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Author Correction: Africa needs context-relevant evidence to shape its clean energy future

Nature Energy 2024年 907页

作者： Yacob Mulugetta Lucas Somavilla Croxatto Meron Tesfamichael Abdulmutalib Yussuff Youba Sokona Philipp A. Trotter Samuel Fankhauser Jessica Omukuti Bjarne Steffen Edo Abraham Jean-Paul Adam Mekalia Paulos Aklilu Yohannes Hailu Linus Mofor S. Nadia Ouedraogo Lawrence Agbemabiese Churchill Agutu Florian Egli Abdulrasheed Isah Tobias S. Schmidt Olakunle Alao Wikus Kruger Peter Twesigye Bothwell Batidzirai Getachew Bekele Anteneh G. Dagnachew Ogunlade Davidson Fatima Denton E. Ogheneruona Diemuodeke Gebrekidan Gebresilassie Eshetu Mulualem Gebreslassie Mamadou Goundiam Haruna Kachalla Gujba Adam D. Hawkes Stephanie Hirmer Helen Hoka Mark Howells Daniel Kammen Francis Kemausuor Ismail Khennas Ifeoma Malo Minette Nago Destenie Nock Chukwumerije Okereke Benedict Probst Maria Schmidt Carlos Shenga Mohamed Sokona Jan Christoph Steckel Sebastian Sterl Bernard Tembo Julia Tomei Jim Watson Harald Winkler Department of Science Technology Engineering & Public Policy University College London London UK Responsible Technology Institute Department of Computer Science University of Oxford Oxford UK Groupe de Reflection et d’Initiatives Novatrices Bamako Mali Schumpeter School of Business and Economics University of Wuppertal Wuppertal Germany Smith School of Enterprise and the Environment University of Oxford Oxford UK Institute for Science Innovation and Society (INSIS) University of Oxford Oxford UK Climate Finance and Policy Group ETH Zurich Zurich Switzerland Department of Water Management Delft University of Technology Delft the Netherlands United Nations Economic Commission for Africa Addis Ababa Ethiopia Center for Energy & Environmental Policy University of Delaware Newark DE USA Energy and Technology Policy Group ETH Zurich Zurich Switzerland Kigali Collaborative Research Centre Kigali Rwanda IIPP Institute for Innovation and Public Purpose University College London London UK Institute of Science Technology and Policy ETH Zurich Zurich Switzerland Power Futures Lab Graduate School of Business University of Cape Town Cape Town South Africa African Union Development Agency (AUDA-NEPAD) Midrand Johannesburg South Africa School of Electrical and Computer Engineering Addis Ababa Institute of Technology Addis Ababa Ethiopia PBL Netherlands Environmental Assessment Agency the Hague the Netherlands Utrecht University Utrecht the Netherlands University of Sierra Leone Freetown Sierra Leone United Nations University–Institute for Natural Resources in Africa (UNU–INRA) Accra Ghana Department of Mechanical Engineering University of Port Harcourt Choba Nigeria Institute for Power Electronics and Electrical Drives RWTH Aachen University Aachen Germany Center of Energy Ethiopian Institute of Technology Mekelle University Mekelle Ethiopia Institute of Engineering University Grenoble Alpes Grenoble France GIZ — Africa–EU Energy Partnership (AEEP) Addis

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Explainable AI: Definition and attributes of a good explanation for health AI

arXiv

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arXiv 2024年

作者： Kyrimi, Evangelia McLachlan, Scott Wohlgemut, Jared M. Perkins, Zane B. Lagnado, David A. Marsh, William Gimson, Alexander Shafti, Ali Ercole, Ari Banerjee, Amitava Glocker, Ben Schafer, Burkhard Gatsonis, Constantine Grosan, Crina Sent, Danielle Berman, David S. Glass, David O'Regan, Declan P. Letsios, Dimitrios Morrissey, Dylan Pisirir, Erhan Leofante, Francesco Soyel, Hamit Williamson, Jon Grieman, Keri Dube, Kudakwashe Mardsen, Max Nagendran, Myura Tai, Nigel Kostopoulou, Olga Jones, Owain Curzon, Paul Stoner, Rebecca S. Tandle, Sankalp Joshi, Shalmali Mossadegh, Somayyeh Buijsman, Stefan Miller, Tim Madai, Vince Istvan School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom School of Nursing Midwifery and Palliative Care King's College London London United Kingdom Centre for Trauma Sciences Blizard Institute Queen Mary University of London London United Kingdom Royal London Hospital Barts Health NHS Trust London United Kingdom Department of Experiment Psychology University College London London United Kingdom Cambridge Liver Unit Addenbrooke's Hospital Cambridge University Hospitals Hills Road Box 210 Cambridge United Kingdom University of Cambridge United Kingdom Cambridge Consultants Ltd. United Kingdom Division of Anaesthesia Cambridge Centre for AI in Medicine University of Cambridge Cambridge United Kingdom Magdelene College University of Cambridge United Kingdom Institute of Health Informatics University College London London United Kingdom Department of Computing Imperial College London London United Kingdom SCRIPT Centre for IT and IP Law School of Law University of Edinburgh United Kingdom Center for Statistical Sciences School of Public Health Brown University Providence United States Research Group Artificial Intelligence HU University of Applied Sciences Utrecht Netherlands Jheronimus Academy of Data Science Tilburg University Eindhoven University of Technology s-Hertogenbosch Netherlands Centre for Theoretical Physics Queen Mary University of London London United Kingdom School of Computing Ulster University Belfast United Kingdom MRC Laboratory of Medical Sciences Imperial College London London United Kingdom Department of Informatics King's College London United Kingdom Physiotherapy Department Barts Health NHS Trust London United Kingdom Sport and Exercise Medicine Queen Mary University of London London United Kingdom School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Department of Computing Imperial College London United Kingdom Departmen

Proposals of artificial intelligence (AI) solutions based on increasingly complex and accurate predictive models are becoming ubiquitous across many disciplines. As the complexity of these models grows, transparency and users' understanding often diminish. This suggests that accurate prediction alone is insufficient for making an AI-based solution truly useful. In the development of healthcare systems, this introduces new issues related to accountability and safety. Understanding how and why an AI system makes a recommendation may require complex explanations of its inner workings and reasoning processes. Although research on explainable AI (XAI) has significantly increased in recent years and there is high demand for XAI in medicine, defining what constitutes a good explanation remains ad hoc, and providing adequate explanations continues to be challenging. To fully realize the potential of AI, it is critical to address two fundamental questions about explanations for safety-critical AI applications, such as health-AI: (1) What is an explanation in health-AI? and (2) What are the attributes of a good explanation in health-AI? In this study, we examined published literature and gathered expert opinions through a two-round Delphi study. The research outputs include (1) a definition of what constitutes an explanation in health-AI and (2) a comprehensive list of attributes that characterize a good explanation in health-AI. © 2024, CC BY.

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Nigrostriatal dopaminergic neurotransmission and resilience to peripheral systemic risk factors for gait slowing upon transition to uneven surfaces in older adult

Aging Brain

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Aging Brain 2025年 8卷

作者： Lana M. Chahine Andrea Rosso Ian Troidl Mary Ganguli Anne Newman Steven Cummings Stephanie Studenski Brian Lopresti Sarah Royse Theodore Huppert Mark Redfern Patrick J. Sparto Nico I. Bohnen Caterina Rosano Department of Neurology School of Medicine University of Pittsburgh Pittsburgh PA USA Department of Epidemiology Graduate School of Public Health University of Pittsburgh Pittsburgh PA USA Departments of Psychiatry School of Medicine University of Pittsburgh Pittsburgh PA USA San Francisco Coordinating Center California Pacific Medical Center Research Institute San Francisco CA USA Department of Epidemiology and Biostatistics University of California San Francisco CA USA Division of Geriatric Medicine University of Pittsburgh School of Medicine PA USA Department of Radiology University of Pittsburgh USA Department of Electrical and Computer Engineering University of Pittsburgh Pittsburgh USA Department of Bioengineering Swanson School of Engineering University of Pittsburgh USA Department of Physical Therapy School of Health and Rehabilitation Sciences University of Pittsburgh USA Department of Radiology University of Michigan Ann Arbor MI USA Departments of Neurology & Radiology School of Medicine University of Michigan Ann Arbor MI USA

Identifying mechanisms that compensate for slow gait speed in older adults is crucial. Dopaminergic neurotransmission curbs deleterious associations of cerebrovascular disease with gait, but whether it compensates for peripheral systemic risk factors (PSRF) for gait slowing has not been studied. In this cross-sectional study of community-dwelling older adults, we examined the relationship between nigrostriatal dopaminergic terminal integrity and gait speed in individuals with and without ≥ 1 PSRF for gait slowing: obesity, joint pain, or reduced muscle strength. The primary outcome was gait speed cost (%GSC) on transition from even to uneven surface. Participants underwent dopaminergic imaging with dihydrotetrabenazine [ 11 C]DTBZ positron emission tomography. Among 197 individuals, (mean (SD) age 74.92 (4.53) years; 61.93 % female; 90.86 % White), 130 (65.99 %) had ≥ 1 PSRF. Relationship between posterior putamen [ 11 C]DTBZ binding and %GSC was modified by PSRF; in those with ≥ 1 PSRF (but not in those with no PSRF), posterior putamen [ 11 C]DTBZ binding was associated with %GSC (β = 0.198, p = 0.03) independent of potential confounders. This cross-sectional study indicates that higher striatal dopaminergic neurotransmission may compensate for the effects of PSRF on gait slowing.

关键词： Dopamine Gait slowing Resilience MYHAT Monongahela-Youghiogheny Healthy Aging Team study PET Positron Emission Tomography PSRF Peripheral Systemic Risk Factors SOMMA Study of Muscle, Mobility and Aging VMAT2 type-2 vesicular monoamine transporter

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A real-world demonstration of machine learning generalizability: Intracranial hemorrhage detection on head CT

arXiv

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arXiv 2021年

作者： Salehinejad, Hojjat Kitamura, Jumpei Ditkofsky, Noah Lin, Amy Bharatha, Aditya Suthiphosuwan, Suradech Lin, Hui-Ming Wilson, Jefferson R. Mamdani, Muhammad Colak, Errol Li Ka Shing Centre for Healthcare Analytics Research & Training St. Michael’s Hospital Toronto Canada Department of Electrical & Computer Engineering University of Toronto Toronto Canada Fujisawa-city Kanagawa Japan Department of Medical Imaging St. Michael’s Hospital Unity Health Toronto Toronto Canada Faculty of Medicine University of Toronto Toronto Canada Division of Neurosurgery Department of Surgery University of Toronto Toronto Canada Leslie Dan Faculty of Pharmacy University of Toronto Toronto Canada Dalla Lana Faculty of Public Health University of Toronto Toronto Canada

Machine learning (ML) holds great promise in transforming healthcare. While published studies have shown the utility of ML models in interpreting medical imaging examinations, these are often evaluated under laboratory settings. The importance of real world evaluation is best illustrated by case studies that have documented successes and failures in the translation of these models into clinical environments. A key prerequisite for the clinical adoption of these technologies is demonstrating generalizable ML model performance under real world circumstances. The purpose of this study was to demonstrate that ML model generalizability is achievable in medical imaging with the detection of intracranial hemorrhage (ICH) on non-contrast computed tomography (CT) scans serving as the use case. An ML model was trained using 21,784 scans from the RSNA Intracranial Hemorrhage CT dataset while generalizability was evaluated using an external validation dataset obtained from our busy trauma and neurosurgical center. This real world external validation dataset consisted of every unenhanced head CT scan (n = 5,965) performed in our emergency department in 2019 without exclusion. The model demonstrated an AUC of 98.4%, sensitivity of 98.8%, and specificity of 98.0%, on the test dataset. On external validation, the model demonstrated an AUC of 95.4%, sensitivity of 91.3%, and specificity of 94.1%. Evaluating the ML model using a real world external validation dataset that is temporally and geographically distinct from the training dataset indicates that ML generalizability is achievable in medical imaging applications. Copyright © 2021, The Authors. All rights reserved.

关键词： Medical imaging

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Alzheimer's disease digital biomarkers multidimensional landscape and AI model scoping review

引用

NPJ digital medicine 2025年第1期8卷 366页

作者： Wenhao Qi Xiaohong Zhu Bin Wang Yankai Shi Chaoqun Dong Shiying Shen Jiaqi Li Kun Zhang Yunfan He Mengjiao Zhao Shiyan Yao Yongze Dong Huajuan Shen Junling Kang Xiaodong Lu Guowei Jiang Lizzy M M Boots Heming Fu Li Pan Hongkai Chen Zhenyu Yan Guoliang Xing Shihua Cao School of Nursing Hangzhou Normal University Hangzhou China. School of Nursing Fujian Medical University Fujian China. School of International Relations and Public Affairs Fudan University Shanghai China. School of Nursing Zhejiang Chinese Medical University Hangzhou China. Nursing Department Zhejiang Provincial People's Hospital Hangzhou China. Department of Neurology The Third Affiliated Hospital of Zhejiang Chinese Medical University Hangzhou China. Department of Neurology Affiliated Hospital of Hangzhou Normal University Hangzhou China. Department of Psychiatry and Neuropsychology and Alzheimer Center Limburg School for Mental Health and Neuroscience Maastricht University Maastricht The Netherlands. Department of Electrical and Computer Engineering Stony Brook University New York USA. Department of Pathology the University of Hong Kong Hong Kong SAR China. Department of Electrical and Engineering The Chinese University of Hong Kong Hong Kong SAR China. Department of Electrical and Engineering The Chinese University of Hong Kong Hong Kong SAR China. glxing@ie.cuhk.edu.hk. School of Nursing Hangzhou Normal University Hangzhou China. csh@***. Key Laboratory of Cognitive Disorder Assessment Technology Hangzhou China. csh@***.

As digital biomarkers gain traction in Alzheimer's disease (AD) diagnosis, understanding recent advancements is crucial. This review conducts a bibliometric analysis of 431 studies from five online databases: Web of Science, PubMed, Embase, IEEE Xplore, and CINAHL, and provides a scoping review of 86 artificial intelligence (AI) models. Research in this field is supported by 224 grants across 54 disciplines and 1403 institutions in 44 countries, with 2571 contributing researchers. Key focuses include motor activity, neurocognitive tests, eye tracking, and speech analysis. Classical machine learning models dominate AI research, though many lack performance reporting. Of 21 AD-focused models, the average AUC is 0.887, while 45 models for mild cognitive impairment show an average AUC of 0.821. Notably, only 2 studies incorporated external validation, and 3 studies performed model calibration. This review highlights the progress and challenges of integrating digital biomarkers into clinical practice.

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A novel approach to multi-class atopic dermatitis disease severity scoring using multi-class SVM

A novel approach to multi-class atopic dermatitis disease se...

引用

2019 IEEE International Conference on Cybernetics and Computational Intelligence, CYBERNETICSCOM 2019

作者： Suhendra, Rivansyah Arnia, Fitri Idroes, Rinaldi Earlia, Nanda Suhartono, Eko Faculty of Engineering Universitas Syiah Kuala Department of Electrical and Computer Banda Aceh Indonesia Faculty of Mathematics and Natural Sciences Universitas Syiah Kuala Department of Pharmacy Banda Aceh Indonesia Dermatology Division Zainoel Abidin Regional Public Hospital Banda Aceh Indonesia Faculty of Medicine Lambung Mangkurat University Banjarmasin Indonesia

ISBN: (纸本)9781728108674

The severity scoring of atopic dermatitis skin disease is a crucial stage for a dermatologist. Accurate and reliable decision-making by a dermatologist are needed for better therapy and risk stratification to the patient. Generally, the dermatologist used visual assessment to make decisions of the severity score. The procedure is very subjective and carries a lack of reliability. The paper presents a novel approach to classify the atopic dermatitis severity scoring based on machine learning algorithms using color, texture, and redness features of the skin. The redness features showed significant indication compared to other features. The overall accuracy achieved by the Multi-class SVM classifier was around 86%, and the multiclass SVM successfully classified the severity score into four classes. © 2019 IEEE.

关键词： Machine learning

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Findings from the Tushirikiane-4-MH (Supporting Each Other for Mental Health) Mobile Health-Supported Virtual Reality Randomized Controlled Trial Among Urban Refugee Youth in Kampala, Uganda

引用

Global Mental Health 2025年 12卷 e12-e12页

作者： Logie, Carmen H. Okumu, Moses Admassu, Zerihun MacKenzie, Frannie Gittings, Lesley Kortenaar, Jean-Luc Khan, Naimul Hakiza, Robert Musoke, Daniel Kibuuka Nakitende, Aidah Katisi, Brenda Kyambadde, Peter Lester, Richard Mbuagbaw, Lawrence Factor-Inwentash Faculty of Social Work University of Toronto Toronto ON Canada Women's College Research Institute Women's College Hospital Toronto ON Canada United Nations University Institute for Water Environment & Health Hamilton ON Canada Centre for Gender and Sexuality Health Equity Vancouver BC Canada School of Social Work University of Illinois Urbana-Champaign United States Uganda Christian University Mukono Uganda School of Health Studies Western University London ON Canada Centre for Social Science Research University of Cape Town South Africa Dalla Lana School of Public Health University of Toronto Toronto ON Canada Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Toronto ON Canada Young African Refugees for Integral Development (YARID) Kampala Uganda International Research Consortium Kampala Uganda National AIDS and STI Control Programme Ministry of Health Kampala Uganda Most At Risk Population Initiative Mulago Hospital Kampala Uganda Department of Medicine University of British Columbia Vancouver BC Canada Department of Health Research Methods Evidence and Impact McMaster University Hamilton ON Canada Department of Anesthesia McMaster University Hamilton ON Canada

Background: Virtual reality (VR) for mental health promotion remains understudied in low income humanitarian settings. We examined the effectiveness of VR in reducing depression with urban refugee youth in Kampala, Uganda. Methods: This randomized controlled trial assessed VR alone (Arm 1), VR followed by group problem management plus (GPM+) (Arm 2), and a control group (Arm 3), with a peer-driven and convenience sample of refugee youth aged 16-25 in Kampala. The primary outcome, depression, was measured with the Patient Health Questionnaire-9. Secondary outcomes included: mental health literacy, mental health stigma, self-compassion, mental wellbeing, and adaptive coping. Analyses were conducted at three timepoints (baseline, 8-weeks, 16-weeks) using generalized estimating equations (GEE). Results: Among participants (n=335, mean age: 20.77, standard deviation: 3.01;cisgender women: n=158, cisgender men: n=173, transgender women: n=4) we found no depression reductions for Arms 1 or 2 at 16-weeks compared with Arm 3. At 16-weeks, mental health literacy was significantly higher for Arm 2 compared with Arm 3, and self compassion was significantly higher in Arms 1 and Arm 2 compared with Arm 3. Discussion: VR alongside GPM+ may benefit self-compassion and MHL among urban refugee youth in Kampala, but these interventions were not effective in reducing depression. © The Author(s), 2025. Published by Cambridge University Press.

关键词： depression refugees self-compassion Uganda virtual reality youth

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Ethical machine learning in health care

arXiv

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arXiv 2020年

作者： Chen, Irene Y. Pierson, Emma Rose, Sherri Joshi, Shalmali Ferryman, Kadija Ghassemi, Marzyeh Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Microsoft Research CambridgeMA02143 United States Center for Health Policy Center for Primary Care and Outcomes Research Stanford University StanfordCA94305 United States Vector Institute TorontoON Canada Department of Technology Culture and Society Tandon School of Engineering New York University BrooklynNY11201 United States Department of Computer Science University of Toronto TorontoON Canada

The use of machine learning (ML) in health care raises numerous ethical concerns, especially as models can amplify existing health inequities. Here, we outline ethical considerations for equitable ML in the advancement of health care. Specifically, we frame ethics of ML in health care through the lens of social justice. We describe ongoing efforts and outline challenges in a proposed pipeline of ethical ML in health, ranging from problem selection to post-deployment considerations. We close by summarizing recommendations to address these challenges. © 2020, CC BY.

关键词： Health care

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Meta-learning techniques to analyze the raman data for optical diagnosis of oral cancer detection

Meta-learning techniques to analyze the raman data for optic...

引用

2019 IEEE International Conferences on Ubiquitous Computing and Communications and Data Science and Computational Intelligence and Smart Computing, Networking and Services, IUCC/DSCI/SmartCNS 2019

作者： Sharma, Mukta Sharma, Lokesh Jeng, Ming-Jer Chang, Liann-Be Huang, Shiang-Fu Wu, Shih-Lin Department of Electronic Engineering Chang Gung University Taoyuan333 Taiwan Department of Otolaryngology-Head and Neck Surgery Chang Gung Memorial Hospital Linkou244 Taiwan Department of Public Health Chang Gung University Taoyuan 333 Taiwan AI Innovation Research Center Chang Gung University Taoyuan333 Taiwan Department of Cardiology Chang Gung Memorial Hospital Taoyuan333 Taiwan Department of Electrical Engineering Ming Chi University of Technology New Taipei City24301 Taiwan Department of Computer Science and Information Engineering Chang Gung University Taoyuan333 Taiwan

ISBN: (纸本)9781728152097

Recently, Machine Learning methods have shown great improvement while analyzing the biomedical data. Raman Spectroscopy (RS), a non-invasive technique, and widely used in screening to diagnose the oral cancer. In order to spot cancer in a smarter and faster way, we have employed Meta-Learning (ML) techniques to learn such as Bagging and Boosting on RS data. Further, we employed normal and tumor tissue class classification by Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) and Adaptive Boosting (AdaBoost) classifiers. The present study aims at examining the RS data with total 110 samples, including 57 tumor and 53 normal ones. To evaluate the performance, we have used the training samples to optimize, and testing samples to generalize the model parameters. The results show that the AdaBoost classifier with Bagging techniques showed the significant changes in accuracy. © 2019 IEEE.

关键词： Adaptive boosting

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On Accelerated Testing for COVID-19 Using Group Testing

arXiv

引用

arXiv 2020年

作者： Narayanan, Krishna R. Heidarzadeh, Anoosheh Laxminarayan, Ramanan Department of Electrical and Computer Engineering Texas A&M University College StationTX77843 United States Center for Disease Dynamics Economics & Policy Silver SpringMD20910 United States Department of Global Health University of Washington SeattleWA98104 United States Princeton Environment Institute Princeton University PrincetonNJ08544 United States

COVID-19 has resulted in a global health crisis that may become even more acute over the upcoming months. One of the main reasons behind the current rapid growth of COVID-19 in the U.S. population is the limited availability of testing kits and the relatively-high cost of screening tests. In this draft, we demonstrate the effectiveness of group testing (pooling) ideas to accelerate testing for COVID-19. This draft is semi-tutorial in nature and is written for a broad audience with interest in mathematical formulations relevant to COVID-19 testing. Therefore, ideas are presented through illustrative examples rather than through purely theoretical formulations. The focus is also on pools of size less than 64 such as what is practical with current RT-PCR technology. Copyright © 2020, The Authors. All rights reserved.

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