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Hutchinson JM, Raffoul A, Pepetone A, Andrade L, Williams TE, McNaughton SA, Leech RM, Reedy J, Shams-White MM, Vena JE, Dodd KW, Bodnar LM, Lamarche B, Wallace MP, Deitchler M, Hussain S, Kirkpatrick SI. Advances in methods for characterizing dietary patterns: A scoping review. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.06.20.24309251. [PMID: 38947003 PMCID: PMC11213084 DOI: 10.1101/2024.06.20.24309251] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
There is a growing focus on better understanding the complexity of dietary patterns and how they relate to health and other factors. Approaches that have not traditionally been applied to characterize dietary patterns, such as machine learning algorithms and latent class analysis methods, may offer opportunities to measure and characterize dietary patterns in greater depth than previously considered. However, there has not been a formal examination of how this wide range of approaches has been applied to characterize dietary patterns. This scoping review synthesized literature from 2005-2022 applying methods not traditionally used to characterize dietary patterns, referred to as novel methods. MEDLINE, CINAHL, and Scopus were searched using keywords including machine learning, latent class analysis, and least absolute shrinkage and selection operator (LASSO). Of 5274 records identified, 24 met the inclusion criteria. Twelve of 24 articles were published since 2020. Studies were conducted across 17 countries. Nine studies used approaches that have applications in machine learning to identify dietary patterns. Fourteen studies assessed associations between dietary patterns that were characterized using novel methods and health outcomes, including cancer, cardiovascular disease, and asthma. There was wide variation in the methods applied to characterize dietary patterns and in how these methods were described. The extension of reporting guidelines and quality appraisal tools relevant to nutrition research to consider specific features of novel methods may facilitate complete and consistent reporting and enable evidence synthesis to inform policies and programs aimed at supporting healthy dietary patterns.
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Affiliation(s)
- Joy M Hutchinson
- School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Amanda Raffoul
- Department of Nutritional Sciences, University of Toronto, Toronto, ON, Canada
| | - Alexandra Pepetone
- School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Lesley Andrade
- School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Tabitha E Williams
- School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Sarah A McNaughton
- Health and Well-Being Centre for Research Innovation, School of Human Movement and Nutrition Sciences, University of Queensland, St. Lucia, QLD, Australia
| | - Rebecca M Leech
- Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Victoria, Geelong, Australia
| | - Jill Reedy
- Office of Dietary Supplements, National Institutes of Health, Bethesda, MD, USA
| | - Marissa M Shams-White
- Population Science Department, American Cancer Society, Washington DC, USA
- Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD, USA
| | - Jennifer E Vena
- Alberta's Tomorrow Project, Alberta Health Services, Edmonton, AB, Canada
| | - Kevin W Dodd
- Division of Cancer Prevention, National Cancer Institute, Bethesda, MD, USA
| | - Lisa M Bodnar
- School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Benoît Lamarche
- Centre Nutrition, santé et société (NUTRISS), Institut sur la nutrition et les aliments fonctionnels (INAF), Université Laval, Québec City, QC, Canada
| | - Michael P Wallace
- Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, Canada
| | - Megan Deitchler
- Intake - Center for Dietary Assessment, FHI Solutions, Washington, DC, USA
| | - Sanaa Hussain
- School of Public Health Sciences, University of Waterloo, Waterloo, ON, Canada
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Bharel M, Auerbach J, Nguyen V, DeSalvo KB. Transforming Public Health Practice With Generative Artificial Intelligence. Health Aff (Millwood) 2024; 43:776-782. [PMID: 38830160 DOI: 10.1377/hlthaff.2024.00050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/05/2024]
Abstract
Public health practice appears poised to undergo a transformative shift as a result of the latest advancements in artificial intelligence (AI). These changes will usher in a new era of public health, charged with responding to deficiencies identified during the COVID-19 pandemic and managing investments required to meet the health needs of the twenty-first century. In this Commentary, we explore how AI is being used in public health, and we describe the advanced capabilities of generative AI models capable of producing synthetic content such as images, videos, audio, text, and other digital content. Viewing the use of AI from the perspective of health departments in the United States, we examine how this new technology can support core public health functions with a focus on near-term opportunities to improve communication, optimize organizational performance, and generate novel insights to drive decision making. Finally, we review the challenges and risks associated with these technologies, offering suggestions for health officials to harness the new tools to accomplish public health goals.
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Kuziemsky CE, Chrimes D, Minshall S, Mannerow M, Lau F. AI Quality Standards in Health Care: Rapid Umbrella Review. J Med Internet Res 2024; 26:e54705. [PMID: 38776538 PMCID: PMC11153979 DOI: 10.2196/54705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Revised: 04/03/2024] [Accepted: 04/04/2024] [Indexed: 05/25/2024] Open
Abstract
BACKGROUND In recent years, there has been an upwelling of artificial intelligence (AI) studies in the health care literature. During this period, there has been an increasing number of proposed standards to evaluate the quality of health care AI studies. OBJECTIVE This rapid umbrella review examines the use of AI quality standards in a sample of health care AI systematic review articles published over a 36-month period. METHODS We used a modified version of the Joanna Briggs Institute umbrella review method. Our rapid approach was informed by the practical guide by Tricco and colleagues for conducting rapid reviews. Our search was focused on the MEDLINE database supplemented with Google Scholar. The inclusion criteria were English-language systematic reviews regardless of review type, with mention of AI and health in the abstract, published during a 36-month period. For the synthesis, we summarized the AI quality standards used and issues noted in these reviews drawing on a set of published health care AI standards, harmonized the terms used, and offered guidance to improve the quality of future health care AI studies. RESULTS We selected 33 review articles published between 2020 and 2022 in our synthesis. The reviews covered a wide range of objectives, topics, settings, designs, and results. Over 60 AI approaches across different domains were identified with varying levels of detail spanning different AI life cycle stages, making comparisons difficult. Health care AI quality standards were applied in only 39% (13/33) of the reviews and in 14% (25/178) of the original studies from the reviews examined, mostly to appraise their methodological or reporting quality. Only a handful mentioned the transparency, explainability, trustworthiness, ethics, and privacy aspects. A total of 23 AI quality standard-related issues were identified in the reviews. There was a recognized need to standardize the planning, conduct, and reporting of health care AI studies and address their broader societal, ethical, and regulatory implications. CONCLUSIONS Despite the growing number of AI standards to assess the quality of health care AI studies, they are seldom applied in practice. With increasing desire to adopt AI in different health topics, domains, and settings, practitioners and researchers must stay abreast of and adapt to the evolving landscape of health care AI quality standards and apply these standards to improve the quality of their AI studies.
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Affiliation(s)
| | - Dillon Chrimes
- School of Health Information Science, University of Victoria, Victoria, BC, Canada
| | - Simon Minshall
- School of Health Information Science, University of Victoria, Victoria, BC, Canada
| | | | - Francis Lau
- School of Health Information Science, University of Victoria, Victoria, BC, Canada
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Huguet N, Chen J, Parikh RB, Marino M, Flocke SA, Likumahuwa-Ackman S, Bekelman J, DeVoe JE. Applying Machine Learning Techniques to Implementation Science. Online J Public Health Inform 2024; 16:e50201. [PMID: 38648094 DOI: 10.2196/50201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 11/15/2023] [Accepted: 03/14/2024] [Indexed: 04/25/2024] Open
Abstract
Machine learning (ML) approaches could expand the usefulness and application of implementation science methods in clinical medicine and public health settings. The aim of this viewpoint is to introduce a roadmap for applying ML techniques to address implementation science questions, such as predicting what will work best, for whom, under what circumstances, and with what predicted level of support, and what and when adaptation or deimplementation are needed. We describe how ML approaches could be used and discuss challenges that implementation scientists and methodologists will need to consider when using ML throughout the stages of implementation.
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Affiliation(s)
- Nathalie Huguet
- Department of Family Medicine, Oregon Health & Science University, Portland, OR, United States
- BRIDGE-C2 Implementation Science Center for Cancer Control, Oregon Health & Science University, Portland, OR, United States
| | - Jinying Chen
- Section of Preventive Medicine and Epidemiology, Department of Medicine, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
- Data Science Core, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, United States
- iDAPT Implementation Science Center for Cancer Control, Wake Forest School of Medicine, Winston-Salem, NC, United States
| | - Ravi B Parikh
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Miguel Marino
- Department of Family Medicine, Oregon Health & Science University, Portland, OR, United States
- BRIDGE-C2 Implementation Science Center for Cancer Control, Oregon Health & Science University, Portland, OR, United States
| | - Susan A Flocke
- Department of Family Medicine, Oregon Health & Science University, Portland, OR, United States
- BRIDGE-C2 Implementation Science Center for Cancer Control, Oregon Health & Science University, Portland, OR, United States
| | - Sonja Likumahuwa-Ackman
- Department of Family Medicine, Oregon Health & Science University, Portland, OR, United States
- BRIDGE-C2 Implementation Science Center for Cancer Control, Oregon Health & Science University, Portland, OR, United States
| | - Justin Bekelman
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Penn Center for Cancer Care Innovation, Abramson Cancer Center, Penn Medicine, Philadelphia, PA, United States
| | - Jennifer E DeVoe
- Department of Family Medicine, Oregon Health & Science University, Portland, OR, United States
- BRIDGE-C2 Implementation Science Center for Cancer Control, Oregon Health & Science University, Portland, OR, United States
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Lippi L, Desimoni F, Canonico M, Massocco G, Turco A, Polverelli M, de Sire A, Invernizzi M. System for Tracking and Evaluating Performance (Step-App®): validation and clinical application of a mobile telemonitoring system in patients with knee and hip total arthroplasty. A prospective cohort study. Eur J Phys Rehabil Med 2024; 60:349-360. [PMID: 38298025 PMCID: PMC11131591 DOI: 10.23736/s1973-9087.24.08128-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Revised: 10/25/2023] [Accepted: 01/16/2024] [Indexed: 02/02/2024]
Abstract
BACKGROUND Technological advances and digital solutions have been proposed to overcome barriers to sustainable rehabilitation programs in patients with musculoskeletal disorders. However, to date, standardized telemonitoring systems able to precisely assess physical performance and functioning are still lacking. AIM To validate a new mobile telemonitoring system, named System for Tracking and Evaluating Performance (Step-App®), to evaluate physical performance in patients undergone knee and hip total arthroplasty. DESIGN Prospective cohort study. METHODS A consecutive series of older adults with knee and hip total arthroplasty participated in a comprehensive rehabilitation program. The Step-App®, a mobile telemonitoring system, was used to remotely monitor the effects of rehabilitation, and the outcomes were assessed before (T0) and after the rehabilitation treatment (T1). The primary outcomes were the 6-Minute Walk Test (6MWT), the 10-Meter Walk Test (10MWT), and the 30-Second Sit-To-Stand Test (30SST). RESULTS Out of 42 patients assessed, 25 older patients were included in the present study. The correlation analysis between the Step-App® measurements and the traditional in-person assessments demonstrated a strong positive correlation for the 6MWT (T0: r2=0.9981, P<0.0001; T1: r2=0.9981, P<0.0001), 10MWT (T0: r2=0.9423, P<0.0001; T1: r2=0.8634, P<0.0001), and 30SST (T0: r2=1, P<0.0001; T1: r2=1, P<0.0001). The agreement analysis, using Bland-Altman plots, showed a good agreement between the Step-App® measurements and the in-person assessments. CONCLUSIONS Therefore, we might conclude that Step-App® could be considered as a validated mobile telemonitoring system for remote assessment that might have a role in telemonitoring personalized rehabilitation programs for knee and hip replacement patients. CLINICAL REHABILITATION IMPACT Our findings might guide clinicians in remote monitoring of physical performance in patients with musculoskeletal conditions, providing new insight into tailored telerehabilitation programs.
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Affiliation(s)
- Lorenzo Lippi
- Unit of Physical and Rehabilitative Medicine, Department of Health Sciences, University of Eastern Piedmont, Novara, Italy
- Unit of Translational Medicine, Dipartimento Attività Integrate Ricerca e Innovazione (DAIRI), Azienda Ospedaliera SS. Antonio e Biagio e Cesare Arrigo, Alessandria, Italy
| | - Francesco Desimoni
- Computer Science Institute, Department of Sciences and Technological Innovation, University of Eastern Piedmont, Alessandria, Italy
| | - Massimo Canonico
- Computer Science Institute, Department of Sciences and Technological Innovation, University of Eastern Piedmont, Alessandria, Italy
| | - Gregorio Massocco
- Unit of Physical and Rehabilitative Medicine, Department of Health Sciences, University of Eastern Piedmont, Novara, Italy
| | - Alessio Turco
- Unit of Physical and Rehabilitative Medicine, Department of Health Sciences, University of Eastern Piedmont, Novara, Italy
| | - Marco Polverelli
- Unit of Rehabilitation, Department of Rehabilitation, Azienda Ospedaliera SS. Antonio e Biagio e Cesare Arrigo, Alessandria, Italy
| | - Alessandro de Sire
- Department of Medical and Surgical Sciences, University of Catanzaro Magna Graecia, Catanzaro, Italy -
- Research Center on Musculoskeletal Health, MusculoSkeletalHealth@UMG, University of Catanzaro Magna Graecia, Catanzaro, Italy
| | - Marco Invernizzi
- Unit of Physical and Rehabilitative Medicine, Department of Health Sciences, University of Eastern Piedmont, Novara, Italy
- Unit of Translational Medicine, Dipartimento Attività Integrate Ricerca e Innovazione (DAIRI), Azienda Ospedaliera SS. Antonio e Biagio e Cesare Arrigo, Alessandria, Italy
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Kao Y, Chu PJ, Chou PC, Chen CC. A dynamic approach to support outbreak management using reinforcement learning and semi-connected SEIQR models. BMC Public Health 2024; 24:751. [PMID: 38462635 PMCID: PMC10926678 DOI: 10.1186/s12889-024-18251-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 03/01/2024] [Indexed: 03/12/2024] Open
Abstract
BACKGROUND Containment measures slowed the spread of COVID-19 but led to a global economic crisis. We establish a reinforcement learning (RL) algorithm that balances disease control and economic activities. METHODS To train the RL agent, we design an RL environment with 4 semi-connected regions to represent the COVID-19 epidemic in Tokyo, Osaka, Okinawa, and Hokkaido, Japan. Every region is governed by a Susceptible-Exposed-Infected-Quarantined-Removed (SEIQR) model and has a transport hub to connect with other regions. The allocation of the synthetic population and inter-regional traveling is determined by population-weighted density. The agent learns the best policy from interacting with the RL environment, which involves obtaining daily observations, performing actions on individual movement and screening, and receiving feedback from the reward function. After training, we implement the agent into RL environments describing the actual epidemic waves of the four regions to observe the agent's performance. RESULTS For all epidemic waves covered by our study, the trained agent reduces the peak number of infectious cases and shortens the epidemics (from 165 to 35 cases and 148 to 131 days for the 5th wave). The agent is generally strict on screening but easy on movement, except for Okinawa, where the agent is easy on both actions. Action timing analyses indicate that restriction on movement is elevated when the number of exposed or infectious cases remains high or infectious cases increase rapidly, and stringency on screening is eased when the number of exposed or infectious cases drops quickly or to a regional low. For Okinawa, action on screening is tightened when the number of exposed or infectious cases increases rapidly. CONCLUSIONS Our experiments exhibit the potential of the RL in assisting policy-making and how the semi-connected SEIQR models establish an interactive environment for imitating cross-regional human flows.
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Affiliation(s)
- Yamin Kao
- Geometric Data Vision Laboratory, Department of Biomedical Sciences and Engineering, National Central University, Taoyuan City, Taiwan
| | - Po-Jui Chu
- Geometric Data Vision Laboratory, Department of Biomedical Sciences and Engineering, National Central University, Taoyuan City, Taiwan
| | - Pai-Chien Chou
- Division of Pulmonary Medicine, Department of Internal Medicine, Taipei Medical University Hospital, Taipei, Taiwan
- Division of Thoracic Medicine, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Chien-Chang Chen
- Geometric Data Vision Laboratory, Department of Biomedical Sciences and Engineering, National Central University, Taoyuan City, Taiwan.
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Tiwari A, Ghosh A, Agrawal PK, Reddy A, Singla D, Mehta DN, Girdhar G, Paiwal K. Artificial intelligence in oral health surveillance among under-served communities. Bioinformation 2023; 19:1329-1335. [PMID: 38415032 PMCID: PMC10895529 DOI: 10.6026/973206300191329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 12/31/2023] [Accepted: 12/31/2023] [Indexed: 02/29/2024] Open
Abstract
A sizable percentage of the population in India still does not have easy access to dental facilities. Therefore, it is of interest to document the role of artificial intelligence (AI) in oral surveillance of underserved communities. Available data shows that AI makes it possible to screen, diagnose, track, prioritize, and monitor dental patients remotely via smart devices. As a result, dentists won't have to deal with simple situations that only require standard treatments; freeing them up to focus on more complicated cases. Additionally, this would allow dentists to reach a broader, more underprivileged population in difficult-to-reach places. AI fracture recognition and categorization performance has shown promise in preliminary testing. Methods for detecting aberrations are frequently employed in public health practise and research continues to be focused on them.
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Affiliation(s)
- Anushree Tiwari
- Clinical Quality and Value, American Academy of Orthopaedic Surgeons, Rosemont, USA
| | - Anirbhan Ghosh
- Department of Orthodontics and Dentofacial Orthopedics, Bhabha College of Dental Sciences, Bhopal, M.P., India
| | - Pankaj Kumar Agrawal
- Department of Oral Pathology and Microbiology, Maitri College of Dentistry and Research Centre, Anjora, Durg, Chhattisgarh, India
| | - Arjun Reddy
- Manipal College of Dental Sciences, Manipal, India
| | - Deepika Singla
- Department of Conservative Dentistry and Endodontics, Desh Bhagat Dental College and Hospital, Malout, India
| | - Dhaval Niranjan Mehta
- Department of Oral Medicine and Radiology, Narsinbhai Patel Dental College and Hospital, Sankalchand Patel University, Visnagar, Gujarat, India
| | - Gaurav Girdhar
- Department of Periodontology, Karnavati School of Dentistry Karnavati University, Gandhinagar, Gujarat, India
| | - Kapil Paiwal
- Department of Oral and Maxillofacial Pathology, Daswani Dental College and Research Center, Kota, Rajasthan, India
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Jiang L, Wang C, Tong Y, Jiang J, Zhao D. Web-based nomogram and risk stratification system constructed for predicting the overall survival of older adults with primary kidney cancer after surgical resection. J Cancer Res Clin Oncol 2023; 149:11873-11889. [PMID: 37410141 DOI: 10.1007/s00432-023-05072-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 06/29/2023] [Indexed: 07/07/2023]
Abstract
BACKGROUND Kidney cancer (KC) is one of the most common malignant tumors in adults which particularly affects the survival of elderly patients. We aimed to construct a nomogram to predict overall survival (OS) in elderly KC patients after surgery. METHODS Information on all primary KC patients aged more than 65 years and treated with surgery between 2010 and 2015 was downloaded from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox regression analysis was used to identify the independent prognostic factors. Consistency index (C-index), receiver operating characteristic curve (ROC), the area under curve (AUC), and calibration curve were used to assess the accuracy and validity of the nomogram. Comparison of the clinical benefits of nomogram and the TNM staging system is done by decision curve analysis (DCA) and time-dependent ROC. RESULTS A total of 15,989 elderly KC patients undergoing surgery were included. All patients were randomly divided into training set (N = 11,193, 70%) and validation set (N = 4796, 30%). The nomogram produced C-indexes of 0.771 (95% CI 0.751-0.791) and 0.792 (95% CI 0.763-0.821) in the training and validation sets, respectively, indicating that the nomogram has excellent predictive accuracy. The ROC, AUC, and calibration curves also showed the same excellent results. In addition, DCA and time-dependent ROC showed that the nomogram outperformed the TNM staging system with better net clinical benefits and predictive efficacy. CONCLUSIONS Independent influencing factors for postoperative OS in elderly KC patients were sex, age, histological type, tumor size, grade, surgery, marriage, radiotherapy, and T-, N-, and M-stage. The web-based nomogram and risk stratification system could assist surgeons and patients in clinical decision-making.
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Affiliation(s)
- Liming Jiang
- Department of Orthopedics, The China-Japan Union Hospital of Jilin University, Changchun, 130000, China
| | - Chengcheng Wang
- Department of Oncology, The First People's Hospital of Liangshan Yi Autonomous Prefecture, Xichang, 615099, China
| | - Yuexin Tong
- Department of Orthopedics, The China-Japan Union Hospital of Jilin University, Changchun, 130000, China
| | - Jiajia Jiang
- Department of Orthopedics, The China-Japan Union Hospital of Jilin University, Changchun, 130000, China
| | - Dongxu Zhao
- Department of Orthopedics, The China-Japan Union Hospital of Jilin University, Changchun, 130000, China.
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Yang L, Iwami M, Chen Y, Wu M, van Dam KH. Computational decision-support tools for urban design to improve resilience against COVID-19 and other infectious diseases: A systematic review. PROGRESS IN PLANNING 2023; 168:100657. [PMID: 35280114 PMCID: PMC8904142 DOI: 10.1016/j.progress.2022.100657] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
The COVID-19 pandemic highlighted the need for decision-support tools to help cities become more resilient to infectious diseases. Through urban design and planning, non-pharmaceutical interventions can be enabled, impelling behaviour change and facilitating the construction of lower risk buildings and public spaces. Computational tools, including computer simulation, statistical models, and artificial intelligence, have been used to support responses to the current pandemic as well as to the spread of previous infectious diseases. Our multidisciplinary research group systematically reviewed state-of-the-art literature to propose a toolkit that employs computational modelling for various interventions and urban design processes. We selected 109 out of 8,737 studies retrieved from databases and analysed them based on the pathogen type, transmission mode and phase, design intervention and process, as well as modelling methodology (method, goal, motivation, focus, and indication to urban design). We also explored the relationship between infectious disease and urban design, as well as computational modelling support, including specific models and parameters. The proposed toolkit will help designers, planners, and computer modellers to select relevant approaches for evaluating design decisions depending on the target disease, geographic context, design stages, and spatial and temporal scales. The findings herein can be regarded as stand-alone tools, particularly for fighting against COVID-19, or be incorporated into broader frameworks to help cities become more resilient to future disasters.
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Affiliation(s)
- Liu Yang
- School of Architecture, Southeast University, Nanjing, China
- Research Center of Urban Design, Southeast University, Nanjing, China
| | - Michiyo Iwami
- Department of Infectious Disease, Faculty of Medicine, Imperial College London, UK
| | - Yishan Chen
- Architecture and Urban Design Research Center, China IPPR International Engineering CO., LTD, Beijing, China
| | - Mingbo Wu
- State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Koen H van Dam
- Centre for Process Systems Engineering, Department of Chemical Engineering, Imperial College London, UK
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Sahoo SK, Palai G, Altahan BR, Ahammad SH, Priya PP, Hossain M, Rashed ANZ. An Optimized Deep Learning Approach for the Prediction of Social Distance Among Individuals in Public Places During Pandemic. NEW GENERATION COMPUTING 2023; 41:135-154. [PMID: 36620356 PMCID: PMC9807244 DOI: 10.1007/s00354-022-00202-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 12/27/2022] [Indexed: 06/17/2023]
Abstract
Social distancing is considered as the most effective prevention techniques for combatting pandemic like Covid-19. It is observed in several places where these norms and conditions have been violated by most of the public though the same has been notified by the local government. Hence, till date, there has been no proper structure for monitoring the loyalty of the social-distancing norms by individuals. This research has proposed an optimized deep learning-based model for predicting social distancing at public places. The proposed research has implemented a customized model using detectron2 and intersection over union (IOU) on the input video objects and predicted the proper social-distancing norms continued by individuals. The extensive trials were conducted with popular state-of-the-art object detection model: regions with convolutional neural networks (RCNN) with detectron2 and fast RCNN, RCNN with TWILIO communication platform, YOLOv3 with TL, fast RCNN with YOLO v4, and fast RCNN with YOLO v2. Among all, the proposed (RCNN with detectron2 and fast RCNN) delivers the efficient performance with precision, mean average precision (mAP), total loss (TL) and training time (TT). The outcomes of the proposed model focused on faster R-CNN for social-distancing norms and detectron2 for identifying the human 'person class' towards estimating and evaluating the violation-threat criteria where the threshold (i.e., 0.75) is calculated. The model attained precision at 98% approximately (97.9%) with 87% recall score where intersection over union (IOU) was at 0.5.
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Affiliation(s)
| | - G. Palai
- Department of Electronics and Communication Engineering, Gandhi Institute for Technological Advancement, Bhubaneswar, Odisha India
| | - Baraa Riyadh Altahan
- Medical Instrumentation Techniques Engineering Department, Al-Mustaqbal University College, Hilla, Babil 51001 Iraq
| | - Sk Hasane Ahammad
- Department of ECE, Koneru Lakshmaiah Education Foundation, Andhra Pradesh, Vaddeswaram, 522302 India
| | - P. Poorna Priya
- Department of ECE, Dadi Institute of Engineering and Technology, Anakapalle, Visakhapatnam India
| | - Md.Amzad Hossain
- Institute of Theoretical Electrical Engineering, Faculty of Electrical Engineering and Information Technology, Ruhr University Bochum, 44801 Bochum, Germany
- Department of Electrical and Electronic Engineering, Jashore University of Science and Technology, Jashore, 7408 Bangladesh
| | - Ahmed Nabih Zaki Rashed
- Electronics and Electrical Communications Engineering Department, Faculty of Electronic Engineering, Menoufia University, Menouf, 32951 Egypt
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Muacevic A, Adler JR, Fernandez-Pacheco A, Taylor L, Kahar P, Khanna D. A Survey of Public Health Failures During COVID-19. Cureus 2022; 14:e32437. [PMID: 36644033 PMCID: PMC9833812 DOI: 10.7759/cureus.32437] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 12/12/2022] [Indexed: 12/15/2022] Open
Abstract
The prolonged coronavirus disease 2019 (COVID-19) pandemic has raised concerns about the failures in the public health measures used to manage the spread of this deadly virus. This review focuses its attention on research papers that at their core highlight the individual public health measures instituted by organizations, institutions, and the government of the United States (US) since the start of the COVID-19 pandemic and that were published in 2019 to 2022. Together, these sources help paint a well-rounded view of the US management of this pandemic so that conclusions may be drawn from mistakes that were made and this country may respond better in the future to such situations. This paper is unique because it highlights the areas where improvement is needed, whereas other published work describes the measures taken and how they were carried out, not the failures, which leaves a gap in the literature that this paper hopes to fill. Through a deep dive into public health measures, seven areas in which improvements could be made were pinpointed by the authors. Such measures included mask mandates, social distancing, lockdown/quarantine, hand hygiene, COVID-19 testing, travel screening, and vaccine hesitancy. In exploring each measure, a discussion was carried out about its benefits and shortcomings in alleviating the ramifications of a global pandemic. In addition to the poor supply chain for critical products like personal protective equipment (PPE), the miscommunication between states and federal policies did not allow for the entirety of the US to respond cohesively in the face of the COVID-19 pandemic. This general review is crucial to know what is working and what needs to be changed to increase the benefits provided to the population.
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Sahoo SK. A hybrid deep learning based approach for the prediction of social distancing among individuals in public places during Covid19 pandemic. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022. [DOI: 10.3233/jifs-221174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Social distance is considered one of the most effective prevention techniques to prevent the spread of Covid19 disease. To date, there is no proper system available to monitor whether social distancing protocol is being followed by individuals or not in public places. This research has proposed a hybrid deep learning-based model for predicting whether individuals maintain social distancing in public places through video object detection. This research has implemented a customized deep learning model using Detectron2 and IOU for monitoring the process. The base model adapted is RCNN and the optimization algorithm used is Stochastic Gradient Descent algorithm. The model has been tested on real time images of people gathered in textile shops to demonstrate the real time application of the developed model. The performance evaluation of the proposed model reveals that the precision is 97.9% and the mAP value is 84.46, which makes it clear that the model developed is good in monitoring the adherence of social distancing by individuals.
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Affiliation(s)
- Santosh Kumar Sahoo
- Department of Electronics and Instrumentation Engineering, CVR College of Engineering, Rangareddy (D), Vastunagar, Mangalpalli (V), Ibrahimpatnam, Pocharam, Hyderabad, Telangana, India
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13
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Peng HY, Lin YK, Nguyen PA, Hsu JC, Chou CL, Chang CC, Lin CC, Lam C, Chen CI, Wang KH, Lu CY. Determinants of coronavirus disease 2019 infection by artificial intelligence technology: A study of 28 countries. PLoS One 2022; 17:e0272546. [PMID: 36018862 PMCID: PMC9417026 DOI: 10.1371/journal.pone.0272546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 07/05/2022] [Indexed: 12/02/2022] Open
Abstract
Objectives The coronavirus disease 2019 pandemic has affected countries around the world since 2020, and an increasing number of people are being infected. The purpose of this research was to use big data and artificial intelligence technology to find key factors associated with the coronavirus disease 2019 infection. The results can be used as a reference for disease prevention in practice. Methods This study obtained data from the "Imperial College London YouGov Covid-19 Behaviour Tracker Open Data Hub", covering a total of 291,780 questionnaire results from 28 countries (April 1~August 31, 2020). Data included basic characteristics, lifestyle habits, disease history, and symptoms of each subject. Four types of machine learning classification models were used, including logistic regression, random forest, support vector machine, and artificial neural network, to build prediction modules. The performance of each module is presented as the area under the receiver operating characteristics curve. Then, this study further processed important factors selected by each module to obtain an overall ranking of determinants. Results This study found that the area under the receiver operating characteristics curve of the prediction modules established by the four machine learning methods were all >0.95, and the RF had the highest performance (area under the receiver operating characteristics curve is 0.988). Top ten factors associated with the coronavirus disease 2019 infection were identified in order of importance: whether the family had been tested, having no symptoms, loss of smell, loss of taste, a history of epilepsy, acquired immune deficiency syndrome, cystic fibrosis, sleeping alone, country, and the number of times leaving home in a day. Conclusions This study used big data from 28 countries and artificial intelligence methods to determine the predictors of the coronavirus disease 2019 infection. The findings provide important insights for the coronavirus disease 2019 infection prevention strategies.
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Affiliation(s)
- Hsiao-Ya Peng
- International PhD Program in Biotech and Healthcare Management, College of Management, Taipei Medical University, Taipei, Taiwan
| | - Yen-Kuang Lin
- Biostatistics Center, Office of Data Science, Taipei Medical University, Taipei, Taiwan
| | - Phung-Anh Nguyen
- Clinical Data Center, Office of Data Science, Taipei Medical University, Taipei, Taiwan
- Research Center of Health Care Industry Data Science, College of Management, Taipei Medical University, Taipei, Taiwan
- Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei Medical University, Taipei, Taiwan
- Department of Healthcare Information & Management, Ming Chuan University, Taoyuan, Taiwan
| | - Jason C. Hsu
- International PhD Program in Biotech and Healthcare Management, College of Management, Taipei Medical University, Taipei, Taiwan
- Clinical Data Center, Office of Data Science, Taipei Medical University, Taipei, Taiwan
- Research Center of Health Care Industry Data Science, College of Management, Taipei Medical University, Taipei, Taiwan
- Clinical Big Data Research Center, Taipei Medical University Hospital, Taipei Medical University, Taipei, Taiwan
- * E-mail:
| | - Chun-Liang Chou
- Department of Thoracic Medicine, Taipei Medical University Hospital, Taipei Medical University, Taipei, Taiwan
| | - Chih-Cheng Chang
- Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
| | - Chia-Chi Lin
- International PhD Program in Biotech and Healthcare Management, College of Management, Taipei Medical University, Taipei, Taiwan
| | - Carlos Lam
- Emergency Department, Department of Emergency and Critical Care Medicine, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan
- Department of Emergency, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
- Graduate Institute of Injury Prevention and Control, College of Public Health, Taipei Medical University, Taipei, Taiwan
| | - Chang-I Chen
- Department of Healthcare Administration, School of Management, Taipei Medical University, Taipei, Taiwan
| | - Kai-Hsun Wang
- Graduate Institute of Business Administration, College of Management, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Christine Y. Lu
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, United States of America
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Martinez-Millana A, Saez-Saez A, Tornero-Costa R, Azzopardi-Muscat N, Traver V, Novillo-Ortiz D. Artificial intelligence and its impact on the domains of universal health coverage, health emergencies and health promotion: An overview of systematic reviews. Int J Med Inform 2022; 166:104855. [PMID: 35998421 PMCID: PMC9551134 DOI: 10.1016/j.ijmedinf.2022.104855] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 08/01/2022] [Accepted: 08/11/2022] [Indexed: 12/04/2022]
Abstract
An overview of systematic reviews on the application of AI including 129 studies. AI use is prominent in Universal Health Coverage, featuring image analysis in neoplasms. Half of the reviews did not evaluate validation procedures nor reporting guidelines. Risk of bias was only included un a third of the reviews. There is not sufficient evidence to transfer AI to actual healthcare delivery.
Background Artificial intelligence is fueling a new revolution in medicine and in the healthcare sector. Despite the growing evidence on the benefits of artificial intelligence there are several aspects that limit the measure of its impact in people’s health. It is necessary to assess the current status on the application of AI towards the improvement of people’s health in the domains defined by WHO’s Thirteenth General Programme of Work (GPW13) and the European Programme of Work (EPW), to inform about trends, gaps, opportunities, and challenges. Objective To perform a systematic overview of systematic reviews on the application of artificial intelligence in the people’s health domains as defined in the GPW13 and provide a comprehensive and updated map on the application specialties of artificial intelligence in terms of methodologies, algorithms, data sources, outcomes, predictors, performance, and methodological quality. Methods A systematic search in MEDLINE, EMBASE, Cochrane and IEEEXplore was conducted between January 2015 and June 2021 to collect systematic reviews using a combination of keywords related to the domains of universal health coverage, health emergencies protection, and better health and wellbeing as defined by the WHO’s PGW13 and EPW. Eligibility criteria was based on methodological quality and the inclusion of practical implementation of artificial intelligence. Records were classified and labeled using ICD-11 categories into the domains of the GPW13. Descriptors related to the area of implementation, type of modeling, data entities, outcomes and implementation on care delivery were extracted using a structured form and methodological aspects of the included reviews studies was assessed using the AMSTAR checklist. Results The search strategy resulted in the screening of 815 systematic reviews from which 203 were assessed for eligibility and 129 were included in the review. The most predominant domain for artificial intelligence applications was Universal Health Coverage (N = 98) followed by Health Emergencies (N = 16) and Better Health and Wellbeing (N = 15). Neoplasms area on Universal Health Coverage was the disease area featuring most of the applications (21.7 %, N = 28). The reviews featured analytics primarily over both public and private data sources (67.44 %, N = 87). The most used type of data was medical imaging (31.8 %, N = 41) and predictors based on regions of interest and clinical data. The most prominent subdomain of Artificial Intelligence was Machine Learning (43.4 %, N = 56), in which Support Vector Machine method was predominant (20.9 %, N = 27). Regarding the purpose, the application of Artificial Intelligence I is focused on the prediction of the diseases (36.4 %, N = 47). With respect to the validation, more than a half of the reviews (54.3 %, N = 70) did not report a validation procedure and, whenever available, the main performance indicator was the accuracy (28.7 %, N = 37). According to the methodological quality assessment, a third of the reviews (34.9 %, N = 45) implemented methods for analysis the risk of bias and the overall AMSTAR score below was 5 (4.01 ± 1.93) on all the included systematic reviews. Conclusion Artificial intelligence is being used for disease modelling, diagnose, classification and prediction in the three domains of GPW13. However, the evidence is often limited to laboratory and the level of adoption is largely unbalanced between ICD-11 categoriesand diseases. Data availability is a determinant factor on the developmental stage of artificial intelligence applications. Most of the reviewed studies show a poor methodological quality and are at high risk of bias, which limits the reproducibility of the results and the reliability of translating these applications to real clinical scenarios. The analyzed papers show results only in laboratory and testing scenarios and not in clinical trials nor case studies, limiting the supporting evidence to transfer artificial intelligence to actual care delivery.
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Affiliation(s)
- Antonio Martinez-Millana
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain
| | - Aida Saez-Saez
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain
| | - Roberto Tornero-Costa
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain
| | - Natasha Azzopardi-Muscat
- Division of Country Health Policies and Systems, World Health Organization, Regional Office for Europe, Copenhagen, Denmark
| | - Vicente Traver
- Instituto Universitario de Investigación de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera S/N, Valencia 46022, Spain
| | - David Novillo-Ortiz
- Division of Country Health Policies and Systems, World Health Organization, Regional Office for Europe, Copenhagen, Denmark.
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Gao B, Wang MD, Li Y, Huang F. Risk stratification system and web-based nomogram constructed for predicting the overall survival of primary osteosarcoma patients after surgical resection. Front Public Health 2022; 10:949500. [PMID: 35991065 PMCID: PMC9389295 DOI: 10.3389/fpubh.2022.949500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Accepted: 07/18/2022] [Indexed: 12/02/2022] Open
Abstract
Background Previous prediction models of osteosarcoma have not focused on survival in patients undergoing surgery, nor have they distinguished and compared prognostic differences among amputation, radical and local resection. This study aimed to establish and validate the first reliable prognostic nomogram to accurately predict overall survival (OS) after surgical resection in patients with osteosarcoma. On this basis, we constructed a risk stratification system and a web-based nomogram. Methods We enrolled all patients with primary osteosarcoma who underwent surgery between 2004 and 2015 in the Surveillance, Epidemiology, and End Results (SEER) database. In patients with primary osteosarcoma after surgical resection, univariate and multivariate cox proportional hazards regression analyses were utilized to identify independent prognostic factors and construct a novel nomogram for the 1-, 3-, and 5-year OS. Then the nomogram's predictive performance and clinical utility were evaluated by the concordance index (C-index), receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Result This study recruited 1,396 patients in all, with 837 serving as the training set (60%) and 559 as the validation set (40%). After COX regression analysis, we identified seven independent prognostic factors to develop the nomogram, including age, primary site, histological type, disease stage, AJCC stage, tumor size, and surgical method. The C-index indicated that this nomogram is considerably more accurate than the AJCC stage in predicting OS [Training set (HR: 0.741, 95% CI: 0.726–0.755) vs. (HR: 0.632, 95% CI: 0.619–0.645); Validation set (HR: 0.735, 95% CI: 0.718–0.753) vs. (HR: 0.635, 95% CI: 0.619–0.652)]. Moreover, the area under ROC curves, the calibration curves, and DCA demonstrated that this nomogram was significantly superior to the AJCC stage, with better predictive performance and more net clinical benefits. Conclusion This study highlighted that radical surgery was the first choice for patients with primary osteosarcoma since it provided the best survival prognosis. We have established and validated a novel nomogram that could objectively predict the overall survival of patients with primary osteosarcoma after surgical resection. Furthermore, a risk stratification system and a web-based nomogram could be applied in clinical practice to assist in therapeutic decision-making.
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Affiliation(s)
- Bing Gao
- Department of Orthopedics, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Meng-die Wang
- Department of Neurology, China-Japan Union Hospital of Jilin University, Changchun, China
| | - Yanan Li
- Department of Pediatrics, The First Hospital of Jilin University, Changchun, China
| | - Fei Huang
- Department of Orthopedics, China-Japan Union Hospital of Jilin University, Changchun, China
- *Correspondence: Fei Huang
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Mentrasti G, Cantini L, Zichi C, D'Ostilio N, Gelsomino F, Martinelli E, Chiari R, La Verde N, Bisonni R, Cognigni V, Pinterpe G, Pecci F, Migliore A, Aimar G, De Vita F, Traisci D, Spallanzani A, Martini G, Nicolardi L, Cona MS, Baleani MG, Rocchi MLB, Berardi R. Alarming Drop in Early Stage Colorectal Cancer Diagnoses After COVID-19 Outbreak: A Real-World Analysis from the Italian COVID-DELAY Study. Oncologist 2022; 27:e723-e730. [PMID: 35815922 PMCID: PMC9438923 DOI: 10.1093/oncolo/oyac129] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Accepted: 05/05/2022] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Coronavirus disease 2019 (COVID-19) has triggered the disruption of health care on a global scale. With Italy tangled up in the pandemic response, oncology care has been largely diverted and cancer screenings suspended. Our multicenter Italian study aimed to evaluate whether COVID-19 has impacted access to diagnosis, staging, and treatment for patients newly diagnosed with colorectal cancer (CRC), compared with pre-pandemic time. METHODS All consecutive new CRC patients referred to 8 Italian oncology institutions between March and December 2020 were included. Access rate and temporal intervals between date of symptoms onset, radiological and cytohistological diagnosis, treatment start and first radiological evaluation were analyzed and compared with the same months of 2019. RESULTS A reduction (29%) in newly diagnosed CRC cases was seen when compared with 2019 (360 vs 506). New CRC patients in 2020 were less likely to be diagnosed with early stage (stages I-II-III) CRC (63% vs 78%, P < .01). Gender and sidedness were similar regardless of the year. The percentage of tumors with any mutation among BRAF, NRAS, and KRAS genes were significantly different between the 2 years (61% in 2020 vs 50% in 2019, P = .04). Timing of access to cancer diagnosis, staging, and treatment for patients with CRC has not been negatively affected by the pandemic. Significantly shorter temporal intervals were observed between symptom onset and first oncological appointment (69 vs 79 days, P = .01) and between histological diagnosis and first oncological appointment (34 vs 42 days, P < .01) during 2020 compared with 2019. Fewer CRC cases were discussed in multidisciplinary meetings during 2020 (38% vs 50%, P = .01). CONCLUSIONS Our data highlight a significant drop in CRC diagnosis after COVID-19, especially for early stage disease. The study also reveals a remarkable setback in the multidisciplinary management of patients with CRC. Despite this, Italian oncologists were able to ensure diagnostic-therapeutic pathways proper operation after March 2020.
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Affiliation(s)
- Giulia Mentrasti
- Department of Medical Oncology, Università Politecnica delle Marche, AOU Ospedali Riuniti di Ancona, Ancona, Italy
| | - Luca Cantini
- Department of Medical Oncology, Università Politecnica delle Marche, AOU Ospedali Riuniti di Ancona, Ancona, Italy
| | - Clizia Zichi
- Department of Oncology, University of Turin, Ordine Mauriziano Hospital, Torino, Italy
| | | | - Fabio Gelsomino
- Division of Oncology, Department of Oncology and Hematology, University Hospital of Modena, Modena, Italy
| | - Erika Martinelli
- UOC Oncologia ed Ematologia, Dipartimento di Medicina di Precisione, Università degli Studi della Campania "Luigi Vanvitelli", Napoli, Italy
| | - Rita Chiari
- Medical Oncology, Ospedali Riuniti Padova Sud, Monselice, Italy
| | - Nicla La Verde
- Department of Oncology, Ospedale Luigi Sacco, ASST Fatebenefratelli Sacco, Milano, Italy
| | - Renato Bisonni
- Department of Oncology, Ospedale Augusto Murri di Fermo, Fermo, Italy
| | - Valeria Cognigni
- Department of Medical Oncology, Università Politecnica delle Marche, AOU Ospedali Riuniti di Ancona, Ancona, Italy
| | - Giada Pinterpe
- Department of Medical Oncology, Università Politecnica delle Marche, AOU Ospedali Riuniti di Ancona, Ancona, Italy
| | - Federica Pecci
- Department of Medical Oncology, Università Politecnica delle Marche, AOU Ospedali Riuniti di Ancona, Ancona, Italy
| | - Antonella Migliore
- Department of Medical Oncology, Università Politecnica delle Marche, AOU Ospedali Riuniti di Ancona, Ancona, Italy
| | - Giacomo Aimar
- Department of Oncology, University of Turin, Ordine Mauriziano Hospital, Torino, Italy
| | - Francesca De Vita
- Department of Oncology, University of Turin, Ordine Mauriziano Hospital, Torino, Italy
| | - Donatella Traisci
- Medical Oncology, ASL2 Abruzzo, Ospedale San Pio da Pietralcina, Vasto, Italy
| | - Andrea Spallanzani
- Division of Oncology, Department of Oncology and Hematology, University Hospital of Modena, Modena, Italy
| | - Giulia Martini
- UOC Oncologia ed Ematologia, Dipartimento di Medicina di Precisione, Università degli Studi della Campania "Luigi Vanvitelli", Napoli, Italy
| | - Linda Nicolardi
- Medical Oncology, Ospedali Riuniti Padova Sud, Monselice, Italy
| | - Maria Silvia Cona
- Department of Oncology, Ospedale Luigi Sacco, ASST Fatebenefratelli Sacco, Milano, Italy
| | | | | | - Rossana Berardi
- Department of Medical Oncology, Università Politecnica delle Marche, AOU Ospedali Riuniti di Ancona, Ancona, Italy
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Liu Q, Ding H. Application of Table Tennis Ball Trajectory and Rotation-Oriented Prediction Algorithm Using Artificial Intelligence. Front Neurorobot 2022; 16:820028. [PMID: 35645761 PMCID: PMC9131050 DOI: 10.3389/fnbot.2022.820028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 02/21/2022] [Indexed: 11/16/2022] Open
Abstract
The present work aims to accelerate sports development in China and promote technological innovation in the artificial intelligence (AI) field. After analyzing the application and development of AI, it is introduced into sports and applied to table tennis competitions and training. The principle of the trajectory prediction of the table tennis ball (TTB) based on AI is briefly introduced. It is found that the difficulty of predicting TTB trajectories lies in rotation measurement. Accordingly, the rotation and trajectory of TTB are predicted using some AI algorithms. Specifically, a TTB detection algorithm is designed based on the Feature Fusion Network (FFN). For feature exaction, the cross-layer connection network is used to strengthen the learning ability of convolutional neural networks (CNNs) and streamline network parameters to improve the network detection response. The experimental results demonstrate that the trained CNN can reach a detection accuracy of over 98%, with a detection response within 5.3 ms, meeting the requirements of the robot vision system of the table tennis robot. By comparison, the traditional Color Segmentation Algorithm has advantages in detection response, with unsatisfactory detection accuracy, especially against TTB's color changes. Thus, the algorithm reported here can immediately hit the ball with high accuracy. The research content provides a reference for applying AI to TTB trajectory and rotation prediction and has significant value in popularizing table tennis.
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Affiliation(s)
| | - Hairong Ding
- Shanghai Polytechnic University, Shanghai, China
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Forecasting COVID-19 Case Trends Using SARIMA Models during the Third Wave of COVID-19 in Malaysia. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19031504. [PMID: 35162523 PMCID: PMC8835281 DOI: 10.3390/ijerph19031504] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 12/09/2021] [Accepted: 12/14/2021] [Indexed: 12/29/2022]
Abstract
With many countries experiencing a resurgence in COVID-19 cases, it is important to forecast disease trends to enable effective planning and implementation of control measures. This study aims to develop Seasonal Autoregressive Integrated Moving Average (SARIMA) models using 593 data points and smoothened case and covariate time-series data to generate a 28-day forecast of COVID-19 case trends during the third wave in Malaysia. SARIMA models were developed using COVID-19 case data sourced from the Ministry of Health Malaysia’s official website. Model training and validation was conducted from 22 January 2020 to 5 September 2021 using daily COVID-19 case data. The SARIMA model with the lowest root mean square error (RMSE), mean absolute percentage error (MAE) and Bayesian information criterion (BIC) was selected to generate forecasts from 6 September to 3 October 2021. The best SARIMA model with a RMSE = 73.374, MAE = 39.716 and BIC = 8.656 showed a downward trend of COVID-19 cases during the forecast period, wherein the observed daily cases were within the forecast range. The majority (89%) of the difference between the forecasted and observed values was well within a deviation range of 25%. Based on this work, we conclude that SARIMA models developed in this paper using 593 data points and smoothened data and sensitive covariates can generate accurate forecast of COVID-19 case trends.
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Data-Driven Analytics Leveraging Artificial Intelligence in the Era of COVID-19: An Insightful Review of Recent Developments. Symmetry (Basel) 2021. [DOI: 10.3390/sym14010016] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
This paper presents the role of artificial intelligence (AI) and other latest technologies that were employed to fight the recent pandemic (i.e., novel coronavirus disease-2019 (COVID-19)). These technologies assisted the early detection/diagnosis, trends analysis, intervention planning, healthcare burden forecasting, comorbidity analysis, and mitigation and control, to name a few. The key-enablers of these technologies was data that was obtained from heterogeneous sources (i.e., social networks (SN), internet of (medical) things (IoT/IoMT), cellular networks, transport usage, epidemiological investigations, and other digital/sensing platforms). To this end, we provide an insightful overview of the role of data-driven analytics leveraging AI in the era of COVID-19. Specifically, we discuss major services that AI can provide in the context of COVID-19 pandemic based on six grounds, (i) AI role in seven different epidemic containment strategies (a.k.a non-pharmaceutical interventions (NPIs)), (ii) AI role in data life cycle phases employed to control pandemic via digital solutions, (iii) AI role in performing analytics on heterogeneous types of data stemming from the COVID-19 pandemic, (iv) AI role in the healthcare sector in the context of COVID-19 pandemic, (v) general-purpose applications of AI in COVID-19 era, and (vi) AI role in drug design and repurposing (e.g., iteratively aligning protein spikes and applying three/four-fold symmetry to yield a low-resolution candidate template) against COVID-19. Further, we discuss the challenges involved in applying AI to the available data and privacy issues that can arise from personal data transitioning into cyberspace. We also provide a concise overview of other latest technologies that were increasingly applied to limit the spread of the ongoing pandemic. Finally, we discuss the avenues of future research in the respective area. This insightful review aims to highlight existing AI-based technological developments and future research dynamics in this area.
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