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General General

Proteomic and Metabolomic Characterization of COVID-19 Patient Sera.

In Cell ; h5-index 250.0

Early detection and effective treatment of severe COVID-19 patients remain major challenges. Here, we performed proteomic and metabolomic profiling of sera from 46 COVID-19 and 53 control individuals. We then trained a machine learning model using proteomic and metabolomic measurements from a training cohort of 18 non-severe and 13 severe patients. The model was validated using 10 independent patients, 7 of which were correctly classified. Targeted proteomics and metabolomics assays were employed to further validate this molecular classifier in a second test cohort of 19 COVID-19 patients, leading to 16 correct assignments. We identified molecular changes in the sera of COVID-19 patients compared to other groups implicating dysregulation of macrophage, platelet degranulation, complement system pathways, and massive metabolic suppression. This study revealed characteristic protein and metabolite changes in the sera of severe COVID-19 patients, which might be used in selection of potential blood biomarkers for severity evaluation.

Shen Bo, Yi Xiao, Sun Yaoting, Bi Xiaojie, Du Juping, Zhang Chao, Quan Sheng, Zhang Fangfei, Sun Rui, Qian Liujia, Ge Weigang, Liu Wei, Liang Shuang, Chen Hao, Zhang Ying, Li Jun, Xu Jiaqin, He Zebao, Chen Baofu, Wang Jing, Yan Haixi, Zheng Yufen, Wang Donglian, Zhu Jiansheng, Kong Ziqing, Kang Zhouyang, Liang Xiao, Ding Xuan, Ruan Guan, Xiang Nan, Cai Xue, Gao Huanhuan, Li Lu, Li Sainan, Xiao Qi, Lu Tian, Zhu Yi, Liu Huafen, Chen Haixiao, Guo Tiannan

2020-May-28

COVID-19, metabolomics, proteomics, serum, severity

Public Health Public Health

Machine Learning to Detect Self-Reporting of Symptoms, Testing Access and Recovery Associated with COVID-19 on Twitter: A Retrospective Big-Data Infoveillance Study.

In JMIR public health and surveillance

BACKGROUND : The coronavirus (COVID-19) pandemic is a global health emergency with over 6 million cases worldwide as of the beginning of June 2020. Importantly, the pandemic is historic in scope and precedent given its emergence in an increasing digital era. Importantly, there have been concerns about the accuracy of COVID-19 case counts due to issues such as lack of access to testing and difficulty in measuring recoveries.

OBJECTIVE : The aims of this study were to detect and characterize user-generated conversations that could be associated with COVID-19-related symptoms, experiences with access to testing, and mentions of disease recovery using an unsupervised machine learning approach.

METHODS : Tweets were collected from the Twitter public streaming API from March 3-20, 2020, filtered for general COVID-19-related keywords and then further filtered for terms that could be related to COVID-19 symptoms as self-reported by users. Tweets were analyzed using an unsupervised machine learning approach called the biterm topic model (BTM), where groups of tweets containing the same word-related themes were separated into topic clusters that included conversations about symptoms, testing, and recovery. Tweets in these clusters were then extracted and manually annotated for content analysis and also assessed for their statistical and geographic characteristics.

RESULTS : A total of 4,492,954 tweets were collected that contained terms that could be related to COVID-19 symptoms. After using BTM to identify relevant topic clusters and removing duplicate tweets, we identified a total of 3,465 (<1%) tweets that included user generated conversations about experiences that users associated with possible COVID-19 symptoms and other disease experiences. These tweets were grouped into five main categories including first and second-hand reports of symptoms, symptom reporting concurrent with lack of testing, discussion of recovery, confirmation of negative COVID-19 diagnosis after receiving testing, and users recalling symptoms and questioning whether they might have been previously infected with COVID-19. Co-occurrence of tweets for these themes were statistically significant for users reporting symptoms with lack of testing and with discussion of recovery. Sixty-three percent (n=1112) of tweets were located in the United States.

CONCLUSIONS : This study used unsupervised machine learning for the purposes of characterizing self-reporting of symptoms, experiences with testing, and mentions of recovery related to COVID-19. Many users reported symptoms they thought were related to COVID-19, but also were not able to get tested to confirm their concerns. In the absence of testing availability and confirmation, accurate case estimations for this period of the outbreak may never be known. Future studies should continue to explore the utility of infoveillance approaches to estimate COVID-19 disease severity.

CLINICALTRIAL :

Mackey Tim, Purushothaman Vidya, Li Jiawei, Shah Neal, Nali Matthew, Bardier Cortni, Liang Bryan, Cai Mingxiang, Cuomo Raphael

2020-Jun-03

General General

Deep Learning Based Drug Screening for Novel Coronavirus 2019-nCov.

In Interdisciplinary sciences, computational life sciences

A novel coronavirus, called 2019-nCoV, was recently found in Wuhan, Hubei Province of China, and now is spreading across China and other parts of the world. Although there are some drugs to treat 2019-nCoV, there is no proper scientific evidence about its activity on the virus. It is of high significance to develop a drug that can combat the virus effectively to save valuable human lives. It usually takes a much longer time to develop a drug using traditional methods. For 2019-nCoV, it is now better to rely on some alternative methods such as deep learning to develop drugs that can combat such a disease effectively since 2019-nCoV is highly homologous to SARS-CoV. In the present work, we first collected virus RNA sequences of 18 patients reported to have 2019-nCoV from the public domain database, translated the RNA into protein sequences, and performed multiple sequence alignment. After a careful literature survey and sequence analysis, 3C-like protease is considered to be a major therapeutic target and we built a protein 3D model of 3C-like protease using homology modeling. Relying on the structural model, we used a pipeline to perform large scale virtual screening by using a deep learning based method to accurately rank/identify protein-ligand interacting pairs developed recently in our group. Our model identified potential drugs for 2019-nCoV 3C-like protease by performing drug screening against four chemical compound databases (Chimdiv, Targetmol-Approved_Drug_Library, Targetmol-Natural_Compound_Library, and Targetmol-Bioactive_Compound_Library) and a database of tripeptides. Through this paper, we provided the list of possible chemical ligands (Meglumine, Vidarabine, Adenosine, D-Sorbitol, D-Mannitol, Sodium_gluconate, Ganciclovir and Chlorobutanol) and peptide drugs (combination of isoleucine, lysine and proline) from the databases to guide the experimental scientists and validate the molecules which can combat the virus in a shorter time.

Zhang Haiping, Saravanan Konda Mani, Yang Yang, Hossain Md Tofazzal, Li Junxin, Ren Xiaohu, Pan Yi, Wei Yanjie

2020-Jun-01

3C-like protease, Coronavirus, Deep learning, Drug screening, Homology modeling

General General

Smartphone-Based Self-Testing of COVID-19 Using Breathing Sounds.

In Telemedicine journal and e-health : the official journal of the American Telemedicine Association

Telemedicine could be a key to control the world-wide disruptive and spreading novel coronavirus disease (COVID-19) pandemic. The COVID-19 virus directly targets the lungs, leading to pneumonia-like symptoms and shortness of breath with life-threatening consequences. Despite the fact that self-quarantine and social distancing are indispensable during the pandemic, the procedure for testing COVID-19 contraction is conventionally available through nasal swabs, saliva test kits, and blood work at healthcare settings. Therefore, devising personalized self-testing kits for COVID-19 virus and other similar viruses is heavily admired. Many e-health initiatives have been made possible by the advent of smartphones with embedded software, hardware, high-performance computing, and connectivity capabilities. A careful review of breathing sounds and their implications in identifying breathing complications suggests that the breathing sounds of COVID-19 contracted users may reveal certain acoustic signal patterns, which is worth investigating. To this end, acquiring respiratory data solely from breathing sounds fed to the smartphone's microphone strikes as a very appealing resolution. The acquired breathing sounds can be analyzed using advanced signal processing and analysis in tandem with new deep/machine learning and pattern recognition techniques to separate the breathing phases, estimate the lung volume, oxygenation, and to further classify the breathing data input into healthy or unhealthy cases. The ideas presented have the potential to be deployed as self-test breathing monitoring apps for the ongoing global COVID-19 pandemic, where users can check their breathing sound pattern frequently through the app.

Faezipour Miad, Abuzneid Abdelshakour

2020-Jun-02

e-health, home health monitoring, sensor technology, technology, telemedicine

Radiology Radiology

Weakly Labeled Data Augmentation for Deep Learning: A Study on COVID-19 Detection in Chest X-Rays.

In Diagnostics (Basel, Switzerland)

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a pandemic resulting in over 2.7 million infected individuals and over 190,000 deaths and growing. Assertions in the literature suggest that respiratory disorders due to COVID-19 commonly present with pneumonia-like symptoms which are radiologically confirmed as opacities. Radiology serves as an adjunct to the reverse transcription-polymerase chain reaction test for confirmation and evaluating disease progression. While computed tomography (CT) imaging is more specific than chest X-rays (CXR), its use is limited due to cross-contamination concerns. CXR imaging is commonly used in high-demand situations, placing a significant burden on radiology services. The use of artificial intelligence (AI) has been suggested to alleviate this burden. However, there is a dearth of sufficient training data for developing image-based AI tools. We propose increasing training data for recognizing COVID-19 pneumonia opacities using weakly labeled data augmentation. This follows from a hypothesis that the COVID-19 manifestation would be similar to that caused by other viral pathogens affecting the lungs. We expand the training data distribution for supervised learning through the use of weakly labeled CXR images, automatically pooled from publicly available pneumonia datasets, to classify them into those with bacterial or viral pneumonia opacities. Next, we use these selected images in a stage-wise, strategic approach to train convolutional neural network-based algorithms and compare against those trained with non-augmented data. Weakly labeled data augmentation expands the learned feature space in an attempt to encompass variability in unseen test distributions, enhance inter-class discrimination, and reduce the generalization error. Empirical evaluations demonstrate that simple weakly labeled data augmentation (Acc: 0.5555 and Acc: 0.6536) is better than baseline non-augmented training (Acc: 0.2885 and Acc: 0.5028) in identifying COVID-19 manifestations as viral pneumonia. Interestingly, adding COVID-19 CXRs to simple weakly labeled augmented training data significantly improves the performance (Acc: 0.7095 and Acc: 0.8889), suggesting that COVID-19, though viral in origin, creates a uniquely different presentation in CXRs compared with other viral pneumonia manifestations.

Rajaraman Sivaramakrishnan, Antani Sameer

2020-May-30

COVID-19, augmentation, chest X-rays, convolutional neural network, deep learning, localization, pneumonia

General General

Analysis of RNA sequences of 3636 SARS-CoV-2 collected from 55 countries reveals selective sweep of one virus type.

In The Indian journal of medical research

Background & objectives : SARS-CoV-2 (Severe acute respiratory syndrome coronavirus-2) is evolving with the progression of the pandemic. This study was aimed to investigate the diversity and evolution of the coronavirus SARS-CoV-2 with progression of the pandemic over time and to identify similarities and differences of viral diversity and evolution across geographical regions (countries).

Methods : Publicly available data on type definitions based on whole-genome sequences of the SARS-CoV-2 sampled during December and March 2020 from 3636 infected patients spread over 55 countries were collected. Phylodynamic analyses were performed and the temporal and spatial evolution of the virus was examined.

Results : It was found that (i) temporal variation in frequencies of types of the coronavirus was significant; ancestral viruses of type O were replaced by evolved viruses belonging to type A2a; (ii) spatial variation was not significant; with the spread of SARS-CoV-2, the dominant virus was the A2a type virus in every geographical region; (iii) within a geographical region, there was significant micro-level variation in the frequencies of the different viral types, and (iv) the evolved coronavirus of type A2a swept rapidly across all continents.

Interpretation & conclusions : SARS-CoV-2 belonging to the A2a type possesses a non-synomymous variant (D614G) that possibly eases the entry of the virus into the lung cells of the host. This may be the reason why the A2a type has an advantage to infect and survive and as a result has rapidly swept all geographical regions. Therefore, large-scale sequencing of coronavirus genomes and, as required, of host genomes should be undertaken in India to identify regional and ethnic variation in viral composition and its interaction with host genomes. Further, careful collection of clinical and immunological data of the host can provide deep learning in relation to infection and transmission of the types of coronavirus genomes.

Biswas Nidhan K, Majumder Partha P

2020-May-30

Host genome interaction - phylogeny - RNA sequence - SARS-CoV-2 - viral type coronavirus

Surgery Surgery

Mechanism of baricitinib supports artificial intelligence-predicted testing in COVID-19 patients.

In EMBO molecular medicine

Baricitinib, is an oral Janus kinase (JAK)1/JAK2 inhibitor approved for the treatment of rheumatoid arthritis (RA) that was independently predicted, using artificial intelligence (AI)-algorithms, to be useful for COVID-19 infection via a proposed anti-cytokine effects and as an inhibitor of host cell viral propagation. We evaluated the in vitro pharmacology of baricitinib across relevant leukocyte subpopulations coupled to its in vivo pharmacokinetics and showed it inhibited signaling of cytokines implicated in COVID-19 infection. We validated the AI-predicted biochemical inhibitory effects of baricitinib on human numb-associated kinase (hNAK) members measuring nanomolar affinities for AAK1, BIKE, and GAK. Inhibition of NAKs led to reduced viral infectivity with baricitinib using human primary liver spheroids. These effects occurred at exposure levels seen clinically. In a case series of patients with bilateral COVID-19 pneumonia, baricitinib treatment was associated with clinical and radiologic recovery, a rapid decline in SARS-CoV-2 viral load, inflammatory markers, and IL-6 levels. Collectively, these data support further evaluation of the anti-cytokine and anti-viral activity of baricitinib and supports its assessment in randomized trials in hospitalized COVID-19 patients.

Stebbing Justin, Krishnan Venkatesh, de Bono Stephanie, Ottaviani Silvia, Casalini Giacomo, Richardson Peter J, Monteil Vanessa, Lauschke Volker M, Mirazimi Ali, Youhanna Sonia, Tan Yee-Joo, Baldanti Fausto, Sarasini Antonella, Terres Jorge A Ross, Nickoloff Brian J, Higgs Richard E, Rocha Guilherme, Byers Nicole L, Schlichting Douglas E, Nirula Ajay, Cardoso Anabela, Corbellino Mario

2020-May-30

Baricitinib, COVID-19, anti-cytokine, anti-viral, case series

General General

Research Progress of Coronavirus Based on Bibliometric Analysis.

In International journal of environmental research and public health ; h5-index 73.0

BACKGROUND : COVID-19 has become one of the most serious global epidemics in the 21st Century. This study aims to explore the distribution of research capabilities of countries, institutions, and researchers, and the hotspots and frontiers of coronavirus research in the past two decades. In it, references for funding support of urgent projects and international cooperation among research institutions are provided.

METHOD : the Web of Science core collection database was used to retrieve the documents related to coronavirus published from 2003 to 2020. Citespace.5.6.R2, VOSviewer1.6.12, and Excel 2016 were used for bibliometric analysis.

RESULTS : 11,036 documents were retrieved, of which China and the United States have contributed the most coronavirus studies, Hong Kong University being the top contributor. Regarding journals, the JournalofVirology has contributed the most, while in terms of researchers, Yuen Kwok Yung has made the most contributions. The proportion of documents published by international cooperation has been rising for decades. Vaccines for SARS-CoV-2 are under development, and clinical trials of several drugs are ongoing.

CONCLUSIONS : international cooperation is an important way to accelerate research progress and achieve success. Developing corresponding vaccines and drugs are the current hotspots and research directions.

Zhai Fei, Zhai Yuxuan, Cong Chuang, Song Tingyan, Xiang Rongwu, Feng Tianyi, Liang Zhengxuan, Zeng Ya, Yang Jing, Yang Jie, Liang Jiankun

2020-May-26

COVID-19, SARS-CoV-2, bibliometrics, coronavirus

General General

Prioritizing and Analyzing the Role of Climate and Urban Parameters in the Confirmed Cases of COVID-19 Based on Artificial Intelligence Applications.

In International journal of environmental research and public health ; h5-index 73.0

Nowadays, an infectious disease outbreak is considered one of the most destructive effects in the sustainable development process. The outbreak of new coronavirus (COVID-19) as an infectious disease showed that it has undesirable social, environmental, and economic impacts, and leads to serious challenges and threats. Additionally, investigating the prioritization parameters is of vital importance to reducing the negative impacts of this global crisis. Hence, the main aim of this study is to prioritize and analyze the role of certain environmental parameters. For this purpose, four cities in Italy were selected as a case study and some notable climate parameters-such as daily average temperature, relative humidity, wind speed-and an urban parameter, population density, were considered as input data set, with confirmed cases of COVID-19 being the output dataset. In this paper, two artificial intelligence techniques, including an artificial neural network (ANN) based on particle swarm optimization (PSO) algorithm and differential evolution (DE) algorithm, were used for prioritizing climate and urban parameters. The analysis is based on the feature selection process and then the obtained results from the proposed models compared to select the best one. Finally, the difference in cost function was about 0.0001 between the performances of the two models, hence, the two methods were not different in cost function, however, ANN-PSO was found to be better, because it reached to the desired precision level in lesser iterations than ANN-DE. In addition, the priority of two variables, urban parameter, and relative humidity, were the highest to predict the confirmed cases of COVID-19.

Shaffiee Haghshenas Sina, Pirouz Behrouz, Shaffiee Haghshenas Sami, Pirouz Behzad, Piro Patrizia, Na Kyoung-Sae, Cho Seo-Eun, Geem Zong Woo

2020-May-25

COVID-19, DE, PSO, artificial intelligence, feature selection, sustainable development

Surgery Surgery

Deep learning COVID-19 detection bias: accuracy through artificial intelligence.

In International orthopaedics ; h5-index 43.0

BACKGROUND : Detection of COVID-19 cases' accuracy is posing a conundrum for scientists, physicians, and policy-makers. As of April 23, 2020, 2.7 million cases have been confirmed, over 190,000 people are dead, and about 750,000 people are reported recovered. Yet, there is no publicly available data on tests that could be missing infections. Complicating matters and furthering anxiety are specific instances of false-negative tests.

METHODS : We developed a deep learning model to improve accuracy of reported cases and to precisely predict the disease from chest X-ray scans. Our model relied on convolutional neural networks (CNNs) to detect structural abnormalities and disease categorization that were keys to uncovering hidden patterns. To do so, a transfer learning approach was deployed to perform detections from the chest anterior-posterior radiographs of patients. We used publicly available datasets to achieve this.

RESULTS : Our results offer very high accuracy (96.3%) and loss (0.151 binary cross-entropy) using the public dataset consisting of patients from different countries worldwide. As the confusion matrix indicates, our model is able to accurately identify true negatives (74) and true positives (32); this deep learning model identified three cases of false-positive and one false-negative finding from the healthy patient scans.

CONCLUSIONS : Our COVID-19 detection model minimizes manual interaction dependent on radiologists as it automates identification of structural abnormalities in patient's CXRs, and our deep learning model is likely to detect true positives and true negatives and weed out false positive and false negatives with > 96.3% accuracy.

Vaid Shashank, Kalantar Reza, Bhandari Mohit

2020-May-27

Artificial intelligence, COVID-19, Deep learning, Detection bias

Public Health Public Health

Fighting COVID-19, a Place for Artificial Intelligence.

In Transboundary and emerging diseases ; h5-index 40.0

The emergence of the coronavirus disease 2019 (COVID-19) heralded a new era in the cross-species transmission of severe respiratory illness leading to rapid spread in mainland China and around the world with a case fatality rate of 2.3% in China and 1.8-7.2% outside China (Wu & McGoogan, 2019; Centers for Disease Control and Prevention, 2020; Onder Rezza, & Brusaferro, 2020; World Health Organization, 2020). As of May 15, 2020, a total of 4,338,658 confirmed cases of COVID-19 and 297,119 death cases have been documented globally (World Health Organization, 2020). Several strategies have been adopted to contain the outbreak including classic infection-control and public health measures, nevertheless these measures may not be effective for tackling the scale of COVID-19.

Emile Sameh Hany, Hamid Hytham K S

2020-May-27

General General

Improvements in Patient Monitoring for the Intensive Care Unit: Survey Study.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Due to demographic change and, more recently, the Coronavirus Disease 2019 (COVID-19), the importance of modern intensive care units (ICU) is becoming apparent. One of the key components of an ICU is the continuous monitoring of patients' vital parameters. However, existing advances in informatics, signal processing, or engineering that could alleviate the burden on ICUs have not yet been applied. This could be related to the lack of user involvement in research and development.

OBJECTIVE : This study focused on satisfaction of ICU staff with the current patient monitoring and their suggestions for future improvements. We aimed to identify aspects disturbing patient care, display devices for remote monitoring, use cases for artificial intelligence (AI), and whether ICU staff is willing to improve their digital literacy or contribute to the improvement of patient monitoring. We further desired to uncover differences in the responses of the professional groups.

METHODS : This survey study was realized with ICU staff from four ICUs of a German university hospital between November 2019 and January 2020. We developed a web-based 36-item survey questionnaire by analyzing a preceding qualitative interview study with ICU staff about clinical requirements of future patient monitoring. Statistical analyses of questionnaire results included median values with their bootstrapped 95% confidence intervals, and Chi-square tests to compare the distributions of item responses of the professional groups.

RESULTS : Eighty-six of the 270 ICU physicians and nurses completed the survey questionnaire. The majority stated to feel confident using the patient monitoring, but high rates of false positive alarms and the many sensor cables were considered to disturb patient care. Wireless sensors, reduction of false positive alarms and hospital standard operating procedures (SOP) for alarm management were demanded. Responses to the display devices proposed for remote patient monitoring were split. Regarding its use, most respondents indicated responsibility for multiple wards or earlier alerting. AI for ICUs would be useful for early detection of complications and increased risk of mortality, as well as to have guidelines for therapy and diagnostics proposed. Transparency, interoperability, usability, and staff training were essential to promote usage of an AI. The majority wanted to learn more about new technologies for ICU and desired more time for it. Physicians had fewer reservations than nurses about using mobile phones for remote monitoring, and AI-based intelligent alarm management.

CONCLUSIONS : This survey study among ICU staff revealed key improvements for patient monitoring in intensive care medicine. Hospital providers and medical device manufacturers should focus on reducing false alarms, implementing hospital alarm SOPs, introducing wireless sensors, preparing for the use of AI, and enhancing digital literacy of ICU staff. Our results may contribute to the user-centered transfer of digital technologies into practice to alleviate challenges in intensive care medicine.

CLINICALTRIAL : ClinicalTrials.gov NCT03514173; https://clinicaltrials.gov/ct2/show/NCT03514173.

Poncette Akira-Sebastian, Mosch Lina, Spies Claudia, Schmieding Malte, Schiefenhövel Fridtjof, Krampe Henning, Balzer Felix

2020-May-13

Radiology Radiology

Early Screening of SARS-CoV-2 by Intelligent Analysis of X-Ray Images

ArXiv Preprint

Future SARS-CoV-2 virus outbreak COVID-XX might possibly occur during the next years. However the pathology in humans is so recent that many clinical aspects, like early detection of complications, side effects after recovery or early screening, are currently unknown. In spite of the number of cases of COVID-19, its rapid spread putting many sanitary systems in the edge of collapse has hindered proper collection and analysis of the data related to COVID-19 clinical aspects. We describe an interdisciplinary initiative that integrates clinical research, with image diagnostics and the use of new technologies such as artificial intelligence and radiomics with the aim of clarifying some of SARS-CoV-2 open questions. The whole initiative addresses 3 main points: 1) collection of standardize data including images, clinical data and analytics; 2) COVID-19 screening for its early diagnosis at primary care centers; 3) define radiomic signatures of COVID-19 evolution and associated pathologies for the early treatment of complications. In particular, in this paper we present a general overview of the project, the experimental design and first results of X-ray COVID-19 detection using a classic approach based on HoG and feature selection. Our experiments include a comparison to some recent methods for COVID-19 screening in X-Ray and an exploratory analysis of the feasibility of X-Ray COVID-19 screening. Results show that classic approaches can outperform deep-learning methods in this experimental setting, indicate the feasibility of early COVID-19 screening and that non-COVID infiltration is the group of patients most similar to COVID-19 in terms of radiological description of X-ray. Therefore, an efficient COVID-19 screening should be complemented with other clinical data to better discriminate these cases.

D. Gil, K. Díaz-Chito, C. Sánchez, A. Hernández-Sabaté

2020-05-28

General General

Advanced Digital Health Technologies for COVID-19 and Future Emergencies.

In Telemedicine journal and e-health : the official journal of the American Telemedicine Association

Background: Coronavirus disease 2019 (COVID-19) has led to a national health care emergency in the United States and exposed resource shortages, particularly of health care providers trained to provide critical or intensive care. This article describes how digital health technologies are being or could be used for COVID-19 mitigation. It then proposes the National Emergency Tele-Critical Care Network (NETCCN), which would combine digital health technologies to address this and future crises. Methods: Subject matter experts from the Society of Critical Care Medicine and the Telemedicine and Advanced Technology Research Center examined the peer-reviewed literature and science/technology news to see what digital health technologies have already been or could be implemented to (1) support patients while limiting COVID-19 transmission, (2) increase health care providers' capability and capacity, and (3) predict/prevent future outbreaks. Results: Major technologies identified included telemedicine and mobile care (for COVID-19 as well as routine care), tiered telementoring, telecritical care, robotics, and artificial intelligence for monitoring. Several of these could be assimilated to form an interoperable scalable NETCCN. NETCCN would assist health care providers, wherever they are located, by obtaining real-time patient and supplies data and disseminating critical care expertise. NETCCN capabilities should be maintained between disasters and regularly tested to ensure continual readiness. Conclusions: COVID-19 has demonstrated the impact of a large-scale health emergency on the existing infrastructures. Short term, an approach to meeting this challenge is to adopt existing digital health technologies. Long term, developing a NETCCN may ensure that the necessary ecosystem is available to respond to future emergencies.

Scott Benjamin K, Miller Geoffrey T, Fonda Stephanie J, Yeaw Ronald E, Gaudaen James C, Pavliscsak Holly H, Quinn Matthew T, Pamplin Jeremy C

2020-May-26

coronavirus, critical care, digital health, emergencies, natural disasters, pandemics, telemedicine

General General

COVID-19 and Your Smartphone: BLE-based Smart Contact Tracing

ArXiv Preprint

Contact tracing is of paramount importance when it comes to preventing the spreading of infectious diseases. Contact tracing is usually performed manually by authorized personnel. Manual contact tracing is an inefficient, error-prone, time-consuming process of limited utility to the population at large as those in close contact with infected individuals are informed hours, if not days, later. This paper introduces an alternative way to manual contact tracing. The proposed Smart Contact Tracing (SCT) system utilizes the smartphone's Bluetooth Low Energy (BLE) signals and machine learning classifier to accurately and quickly determined the contact profile. SCT's contribution is two-fold: a) classification of the user's contact as high/low-risk using precise proximity sensing, and b) user anonymity using a privacy-preserving communications protocol. SCT leverages BLE's non-connectable advertising feature to broadcast a signature packet when the user is in the public space. Both broadcasted and observed signatures are stored in the user's smartphone and they are only uploaded to a secure signature database when a user is confirmed by public health authorities to be infected. Using received signal strength (RSS) each smartphone estimates its distance from other user's phones and issues real-time alerts when social distancing rules are violated. The paper includes extensive experimentation utilizing real-life smartphone positions and a comparative evaluation of five machine learning classifiers. Reported results indicate that a decision tree classifier outperforms other states of the art classification methods in terms of accuracy. Lastly, to facilitate research in this area, and to contribute to the timely development of advanced solutions the entire data set of six experiments with about 123,000 data points is made publicly available.

Pai Chet Ng, Petros Spachos, Konstantinos Plataniotis

2020-05-28

Public Health Public Health

Using Reports of Own and Others' Symptoms and Diagnosis on Social Media to Predict COVID-19 Case Counts: Observational Infoveillance Study in Mainland China.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : COVID-19 has affected more than 200 countries and territories worldwide. It poses an extraordinary challenge for public health systems, because screening and surveillance capacity-especially during the beginning of the outbreak-is often severely limited, fueling the outbreak as many patients unknowingly infect others.

OBJECTIVE : We present an effort to collect and analyze COVID-19 related posts on the popular Twitter-like social media site in China, Weibo. To our knowledge, this infoveillance study employs the largest, most comprehensive and fine-grained social media data to date to predict COVID-19 case counts in mainland China.

METHODS : We built a Weibo user pool of 250 million, approximately half of the entire monthly active Weibo user population. Using a comprehensive list of 167 keywords, we retrieved and analyzed around 15 million COVID-19 related posts from our user pool, from November 1, 2019 to March 31, 2020. We developed a machine learning classifier to identify "sick posts," which are reports of one's own and other people's symptoms and diagnosis related to COVID-19. Using officially reported case counts as the outcome, we then estimated the Granger causality of sick posts and other COVID-19 posts on daily case counts. For a subset of geotagged posts (3.10% of all retrieved posts), we also ran separate predictive models for Hubei province, the epicenter of the initial outbreak, and the rest of mainland China.

RESULTS : We found that reports of symptoms and diagnosis of COVID-19 significantly predicted daily case counts, up to 14 days ahead of official statistics, whereas other COVID-19 posts did not have similar predictive power. For the subset of geotagged posts, we found that the predictive pattern held true for both Hubei province and the rest of mainland China, regardless of unequal distribution of healthcare resources and outbreak timeline.

CONCLUSIONS : Public social media data can be usefully harnessed to predict infection cases and inform timely responses. Researchers and disease control agencies should pay close attention to the social media infosphere regarding COVID-19. On top of monitoring overall search and posting activities, leveraging machine learning approaches and theoretical understandings of information sharing behaviors is a promising approach to identifying true disease signals and improving the effectiveness of infoveillance.

CLINICALTRIAL :

Shen Cuihua, Chen Anfan, Luo Chen, Zhang Jingwen, Feng Bo, Liao Wang

2020-May-25

General General

An up-to-date overview of computational polypharmacology in modern drug discovery.

In Expert opinion on drug discovery ; h5-index 34.0

INTRODUCTION : In recent years, computational polypharmacology has gained significant attention to study the promiscuous nature of drugs. Despite tremendous challenges, community-wide efforts have led to a variety of novel approaches for predicting drug polypharmacology. In particular, some rapid advances using machine learning and artificial intelligence have been reported with great success.

AREAS COVERED : In this article, the authors provide a comprehensive update on the current state-of-the-art polypharmacology approaches and their applications, focusing on those reports published after our 2017 review article. The authors particularly discuss some novel, groundbreaking concepts, and methods that have been developed recently and applied to drug polypharmacology studies.

EXPERT OPINION : Polypharmacology is evolving and novel concepts are being introduced to counter the current challenges in the field. However, major hurdles remain including incompleteness of high-quality experimental data, lack of in vitro and in vivo assays to characterize multi-targeting agents, shortage of robust computational methods, and challenges to identify the best target combinations and design effective multi-targeting agents. Fortunately, numerous national/international efforts including multi-omics and artificial intelligence initiatives as well as most recent collaborations on addressing the COVID-19 pandemic have shown significant promise to propel the field of polypharmacology forward.

Chaudhari Rajan, Fong Long Wolf, Tan Zhi, Huang Beibei, Zhang Shuxing

2020-May-26

Drug Polypharmacology, artificial Intelligence, deep Learning, drug Repurposing, molecular Promiscuity, multi-omics, multi-targeting Design, network Pharmacology, off-targets

Public Health Public Health

Role of biological Data Mining and Machine Learning Techniques in Detecting and Diagnosing the Novel Coronavirus (COVID-19): A Systematic Review.

In Journal of medical systems ; h5-index 48.0

Coronaviruses (CoVs) are a large family of viruses that are common in many animal species, including camels, cattle, cats and bats. Animal CoVs, such as Middle East respiratory syndrome-CoV, severe acute respiratory syndrome (SARS)-CoV, and the new virus named SARS-CoV-2, rarely infect and spread among humans. On January 30, 2020, the International Health Regulations Emergency Committee of the World Health Organisation declared the outbreak of the resulting disease from this new CoV called 'COVID-19', as a 'public health emergency of international concern'. This global pandemic has affected almost the whole planet and caused the death of more than 315,131 patients as of the date of this article. In this context, publishers, journals and researchers are urged to research different domains and stop the spread of this deadly virus. The increasing interest in developing artificial intelligence (AI) applications has addressed several medical problems. However, such applications remain insufficient given the high potential threat posed by this virus to global public health. This systematic review addresses automated AI applications based on data mining and machine learning (ML) algorithms for detecting and diagnosing COVID-19. We aimed to obtain an overview of this critical virus, address the limitations of utilising data mining and ML algorithms, and provide the health sector with the benefits of this technique. We used five databases, namely, IEEE Xplore, Web of Science, PubMed, ScienceDirect and Scopus and performed three sequences of search queries between 2010 and 2020. Accurate exclusion criteria and selection strategy were applied to screen the obtained 1305 articles. Only eight articles were fully evaluated and included in this review, and this number only emphasised the insufficiency of research in this important area. After analysing all included studies, the results were distributed following the year of publication and the commonly used data mining and ML algorithms. The results found in all papers were discussed to find the gaps in all reviewed papers. Characteristics, such as motivations, challenges, limitations, recommendations, case studies, and features and classes used, were analysed in detail. This study reviewed the state-of-the-art techniques for CoV prediction algorithms based on data mining and ML assessment. The reliability and acceptability of extracted information and datasets from implemented technologies in the literature were considered. Findings showed that researchers must proceed with insights they gain, focus on identifying solutions for CoV problems, and introduce new improvements. The growing emphasis on data mining and ML techniques in medical fields can provide the right environment for change and improvement.

Albahri A S, Hamid Rula A, Alwan Jwan K, Al-Qays Z T, Zaidan A A, Zaidan B B, Albahri A O S, AlAmoodi A H, Khlaf Jamal Mawlood, Almahdi E M, Thabet Eman, Hadi Suha M, Mohammed K I, Alsalem M A, Al-Obaidi Jameel R, Madhloom H T

2020-May-25

Artificial Intelligence, Biological Data Mining, COVID-19, Coronaviruses, MERS-CoV, Machine Learning, SARS-CoV-2

General General

An AI approach to COVID-19 infection risk assessment in virtual visits: a case report.

In Journal of the American Medical Informatics Association : JAMIA

OBJECTIVE : In an effort to improve the efficiency of computer algorithms applied to screening for COVID-19 testing, we used natural language processing (NLP) and artificial intelligence (AI)-based methods with unstructured patient data collected through telehealth visits.

METHODS : After segmenting and parsing documents, we conducted analysis of overrepresented words in patient symptoms. We then developed a word embedding-based convolutional neural network for predicting COVID-19 test results based on patients' self-reported symptoms.

RESULTS : Text analytics revealed that concepts such as "smell" and "taste" were more prevalent than expected in patients testing positive. As a result, screening algorithms were adapted to include these symptoms. The deep learning model yielded an AUC of 0.729 for predicting positive results and was subsequently applied to prioritize testing appointment scheduling.

DISCUSSION : Informatics tools such as NLP and AI methods can have significant clinical impacts when applied to data streams early in the development of clinical systems for outbreak response.

Obeid Jihad S, Davis Matthew, Turner Matthew, Meystre Stephane M, Heider Paul M, Lenert Leslie A

2020-May-25

AI, COVID-19, artificial intelligence, risk assessment, text analytics

Radiology Radiology

Towards computer-aided severity assessment: training and validation of deep neural networks for geographic extent and opacity extent scoring of chest X-rays for SARS-CoV-2 lung disease severity

ArXiv Preprint

Background: A critical step in effective care and treatment planning for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the assessment of the severity of disease progression. Chest x-rays (CXRs) are often used to assess SARS-CoV-2 severity, with two important assessment metrics being extent of lung involvement and degree of opacity. In this proof-of-concept study, we assess the feasibility of computer-aided scoring of CXRs of SARS-CoV-2 lung disease severity using a deep learning system. Materials and Methods: Data consisted of 130 CXRs from SARS-CoV-2 positive patient cases from the Cohen study. Geographic extent and opacity extent were scored by two board-certified expert chest radiologists (with 20+ years of experience) and a 2nd-year radiology resident. The deep neural networks used in this study are based on a COVID-Net network architecture. 100 versions of the network were independently learned (50 to perform geographic extent scoring and 50 to perform opacity extent scoring) using random subsets of CXRs from the Cohen study, and evaluated the networks using stratified Monte Carlo cross-validation experiments. Findings: The deep neural networks yielded R$^2$ of 0.673 $\pm$ 0.004 and 0.636 $\pm$ 0.002 between predicted scores and radiologist scores for geographic extent and opacity extent, respectively, in stratified Monte Carlo cross-validation experiments. The best performing networks achieved R$^2$ of 0.865 and 0.746 between predicted scores and radiologist scores for geographic extent and opacity extent, respectively. Interpretation: The results are promising and suggest that the use of deep neural networks on CXRs could be an effective tool for computer-aided assessment of SARS-CoV-2 lung disease severity, although additional studies are needed before adoption for routine clinical use.

Alexander Wong, Zhong Qiu Lin, Linda Wang, Audrey G. Chung, Beiyi Shen, Almas Abbasi, Mahsa Hoshmand-Kochi, Timothy Q. Duong

2020-05-26

General General

BBMRI-ERIC's contributions to research and knowledge exchange on COVID-19.

In European journal of human genetics : EJHG

During the COVID-19 pandemic, the European biobanking infrastructure is in a unique position to preserve valuable biological material complemented with detailed data for future research purposes. Biobanks can be either integrated into healthcare, where preservation of the biological material is a fork in clinical routine diagnostics and medical treatment processes or they can also host prospective cohorts or material related to clinical trials. The paper discussed objectives of BBMRI-ERIC, the European research infrastructure established to facilitate access to quality-defined biological materials and data for research purposes, with respect to the COVID-19 crisis: (a) to collect information on available European as well as non-European COVID-19-relevant biobanking resources in BBMRI-ERIC Directory and to facilitate access to these via BBMRI-ERIC Negotiator platform; (b) to help harmonizing guidelines on how data and biological material is to be collected to maximize utility for future research, including large-scale data processing in artificial intelligence, by participating in activities such as COVID-19 Host Genetics Initiative; (c) to minimize risks for all involved parties dealing with (potentially) infectious material by developing recommendations and guidelines; (d) to provide a European-wide platform of exchange in relation to ethical, legal, and societal issues (ELSI) specific to the collection of biological material and data during the COVID-19 pandemic.

Holub Petr, Kozera Lukasz, Florindi Francesco, van Enckevort Esther, Swertz Morris, Reihs Robert, Wutte Andrea, Valík Dalibor, Mayrhofer Michaela Th

2020-May-22

Radiology Radiology

A Fully Automatic Deep Learning System for COVID-19 Diagnostic and Prognostic Analysis.

In The European respiratory journal

Coronavirus disease 2019 (COVID-19) has spread globally, and medical resources become insufficient in many regions. Fast diagnosis of COVID-19, and finding high-risk patients with worse prognosis for early prevention and medical resources optimisation is important. Here, we proposed a fully automatic deep learning system for COVID-19 diagnostic and prognostic analysis by routinely used computed tomography.We retrospectively collected 5372 patients with computed tomography images from 7 cities or provinces. Firstly, 4106 patients with computed tomography images were used to pre-train the DL system, making it learn lung features. Afterwards, 1266 patients (924 with COVID-19, and 471 had follow-up for 5+ days; 342 with other pneumonia) from 6 cities or provinces were enrolled to train and externally validate the performance of the deep learning system.In the 4 external validation sets, the deep learning system achieved good performance in identifying COVID-19 from other pneumonia (AUC=0.87 and 0.88) and viral pneumonia (AUC=0.86). Moreover, the deep learning system succeeded to stratify patients into high-risk and low-risk groups whose hospital-stay time have significant difference (p=0.013 and 0.014). Without human-assistance, the deep learning system automatically focused on abnormal areas that showed consistent characteristics with reported radiological findings.Deep learning provides a convenient tool for fast screening COVID-19 and finding potential high-risk patients, which may be helpful for medical resource optimisation and early prevention before patients show severe symptoms.

Wang Shuo, Zha Yunfei, Li Weimin, Wu Qingxia, Li Xiaohu, Niu Meng, Wang Meiyun, Qiu Xiaoming, Li Hongjun, Yu He, Gong Wei, Bai Yan, Li Li, Zhu Yongbei, Wang Liusu, Tian Jie

2020-May-22

Radiology Radiology

Intensive Care Risk Estimation in COVID-19 Pneumonia Based on Clinical and Imaging Parameters: Experiences from the Munich Cohort.

In Journal of clinical medicine

The evolving dynamics of coronavirus disease 2019 (COVID-19) and the increasing infection numbers require diagnostic tools to identify patients at high risk for a severe disease course. Here we evaluate clinical and imaging parameters for estimating the need of intensive care unit (ICU) treatment. We collected clinical, laboratory and imaging data from 65 patients with confirmed COVID-19 infection based on polymerase chain reaction (PCR) testing. Two radiologists evaluated the severity of findings in computed tomography (CT) images on a scale from 1 (no characteristic signs of COVID-19) to 5 (confluent ground glass opacities in over 50% of the lung parenchyma). The volume of affected lung was quantified using commercially available software. Machine learning modelling was performed to estimate the risk for ICU treatment. Patients with a severe course of COVID-19 had significantly increased interleukin (IL)-6, C-reactive protein (CRP), and leukocyte counts and significantly decreased lymphocyte counts. The radiological severity grading was significantly increased in ICU patients. Multivariate random forest modelling showed a mean ± standard deviation sensitivity, specificity and accuracy of 0.72 ± 0.1, 0.86 ± 0.16 and 0.80 ± 0.1 and a receiver operating characteristic-area under curve (ROC-AUC) of 0.79 ± 0.1. The need for ICU treatment is independently associated with affected lung volume, radiological severity score, CRP, and IL-6.

Burian Egon, Jungmann Friederike, Kaissis Georgios A, Lohöfer Fabian K, Spinner Christoph D, Lahmer Tobias, Treiber Matthias, Dommasch Michael, Schneider Gerhard, Geisler Fabian, Huber Wolfgang, Protzer Ulrike, Schmid Roland M, Schwaiger Markus, Makowski Marcus R, Braren Rickmer F

2020-May-18

COVID-19, clinical parameters, computed tomography, intensive care unit, radiological parameters, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)

General General

GeoCoV19: A Dataset of Hundreds of Millions of Multilingual COVID-19 Tweets with Location Information

ArXiv Preprint

The past several years have witnessed a huge surge in the use of social media platforms during mass convergence events such as health emergencies, natural or human-induced disasters. These non-traditional data sources are becoming vital for disease forecasts and surveillance when preparing for epidemic and pandemic outbreaks. In this paper, we present GeoCoV19, a large-scale Twitter dataset containing more than 524 million multilingual tweets posted over a period of 90 days since February 1, 2020. Moreover, we employ a gazetteer-based approach to infer the geolocation of tweets. We postulate that this large-scale, multilingual, geolocated social media data can empower the research communities to evaluate how societies are collectively coping with this unprecedented global crisis as well as to develop computational methods to address challenges such as identifying fake news, understanding communities' knowledge gaps, building disease forecast and surveillance models, among others.

Umair Qazi, Muhammad Imran, Ferda Ofli

2020-05-22

Radiology Radiology

A review on the use of artificial intelligence for medical imaging of the lungs of patients with coronavirus disease 2019.

In Diagnostic and interventional radiology (Ankara, Turkey)

The results of research on the use of artificial intelligence (AI) for medical imaging of the lungs of patients with coronavirus disease 2019 (COVID-19) has been published in various forms. In this study, we reviewed the AI for diagnostic imaging of COVID-19 pneumonia. PubMed, arXiv, medRxiv, and Google scholar were used to search for AI studies. There were 15 studies of COVID-19 that used AI for medical imaging. Of these, 11 studies used AI for computed tomography (CT) and 4 used AI for chest radiography. Eight studies presented independent test data, 5 used disclosed data, and 4 disclosed the AI source codes. The number of datasets ranged from 106 to 5941, with sensitivities ranging from 0.67-1.00 and specificities ranging from 0.81-1.00 for prediction of COVID-19 pneumonia. Four studies with independent test datasets showed a breakdown of the data ratio and reported prediction of COVID-19 pneumonia with sensitivity, specificity, and area under the curve (AUC). These 4 studies showed very high sensitivity, specificity, and AUC, in the range of 0.9-0.98, 0.91-0.96, and 0.96-0.99, respectively.

Ito Rintaro, Iwano Shingo, Naganawa Shinji

2020-May-21

Radiology Radiology

SODA: Detecting Covid-19 in Chest X-rays with Semi-supervised Open Set Domain Adaptation

ArXiv Preprint

The global pandemic of COVID-19 has infected millions of people since its first outbreak in last December. A key challenge for preventing and controlling COVID-19 is how to quickly, widely, and effectively implement the test for the disease, because testing is the first step to break the chains of transmission. To assist the diagnosis of the disease, radiology imaging is used to complement the screening process and triage patients into different risk levels. Deep learning methods have taken a more active role in automatically detecting COVID-19 disease in chest x-ray images, as witnessed in many recent works. Most of these works first train a CNN on an existing large-scale chest x-ray image dataset and then fine-tune it with a COVID-19 dataset at a much smaller scale. However, direct transfer across datasets from different domains may lead to poor performance due to visual domain shift. Also, the small scale of the COVID-19 dataset on the target domain can make the training fall into the overfitting trap. To solve all these crucial problems and fully exploit the available large-scale chest x-ray image dataset, we formulate the problem of COVID-19 chest x-ray image classification in a semi-supervised open set domain adaptation setting, through which we are motivated to reduce the domain shift and avoid overfitting when training on a very small dataset of COVID-19. In addressing this formulated problem, we propose a novel Semi-supervised Open set Domain Adversarial network (SODA), which is able to align the data distributions across different domains in a general domain space and also in a common subspace of source and target data. In our experiments, SODA achieves a leading classification performance compared with recent state-of-the-art models, as well as effectively separating COVID-19 with common pneumonia.

Jieli Zhou, Baoyu Jing, Zeya Wang

2020-05-22

Public Health Public Health

No Place Like Home: A Cross-National Assessment of the Efficacy of Social Distancing during the COVID-19 Pandemic.

In JMIR public health and surveillance

BACKGROUND : In the absence of a cure in the time of pandemics, social distancing measures seem to be the most effective intervention to slow down the spread of disease. Various simulation-based studies have been conducted in the past to investigate the effectiveness of such measures. While those studies unanimously confirm the mitigating effect of social distancing on the disease spread, the reported effectiveness varies from 10% to more than 90% reduction in the number of infections. This level of uncertainty is mostly due to the complex dynamics of epidemics and their time-variant parameters. A real transactional data, however, can reduce the uncertainty and provide a less noisy picture of social distancing effectiveness.

OBJECTIVE : In this paper, we integrate multiple transactional data sets (GPS mobility data from Google and Apple as well as disease statistics data from ECDC) to study the role of social distancing policies in 26 countries wherein the transmission rate of the COVID-19 pandemic is analyzed over the course of five weeks.

METHODS : Relying on the SIR model and official COVID-19 reports, we first calculated the weekly transmission rate (β) of the coronavirus disease in 26 countries for five consecutive weeks. Then we integrated that with the Google's and Apple's mobility data sets for the same time frame and used a machine learning approach to investigate the relationship between mobility factors and β values.

RESULTS : Gradient Boosted Trees (GBT) regression analysis showed that changes in mobility patterns, resulted from social distancing policies, explain around 47% of the variation in the disease transmission rate.

CONCLUSIONS : Consistent with simulation-based studies, real cross-national transactional data confirms the effectiveness of social distancing interventions in slowing down the spread of the disease. Apart from providing less noisy and more generalizable support for the whole social distancing idea, we provide specific insights for public health policy-makers as to what locations should be given a higher priority for enforcing social distancing measures.

CLINICALTRIAL :

Delen Dursun, Eryarsoy Enes, Davazdahemami Behrooz

2020-May-20

Radiology Radiology

Artificial intelligence-enabled rapid diagnosis of patients with COVID-19.

In Nature medicine ; h5-index 170.0

For diagnosis of coronavirus disease 2019 (COVID-19), a SARS-CoV-2 virus-specific reverse transcriptase polymerase chain reaction (RT-PCR) test is routinely used. However, this test can take up to 2 d to complete, serial testing may be required to rule out the possibility of false negative results and there is currently a shortage of RT-PCR test kits, underscoring the urgent need for alternative methods for rapid and accurate diagnosis of patients with COVID-19. Chest computed tomography (CT) is a valuable component in the evaluation of patients with suspected SARS-CoV-2 infection. Nevertheless, CT alone may have limited negative predictive value for ruling out SARS-CoV-2 infection, as some patients may have normal radiological findings at early stages of the disease. In this study, we used artificial intelligence (AI) algorithms to integrate chest CT findings with clinical symptoms, exposure history and laboratory testing to rapidly diagnose patients who are positive for COVID-19. Among a total of 905 patients tested by real-time RT-PCR assay and next-generation sequencing RT-PCR, 419 (46.3%) tested positive for SARS-CoV-2. In a test set of 279 patients, the AI system achieved an area under the curve of 0.92 and had equal sensitivity as compared to a senior thoracic radiologist. The AI system also improved the detection of patients who were positive for COVID-19 via RT-PCR who presented with normal CT scans, correctly identifying 17 of 25 (68%) patients, whereas radiologists classified all of these patients as COVID-19 negative. When CT scans and associated clinical history are available, the proposed AI system can help to rapidly diagnose COVID-19 patients.

Mei Xueyan, Lee Hao-Chih, Diao Kai-Yue, Huang Mingqian, Lin Bin, Liu Chenyu, Xie Zongyu, Ma Yixuan, Robson Philip M, Chung Michael, Bernheim Adam, Mani Venkatesh, Calcagno Claudia, Li Kunwei, Li Shaolin, Shan Hong, Lv Jian, Zhao Tongtong, Xia Junli, Long Qihua, Steinberger Sharon, Jacobi Adam, Deyer Timothy, Luksza Marta, Liu Fang, Little Brent P, Fayad Zahi A, Yang Yang

2020-May-19

General General

Digital Translucence: Adapting Telemedicine Delivery Post-COVID-19.

In Telemedicine journal and e-health : the official journal of the American Telemedicine Association

In nearly 1 month, with a rapidly expanding corona virus disease 2019 (COVID-19), telemedicine has been transformed into an essential service for delivering routine clinical care. This transformation occurred as a crisis management response-driven by the need to provide care for patients with physical distancing measures in place. However, the current rapid adoption of telemedicine presents a transitional state between one that existed before the pandemic and one that could potentially be better aligned with the delivery of a personalized model of care. Using the conceptual framework of digital translucence-situating virtual encounters with more nuanced information regarding patients-we describe the role of integrated remote monitoring and virtual care tools aligned with the patient's electronic health record for adapting telemedicine delivery post-COVID-19.

Kannampallil Thomas, Ma Jun

2020-May-18

artificial intelligence, electronic health records, m-health, pandemic, telemedicine

Radiology Radiology

An automated Residual Exemplar Local Binary Pattern and iterative ReliefF based corona detection method using lung X-ray image.

In Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society

Coronavirus is normally transmitted from animal to person, but nowadays it is transmitted from person to person by changing its form. Covid-19 appeared as a very dangerous virus and unfortunately caused a worldwide pandemic disease. Radiology doctors use X-ray or CT images for the diagnosis of Covid-19. It has become crucial to help diagnose such images using image processing methods. Therefore, we proposed a novel intelligent computer vision method to automatically detect the Covid-19 virus. The proposed automatic Covid-19 detection method consists of preprocessing, feature extraction and feature selection stages. Image resizing and grayscale conversion are used in the preprocessing phase. The proposed feature generation method is called as Residual Exemplar Local Binary Pattern (ResExLBP). In the feature selection phase, a novel iterative ReliefF (IRF) based feature selection is used. Decision tree (DT), linear discriminant (LD), support vector machine (SVM), k nearest neighborhood (kNN) and subspace discriminant (SD) methods are chosen as classifiers in the classification phase. Leave one out cross-validation (LOOCV) and 10-fold cross-validation are used for training and testing. In this work, SVM classifier achieved 100.0% classification accuracy by using 10-fold cross-validation. This result clearly has shown that we reached the perfect classification rate by using X-ray image for Covid-19 detection.

Tuncer Turker, Dogan Sengul, Ozyurt Fatih

2020-May-18

Classification, Covid-19, Iterative ReliefF, Machine learning, Residual exemplar LBP

General General

Artificial intelligence approach fighting COVID-19 with repurposing drugs.

In Biomedical journal

Background : The ongoing COVID-19 pandemic has caused more than 193,825 deaths during the past few months. A quick-to-be-identified cure for the disease will be a therapeutic medicine that has prior use experiences in patients in order to resolve the current pandemic situation before it could become worsening. Artificial intelligence (AI) technology is hereby applied to identify the marketed drugs with potential for treating COVID-19.

Material and methods : An AI platform was established to identify potential old drugs with anti-coronavirus activities by using two different learning databases; one consisted of the compounds reported or proven active against SARS-CoV, SARS-CoV-2, human immunodeficiency virus, influenza virus, and the other one containing the known 3C-like protease inhibitors. All AI predicted drugs were then tested for activities against a feline coronavirus in in vitro cell-based assay. These assay results were feedbacks to the AI system for relearning and thus to generate a modified AI model to search for old drugs again.

Results : After a few runs of AI learning and prediction processes, the AI system identified 80 marketed drugs with potential. Among them, 8 drugs (bedaquiline, brequinar, celecoxib, clofazimine, conivaptan, gemcitabine, tolcapone, and vismodegib) showed in vitro activities against the proliferation of a feline infectious peritonitis (FIP) virus in Fcwf-4 cells. In addition, 5 other drugs (boceprevir, chloroquine, homoharringtonine, tilorone, and salinomycin) were also found active during the exercises of AI approaches.

Conclusion : Having taken advantages of AI, we identified old drugs with activities against FIP coronavirus. Further studies are underway to demonstrate their activities against SARS-CoV-2 in vitro and in vivo at clinically achievable concentrations and doses. With prior use experiences in patients, these old drugs if proven active against SARS-CoV-2 can readily be applied for fighting COVID-19 pandemic.

Ke Yi-Yu, Peng Tzu-Ting, Yeh Teng-Kuang, Huang Wen-Zheng, Chang Shao-En, Wu Szu-Huei, Hung Hui-Chen, Hsu Tsu-An, Lee Shiow-Ju, Song Jeng-Shin, Lin Wen-Hsing, Chiang Tung-Jung, Lin Jiunn-Horng, Sytwu Huey-Kang, Chen Chiung-Tong

2020-May-15

AI, COVID-19, DNN, Drug repurposing, Feline coronavirus, SARS-CoV-2

Cardiology Cardiology

Cardiovascular implications of the COVID-19 pandemic: a global perspective.

In The Canadian journal of cardiology

The Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), represents the pandemic of the century, with approximately 3.5 million cases and 250,000 deaths worldwide as of May 2020. Although respiratory symptoms usually dominate the clinical presentation, COVID-19 is now known to also have potentially serious cardiovascular consequences, including myocardial injury, myocarditis, acute coronary syndromes, pulmonary embolism, stroke, arrhythmias, heart failure, and cardiogenic shock. The cardiac manifestations of COVID-19 might be related to the adrenergic drive, systemic inflammatory milieu and cytokine-release syndrome caused by SARS-CoV-2, direct viral infection of myocardial and endothelial cells, hypoxia due to respiratory failure, electrolytic imbalances, fluid overload, and side effects of certain COVID-19 medications. COVID-19 has profoundly reshaped usual care of both ambulatory and acute cardiac patients, by leading to the cancellation of elective procedures and by reducing the efficiency of existing pathways of urgent care, respectively. Decreased utilization of healthcare services for acute conditions by non-COVID-19 patients has also been reported and attributed to concerns about acquiring in-hospital infection. Innovative approaches that leverage modern technologies to tackle the COVID-19 pandemic have been introduced, which include telemedicine, dissemination of educational material over social media, smartphone apps for case tracking, and artificial intelligence for pandemic modelling, among others. This article provides a comprehensive overview of the pathophysiology and cardiovascular implications of COVID-19, its impact on existing pathways of care, the role of modern technologies to tackle the pandemic, and a proposal of novel management algorithms for the most common acute cardiac conditions.

Boukhris Marouane, Hillani Ali, Moroni Francesco, Annabi Mohamed Salah, Addad Faouzi, Ribeiro Marcelo Harada, Mansour Samer, Zhao Xiaohui, Ybarra Luiz Fernando, Abbate Antonio, Vilca Luz Maria, Azzalini Lorenzo

2020-May-16

Public Health Public Health

Artificial Intelligence-Empowered Mobilization of Assessments in COVID-19-like Pandemics: A Case Study for Early Flattening of the Curve.

In International journal of environmental research and public health ; h5-index 73.0

The global outbreak of the Coronavirus Disease 2019 (COVID-19) pandemic has uncovered the fragility of healthcare and public health preparedness and planning against epidemics/pandemics. In addition to the medical practice for treatment and immunization, it is vital to have a thorough understanding of community spread phenomena as related research reports 17.9-30.8% confirmed cases to remain asymptomatic. Therefore, an effective assessment strategy is vital to maximize tested population in a short amount of time. This article proposes an Artificial Intelligence (AI)-driven mobilization strategy for mobile assessment agents for epidemics/pandemics. To this end, a self-organizing feature map (SOFM) is trained by using data acquired from past mobile crowdsensing (MCS) campaigns to model mobility patterns of individuals in multiple districts of a city so to maximize the assessed population with minimum agents in the shortest possible time. Through simulation results for a real street map on a mobile crowdsensing simulator and considering the worst case analysis, it is shown that on the 15th day following the first confirmed case in the city under the risk of community spread, AI-enabled mobilization of assessment centers can reduce the unassessed population size down to one fourth of the unassessed population under the case when assessment agents are randomly deployed over the entire city.

Simsek Murat, Kantarci Burak

2020-May-14

Artificial Intelligence, COVID-19, epidemics, mobile assessment centers, neural networks, optimum route planning, pandemics, public health, self-organizing feature map

Pathology Pathology

Deep learning classification of chest x-ray images

ArXiv Preprint

We propose a deep learning based method for classification of commonly occurring pathologies in chest X-ray images. The vast number of publicly available chest X-ray images provides the data necessary for successfully employing deep learning methodologies to reduce the misdiagnosis of thoracic diseases. We applied our method to the classification of two example pathologies, pulmonary nodules and cardiomegaly, and we compared the performance of our method to three existing methods. The results show an improvement in AUC for detection of nodules and cardiomegaly compared to the existing methods.

Mohammad S. Majdi, Khalil N. Salman, Michael F. Morris, Nirav C. Merchant, Jeffrey J. Rodriguez

2020-05-19

General General

The Effect of Moderation on Online Mental Health Conversations

ArXiv Preprint

Many people struggling with mental health issues are unable to access adequate care due to high costs and a shortage of mental health professionals, leading to a global mental health crisis. Online mental health communities can help mitigate this crisis by offering a scalable, easily accessible alternative to in-person sessions with therapists or support groups. However, people seeking emotional or psychological support online may be especially vulnerable to the kinds of antisocial behavior that sometimes occur in online discussions. Moderation can improve online discourse quality, but we lack an understanding of its effects on online mental health conversations. In this work, we leveraged a natural experiment, occurring across 200,000 messages from 7,000 conversations hosted on a mental health mobile application, to evaluate the effects of moderation on online mental health discussions. We found that participation in group mental health discussions led to improvements in psychological perspective, and that these improvements were larger in moderated conversations. The presence of a moderator increased user engagement, encouraged users to discuss negative emotions more candidly, and dramatically reduced bad behavior among chat participants. Moderation also encouraged stronger linguistic coordination, which is indicative of trust building. In addition, moderators who remained active in conversations were especially successful in keeping conversations on topic. Our findings suggest that moderation can serve as a valuable tool to improve the efficacy and safety of online mental health conversations. Based on these findings, we discuss implications and trade-offs involved in designing effective online spaces for mental health support.

David Wadden, Tal August, Qisheng Li, Tim Althoff

2020-05-19

Dermatology Dermatology

The Skincare project, an interactive deep learning system for differential diagnosis of malignant skin lesions. Technical Report

ArXiv Preprint

A shortage of dermatologists causes long wait times for patients who seek dermatologic care. In addition, the diagnostic accuracy of general practitioners has been reported to be lower than the accuracy of artificial intelligence software. This article describes the Skincare project (H2020, EIT Digital). Contributions include enabling technology for clinical decision support based on interactive machine learning (IML), a reference architecture towards a Digital European Healthcare Infrastructure (also cf. EIT MCPS), technical components for aggregating digitised patient information, and the integration of decision support technology into clinical test-bed environments. However, the main contribution is a diagnostic and decision support system in dermatology for patients and doctors, an interactive deep learning system for differential diagnosis of malignant skin lesions. In this article, we describe its functionalities and the user interfaces to facilitate machine learning from human input. The baseline deep learning system, which delivers state-of-the-art results and the potential to augment general practitioners and even dermatologists, was developed and validated using de-identified cases from a dermatology image data base (ISIC), which has about 20000 cases for development and validation, provided by board-certified dermatologists defining the reference standard for every case. ISIC allows for differential diagnosis, a ranked list of eight diagnoses, that is used to plan treatments in the common setting of diagnostic ambiguity. We give an overall description of the outcome of the Skincare project, and we focus on the steps to support communication and coordination between humans and machine in IML. This is an integral part of the development of future cognitive assistants in the medical domain, and we describe the necessary intelligent user interfaces.

Daniel Sonntag, Fabrizio Nunnari, Hans-Jürgen Profitlich

2020-05-19

Radiology Radiology

Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography.

In Cell ; h5-index 250.0

Many COVID-19 patients infected by SARS-CoV-2 virus develop pneumonia (called novel coronavirus pneumonia, NCP) and rapidly progress to respiratory failure. However, rapid diagnosis and identification of high-risk patients for early intervention are challenging. Using a large computed tomography (CT) database from 3,777 patients, we developed an AI system that can diagnose NCP and differentiate it from other common pneumonia and normal controls. The AI system can assist radiologists and physicians in performing a quick diagnosis especially when the health system is overloaded. Significantly, our AI system identified important clinical markers that correlated with the NCP lesion properties. Together with the clinical data, our AI system was able to provide accurate clinical prognosis that can aid clinicians to consider appropriate early clinical management and allocate resources appropriately. We have made this AI system available globally to assist the clinicians to combat COVID-19.

Zhang Kang, Liu Xiaohong, Shen Jun, Li Zhihuan, Sang Ye, Wu Xingwang, Zha Yunfei, Liang Wenhua, Wang Chengdi, Wang Ke, Ye Linsen, Gao Ming, Zhou Zhongguo, Li Liang, Wang Jin, Yang Zehong, Cai Huimin, Xu Jie, Yang Lei, Cai Wenjia, Xu Wenqin, Wu Shaoxu, Zhang Wei, Jiang Shanping, Zheng Lianghong, Zhang Xuan, Wang Li, Lu Liu, Li Jiaming, Yin Haiping, Wang Winston, Li Oulan, Zhang Charlotte, Liang Liang, Wu Tao, Deng Ruiyun, Wei Kang, Zhou Yong, Chen Ting, Lau Johnson Yiu-Nam, Fok Manson, He Jianxing, Lin Tianxin, Li Weimin, Wang Guangyu

2020-May-04

AI, COVID-19, SARS-CoV-2, automated diagnosis, computed tomography, deep learning, pneumonia, prognosis analysis

General General

Robust computational design and evaluation of peptide vaccines for cellular immunity with application to SARS-CoV-2

bioRxiv Preprint

We present a combinatorial machine learning method to evaluate and optimize peptide vaccine formulations, and we find for SARS-CoV-2 that it provides superior predicted display of viral epitopes by MHC class I and MHC class II molecules over populations when compared to other candidate vaccines. Our method is robust to idiosyncratic errors in the prediction of MHC peptide display and considers target population HLA haplotype frequencies during optimization. To minimize clinical development time our methods validate vaccines with multiple peptide presentation algorithms to increase the probability that a vaccine will be effective. We optimize an objective function that is based on the presentation likelihood of a diverse set of vaccine peptides conditioned on a target population HLA haplotype distribution and expected epitope drift. We produce separate peptide formulations for MHC class I loci (HLA-A, HLA-B, and HLA-C) and class II loci (HLA-DP, HLA-DQ, and HLA-DR) to permit signal sequence based cell compartment targeting using nucleic acid based vaccine platforms. Our SARS-CoV-2 MHC class I vaccine formulations provide 93.21% predicted population coverage with at least five vaccine peptide-HLA hits on average in an individual ([≥] 1 peptide 99.91%) with all vaccine peptides perfectly conserved across 4,690 geographically sampled SARS-CoV-2 genomes. Our MHC class II vaccine formulations provide 90.17% predicted coverage with at least five vaccine peptide-HLA hits on average in an individual with all peptides having observed mutation probability [≤] 0.001. We evaluate 29 previously published peptide vaccine designs with our evaluation tool with the requirement of having at least five vaccine peptide-HLA hits per individual, and they have a predicted maximum of 58.51% MHC class I coverage and 71.65% MHC class II coverage given haplotype based analysis. We provide an open source implementation of our design methods (OptiVax), vaccine evaluation tool (EvalVax), as well as the data used in our design efforts.

Liu, G.; Carter, B.; Bricken, T.; Jain, S.; Viard, M.; Carrington, M.; Gifford, D. K.

2020-05-17

General General

A review of modern technologies for tackling COVID-19 pandemic.

In Diabetes & metabolic syndrome

OBJECTIVE : Science and technology sector constituting of data science, machine learning and artificial intelligence are contributing towards COVID-19. The aim of the present study is to discuss the various aspects of modern technology used to fight against COVID-19 crisis at different scales, including medical image processing, disease tracking, prediction outcomes, computational biology and medicines.

METHODS : A progressive search of the database related to modern technology towards COVID-19 is made. Further, a brief review is done on the extracted information by assessing the various aspects of modern technologies for tackling COVID-19 pandemic.

RESULTS : We provide a window of thoughts on review of the technology advances used to decrease and smother the substantial impact of the outburst. Though different studies relating to modern technology towards COVID-19 have come up, yet there are still constrained applications and contributions of technology in this fight.

CONCLUSIONS : On-going progress in the modern technology has contributed in improving people's lives and hence there is a solid conviction that validated research plans including artificial intelligence will be of significant advantage in helping people to fight this infection.

Kumar Aishwarya, Gupta Puneet Kumar, Srivastava Ankita

2020-May-07

Artificial intelligence, COVID-19, Epidemic, Machine learning

Radiology Radiology

Deep learning-based multi-view fusion model for screening 2019 novel coronavirus pneumonia: A multicentre study.

In European journal of radiology ; h5-index 47.0

PURPOSE : To develop a deep learning-based method to assist radiologists to fast and accurately identify patients with COVID-19 by CT images.

METHODS : We retrospectively collected chest CT images of 495 patients from three hospitals in China. 495 datasets were randomly divided into 395 cases (80%, 294 of COVID-19, 101 of other pneumonia) of the training set, 50 cases (10%, 37 of COVID-19, 13 of other pneumonia) of the validation set and 50 cases (10%, 37 of COVID-19, 13 of other pneumonia) of the testing set. We trained a multi-view fusion model using deep learning network to screen patients with COVID-19 using CT images with the maximum lung regions in axial, coronal and sagittal views. The performance of the proposed model was evaluated by both the validation and testing sets.

RESULTS : The multi-view deep learning fusion model achieved the area under the receiver-operating characteristics curve (AUC) of 0.732, accuracy of 0.700, sensitivity of 0.730 and specificity of 0.615 in validation set. In the testing set, we can achieve AUC, accuracy, sensitivity and specificity of 0.819, 0.760, 0.811 and 0.615 respectively.

CONCLUSIONS : Based on deep learning method, the proposed diagnosis model trained on multi-view images of chest CT images showed great potential to improve the efficacy of diagnosis and mitigate the heavy workload of radiologists for the initial screening of COVID-19 pneumonia.

Wu Xiangjun, Hui Hui, Niu Meng, Li Liang, Wang Li, He Bingxi, Yang Xin, Li Li, Li Hongjun, Tian Jie, Zha Yunfei

2020-May-05

Computed tomography, Coronavirus disease 2019, Deep learning, Multi-view model

General General

Deep learning for classification and localization of COVID-19 markers in point-of-care lung ultrasound.

In IEEE transactions on medical imaging ; h5-index 74.0

Deep learning (DL) has proved successful in medical imaging and, in the wake of the recent COVID-19 pandemic, some works have started to investigate DLbased solutions for the assisted diagnosis of lung diseases. While existing works focus on CT scans, this paper studies the application of DL techniques for the analysis of lung ultrasonography (LUS) images. Specifically, we present a novel fully-annotated dataset of LUS images collected from several Italian hospitals, with labels indicating the degree of disease severity at a frame-level, videolevel, and pixel-level (segmentation masks). Leveraging these data, we introduce several deep models that address relevant tasks for the automatic analysis of LUS images. In particular, we present a novel deep network, derived from Spatial Transformer Networks, which simultaneously predicts the disease severity score associated to a input frame and provides localization of pathological artefacts in a weakly-supervised way. Furthermore, we introduce a new method based on uninorms for effective frame score aggregation at a video-level. Finally, we benchmark state of the art deep models for estimating pixel-level segmentations of COVID-19 imaging biomarkers. Experiments on the proposed dataset demonstrate satisfactory results on all the considered tasks, paving the way to future research on DL for the assisted diagnosis of COVID-19 from LUS data.

Roy Subhankar, Menapace Willi, Oei Sebastiaan, Luijten Ben, Fini Enrico, Saltori Cristiano, Huijben Iris, Chennakeshava Nishith, Mento Federico, Sentelli Alessandro, Peschiera Emanuele, Trevisan Riccardo, Maschietto Giovanni, Torri Elena, Inchingolo Riccardo, Smargiassi Andrea, Soldati Gino, Rota Paolo, Passerini Andrea, Van Sloun Ruud J G, Ricci Elisa, Demi Libertario

2020-May-14

General General

Decreased T cell populations contribute to the increased severity of COVID-19.

In Clinica chimica acta; international journal of clinical chemistry

Background : We observe changes of the main lymphocyte subsets (CD16+CD56、CD19、CD3、CD4、and CD8) in COVID-19-infected patients and explore whether the changes are associated with disease severity.

Methods : One-hundred fifty-four cases of COVID-19-infected patients were selected and divided into 3 groups (moderate group, severe group and critical group). The flow cytometry assay was performed to examine the numbers of lymphocyte subsets.

Results : CD3+, CD4+ and CD8+ T lymphocyte subsets were decreased in COVID-19-infected patients. Compared with the moderate group and the sever group, CD3+, CD4+ and CD8+ T cells in the critical group decreased greatly (P < 0.001, P= 0.005 or P = 0.001).

Conclusions : Reduced CD3+, CD4+, CD8+ T lymphocyte counts may reflect the severity of the COVID-19. Monitoring T cell changes has important implications for the diagnosis and treatment of severe patients who may become critically ill.

Liu Rui, Wang Ying, Li Jie, Han Huan, Xia Zunen, Liu Fang, Wu Kailang, Yang Lan, Liu Xinghui, Zhu Chengliang

2020-May-13

CD3+ T cells, CD4+ T cells, CD8+ T cells, COVID-19, lymphocyte subsets

General General

A chatbot architecture for promoting youth resilience

ArXiv Preprint

E-health technologies have the potential to provide scalable and accessible interventions for youth mental health. As part of a developing an ecosystem of e-screening and e-therapy tools for New Zealand young people, a dialog agent, Headstrong, has been designed to promote resilience with methods grounded in cognitive behavioral therapy and positive psychology. This paper describes the architecture underlying the chatbot. The architecture supports a range of over 20 activities delivered in a 4-week program by relatable personas. The architecture provides a visual authoring interface to its content management system. In addition to supporting the original adolescent resilience chatbot, the architecture has been reused to create a 3-week 'stress-detox' intervention for undergraduates, and subsequently for a chatbot to support young people with the impacts of the COVID-19 pandemic, with all three systems having been used in field trials. The Headstrong architecture illustrates the feasibility of creating a domain-focused authoring environment in the context of e-therapy that supports non-technical expert input and rapid deployment.

Chester Holt-Quick, Jim Warren, Karolina Stasiak, Ruth Williams, Grant Christie, Sarah Hetrick, Sarah Hopkins, Tania Cargo, Sally Merry

2020-05-15

Public Health Public Health

Mining the characteristics of COVID-19 Patients: Based on Social media of China.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : The SARS-CoV-2 epidemic spread rapidly by human-to-human transmission in Wuhan China. Social media, especially, Sina Weibo (a major Chinese microblogging social media platform), has become an important platform for the public to obtain information and express their demand.

OBJECTIVE : This study aims to analyze the characteristics of the suspected or laboratory-confirmed COVID-19 pneumonia patients who asked for help on the Sina Weibo.

METHODS : We conducted data mining on the Chinese social media site Sina Weibo and extracted the data of 485 patients that presented with at least clinical symptoms and imaging descriptions of suspected or laboratory-confirmed COVID-19 pneumonia from 9,878 posts seeking help on Weibo from February 3rd to 20th, 2020.We used descriptive research method to describe the distribution and other epidemiological characteristics of the patients with suspected or laboratory-confirmed SARS-CoV-2 infection. The distance from their home to the nearest hospital was calculated using geographic information systems (ArcGIS).

RESULTS : All patients seeking help on Weibo in this study lived in Wuhan, with a median age of 63.0 years (IQR, 55.0-71.0). Fever (84.12%, 408/485) was the most common symptom. Ground-glass opacity (75.48%, 237/314) was the most common pattern on chest computed tomography.39.67% (167/421) of families had laboratory-confirmed and/or suspected COVID-19 family members. 36.58% (154/421) of families had 1 or 2 laboratory-confirmed and/or suspected family members.70.52% (232/329) of patients needed to rely on their descendants for help. The median time from illness onset to real-time reverse-transcriptase polymerase-chain-reaction (RT-PCR) testing was 8 days (IQR, 5.0-10.0), and the median time from illness onset to online help was 10 days (IQR, 6.0-12.0). 32.22% (155/481) of patients lived more than 3 kilometers away from the nearest hospital.

CONCLUSIONS : The patients seeking help on Weibo lived in Wuhan and most of the patients were elderly. Most of patients had fever symptoms and chest computed tomography mainly manifested as ground-glass opacity pattern. The onset of the disease was characterized by family clustering and most of families lived far from the hospital. Therefore, (1) We recommend that the most stringent centralized medical observation measures should be taken to avoid transmission of family cluster; (2) Social media can help these patients get early attention during the closure of Wuhan. These findings can help the government and the health department identify high-risk patients and accelerate emergency responses following public help demands.

CLINICALTRIAL :

Huang Chunmei, Xu XinJie, Cai Yuyang, Ge QinMin, Zeng GuangWang, Li XiaoPan, Zhang Weide, Ji Chen, Yang Ling

2020-May-12

General General

Using X-ray Images and Deep Learning for Automated Detection of Coronavirus Disease.

In Journal of biomolecular structure & dynamics

Coronavirus is still the leading cause of death worldwide. There are a set number of COVID-19 test units accessible in emergency clinics because of the expanding cases daily. Therefore, it is important to implement an automatic detection and classification system as a speedy elective finding choice to forestall COVID-19 spreading among individuals. Medical images analysis is one of the most promising research areas, it provides facilities for diagnosis and making decisions of a number of diseases such as Coronavirus. This paper conducts a comparative study of the use of the recent deep learning models (VGG16, VGG19, DenseNet201, Inception_ResNet_V2, Inception_V3, Resnet50, and MobileNet_V2) to deal with detection and classification of coronavirus pneumonia. The experiments were conducted using chest X-ray & CT dataset of 6087 images (2780 images of bacterial pneumonia, 1493 of coronavirus, 231 of Covid19, and 1583 normal) and confusion matrices are used to evaluate model performances. Results found out that the use of inception_Resnet_V2 and Densnet201 provide better results compared to other models used in this work (92.18% accuracy for Inception-ResNetV2 and 88.09% accuracy for Densnet201).

Elasnaoui Khalid, Chawki Youness

2020-May-13

CT and X-ray images, Computer-Aided Diagnosis, Coronavirus automatic detection, Covid-19, Deep learning, Pneumonia

General General

India nudges to contain COVID-19 pandemic: a reactive public policy analysis using machine-learning based topic modelling

ArXiv Preprint

India locked down 1.3 billion people on March 25, 2020 in the wake of COVID-19 pandemic. The economic cost of it was estimated at USD 98 billion, while the social costs are still unknown. This study investigated how government formed reactive policies to fight coronavirus across its policy sectors. Primary data was collected from the Press Information Bureau (PIB) in the form press releases of government plans, policies, programme initiatives and achievements. A text corpus of 260,852 words was created from 396 documents from the PIB. An unsupervised machine-based topic modelling using Latent Dirichlet Allocation (LDA) algorithm was performed on the text corpus. It was done to extract high probability topics in the policy sectors. The interpretation of the extracted topics was made through a nudge theoretic lens to derive the critical policy heuristics of the government. Results showed that most interventions were targeted to generate endogenous nudge by using external triggers. Notably, the nudges from the Prime Minister of India was critical in creating herd effect on lockdown and social distancing norms across the nation. A similar effect was also observed around the public health (e.g., masks in public spaces; Yoga and Ayurveda for immunity), transport (e.g., old trains converted to isolation wards), micro, small and medium enterprises (e.g., rapid production of PPE and masks), science and technology sector (e.g., diagnostic kits, robots and nano-technology), home affairs (e.g., surveillance and lockdown), urban (e.g. drones, GIS-tools) and education (e.g., online learning). A conclusion was drawn on leveraging these heuristics are crucial for lockdown easement planning.

Ramit Debnath, Ronita Bardhan

2020-05-14

General General

Predicting COVID-19 in China Using Hybrid AI Model.

In IEEE transactions on cybernetics

The coronavirus disease 2019 (COVID-19) breaking out in late December 2019 is gradually being controlled in China, but it is still spreading rapidly in many other countries and regions worldwide. It is urgent to conduct prediction research on the development and spread of the epidemic. In this article, a hybrid artificial-intelligence (AI) model is proposed for COVID-19 prediction. First, as traditional epidemic models treat all individuals with coronavirus as having the same infection rate, an improved susceptible-infected (ISI) model is proposed to estimate the variety of the infection rates for analyzing the transmission laws and development trend. Second, considering the effects of prevention and control measures and the increase of the public's prevention awareness, the natural language processing (NLP) module and the long short-term memory (LSTM) network are embedded into the ISI model to build the hybrid AI model for COVID-19 prediction. The experimental results on the epidemic data of several typical provinces and cities in China show that individuals with coronavirus have a higher infection rate within the third to eighth days after they were infected, which is more in line with the actual transmission laws of the epidemic. Moreover, compared with the traditional epidemic models, the proposed hybrid AI model can significantly reduce the errors of the prediction results and obtain the mean absolute percentage errors (MAPEs) with 0.52%, 0.38%, 0.05%, and 0.86% for the next six days in Wuhan, Beijing, Shanghai, and countrywide, respectively.

Zheng Nanning, Du Shaoyi, Wang Jianji, Zhang He, Cui Wenting, Kang Zijian, Yang Tao, Lou Bin, Chi Yuting, Long Hong, Ma Mei, Yuan Qi, Zhang Shupei, Zhang Dong, Ye Feng, Xin Jingmin

2020-May-08

General General

Deep Learning COVID-19 Features on CXR using Limited Training Data Sets.

In IEEE transactions on medical imaging ; h5-index 74.0

Under the global pandemic of COVID-19, the use of artificial intelligence to analyze chest X-ray (CXR) image for COVID-19 diagnosis and patient triage is becoming important. Unfortunately, due to the emergent nature of the COVID-19 pandemic, a systematic collection of CXR data set for deep neural network training is difficult. To address this problem, here we propose a patch-based convolutional neural network approach with a relatively small number of trainable parameters for COVID-19 diagnosis. The proposed method is inspired by our statistical analysis of the potential imaging biomarkers of the CXR radiographs. Experimental results show that our method achieves state-of-the-art performance and provides clinically interpretable saliency maps, which are useful for COVID-19 diagnosis and patient triage.

Oh Yujin, Park Sangjoon, Ye Jong Chul

2020-May-08

General General

Origin of Novel Coronavirus (COVID-19): A Computational Biology Study using Artificial Intelligence

bioRxiv Preprint

Origin of the COVID-19 virus has been intensely debated in the scientific community since the first infected cases were detected in December 2019. The disease has caused a global pandemic, leading to deaths of thousands of people across the world and thus finding origin of this novel coronavirus is important in responding and controlling the pandemic. Recent research results suggest that either bat or pangolin might be the original host of the virus based on comparative studies using its genomic sequences. This paper investigates the COVID-19 virus origin by using artificial intelligence (AI) and raw genomic sequences of the virus. More than 300 genome sequences of COVID-19 infected cases collected from different countries are explored and analysed using unsupervised clustering methods. The results obtained from various AI-enabled experiments using clustering algorithms demonstrate that all examined COVID-19 virus genomes belong to a cluster that also contains bat and pangolin coronavirus genomes. This provides evidences strongly supporting scientific hypotheses that bats and pangolins are probable hosts for the COVID-19 novel coronavirus. At the whole genome analysis level, our findings also indicate that bats are more likely the host for the COVID-19 virus than pangolins.

Nguyen, T. T.; Abdelrazek, M.; Nguyen, D. T.; Aryal, S.; Nguyen, D. T.; Khatami, A.

2020-05-13

Radiology Radiology

Deep learning for detecting corona virus disease 2019 (COVID-19) on high-resolution computed tomography: a pilot study.

In Annals of translational medicine

Background : To evaluate the diagnostic efficacy of Densely Connected Convolutional Networks (DenseNet) for detection of COVID-19 features on high resolution computed tomography (HRCT).

Methods : The Ethic Committee of our institution approved the protocol of this study and waived the requirement for patient informed consent. Two hundreds and ninety-five patients were enrolled in this study (healthy person: 149; COVID-19 patients: 146), which were divided into three separate non-overlapping cohorts (training set, n=135, healthy person, n=69, patients, n=66; validation set, n=20, healthy person, n=10, patients, n=10; test set, n=140, healthy person, n=70, patients, n=70). The DenseNet was trained and tested to classify the images as having manifestation of COVID-19 or as healthy. A radiologist also blindly evaluated all the test images and rechecked the misdiagnosed cases by DenseNet. Receiver operating characteristic curves (ROC) and areas under the curve (AUCs) were used to assess the model performance. The sensitivity, specificity and accuracy of DenseNet model and radiologist were also calculated.

Results : The DenseNet algorithm model yielded an AUC of 0.99 (95% CI: 0.958-1.0) in the validation set and 0.98 (95% CI: 0.972-0.995) in the test set. The threshold value was selected as 0.8, while for validation and test sets, the accuracies were 95% and 92%, the sensitivities were 100% and 97%, the specificities were 90% and 87%, and the F1 values were 95% and 93%, respectively. The sensitivity of radiologist was 94%, the specificity was 96%, while the accuracy was 95%.

Conclusions : Deep learning (DL) with DenseNet can accurately classify COVID-19 on HRCT with an AUC of 0.98, which can reduce the miss diagnosis rate (combined with radiologists' evaluation) and radiologists' workload.

Yang Shuyi, Jiang Longquan, Cao Zhuoqun, Wang Liya, Cao Jiawang, Feng Rui, Zhang Zhiyong, Xue Xiangyang, Shi Yuxin, Shan Fei

2020-Apr

COVID-19, deep learning (DL), high resolution computed tomography (HRCT)

General General

SUPER RESOLUTION MICROSCOPY AND DEEP LEARNING IDENTIFY ZIKA VIRUS REORGANIZATION OF THE ENDOPLASMIC RETICULUM

bioRxiv Preprint

Flaviviruses such as Zika virus (ZIKV) induce reorganization of endoplasmic reticulum (ER) membranes to facilitate viral replication. Here, using 3D super resolution microscopy, ZIKV infection is shown to induce the formation of dense tubular matrices associated with viral replication in the central ER to. Viral non-structural proteins NS4B and NS2B are enriched within the ZIKV-induced tubular matrix where they associate with replication complexes but exhibit distinct ER distributions outside this central ER region. To determine if ZIKV-induced ER reorganization can be used as an indicator of viral infection, we trained a deep convolutional neural network to classify cells as ZIKV-infected or non-infected based on ER morphology. The network successfully identified ZIKV-induced central ER reorganization as a predictor of viral infection. Given the need to combat ER reorganizing viruses, such as SARS-CoV-2 responsible for COVID-19, this deep learning-based methodology could be used to rapidly screen for drugs that inhibit viral infection.

Long, R. K. M.; Moriarty, K. P.; Cardoen, B.; Gao, G.; Vogl, A. W.; Jean, F.; Hamarneh, G.; Nabi, I. R.

2020-05-13

General General

Modeling the trend of coronavirus disease 2019 and restoration of operational capability of metropolitan medical service in China: a machine learning and mathematical model-based analysis.

In Global health research and policy

Background : To contain the outbreak of coronavirus disease 2019 (COVID-19) in China, many unprecedented intervention measures are adopted by the government. However, these measures may interfere in the normal medical service. We sought to model the trend of COVID-19 and estimate the restoration of operational capability of metropolitan medical service in China.

Methods : Real-time data of COVID-19 and population mobility data were extracted from open sources. SEIR (Susceptible, Exposed, Infectious, Recovered) and neural network models (NNs) were built to model disease trends in Wuhan, Beijing, Shanghai and Guangzhou. Combined with public transportation data, Autoregressive Integrated Moving Average (ARIMA) model was used to estimate the accumulated demands for nonlocal hospitalization during the epidemic period in Beijing, Shanghai and Guangzhou.

Results : The number of infected people and deaths would increase by 45% and 567% respectively, given that the government only has implemented traffic control in Wuhan without additional medical professionals. The epidemic of Wuhan (measured by cumulative confirmed cases) was predicted to reach turning point at the end of March and end in later April, 2020. The outbreak in Beijing, Shanghai and Guangzhou was predicted to end at the end of March and the medical service could be fully back to normal in middle of April. During the epidemic, the number of nonlocal inpatient hospitalizations decreased by 69.86%, 57.41% and 66.85% in Beijing, Shanghai and Guangzhou respectively. After the end of epidemic, medical centers located in these metropolises may face 58,799 (95% CI 48926-67,232) additional hospitalization needs in the first month.

Conclusion : The COVID-19 epidemic in China has been effectively contained and medical service across the country is expected to return to normal in April. However, the huge unmet medical needs for other diseases could result in massive migration of patients and their families, bringing tremendous challenges for medical service in major metropolis and disease control for the potential asymptomatic virus carrier.

Liu Zeye, Huang Shuai, Lu Wenlong, Su Zhanhao, Yin Xin, Liang Huiying, Zhang Hao

2020

General General

ONLINE FORECASTING OF COVID-19 CASES IN NIGERIA USING LIMITED DATA.

In Data in brief

The novel Coronavirus disease (COVID-19) was first identified in Wuhan, China in December 2019 but later spread to other parts of the world. The disease as at the point of writing this paper has been declared a pandemic by the World Health Organization (WHO). The application of mathematical models, artificial intelligence, big data, and similar methodologies are potential tools to predict the extent of the spread and effectiveness of containment strategies to stem the transmission of this disease. In societies with constrained data infrastructures, modeling and forecasting COVID-19 becomes an extremely difficult endeavor. Nonetheless, we propose an online forecasting mechanism that streams data from the Nigeria Center for Disease Control to update the parameters of an ensemble model which in turn provides updated COVID-19 forecasts every 24 hours. The ensemble combines an Auto-Regressive Integrated Moving Average model (ARIMA), Prophet - an additive regression model developed by Facebook, and a Holt-Winters Exponential Smoothing model combined with Generalized Autoregressive Conditional Heteroscedasticity (GARCH). The outcomes of these efforts are expected to provide academic thrust in guiding the policymakers in the deployment of containment strategies and/or assessment of containment interventions in stemming the spread of the disease in Nigeria.

Abdulmajeed Kabir, Adeleke Monsuru, Popoola Labode

2020-May-08

Analytic Modeling, Coronavirus COVID-19, Ensembles, Nigeria NCDC, Small Data, Timeseries forecasting

General General

Time Series Forecasting of COVID-19 transmission in Canada Using LSTM Networks.

In Chaos, solitons, and fractals

On March 11 th 2020, World Health Organization (WHO) declared the 2019 novel corona virus as global pandemic. Corona virus, also known as COVID-19 was first originated in Wuhan, Hubei province in China around December 2019 and spread out all over the world within few weeks. Based on the public datasets provided by John Hopkins university and Canadian health authority, we have developed a forecasting model of COVID-19 outbreak in Canada using state-of-the-art Deep Learning (DL) models. In this novel research, we evaluated the key features to predict the trends and possible stopping time of the current COVID-19 outbreak in Canada and around the world. In this paper we presented the Long short-term memory (LSTM) networks, a deep learning approach to forecast the future COVID-19 cases. Based on the results of our Long short-term memory (LSTM) network, we predicted the possible ending point of this outbreak will be around June 2020. In addition to that, we compared transmission rates of Canada with Italy and USA. Here we also presented the 2, 4, 6, 8, 10, 12 and 14 th day predictions for 2 successive days. Our forecasts in this paper is based on the available data until March 31, 2020. To the best of our knowledge, this of the few studies to use LSTM networks to forecast the infectious diseases.

Chimmula Vinay Kumar Reddy, Zhang Lei

2020-May-08

COVID-19, Corona Virus, Epidemic transmission, Long Short Term Memory (LSTM) Networks, Machine Learning, Time Series Forecasting

General General

Diagnosis of Coronavirus Disease 2019 (COVID-19) with Structured Latent Multi-View Representation Learning.

In IEEE transactions on medical imaging ; h5-index 74.0

Recently, the outbreak of Coronavirus Disease 2019 (COVID-19) has spread rapidly across the world. Due to the large number of infected patients and heavy labor for doctors, computer-aided diagnosis with machine learning algorithm is urgently needed, and could largely reduce the efforts of clinicians and accelerate the diagnosis process. Chest computed tomography (CT) has been recognized as an informative tool for diagnosis of the disease. In this study, we propose to conduct the diagnosis of COVID-19 with a series of features extracted from CT images. To fully explore multiple features describing CT images from different views, a unified latent representation is learned which can completely encode information from different aspects of features and is endowed with promising class structure for separability. Specifically, the completeness is guaranteed with a group of backward neural networks (each for one type of features), while by using class labels the representation is enforced to be compact within COVID-19/community-acquired pneumonia (CAP) and also a large margin is guaranteed between different types of pneumonia. In this way, our model can well avoid overfitting compared to the case of directly projecting highdimensional features into classes. Extensive experimental results show that the proposed method outperforms all comparison methods, and rather stable performances are observed when varying the number of training data.

Kang Hengyuan, Xia Liming, Yan Fuhua, Wan Zhibin, Shi Feng, Yuan Huan, Jiang Huiting, Wu Dijia, Sui He, Zhang Changqing, Shen Dinggang

2020-May-05

Radiology Radiology

Value of CT application in the screening,diagnosis,and treatment of COVID-19.

In Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences

The coronavirus disease 2019 (COVID-19) has attracted extensive attention all around the world recently. Early screening, early diagnosis, early isolation, and early treatment remain the most effective prevention and control measures. Computed tomography (CT) plays a vital role in the screening, diagnosis, treatment, and follow-up of COVID-19, especially in the early screening, with a higher sensitivity than that of real-time fluorescence RT-PCR. The combination of CT and artificial intelligence has the potential to help clinicians in improving the diagnostic accuracy and working efficiency.

Li Ge, Xiong Zeng, Zhou Hui, Xie Jiangping, Chen Wei, Zhou Moling, Zhu Zhiming, Zhou Gaofeng, Liu Jinkang

2020-Mar-28

artificial intelligence, computed tomography, coronavirus disease 2019, diagnosis, follow-up, screening, severe acute respiratory syndrome coronavirus 2

Surgery Surgery

Using Machine Learning to Estimate Unobserved COVID-19 Infections in North America.

In The Journal of bone and joint surgery. American volume

BACKGROUND : The detection of coronavirus disease 2019 (COVID-19) cases remains a huge challenge. As of April 22, 2020, the COVID-19 pandemic continues to take its toll, with >2.6 million confirmed infections and >183,000 deaths. Dire projections are surfacing almost every day, and policymakers worldwide are using projections for critical decisions. Given this background, we modeled unobserved infections to examine the extent to which we might be grossly underestimating COVID-19 infections in North America.

METHODS : We developed a machine-learning model to uncover hidden patterns based on reported cases and to predict potential infections. First, our model relied on dimensionality reduction to identify parameters that were key to uncovering hidden patterns. Next, our predictive analysis used an unbiased hierarchical Bayesian estimator approach to infer past infections from current fatalities.

RESULTS : Our analysis indicates that, when we assumed a 13-day lag time from infection to death, the United States, as of April 22, 2020, likely had at least 1.3 million undetected infections. With a longer lag time-for example, 23 days-there could have been at least 1.7 million undetected infections. Given these assumptions, the number of undetected infections in Canada could have ranged from 60,000 to 80,000. Duarte's elegant unbiased estimator approach suggested that, as of April 22, 2020, the United States had up to >1.6 million undetected infections and Canada had at least 60,000 to 86,000 undetected infections. However, the Johns Hopkins University Center for Systems Science and Engineering data feed on April 22, 2020, reported only 840,476 and 41,650 confirmed cases for the United States and Canada, respectively.

CONCLUSIONS : We have identified 2 key findings: (1) as of April 22, 2020, the United States may have had 1.5 to 2.029 times the number of reported infections and Canada may have had 1.44 to 2.06 times the number of reported infections and (2) even if we assume that the fatality and growth rates in the unobservable population (undetected infections) are similar to those in the observable population (confirmed infections), the number of undetected infections may be within ranges similar to those described above. In summary, 2 different approaches indicated similar ranges of undetected infections in North America.

LEVEL OF EVIDENCE : Prognostic Level V. See Instructions for Authors for a complete description of levels of evidence.

Vaid Shashank, Cakan Caglar, Bhandari Mohit

2020-May-07

Radiology Radiology

COVID-19 on the Chest Radiograph: A Multi-Reader Evaluation of an AI System.

In Radiology ; h5-index 91.0

Background Chest radiography (CXR) may play an important role in triage for COVID-19, particularly in low-resource settings. Purpose To evaluate the performance of an artificial intelligence (AI) system for detection of COVID-19 pneumonia on chest radiographs. Methods An AI system (CAD4COVID-Xray) was trained on 24,678 CXR images including 1,540 used only for validation while training. The test set consisted of a set of continuously acquired CXR images (n=454) obtained in patients suspected for COVID-19 pneumonia between March 4th and April 6th 2020 in a single center (223 RT-PCR positive subjects, 231 RT-PCR negative subjects). The radiographs were independently analyzed by six readers and by the AI system. Diagnostic performance was performed by receiver operating characteristic curve analysis. Results For the test set, the mean age of the patients was 67.3 (+/-14.4) years (56% male). Using RT-PCR test results as the reference standard, the AI system correctly classified CXR images as COVID-19 pneumonia with an AUC of 0.81. The system significantly outperforms each reader (p < 0.001 using McNemar test) at their highest possible sensitivities. At their lowest sensitivities, only one reader can significantly outperform the AI system (p=0.04). Conclusions An AI system for detection of COVID-19 on chest radiographs was comparable to six independent readers.

Murphy Keelin, Smits Henk, Knoops Arnoud J G, Korst Mike B J M, Samson Tijs, Scholten Ernst T, Schalekamp Steven, Schaefer-Prokop Cornelia M, Philipsen Rick H H M, Meijers Annet, Melendez Jaime, van Ginneken Bram, Rutten Matthieu

2020-May-08

Surgery Surgery

Using Machine Learning to Estimate Unobserved COVID-19 Infections in North America.

In The Journal of bone and joint surgery. American volume

BACKGROUND : The detection of coronavirus disease 2019 (COVID-19) cases remains a huge challenge. As of April 22, 2020, the COVID-19 pandemic continues to take its toll, with >2.6 million confirmed infections and >183,000 deaths. Dire projections are surfacing almost every day, and policymakers worldwide are using projections for critical decisions. Given this background, we modeled unobserved infections to examine the extent to which we might be grossly underestimating COVID-19 infections in North America.

METHODS : We developed a machine-learning model to uncover hidden patterns based on reported cases and to predict potential infections. First, our model relied on dimensionality reduction to identify parameters that were key to uncovering hidden patterns. Next, our predictive analysis used an unbiased hierarchical Bayesian estimator approach to infer past infections from current fatalities.

RESULTS : Our analysis indicates that, when we assumed a 13-day lag time from infection to death, the United States, as of April 22, 2020, likely had at least 1.3 million undetected infections. With a longer lag time-for example, 23 days-there could have been at least 1.7 million undetected infections. Given these assumptions, the number of undetected infections in Canada could have ranged from 60,000 to 80,000. Duarte's elegant unbiased estimator approach suggested that, as of April 22, 2020, the United States had up to >1.6 million undetected infections and Canada had at least 60,000 to 86,000 undetected infections. However, the Johns Hopkins University Center for Systems Science and Engineering data feed on April 22, 2020, reported only 840,476 and 41,650 confirmed cases for the United States and Canada, respectively.

CONCLUSIONS : We have identified 2 key findings: (1) as of April 22, 2020, the United States may have had 1.5 to 2.029 times the number of reported infections and Canada may have had 1.44 to 2.06 times the number of reported infections and (2) even if we assume that the fatality and growth rates in the unobservable population (undetected infections) are similar to those in the observable population (confirmed infections), the number of undetected infections may be within ranges similar to those described above. In summary, 2 different approaches indicated similar ranges of undetected infections in North America.

LEVEL OF EVIDENCE : Prognostic Level V. See Instructions for Authors for a complete description of levels of evidence.

Vaid Shashank, Cakan Caglar, Bhandari Mohit

2020-May-07

Radiology Radiology

COVID-19 on the Chest Radiograph: A Multi-Reader Evaluation of an AI System.

In Radiology ; h5-index 91.0

Background Chest radiography (CXR) may play an important role in triage for COVID-19, particularly in low-resource settings. Purpose To evaluate the performance of an artificial intelligence (AI) system for detection of COVID-19 pneumonia on chest radiographs. Methods An AI system (CAD4COVID-Xray) was trained on 24,678 CXR images including 1,540 used only for validation while training. The test set consisted of a set of continuously acquired CXR images (n=454) obtained in patients suspected for COVID-19 pneumonia between March 4th and April 6th 2020 in a single center (223 RT-PCR positive subjects, 231 RT-PCR negative subjects). The radiographs were independently analyzed by six readers and by the AI system. Diagnostic performance was performed by receiver operating characteristic curve analysis. Results For the test set, the mean age of the patients was 67.3 (+/-14.4) years (56% male). Using RT-PCR test results as the reference standard, the AI system correctly classified CXR images as COVID-19 pneumonia with an AUC of 0.81. The system significantly outperforms each reader (p < 0.001 using McNemar test) at their highest possible sensitivities. At their lowest sensitivities, only one reader can significantly outperform the AI system (p=0.04). Conclusions An AI system for detection of COVID-19 on chest radiographs was comparable to six independent readers.

Murphy Keelin, Smits Henk, Knoops Arnoud J G, Korst Mike B J M, Samson Tijs, Scholten Ernst T, Schalekamp Steven, Schaefer-Prokop Cornelia M, Philipsen Rick H H M, Meijers Annet, Melendez Jaime, van Ginneken Bram, Rutten Matthieu

2020-May-08

Radiology Radiology

CT quantification of pneumonia lesions in early days predicts progression to severe illness in a cohort of COVID-19 patients.

In Theranostics

Rationale: Some patients with coronavirus disease 2019 (COVID-19) rapidly develop respiratory failure or even die, underscoring the need for early identification of patients at elevated risk of severe illness. This study aims to quantify pneumonia lesions by computed tomography (CT) in the early days to predict progression to severe illness in a cohort of COVID-19 patients. Methods: This retrospective cohort study included confirmed COVID-19 patients. Three quantitative CT features of pneumonia lesions were automatically calculated using artificial intelligence algorithms, representing the percentages of ground-glass opacity volume (PGV), semi-consolidation volume (PSV), and consolidation volume (PCV) in both lungs. CT features, acute physiology and chronic health evaluation II (APACHE-II) score, neutrophil-to-lymphocyte ratio (NLR), and d-dimer, on day 0 (hospital admission) and day 4, were collected to predict the occurrence of severe illness within a 28-day follow-up using both logistic regression and Cox proportional hazard models. Results: We included 134 patients, of whom 19 (14.2%) developed any severe illness. CT features on day 0 and day 4, as well as their changes from day 0 to day 4, showed predictive capability. Changes in CT features from day 0 to day 4 performed the best in the prediction (area under the receiver operating characteristic curve = 0.93, 95% confidence interval [CI] 0.87~0.99; C-index=0.88, 95% CI 0.81~0.95). The hazard ratios of PGV and PCV were 1.39 (95% CI 1.05~1.84, P=0.023) and 1.67 (95% CI 1.17~2.38, P=0.005), respectively. CT features, adjusted for age and gender, on day 4 and in terms of changes from day 0 to day 4 outperformed APACHE-II, NLR, and d-dimer. Conclusions: CT quantification of pneumonia lesions can early and non-invasively predict the progression to severe illness, providing a promising prognostic indicator for clinical management of COVID-19.

Liu Fengjun, Zhang Qi, Huang Chao, Shi Chunzi, Wang Lin, Shi Nannan, Fang Cong, Shan Fei, Mei Xue, Shi Jing, Song Fengxiang, Yang Zhongcheng, Ding Zezhen, Su Xiaoming, Lu Hongzhou, Zhu Tongyu, Zhang Zhiyong, Shi Lei, Shi Yuxin

2020

Artificial intelligence, COVID-19, Chest CT, Retrospective cohort, Severe illness

Dermatology Dermatology

How Big Data and Artificial Intelligence Can Help Better Manage the COVID-19 Pandemic.

In International journal of environmental research and public health ; h5-index 73.0

SARS-CoV2 is a novel coronavirus, responsible for the COVID-19 pandemic declared by the World Health Organization. Thanks to the latest advancements in the field of molecular and computational techniques and information and communication technologies (ICTs), artificial intelligence (AI) and Big Data can help in handling the huge, unprecedented amount of data derived from public health surveillance, real-time epidemic outbreaks monitoring, trend now-casting/forecasting, regular situation briefing and updating from governmental institutions and organisms, and health facility utilization information. The present review is aimed at overviewing the potential applications of AI and Big Data in the global effort to manage the pandemic.

Bragazzi Nicola Luigi, Dai Haijiang, Damiani Giovanni, Behzadifar Masoud, Martini Mariano, Wu Jianhong

2020-May-02

Big Data, artificial intelligence, epidemiology, public health, viral outbreak

Radiology Radiology

Artificial intelligence to codify lung CT in Covid-19 patients.

In La Radiologia medica

The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has already assumed pandemic proportions, affecting over 100 countries in few weeks. A global response is needed to prepare health systems worldwide. Covid-19 can be diagnosed both on chest X-ray and on computed tomography (CT). Asymptomatic patients may also have lung lesions on imaging. CT investigation in patients with suspicion Covid-19 pneumonia involves the use of the high-resolution technique (HRCT). Artificial intelligence (AI) software has been employed to facilitate CT diagnosis. AI software must be useful categorizing the disease into different severities, integrating the structured report, prepared according to subjective considerations, with quantitative, objective assessments of the extent of the lesions. In this communication, we present an example of a good tool for the radiologist (Thoracic VCAR software, GE Healthcare, Italy) in Covid-19 diagnosis (Pan et al. in Radiology, 2020. https://doi.org/10.1148/radiol.2020200370). Thoracic VCAR offers quantitative measurements of the lung involvement. Thoracic VCAR can generate a clear, fast and concise report that communicates vital medical information to referring physicians. In the post-processing phase, software, thanks to the help of a colorimetric map, recognizes the ground glass and differentiates it from consolidation and quantifies them as a percentage with respect to the healthy parenchyma. AI software therefore allows to accurately calculate the volume of each of these areas. Therefore, keeping in mind that CT has high diagnostic sensitivity in identifying lesions, but not specific for Covid-19 and similar to other infectious viral diseases, it is mandatory to have an AI software that expresses objective evaluations of the percentage of ventilated lung parenchyma compared to the affected one.

Belfiore Maria Paola, Urraro Fabrizio, Grassi Roberta, Giacobbe Giuliana, Patelli Gianluigi, Cappabianca Salvatore, Reginelli Alfonso

2020-May-04

Artificial intelligence, Sars-Cov-2, Structured report

General General

An Evidence Based Perspective on mRNA-SARS-CoV-2 Vaccine Development.

In Medical science monitor : international medical journal of experimental and clinical research

The first outbreak of coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) occurred in Wuhan, Hubei Province, China, in late 2019. The subsequent COVID-19 pandemic rapidly affected the health and economy of the world. The global approach to the pandemic was to isolate populations to reduce the spread of this deadly virus while vaccines began to be developed. In March 2020, the first phase I clinical trial of a novel lipid nanoparticle (LNP)-encapsulated mRNA-based vaccine, mRNA-1273, which encodes the spike protein (S protein) of SARS-CoV-2, began in the United States (US). The production of mRNA-based vaccines is a promising recent development in the production of vaccines. However, there remain significant challenges in the development and testing of vaccines as rapidly as possible to control COVID-19, which requires international collaboration. This review aims to describe the background to the rationale for the development of mRNA-based SARS-CoV-2 vaccines and the current status of the mRNA-1273 vaccine.

Wang Fuzhou, Kream Richard M, Stefano George B

2020-May-05

General General

Composite Monte Carlo decision making under high uncertainty of novel coronavirus epidemic using hybridized deep learning and fuzzy rule induction.

In Applied soft computing

In the advent of the novel coronavirus epidemic since December 2019, governments and authorities have been struggling to make critical decisions under high uncertainty at their best efforts. In computer science, this represents a typical problem of machine learning over incomplete or limited data in early epidemic Composite Monte-Carlo (CMC) simulation is a forecasting method which extrapolates available data which are broken down from multiple correlated/casual micro-data sources into many possible future outcomes by drawing random samples from some probability distributions. For instance, the overall trend and propagation of the infested cases in China are influenced by the temporal-spatial data of the nearby cities around the Wuhan city (where the virus is originated from), in terms of the population density, travel mobility, medical resources such as hospital beds and the timeliness of quarantine control in each city etc. Hence a CMC is reliable only up to the closeness of the underlying statistical distribution of a CMC, that is supposed to represent the behaviour of the future events, and the correctness of the composite data relationships. In this paper, a case study of using CMC that is enhanced by deep learning network and fuzzy rule induction for gaining better stochastic insights about the epidemic development is experimented. Instead of applying simplistic and uniform assumptions for a MC which is a common practice, a deep learning-based CMC is used in conjunction of fuzzy rule induction techniques. As a result, decision makers are benefited from a better fitted MC outputs complemented by min-max rules that foretell about the extreme ranges of future possibilities with respect to the epidemic.

Fong Simon James, Li Gloria, Dey Nilanjan, Crespo Rubén González, Herrera-Viedma Enrique

2020-Apr-09

2019-nCoV, COVID-19, Coronavirus, Decision support, Monte Carlo simulation

General General

Insights into the inhibitory potential of selective phytochemicals against Mpro of 2019-nCoV: a computer-aided study.

In Structural chemistry

At the end of December 2019, a novel strain of coronavirus, given the name of 2019-nCoV, emerged for exhibiting symptoms of severe acute respiratory syndrome. The virus is spreading rapidly in China and around the globe, affecting thousands of people leading to a pandemic. To control the mortality rate associated with the 2019-nCoV, prompt steps are needed. Until now there is no effective treatment or drug present to control its life-threatening effects in the humans. The scientist is struggling to find new inhibitors of this deadly virus. In this study, to identify the effective inhibitor candidates against the main protease (Mpro) of 2019-nCoV, computational approaches were adopted. Phytochemicals having immense medicinal properties as ligands were docked against the Mpro of 2019-nCoV to study their binding properties. ADMET and DFT analyses were also further carried out to analyze the potential of these phytochemicals as an effective inhibitor against Mpro of 2019-nCoV.

Rasool Nouman, Akhtar Ammara, Hussain Waqar

2020-May-01

2019-nCoV, ADMET, DFT, Docking, Main protease, Phytochemicals

General General

COVIDier: A Deep-learning Tool For Coronaviruses Genome And Virulence Proteins Classification

bioRxiv Preprint

COVID-19, caused by SARS-CoV-2 infection, has already reached pandemic proportions in a matter of a few weeks. At the time of writing this manuscript, the unprecedented public health crisis caused more than 2.5 million cases with a mortality range of 5-7%. The SARS-CoV-2, also called novel Coronavirus, is related to both SARS-CoV and bat SARS. Great efforts have been spent to control the pandemic that has become a significant burden on the health systems in a short time. Since the emergence of the crisis, a great number of researchers started to use the AI tools to identify drugs, diagnosing using CT scan images, scanning body temperature, and classifying the severity of the disease. The emergence of variants of the SARS-CoV-2 genome is a challenging problem with expected serious consequences on the management of the disease. Here, we introduce COVIDier, a deep learning-based software that is enabled to classify the different genomes of Alpha coronavirus, Beta coronavirus, MERS, SARS-CoV-1, SARS-CoV-2, and bronchitis-CoV. COVIDier was trained on 1925 genomes, belonging to the three families of SARS retrieved from NCBI Database to propose a new method to train deep learning model trained on genome data using Multi-layer Perceptron Classifier (MLPClassifier), a deep learning algorithm, that could blindly predict the virus family name from the genome of by predicting the statistically similar genome from training data to the given genome. COVIDier able to predict how close the emerging novel genomes of SARS to the known genomes with accuracy 99%. COVIDier can replace tools like BLAST that consume higher CPU and time.

Habib, P.; Alsamman, A. M.; Saber-Ayad, M.; Hassanein, S. E.; Hamwieh, A.

2020-05-05

General General

Emergence of Drift Variants That May Affect COVID-19 Vaccine Development and Antibody Treatment.

In Pathogens (Basel, Switzerland)

New coronavirus (SARS-CoV-2) treatments and vaccines are under development to combat COVID-19. Several approaches are being used by scientists for investigation, including (1) various small molecule approaches targeting RNA polymerase, 3C-like protease, and RNA endonuclease; and (2) exploration of antibodies obtained from convalescent plasma from patients who have recovered from COVID-19. The coronavirus genome is highly prone to mutations that lead to genetic drift and escape from immune recognition; thus, it is imperative that sub-strains with different mutations are also accounted for during vaccine development. As the disease has grown to become a pandemic, B-cell and T-cell epitopes predicted from SARS coronavirus have been reported. Using the epitope information along with variants of the virus, we have found several variants which might cause drifts. Among such variants, 23403A>G variant (p.D614G) in spike protein B-cell epitope is observed frequently in European countries, such as the Netherlands, Switzerland, and France, but seldom observed in China.

Koyama Takahiko, Weeraratne Dilhan, Snowdon Jane L, Parida Laxmi

2020-Apr-26

COVID-19, SARS-CoV-2, antibody, convalescent plasma, genomic drift, immune escape, spike protein, vaccine, variant

General General

The role of imaging in the detection and management of COVID-19: a review.

In IEEE reviews in biomedical engineering

Coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is spreading rapidly around the world, resulting in a massive death toll. Lung infection or pneumonia is the common complication of COVID-19, and imaging techniques, especially computed tomography (CT), have played an important role in diagnosis and treatment assessment of the disease. Herein, we review the imaging characteristics and computing models that have been applied for the management of COVID-19. CT, positron emission tomography - CT (PET/CT), lung ultrasound, and magnetic resonance imaging (MRI) have been used for detection, treatment, and follow-up. The quantitative analysis of imaging data using artificial intelligence (AI) is also explored. Our findings indicate that typical imaging characteristics and their changes can play crucial roles in the detection and management of COVID-19. In addition, AI or other quantitative image analysis methods are urgently needed to maximize the value of imaging in the management of COVID-19.

Dong Di, Tang Zhenchao, Wang Shuo, Hui Hui, Gong Lixin, Lu Yao, Xue Zhong, Liao Hongen, Chen Fang, Yang Fan, Jin Ronghua, Wang Kun, Liu Zhenyu, Wei Jingwei, Mu Wei, Zhang Hui, Jiang Jingying, Tian Jie, Li Hongjun

2020-Apr-27

General General

COVID-19 pandemic and personal protective equipment shortage: protective efficacy comparing masks and scientific methods for respirator reuse.

In Gastrointestinal endoscopy ; h5-index 72.0

BACKGROUND AND AIMS : The abrupt outbreak of COVID-19 and its rapid spread over many health care systems in the world led to personal protective equipment (PPE) shortening, which cannot be faced only by the reduction in their consumption nor by the expensive and time-requiring implementation of their production. It is thus necessary to promote PPE rational use, highlighting possible differences in terms of efficacy among them and promoting an effective technique to reuse them.

METHODS : A literature search was performed on PubMed, Scopus, Cochrane database, and Google Scholar and from 25 top cited papers, 15 were selected for relevance and impact.

RESULTS : Most studies on prior respiratory virus epidemic to date suggest surgical masks not to be inferior compared with N95 respirators in terms of protective efficacy among health care workers. The use of N95 respirators should be then limited in favor of high-risk situations. Concerning respirators reuse, highly energetic short-wave ultraviolet germicidal irradiation (UVGI) at 254 nm was proficiently applied to determine N95 respirators decontamination from viral respiratory agents, but it requires careful consideration of the type of respirator and of the biological target.

CONCLUSIONS : Rational use and successful reuse of respirators can help facing PPE shortening during a pandemic. Further evidences testing UVGI and other decontamination techniques are an unmet need. The definitive answer to pandemic issues can be found in artificial intelligence and deep learning: these groundbreaking modalities could help in identifying high-risk patients and in suggesting appropriate types and use of PPE.

Boškoski Ivo, Gallo Camilla, Wallace Michael B, Costamagna Guido

2020-Apr-27

Public Health Public Health

Epidemiology and transmission of COVID-19 in 391 cases and 1286 of their close contacts in Shenzhen, China: a retrospective cohort study.

In The Lancet. Infectious diseases

BACKGROUND : Rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Wuhan, China, prompted heightened surveillance in Shenzhen, China. The resulting data provide a rare opportunity to measure key metrics of disease course, transmission, and the impact of control measures.

METHODS : From Jan 14 to Feb 12, 2020, the Shenzhen Center for Disease Control and Prevention identified 391 SARS-CoV-2 cases and 1286 close contacts. We compared cases identified through symptomatic surveillance and contact tracing, and estimated the time from symptom onset to confirmation, isolation, and admission to hospital. We estimated metrics of disease transmission and analysed factors influencing transmission risk.

FINDINGS : Cases were older than the general population (mean age 45 years) and balanced between males (n=187) and females (n=204). 356 (91%) of 391 cases had mild or moderate clinical severity at initial assessment. As of Feb 22, 2020, three cases had died and 225 had recovered (median time to recovery 21 days; 95% CI 20-22). Cases were isolated on average 4·6 days (95% CI 4·1-5·0) after developing symptoms; contact tracing reduced this by 1·9 days (95% CI 1·1-2·7). Household contacts and those travelling with a case were at higher risk of infection (odds ratio 6·27 [95% CI 1·49-26·33] for household contacts and 7·06 [1·43-34·91] for those travelling with a case) than other close contacts. The household secondary attack rate was 11·2% (95% CI 9·1-13·8), and children were as likely to be infected as adults (infection rate 7·4% in children <10 years vs population average of 6·6%). The observed reproductive number (R) was 0·4 (95% CI 0·3-0·5), with a mean serial interval of 6·3 days (95% CI 5·2-7·6).

INTERPRETATION : Our data on cases as well as their infected and uninfected close contacts provide key insights into the epidemiology of SARS-CoV-2. This analysis shows that isolation and contact tracing reduce the time during which cases are infectious in the community, thereby reducing the R. The overall impact of isolation and contact tracing, however, is uncertain and highly dependent on the number of asymptomatic cases. Moreover, children are at a similar risk of infection to the general population, although less likely to have severe symptoms; hence they should be considered in analyses of transmission and control.

FUNDING : Emergency Response Program of Harbin Institute of Technology, Emergency Response Program of Peng Cheng Laboratory, US Centers for Disease Control and Prevention.

Bi Qifang, Wu Yongsheng, Mei Shujiang, Ye Chenfei, Zou Xuan, Zhang Zhen, Liu Xiaojian, Wei Lan, Truelove Shaun A, Zhang Tong, Gao Wei, Cheng Cong, Tang Xiujuan, Wu Xiaoliang, Wu Yu, Sun Binbin, Huang Suli, Sun Yu, Zhang Juncen, Ma Ting, Lessler Justin, Feng Tiejian

2020-Apr-27

Radiology Radiology

Infection Control for CT Equipment and Radiographers' Personal Protection During the Coronavirus Disease (COVID-19) Outbreak in China.

In AJR. American journal of roentgenology

OBJECTIVE. Because CT plays an important role in diagnosis, isolation, treatment, and effective evaluation of coronavirus disease (COVID-19), infection prevention and control management of CT examination rooms is important. CONCLUSION. We describe modifications to the CT examination process, strict disinfection of examination rooms, arrangement of waiting areas, and efforts to increase radiographers' awareness of personal protection made at our institution during the COVID-19 outbreak. In addition, we discuss the potential of using artificial intelligence in imaging patients with contagious diseases.

Qu Jieming, Yang Wenjie, Yang Yanzhao, Qin Le, Yan Fuhua

2020-Apr-30

AI, COVID-19, SARS-CoV-2, artificial intelligence, coronavirus disease, infection control, personal protection, radiology operations

Radiology Radiology

Use of CT and artificial intelligence in suspected or COVID-19 positive patients: statement of the Italian Society of Medical and Interventional Radiology.

In La Radiologia medica

The COVID-19 pandemic started in Italy in February 2020 with an exponential growth that has exceeded the number of cases reported in China. Italian radiology departments found themselves at the forefront in the management of suspected and positive COVID cases, both in diagnosis, in estimating the severity of the disease and in follow-up. In this context SIRM recommends chest X-ray as first-line imaging tool, CT as additional tool that shows typical features of COVID pneumonia, and ultrasound of the lungs as monitoring tool. SIRM recommends, as high priority, to ensure appropriate sanitation procedures on the scan equipment after detecting any suspected or positive COVID-19 patients. In this emergency situation, several expectations have been raised by the scientific community about the role that artificial intelligence can have in improving the diagnosis and treatment of coronavirus infection, and SIRM wishes to deliver clear statements to the radiological community, on the usefulness of artificial intelligence as a radiological decision support system in COVID-19 positive patients. (1) SIRM supports the research on the use of artificial intelligence as a predictive and prognostic decision support system, especially in hospitalized patients and those admitted to intensive care, and welcomes single center of multicenter studies for a clinical validation of the test. (2) SIRM does not support the use of CT with artificial intelligence for screening or as first-line test to diagnose COVID-19. (3) Chest CT with artificial intelligence cannot replace molecular diagnosis tests with nose-pharyngeal swab (rRT-PCR) in suspected for COVID-19 patients.

Neri Emanuele, Miele Vittorio, Coppola Francesca, Grassi Roberto

2020-Apr-29

Artificial intellingence, COVID-19, Computed tomography, Ethics, Imaging

General General

Facilitating Access to Multilingual COVID-19 Information via Neural Machine Translation

ArXiv Preprint

Every day, more people are becoming infected and dying from exposure to COVID-19. Some countries in Europe like Spain, France, the UK and Italy have suffered particularly badly from the virus. Others such as Germany appear to have coped extremely well. Both health professionals and the general public are keen to receive up-to-date information on the effects of the virus, as well as treatments that have proven to be effective. In cases where language is a barrier to access of pertinent information, machine translation (MT) may help people assimilate information published in different languages. Our MT systems trained on COVID-19 data are freely available for anyone to use to help translate information published in German, French, Italian, Spanish into English, as well as the reverse direction.

Andy Way, Rejwanul Haque, Guodong Xie, Federico Gaspari, Maja Popovic, Alberto Poncelas

2020-05-01