian, Mohammad Hassen, Mohammed Bheser Hay, Simon I. Hebert, Jeffrey J. Heidari, Golnaz Hoveidaei, Amir Human Hsiao, Alexander Kevin Ibitoye, Segun Emmanuel Iwu, Chidozie C.D. Jacob, Louis Janodia, Manthan Dilipkumar Jin, Yingzhao Jonas, Jost B. Joshua, Charity Ehimwenma Kandel, Himal Khader, Yousef Saleh Khajuria, Himanshu Khan, Ejaz Ahmad Khan, Moien A.B. Khatatbeh, Moawiah Mohammad Khateri, Sorour Khayat Kashani, Hamid Reza Khonji, Mohammad Saeid Department of Orthopaedics The Second Affiliated Hospital of Wenzhou Medical University Wenzhou China Faculty of Medicine and Health University of Sydney Sydney NSW Australia Global Alliance for Musculoskeletal Health Sydney NSW Australia Kolling Institute of Medical Research University of Sydney Northern Sydney Local Health District St Leonards NSW Australia Department of Physical Therapy and Human Movement Sciences Northwestern University Chicago IL United States Institute for Health Metrics and Evaluation University of Washington Seattle WA United States School of Population and Public Health University of British Columbia Vancouver BC Canada Arthritis Research Canada Richmond BC Canada US Department of Veterans Affairs (VA) Birmingham AL United States Centre for Health Policy University of Melbourne Melbourne VIC Australia Bone and Joint Research Group Royal Cornwall Hospital Truro United Kingdom Department of Family and Community Medicine University of Toronto Toronto ON Canada Department of Research and Innovation Canadian Memorial Chiropractic College Toronto ON Canada Department of Physical Therapy University of Sydney Sydney NSW Australia School of Physical Therapy Western University London ON Canada School of Health and Rehabilitation Sciences The University of Queensland Brisbane Australia Center of Musculoskeletal Surgery Charité Universitätsmedizin Berlin (Charité Medical University Berlin) Berlin Germany London United Kingdom Department of Internal Medicine Rafsanjan University of Medical Sciences Rafsanjan Iran Clinical Research Development Unit Rafsanjan University of Medical Sciences Rafsanjan Iran Social Determinants of Health Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Non-communicable Diseases Research Center Tehran University of Medical Sciences Tehran Iran Tehran University of Medical Sciences Tehran Iran Centre for Social Research in Health University of New South Wales Sydney NSW

Background: Neck pain is a highly prevalent condition that leads to considerable pain, disability, and economic cost. We present the most current estimates of neck pain prevalence and years lived with disability (YLDs) from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) by age, sex, and location, with forecasted prevalence to 2050. Methods: Systematic reviews identified population-representative surveys used to estimate the prevalence of and YLDs from neck pain in 204 countries and territories, spanning from 1990 to 2020, with additional data from opportunistic review. Medical claims data from Taiwan (province of China) were also included. Input data were pooled using DisMod-MR 2.1, a Bayesian meta-regression tool. Prevalence was forecast to 2050 using a mixed-effects model using Socio-demographic Index as a predictor and multiplying by projected population estimates. We present 95% UIs for every metric based on the 2·5th and 97·5th percentiles of 100 draws of the posterior distribution. Findings: Globally, in 2020, neck pain affected 203 million (95% uncertainty interval [UI] 163–253) people. The global age-standardised prevalence rate of neck pain was estimated to be 2450 (1960–3040) per 100 000 population and global age-standardised YLD rate was estimated to be 244 (165–346) per 100 000. The age-standardised prevalence rate remained stable between 1990 and 2020 (percentage change 0·2% [–1·3 to 1·7]). Globally, females had a higher age-standardised prevalence rate (2890 [2330–3620] per 100 000) than males (2000 [1600–2480] per 100 000), with the prevalence peaking between 45 years and 74 years in male and female sexes. By 2050, the estimated global number of neck pain cases is projected to be 269 million (219–322), with an increase of 32·5% (23·9–42·3) from 2020 to 2050. Decomposition analysis of the projections showed population growth was the primary contributing factor, followed by population ageing. Interpretation: Although age-standardi

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Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy

arXiv

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arXiv 2020年

作者： Ali, Sharib Dmitrieva, Mariia Ghatwary, Noha Bano, Sophia Polat, Gorkem Temizel, Alptekin Krenzer, Adrian Hekalo, Amar Guo, Yun Bo Matuszewski, Bogdan Gridach, Mourad Voiculescu, Irina Yoganand, Vishnusai Chavan, Arnav Raj, Aryan Nguyen, Nhan T. Tran, Dat Q. Huynh, Le Duy Boutry, Nicolas Rezvy, Shahadate Chen, Haijian Choi, Yoon Ho Subramanian, Anand Balasubramanian, Velmurugan Gao, Xiaohong W. Hu, Hongyu Liao, Yusheng Stoyanov, Danail Daul, Christian Realdon, Stefano Cannizzaro, Renato Lamarque, Dominique Tran-Nguyen, Terry Bailey, Adam Braden, Barbara East, James Rittscher, Jens Institute of Biomedical Engineering and Big Data Institute Old Road Campus University of Oxford Oxford United Kingdom Translational Gastroenterology Unit Experimental Medicine Div. John Radcliffe Hospital University of Oxford Oxford United Kingdom Oxford NIHR Biomedical Research Centre Oxford United Kingdom Université de Versailles St-Quentin en Yvelines Hôpital Ambroise Paré France Instituto Onclologico Veneto IOV-IRCCS Padova Italy CRO Centro Riferimento Oncologico IRCCS Aviano Italy Computer Engineering Department Arab Academy for Science and Technology Alexandria Egypt Department of Computer Science University College London London United Kingdom CRAN UMR 7039 University of Lorraine CNRS Nancy France Graduate School of Informatics Middle East Technical University Ankara Turkey Department of Artificial Intelligence and Knowledge Systems University of Würzburg Germany School of Engineering University of Central Lancashire United Kingdom Department of Computer Science University of Oxford United Kingdom Mimyk Medical Simulations Pvt Ltd Indian Institute of Science Bengaluru India Dhanbad India Hanoi Viet Nam Le Kremlin-BicêtreF-94270 France School of Science and Technology Middlesex University London United Kingdom Department of Computer Science School of Informatics Xiamen University China Sungkyunkwan University Seoul Korea Republic of Claritrics India Pvt Ltd Chennai India School of Medical Science and Technology Indian Institute of Technology KharagpurWest Bengal India Shanghai Jiaotong University Shanghai China Ibn Zohr University Computer Science HIT Agadir Morocco

The Endoscopy Computer Vision Challenge (EndoCV) is a crowd-sourcing initiative to address eminent problems in developing reliable computer aided detection and diagnosis endoscopy systems and suggest a pathway for clinical translation of technologies. Whilst endoscopy is a widely used diagnostic and treatment tool for hollow-organs, there are several core challenges often faced by endoscopists, mainly: 1) presence of multi-class artefacts that hinder their visual interpretation, and 2) difficulty in identifying subtle precancerous precursors and cancer abnormalities. Artefacts often affect the robustness of deep learning methods applied to the gastrointestinal tract organs as they can be confused with tissue of interest. EndoCV2020 challenges are designed to address research questions in these remits. In this paper, we present a summary of methods developed by the top 17 teams and provide an objective comparison of state-of-the-art methods and methods designed by the participants for two sub-challenges: i) artefact detection and segmentation (EAD2020), and ii) disease detection and segmentation (EDD2020). Multi-center, multi-organ, multi-class, and multi-modal clinical endoscopy datasets were compiled for both EAD2020 and EDD2020 sub-challenges. The out-of-sample generalization ability of detection algorithms was also evaluated. Whilst most teams focused on accuracy improvements, only a few methods hold credibility for clinical usability. The best performing teams provided solutions to tackle class imbalance, and variabilities in size, origin, modality and occurrences by exploring data augmentation, data fusion, and optimal class thresholding techniques. © 2020, CC BY-NC-SA.

关键词： Endoscopy

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Global investments in pandemic preparedness and COVID-19: development assistance and domestic spending on health between 1990 and 2026

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The Lancet Global Health 2023年第3期11卷 e385-e413页

作者： Micah, Angela E. Bhangdia, Kayleigh Cogswell, Ian E. Lasher, Dylan Lidral-Porter, Brendan Maddison, Emilie R. Nguyen, Trang Nhu Ngoc Patel, Nishali Pedroza, Paola Solorio, Juan Stutzman, Hayley Tsakalos, Golsum Wang, Yifeng Warriner, Wesley Zhao, Yingxi Zlavog, Bianca S. Abbafati, Cristiana Abbas, Jaffar Abbasi-Kangevari, Mohsen Abbasi-Kangevari, Zeinab Abdelmasseh, Michael Abdulah, Deldar Morad Abedi, Aidin Abegaz, Kedir Hussein Abhilash, E.S. Aboagye, Richard Gyan Abolhassani, Hassan Abrigo, Michael R.M. Abubaker Ali, Hiwa Abu-Gharbieh, Eman Adem, Mohammed Hussien Afzal, Muhammad Sohail Ahmadi, Ali Ahmed, Haroon Ahmed Rashid, Tarik Aji, Budi Akbarialiabad, Hossein Akelew, Yibeltal Al Hamad, Hanadi Alam, Khurshid Alanezi, Fahad Mashhour Alanzi, Turki M. Al-Hanawi, Mohammed Khaled Alhassan, Robert Kaba Aljunid, Syed Mohamed Almustanyir, Sami Al-Raddadi, Rajaa M. Alvis-Guzman, Nelson Alvis-Zakzuk, Nelson J. Amare, Azmeraw T. Ameyaw, Edward Kwabena Amini-Rarani, Mostafa Amu, Hubert Ancuceanu, Robert Andrei, Tudorel Anwar, Sumadi Lukman Appiah, Francis Aqeel, Muhammad Arabloo, Jalal Arab-Zozani, Morteza Aravkin, Aleksandr Y. Aremu, Olatunde Aruleba, Raphael Taiwo Athari, Seyyed Shamsadin Avila-Burgos, Leticia Ayanore, Martin Amogre Azari, Samad Baig, Atif Amin Bantie, Abere Tilahun Barrow, Amadou Baskaran, Pritish Basu, Sanjay Batiha, Abdul-Monim Mohammad Baune, Bernhard T. Berezvai, Zombor Bhardwaj, Nikha Bhardwaj, Pankaj Bhaskar, Sonu Boachie, Micheal Kofi Bodolica, Virginia Botelho, João Silva Botelho Braithwaite, Dejana Breitborde, Nicholas J.K. Busse, Reinhard Cahuana-Hurtado, Lucero Catalá-López, Ferrán Chansa, Collins Charan, Jaykaran Chattu, Vijay Kumar Chen, Simiao Chukwu, Isaac Sunday Dadras, Omid Dandona, Lalit Dandona, Rakhi Dargahi, Abdollah Debela, Sisay Abebe Denova-Gutiérrez, Edgar Desye, Belay Dharmaratne, Samath Dhamminda Diao, Nancy Doan, Linh Phuong Dodangeh, Milad dos Santos, Wendel Mombaque Doshmangir, Leila Dube, John Eini, Ebrahim El Sayed Zaki, Maysaa El Tantawi, Maha Enyew, Daniel Berhanie Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Applied Mathematics University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Nuffield Department of Medicine University of Oxford Oxford United Kingdom Department of Juridical and Economic Studies La Sapienza University Rome Italy Antai College of Economics Shanghai Jiao Tong University Shanghai China Non-communicable Diseases Research Center Tehran University of Medical Sciences Tehran Iran Research Center for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Multiple Sclerosis Research Center Tehran University of Medical Sciences Tehran Iran Children’s Medical Center Tehran University of Medical Sciences Tehran Iran Department of Epidemiology and Biostatistics Tehran University of Medical Sciences Tehran Iran Tehran Heart Center Tehran University of Medical Sciences Tehran Iran Faculty of Medicine Tehran University of Medical Sciences Tehran Iran National Institute for Health Research Tehran University of Medical Sciences Tehran Iran Department of Pharmacology Tehran University of Medical Sciences Tehran Iran Department of Cardiology Tehran University of Medical Sciences Tehran Iran Sina Trauma and Surgery Research Center Tehran University of Medical Sciences Tehran Iran Digestive Diseases Research Institute Tehran University of Medical Sciences Tehran Iran Department of Neurology Tehran University of Medical Sciences Tehran Iran Social Determinants of Health Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Department of Epidemiology Shahid Beheshti University of Medical Sciences Tehran Iran Department of Health Economics and Statistics Shahid Beheshti University of Medical Sciences Tehran Iran Department of Neurosurgery Shahid Beheshti University of Medical Sciences Tehran Iran Ophthalmic R

Background: The COVID-19 pandemic highlighted gaps in health surveillance systems, disease prevention, and treatment globally. Among the many factors that might have led to these gaps is the issue of the financing of national health systems, especially in low-income and middle-income countries (LMICs), as well as a robust global system for pandemic preparedness. We aimed to provide a comparative assessment of global health spending at the onset of the pandemic;characterise the amount of development assistance for pandemic preparedness and response disbursed in the first 2 years of the COVID-19 pandemic;and examine expectations for future health spending and put into context the expected need for investment in pandemic preparedness. Methods: In this analysis of global health spending between 1990 and 2021, and prediction from 2021 to 2026, we estimated four sources of health spending: development assistance for health (DAH), government spending, out-of-pocket spending, and prepaid private spending across 204 countries and territories. We used the Organisation for Economic Co-operation and Development (OECD)'s Creditor Reporting System (CRS) and the WHO Global Health Expenditure database (GHED) to estimate spending. We estimated development assistance for general health, COVID-19 response, and pandemic preparedness and response using a keyword search. Health spending estimates were combined with estimates of resources needed for pandemic prevention and preparedness to analyse future health spending patterns, relative to need. Findings: In 2019, at the onset of the COVID-19 pandemic, US$9·2 trillion (95% uncertainty interval [UI] 9·1–9·3) was spent on health worldwide. We found great disparities in the amount of resources devoted to health, with high-income countries spending $7·3 trillion (95% UI 7·2–7·4) in 2019;293·7 times the $24·8 billion (95% UI 24·3–25·3) spent by low-income countries in 2019. That same year, $43·1 billion in development assistance was provided

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