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1.
Bioorg Med Chem ; 70: 116939, 2022 09 15.
Article in English | MEDLINE | ID: covidwho-2176835
2.
Am J Gastroenterol ; 117(11): 1871-1873, 2022 11 01.
Article in English | MEDLINE | ID: covidwho-2155855

ABSTRACT

The performance of artificial intelligence-aided colonoscopy (AIAC) in a real-world setting has not been described. We compared adenoma and polyp detection rates (ADR/PDR) in a 6-month period before (pre-AIAC) and after introduction of AIAC (GI Genius, Medtronic) in all endoscopy suites in our large-volume center. The ADR and PDR in the AIAC group was lower compared with those in the pre-AIAC group (30.3% vs 35.2%, P < 0.001; 36.5% vs 40.9%, P = 0.004, respectively); procedure time was significantly shorter in the AIAC group. In summary, introduction of AIAC did not result in performance improvement in our large-center cohort, raising important questions on AI-human interactions in medicine.


Subject(s)
Adenoma , Adenomatous Polyps , Colonic Polyps , Colorectal Neoplasms , Humans , Colonic Polyps/diagnosis , Artificial Intelligence , Colonoscopy/methods , Adenoma/diagnosis , Adenomatous Polyps/diagnosis , Colorectal Neoplasms/diagnosis
3.
Nat Commun ; 13(1): 6815, 2022 Nov 17.
Article in English | MEDLINE | ID: covidwho-2151032

ABSTRACT

Bank bailouts are controversial governmental decisions, putting taxpayers' money at risk to avoid a domino effect through the network of claims between financial institutions. Yet very few studies address quantitatively the convenience of government investments in failing banks from the taxpayers' standpoint. We propose a dynamic financial network framework incorporating bailout decisions as a Markov Decision Process and an artificial intelligence technique that learns the optimal bailout actions to minimise the expected taxpayers' losses. Considering the European global systemically important institutions, we find that bailout decisions become optimal only if the taxpayers' stakes exceed some critical level, endogenously determined by all financial network's characteristics. The convenience to intervene increases with the network's distress, taxpayers' stakes, bank bilateral credit exposures and crisis duration. Moreover, the government should optimally keep bailing-out banks that received previous investments, creating moral hazard for rescued banks that could increase their risk-taking, reckoning on government intervention.


Subject(s)
Artificial Intelligence , Government , Markov Chains
4.
Front Public Health ; 10: 1016680, 2022.
Article in English | MEDLINE | ID: covidwho-2142349

ABSTRACT

Cognitive decline is a gradual neurodegenerative process that is affected by genetic and environmental factors. The doctor-patient relationship in the healthcare for cognitive decline is in a "shallow" medical world. With the development of data science, virtual reality, artificial intelligence, and digital twin, the introduction of the concept of the metaverse in medicine has brought alternative and complementary strategies in the intervention of cognitive decline. This article technically analyzes the application scenarios and paradigms of the metaverse in medicine in the field of mental health, such as hospital management, diagnosis, prediction, prevention, rehabilitation, progression delay, assisting life, companionship, and supervision. The metaverse in medicine has made primary progress in education, immersive consultation, dental disease, and Parkinson's disease, bringing revolutionary prospects for non-pharmacological complementary treatment of cognitive decline and other mental problems. In particular, with the demand for non-face-to-face communication generated by the global COVID-19 epidemic, the needs for uncontactable healthcare service for the elderly have increased. The paradigm of self-monitoring, self-healing, and healthcare experienced by the elderly through the metaverse in medicine, especially from meta-platform, meta-community, and meta-hospital, will be generated, which will reconstruct the service modes for the elderly people. The future map of the metaverse in medicine is huge, which depends on the co-construction of community partners.


Subject(s)
COVID-19 , Cognitive Dysfunction , Humans , Aged , Physician-Patient Relations , Artificial Intelligence , Cognitive Dysfunction/therapy , Mental Health
5.
PLoS One ; 17(11): e0276767, 2022.
Article in English | MEDLINE | ID: covidwho-2140606

ABSTRACT

COVID-19, the disease caused by the novel coronavirus (SARS-CoV-2), first emerged in Wuhan, China late in December 2019. Not long after, the virus spread worldwide and was declared a pandemic by the World Health Organization in March 2020. This caused many changes around the world and in the United States, including an educational shift towards online learning. In this paper, we seek to understand how the COVID-19 pandemic and the increase in online learning impact college students' emotional wellbeing. We use several machine learning and statistical models to analyze data collected by the Faculty of Public Administration at the University of Ljubljana, Slovenia in conjunction with an international consortium of universities, other higher education institutions, and students' associations. Our results indicate that features related to students' academic life have the largest impact on their emotional wellbeing. Other important factors include students' satisfaction with their university's and government's handling of the pandemic as well as students' financial security.


Subject(s)
COVID-19 , Education, Distance , Humans , Mental Health , COVID-19/epidemiology , Pandemics , Artificial Intelligence , SARS-CoV-2 , Students/psychology
7.
Environ Sci Pollut Res Int ; 28(30): 40515-40532, 2021 Aug.
Article in English | MEDLINE | ID: covidwho-2115889

ABSTRACT

The world has never been prepared for global pandemics like the COVID-19, currently posing an immense threat to the public and consistent pressure on the global healthcare systems to navigate optimized tools, equipments, medicines, and techno-driven approaches to retard the infection spread. The synergized outcome of artificial intelligence paradigms and human-driven control measures elicit a significant impact on screening, analysis, prediction, and tracking the currently infected individuals, and likely the future patients, with precision and accuracy, generating regular international and national data on confirmed, recovered, and death cases, as the current status of 3,820,869 infected patients worldwide. Artificial intelligence is a frontline concept, with time-saving, cost-effective, and productive access to disease management, rendering positive results in physician assistance in high workload conditions, radiology imaging, computational tomography, and database formulations, to facilitate availability of information accessible to researchers all over the globe. The review tends to elaborate the role of industry 4.0 technology, fast diagnostic procedures, and convolutional neural networks, as artificial intelligence aspects, in potentiating the COVID-19 management criteria and differentiating infection in SARS-CoV-2 positive and negative groups. Therefore, the review successfully supplements the processes of vaccine development, disease management, diagnosis, patient records, transmission inhibition, social distancing, and future pandemic predictions, with artificial intelligence revolution and smart techno processes to ensure that the human race wins this battle with COVID-19 and many more combats in the future.


Subject(s)
COVID-19 , Communicable Diseases , Artificial Intelligence , Humans , Pandemics , SARS-CoV-2
8.
Front Public Health ; 10: 876691, 2022.
Article in English | MEDLINE | ID: covidwho-2119660

ABSTRACT

As COVID-19 continues to impact the United States and the world at large it is becoming increasingly necessary to develop methods which predict local scale spread of the disease. This is especially important as newer variants of the virus are likely to emerge and threaten community spread. We develop a Dynamic Bayesian Network (DBN) to predict community-level relative risk of COVID-19 infection at the census tract scale in the U.S. state of Indiana. The model incorporates measures of social and environmental vulnerability-including environmental determinants of COVID-19 infection-into a spatial temporal prediction of infection relative risk 1-month into the future. The DBN significantly outperforms five other modeling techniques used for comparison and which are typically applied in spatial epidemiological applications. The logic behind the DBN also makes it very well-suited for spatial-temporal prediction and for "what-if" analysis. The research results also highlight the need for further research using DBN-type approaches that incorporate methods of artificial intelligence into modeling dynamic processes, especially prominent within spatial epidemiologic applications.


Subject(s)
COVID-19 , Humans , United States/epidemiology , Risk , Bayes Theorem , COVID-19/epidemiology , Artificial Intelligence , Indiana/epidemiology
9.
PLoS One ; 17(11): e0276250, 2022.
Article in English | MEDLINE | ID: covidwho-2119372

ABSTRACT

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19). Imaging tests such as chest X-ray (CXR) and computed tomography (CT) can provide useful information to clinical staff for facilitating a diagnosis of COVID-19 in a more efficient and comprehensive manner. As a breakthrough of artificial intelligence (AI), deep learning has been applied to perform COVID-19 infection region segmentation and disease classification by analyzing CXR and CT data. However, prediction uncertainty of deep learning models for these tasks, which is very important to safety-critical applications like medical image processing, has not been comprehensively investigated. In this work, we propose a novel ensemble deep learning model through integrating bagging deep learning and model calibration to not only enhance segmentation performance, but also reduce prediction uncertainty. The proposed method has been validated on a large dataset that is associated with CXR image segmentation. Experimental results demonstrate that the proposed method can improve the segmentation performance, as well as decrease prediction uncertainty.


Subject(s)
COVID-19 , Deep Learning , Humans , COVID-19/diagnostic imaging , SARS-CoV-2 , Artificial Intelligence , X-Rays , Semantics
10.
Radiology ; 305(3): 538-554, 2022 Dec.
Article in English | MEDLINE | ID: covidwho-2117945

ABSTRACT

This review focuses on three key noninvasive cardiac imaging modalities-cardiac CT angiography (CTA), MRI, and PET/CT-and summarizes key publications in 2021 relevant to radiologists in clinical practice. Although this review focuses primarily on articles published in Radiology, important studies from other major journals are included to highlight "must-know" articles in the field of cardiovascular imaging. Cardiac CTA has been established as the first-line test for patients with stable chest pain and no known coronary artery disease, and its value remains central to the assessment of surgical or transcatheter aortic valve replacement. Artificial intelligence continues to evolve in a number of applications in cardiovascular disease. In cardiac MRI studies, 2021 has seen an emphasis on nonischemic cardiomyopathies, valvular heart disease, and COVID-19 disease cardiac manifestations and the authors highlight the key articles on these topics. A section featuring the increasing role of cardiac PET/CT in the assessment of cardiac sarcoidosis and prosthetic valves is also provided.


Subject(s)
COVID-19 , Positron Emission Tomography Computed Tomography , Humans , Artificial Intelligence , Tomography, X-Ray Computed/methods , Magnetic Resonance Imaging
11.
Sci Rep ; 12(1): 19267, 2022 Nov 10.
Article in English | MEDLINE | ID: covidwho-2116889

ABSTRACT

The COVID-19 global pandemic has caused unprecedented worldwide changes in healthcare delivery. While containment and mitigation approaches have been intensified, the progressive increase in the number of cases has overwhelmed health systems globally, highlighting the need for anticipation and prediction to be the basis of an efficient response system. This study demonstrates the role of population health metrics as early warning signs of future health crises. We retrospectively collected data from the emergency department of a large academic hospital in the northeastern United States from 01/01/2019 to 08/07/2021. A total of 377,694 patient records and 303 features were included for analysis. Departing from a multivariate artificial intelligence (AI) model initially developed to predict the risk of high-flow oxygen therapy or mechanical ventilation requirement during the COVID-19 pandemic, a total of 19 original variables and eight engineered features showing to be most predictive of the outcome were selected for further analysis. The temporal trends of the selected variables before and during the pandemic were characterized to determine their potential roles as early warning signs of future health crises. Temporal analysis of the individual variables included in the high-flow oxygen model showed that at a population level, the respiratory rate, temperature, low oxygen saturation, number of diagnoses during the first encounter, heart rate, BMI, age, sex, and neutrophil percentage demonstrated observable and traceable changes eight weeks before the first COVID-19 public health emergency declaration. Additionally, the engineered rule-based features built from the original variables also exhibited a pre-pandemic surge that preceded the first pandemic wave in spring 2020. Our findings suggest that the changes in routine population health metrics may serve as early warnings of future crises. This justifies the development of patient health surveillance systems, that can continuously monitor population health features, and alarm of new approaching public health crises before they become devastating.


Subject(s)
COVID-19 , Pandemics , Humans , Infant , COVID-19/diagnosis , COVID-19/epidemiology , Artificial Intelligence , Retrospective Studies , Medical Records , Oxygen
12.
Sci Rep ; 12(1): 19186, 2022 Nov 10.
Article in English | MEDLINE | ID: covidwho-2116888

ABSTRACT

Covid-19 has been a global concern since 2019, crippling the world economy and health. Biological diagnostic tools have since been developed to identify the virus from bodily fluids and since the virus causes pneumonia, which results in lung inflammation, the presence of the virus can also be detected using medical imaging by expert radiologists. The success of each diagnostic method is measured by the hit rate for identifying Covid infections. However, the access for people to each diagnosis tool can be limited, depending on the geographic region and, since Covid treatment denotes a race against time, the diagnosis duration plays an important role. Hospitals with X-ray opportunities are widely distributed all over the world, so a method investigating lung X-ray images for possible Covid-19 infections would offer itself. Promising results have been achieved in the literature in automatically detecting the virus using medical images like CT scans and X-rays using supervised artificial neural network algorithms. One of the major drawbacks of supervised learning models is that they require enormous amounts of data to train, and generalize on new data. In this study, we develop a Swish activated, Instance and Batch normalized Residual U-Net GAN with dense blocks and skip connections to create synthetic and augmented data for training. The proposed GAN architecture, due to the presence of instance normalization and swish activation, can deal with the randomness of luminosity, that arises due to different sources of X-ray images better than the classical architecture and generate realistic-looking synthetic data. Also, the radiology equipment is not generally computationally efficient. They cannot efficiently run state-of-the-art deep neural networks such as DenseNet and ResNet effectively. Hence, we propose a novel CNN architecture that is 40% lighter and more accurate than state-of-the-art CNN networks. Multi-class classification of the three classes of chest X-rays (CXR), ie Covid-19, healthy and Pneumonia, is performed using the proposed model which had an extremely high test accuracy of 99.2% which has not been achieved in any previous studies in the literature. Based on the mentioned criteria for developing Corona infection diagnosis, in the present study, an Artificial Intelligence based method is proposed, resulting in a rapid diagnostic tool for Covid infections based on generative adversarial and convolutional neural networks. The benefit will be a high accuracy of lung infection identification with 99% accuracy. This could lead to a support tool that helps in rapid diagnosis, and an accessible Covid identification method using CXR images.


Subject(s)
COVID-19 , Deep Learning , Pneumonia , Humans , COVID-19/diagnostic imaging , SARS-CoV-2 , Artificial Intelligence
13.
Nature ; 611(7934): 192-193, 2022 11.
Article in English | MEDLINE | ID: covidwho-2106382
14.
Psychiatr Danub ; 32(1): 25-31, 2020.
Article in English | MEDLINE | ID: covidwho-2100748

ABSTRACT

Deep emotional traumas in societies overwhelmed by large-scale human disasters, like, global pandemic diseases, natural disasters, man-made tragedies, war conflicts, social crises, etc., can cause massive stress-related disorders. Motivated by the ongoing global coronavirus pandemic, the article provides an overview of scientific evidence regarding adverse impact of diverse human disasters on mental health in afflicted groups and societies. Following this broader context, psychosocial impact of COVID-19 as a specific global human disaster is presented, with an emphasis on disturbing mental health aspects of the ongoing pandemic. Limited resources of mental health services in a number of countries around the world are illustrated, which will be further stretched by the forthcoming increase in demand for mental health services due to the global COVID-19 pandemic. Mental health challenges are particularly important for the Republic of Croatia in the current situation, due to disturbing stress of the 2020 Zagreb earthquake and the high pre-pandemic prevalence of chronic Homeland-War-related posttraumatic stress disorders. Comprehensive approach based on digital psychiatry is proposed to address the lack of access to psychiatric services, which includes artificial intelligence, telepsychiatry and an array of new technologies, like internet-based computer-aided mental health tools and services. These tools and means should be utilized as an important part of the whole package of measures to mitigate negative mental health effects of the global coronavirus pandemic. Our scientific and engineering experiences in the design and development of digital tools and means in mitigation of stress-related disorders and assessment of stress resilience are presented. Croatian initiative on enhancement of interdisciplinary research of psychiatrists, psychologists and computer scientists on the national and EU level is important in addressing pressing mental health concerns related to the ongoing pandemic and similar human disasters.


Subject(s)
Coronavirus Infections/psychology , Disasters , Mental Health Services , Mental Health , Pneumonia, Viral/psychology , Psychiatry , Telemedicine , Artificial Intelligence , Betacoronavirus , COVID-19 , Croatia , Humans , Internet , Pandemics , Psychiatry/trends , SARS-CoV-2 , Telemedicine/trends , User-Computer Interface
15.
Sensors (Basel) ; 22(21)2022 Nov 01.
Article in English | MEDLINE | ID: covidwho-2099737

ABSTRACT

The rapid growth of the world population has increased the food demand as well as the need for assurance of food quality, safety, and sustainability. However, food security can easily be compromised by not only natural hazards but also changes in food preferences, political conflicts, and food frauds. In order to contribute to building a more sustainable food system-digitally visible and processes measurable-within this review, we summarized currently available evidence for various information and communication technologies (ICTs) that can be utilized to support collaborative actions, prevent fraudulent activities, and remotely perform real-time monitoring, which has become essential, especially during the COVID-19 pandemic. The Internet of Everything, 6G, blockchain, artificial intelligence, and digital twin are gaining significant attention in recent years in anticipation of leveraging the creativity of human experts in collaboration with efficient, intelligent, and accurate machines, but with limited consideration in the food supply chain. Therefore, this paper provided a thorough review of the food system by showing how various ICT tools can help sense and quantify the food system and highlighting the key enhancements that Industry 5.0 technologies can bring. The vulnerability of the food system can be effectively mitigated with the utilization of various ICTs depending on not only the nature and severity of crisis but also the specificity of the food supply chain. There are numerous ways of implementing these technologies, and they are continuously evolving.


Subject(s)
Blockchain , COVID-19 , Humans , Pandemics/prevention & control , Artificial Intelligence , Food Security
16.
J Med Libr Assoc ; 110(2): 273-275, 2022 Apr 01.
Article in English | MEDLINE | ID: covidwho-1801934

ABSTRACT

World Health Organization's Early AI-supported Response with Social Listening Platform (WHO EARS). WHO HQ, Avenue Appia 20, 1211, Geneva 27, Switzerland; https://www.who-ears.com/; free.


Subject(s)
Artificial Intelligence , Global Health , World Health Organization
17.
Stud Health Technol Inform ; 299: 235-241, 2022 Nov 03.
Article in English | MEDLINE | ID: covidwho-2099074

ABSTRACT

The spread of a new coronavirus infection in the last two years together with HIV infection preserves and even increases the potential for the spread of tuberculosis in the world. Sverdlovsk oblast (SO) of Russian Federation is the region with high levels of HIV and tuberculosis (TB). The search for new methods of forecasting of the future epidemic situation for tuberculosis has become particularly relevant. The aim was to develop an effective method for predicting the epidemic situation of tuberculosis using an artificial intelligence (AI) method in the format of a dynamic simulation model based on AI technologies. Statistical data was loaded from the state statistical reporting on TB patients for the period 2007-2017. The parameters were controlled through a system of inequalities. The proposed SDM made it possible to identify and reliably calculate trends of TB epidemiological indicators. Comparison of the predicted values made in 2017 with the actual values of 2018-2021 revealed a reliable coincidence of the trend of movement of TB epidemiological indicators in the region, the maximum deviation was no more than 14.82%. The forecast results obtained with SDM are quite suitable for practical use. Especially, in operational resource planning of measures to counteract the spread of tuberculosis at the regional level.


Subject(s)
Epidemics , HIV Infections , Tuberculosis , Humans , HIV Infections/epidemiology , Artificial Intelligence , Tuberculosis/epidemiology , Forecasting , Russia/epidemiology
18.
Infect Control Hosp Epidemiol ; 41(7): 869-870, 2020 07.
Article in English | MEDLINE | ID: covidwho-2096241
19.
Int J Med Sci ; 19(12): 1743-1752, 2022.
Article in English | MEDLINE | ID: covidwho-2090803

ABSTRACT

This systematic review focuses on using artificial intelligence (AI) to detect COVID-19 infection with the help of X-ray images. Methodology: In January 2022, the authors searched PubMed, Embase and Scopus using specific medical subject headings terms and filters. All articles were independently reviewed by two reviewers. All conflicts resulting from a misunderstanding were resolved by a third independent researcher. After assessing abstracts and article usefulness, eliminating repetitions and applying inclusion and exclusion criteria, six studies were found to be qualified for this study. Results: The findings from individual studies differed due to the various approaches of the authors. Sensitivity was 72.59%-100%, specificity was 79%-99.9%, precision was 74.74%-98.7%, accuracy was 76.18%-99.81%, and the area under the curve was 95.24%-97.7%. Conclusion: AI computational models used to assess chest X-rays in the process of diagnosing COVID-19 should achieve sufficiently high sensitivity and specificity. Their results and performance should be repeatable to make them dependable for clinicians. Moreover, these additional diagnostic tools should be more affordable and faster than the currently available procedures. The performance and calculations of AI-based systems should take clinical data into account.


Subject(s)
COVID-19 , Humans , COVID-19/diagnostic imaging , Artificial Intelligence , X-Rays , Sensitivity and Specificity , Radiography
20.
Int J Environ Res Public Health ; 19(21)2022 Oct 31.
Article in English | MEDLINE | ID: covidwho-2090191

ABSTRACT

Anatomy is taught in the early years of an undergraduate medical curriculum. The subject is volatile and of voluminous content, given the complex nature of the human body. Students frequently face learning constraints in these fledgling years of medical education, often resulting in a spiraling dwindling academic performance. Hence, there have been continued efforts directed at developing new curricula and incorporating new methods of teaching, learning and assessment that are aimed at logical learning and long-term retention of anatomical knowledge, which is a mainstay of all medical practice. In recent years, artificial intelligence (AI) has gained in popularity. AI uses machine learning models to store, compute, analyze and even augment huge amounts of data to be retrieved when needed, while simultaneously the machine itself can be programmed for deep learning, improving its own efficiency through complex neural networks. There are numerous specific benefits to incorporating AI in education, which include in-depth learning, storage of large electronic data, teaching from remote locations, engagement of fewer personnel in teaching, quick feedback from responders, innovative assessment methods and user-friendly alternatives. AI has long been a part of medical diagnostics and treatment planning. Extensive literature is available on uses of AI in clinical settings, e.g., in Radiology, but to the best of our knowledge there is a paucity of published data on AI used for teaching, learning and assessment in anatomy. In the present review, we highlight recent novel and advanced AI techniques such as Artificial Neural Networks (ANN), or more complex Convoluted Neural Networks (CNN) and Bayesian U-Net, which are used for teaching anatomy. We also address the main advantages and limitations of the use of AI in medical education and lessons learnt from AI application during the COVID-19 pandemic. In the future, studies with AI in anatomy education could be advantageous for both students to develop professional expertise and for instructors to develop improved teaching methods for this vast and complex subject, especially with the increasing paucity of cadavers in many medical schools. We also suggest some novel examples of how AI could be incorporated to deliver augmented reality experiences, especially with reference to complex regions in the human body, such as neural pathways in the brain, complex developmental processes in the embryo or in complicated miniature regions such as the middle and inner ear. AI can change the face of assessment techniques and broaden their dimensions to suit individual learners.


Subject(s)
Artificial Intelligence , COVID-19 , Humans , Bayes Theorem , Pandemics , Curriculum , Teaching
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