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Radiology Radiology

BSR 2020 Annual Meeting: Program.

In Journal of the Belgian Society of Radiology

Different times call for different measures. The COVID-19 pandemic has forced us to search for alternative methods to provide an annual meeting which is equally interesting and has quality. For the Belgian Society of Radiology (BSR) 2020 Annual Meeting, the sections on Abdominal Imaging, Thoracic Imaging and the Young Radiologist Section (YRS) joined forces to organize a meeting which is quite different from the ones we have organised in the past. We have chosen to create a compact - approximately 5 hour - and entirely virtual meeting with the possibility of live interaction with the speakers during the question and answer sessions. The meeting kicks off with a message from the BSR president about radiology in 2020, followed by three abdominal talks. The second session combines an abdominal talk with COVID-related talks. We have chosen to include not only thoracic findings in COVID-19, but to take it further and discuss neurological patterns, long-term clinical findings and the progress in artificial intelligence in COVID-19. Lastly, the annual meeting closes off with a short movie about the (re)discovery of Röntgens X-ray, presented to us by the Belgian Museum for Radiology, Military Hospital, Brussels.

Vanhoenacker Anne-Sophie, Grandjean Flavien, Lieven Van Hoe, Snoeckx Annemie, Vanhoenacker Piet, Oyen Raymond

2020-Nov-13

2020, Annual Symposium, BSR

General General

COVID-CheXNet: hybrid deep learning framework for identifying COVID-19 virus in chest X-rays images.

In Soft computing

The outbreaks of Coronavirus (COVID-19) epidemic have increased the pressure on healthcare and medical systems worldwide. The timely diagnosis of infected patients is a critical step to limit the spread of the COVID-19 epidemic. The chest radiography imaging has shown to be an effective screening technique in diagnosing the COVID-19 epidemic. To reduce the pressure on radiologists and control of the epidemic, fast and accurate a hybrid deep learning framework for diagnosing COVID-19 virus in chest X-ray images is developed and termed as the COVID-CheXNet system. First, the contrast of the X-ray image was enhanced and the noise level was reduced using the contrast-limited adaptive histogram equalization and Butterworth bandpass filter, respectively. This was followed by fusing the results obtained from two different pre-trained deep learning models based on the incorporation of a ResNet34 and high-resolution network model trained using a large-scale dataset. Herein, the parallel architecture was considered, which provides radiologists with a high degree of confidence to discriminate between the healthy and COVID-19 infected people. The proposed COVID-CheXNet system has managed to correctly and accurately diagnose the COVID-19 patients with a detection accuracy rate of 99.99%, sensitivity of 99.98%, specificity of 100%, precision of 100%, F1-score of 99.99%, MSE of 0.011%, and RMSE of 0.012% using the weighted sum rule at the score-level. The efficiency and usefulness of the proposed COVID-CheXNet system are established along with the possibility of using it in real clinical centers for fast diagnosis and treatment supplement, with less than 2 s per image to get the prediction result.

Al-Waisy Alaa S, Al-Fahdawi Shumoos, Mohammed Mazin Abed, Abdulkareem Karrar Hameed, Mostafa Salama A, Maashi Mashael S, Arif Muhammad, Garcia-Zapirain Begonya

2020-Nov-21

Chest X-ray images, Chest radiography imaging, Coronavirus COVID-19 epidemic, Deep learning, ResNet34 model, Transfer learning

Radiology Radiology

CoroDet: A deep learning based classification for COVID-19 detection using chest X-ray images.

In Chaos, solitons, and fractals

Background and Objective : The Coronavirus 2019, or shortly COVID-19, is a viral disease that causes serious pneumonia and impacts our different body parts from mild to severe depending on patient's immune system. This infection was first reported in Wuhan city of China in December 2019, and afterward, it became a global pandemic spreading rapidly around the world. As the virus spreads through human to human contact, it has affected our lives in a devastating way, including the vigorous pressure on the public health system, the world economy, education sector, workplaces, and shopping malls. Preventing viral spreading requires early detection of positive cases and to treat infected patients as quickly as possible. The need for COVID-19 testing kits has increased, and many of the developing countries in the world are facing a shortage of testing kits as new cases are increasing day by day. In this situation, the recent research using radiology imaging (such as X-ray and CT scan) techniques can be proven helpful to detect COVID-19 as X-ray and CT scan images provide important information about the disease caused by COVID-19 virus. The latest data mining and machine learning techniques such as Convolutional Neural Network (CNN) can be applied along with X-ray and CT scan images of the lungs for the accurate and rapid detection of the disease, assisting in mitigating the problem of scarcity of testing kits.

Methods : Hence a novel CNN model called CoroDet for automatic detection of COVID-19 by using raw chest X-ray and CT scan images have been proposed in this study. CoroDet is developed to serve as an accurate diagnostics for 2 class classification (COVID and Normal), 3 class classification (COVID, Normal, and non-COVID pneumonia), and 4 class classification (COVID, Normal, non-COVID viral pneumonia, and non-COVID bacterial pneumonia).

Results : The performance of our proposed model was compared with ten existing techniques for COVID detection in terms of accuracy. A classification accuracy of 99.1% for 2 class classification, 94.2% for 3 class classification, and 91.2% for 4 class classification was produced by our proposed model, which is obviously better than the state-of-the-art-methods used for COVID-19 detection to the best of our knowledge. Moreover, the dataset with x-ray images that we prepared for the evaluation of our method is the largest datasets for COVID detection as far as our knowledge goes.

Conclusion : The experimental results of our proposed method CoroDet indicate the superiority of CoroDet over the existing state-of-the-art-methods. CoroDet may assist clinicians in making appropriate decisions for COVID-19 detection and may also mitigate the problem of scarcity of testing kits.

Hussain Emtiaz, Hasan Mahmudul, Rahman Md Anisur, Lee Ickjai, Tamanna Tasmi, Parvez Mohammad Zavid

2020-Nov-23

Accuracy, COVID-19, Confusion matrix, Convolutional neural network, Deep learning, Pneumonia-bacterial, Pneumonia-viral, X-ray

General General

CPAS: the UK's national machine learning-based hospital capacity planning system for COVID-19.

In Machine learning

The coronavirus disease 2019 (COVID-19) global pandemic poses the threat of overwhelming healthcare systems with unprecedented demands for intensive care resources. Managing these demands cannot be effectively conducted without a nationwide collective effort that relies on data to forecast hospital demands on the national, regional, hospital and individual levels. To this end, we developed the COVID-19 Capacity Planning and Analysis System (CPAS)-a machine learning-based system for hospital resource planning that we have successfully deployed at individual hospitals and across regions in the UK in coordination with NHS Digital. In this paper, we discuss the main challenges of deploying a machine learning-based decision support system at national scale, and explain how CPAS addresses these challenges by (1) defining the appropriate learning problem, (2) combining bottom-up and top-down analytical approaches, (3) using state-of-the-art machine learning algorithms, (4) integrating heterogeneous data sources, and (5) presenting the result with an interactive and transparent interface. CPAS is one of the first machine learning-based systems to be deployed in hospitals on a national scale to address the COVID-19 pandemic-we conclude the paper with a summary of the lessons learned from this experience.

Qian Zhaozhi, Alaa Ahmed M, van der Schaar Mihaela

2020-Nov-24

Automated machine learning, COVID-19, Compartmental models, Gaussian processes, Healthcare, Resource planning

General General

The automation of bias in medical Artificial Intelligence (AI): Decoding the past to create a better future.

In Artificial intelligence in medicine ; h5-index 34.0

Medicine is at a disciplinary crossroads. With the rapid integration of Artificial Intelligence (AI) into the healthcare field the future care of our patients will depend on the decisions we make now. Demographic healthcare inequalities continue to persist worldwide and the impact of medical biases on different patient groups is still being uncovered by the research community. At a time when clinical AI systems are scaled up in response to the Covid19 pandemic, the role of AI in exacerbating health disparities must be critically reviewed. For AI to account for the past and build a better future, we must first unpack the present and create a new baseline on which to develop these tools. The means by which we move forwards will determine whether we project existing inequity into the future, or whether we reflect on what we hold to be true and challenge ourselves to be better. AI is an opportunity and a mirror for all disciplines to improve their impact on society and for medicine the stakes could not be higher.

Straw Isabel

2020-Nov

Artificial intelligence, Bias, Data science, Digital health, Disparities, Health, Healthcare, Inequality, Medicine

Cardiology Cardiology

Derivation with Internal Validation of a Multivariable Predictive Model to Predict COVID-19 Test Results in Emergency Department Patients.

In Academic emergency medicine : official journal of the Society for Academic Emergency Medicine

OBJECTIVES : The COVID-19 pandemic has placed acute care providers in demanding situations in predicting disease given the clinical variability, desire to cohort patients, and high variance in testing availability. An approach to stratify patients by likelihood of disease based on rapidly available emergency department (ED) clinical data would offer significant operational and clinical value. The purpose of this study was to develop and internally validate a predictive model to aid in the discrimination of patients undergoing investigation for COVID-19.

METHODS : All patients greater than 18 years presenting to a single academic ED who were tested for COVID-19 during this index ED evaluation were included. Outcome was defined as the result of COVID-19 PCR testing during the index visit or any positive result within the following 7 days. Variables included chest radiograph interpretation, disease specific screening questions, and laboratory data. Three models were developed with a split-sample approach to predict outcome of the PCR test utilizing logistic regression, random forest, and gradient boosted decision-tree methods. Model discrimination was evaluated comparing AUC and point statistics at a predefined threshold.

RESULTS : 1026 patients were included in the study collected between March and April 2020. Overall, there was disease prevalence of 9.6% in the population under study during this time frame. The logistic regression model was found to have an AUC of 0.89 (95% CI 0.84 - 0.94) when including four features: exposure history, temperature, WBC, and chest radiograph result. Random forest method resulted in AUC of 0.86 (95% CI 0.79 - 0.92) and gradient boosting had an AUC of 0.85 (95% CI 0.79-0.91). With a consistently held negative predictive value, the logistic regression model had a positive predictive value of 0.29 (0.2-0.39) compared to 0.2 (0.14-0.28) for random forest and 0.22 (0.15 - 0.3) for the gradient boosted method.

CONCLUSION : The derived predictive models offer good discriminating capacity for COVID-19 disease and provide interpretable and usable methods for those providers caring for these patients at the important crossroads of the community and the health system. We found utilization of the logistic regression model utilizing exposure history, temperature, WBC, and Chest XR result had the greatest discriminatory capacity with the most interpretable model. Integrating a predictive model-based approach to COVID-19 testing decisions and patient care pathways and locations could add efficiency and accuracy to decrease uncertainty.

McDonald Samuel A, Medford Richard J, Basit Mujeeb A, Diercks Deborah B, Courtney D Mark

2020-Nov-28

COVID-19, Clinical Prediction Models, Informatics, Machine Learning

General General

A Tiny CNN Architecture for Medical Face Mask Detection for Resource-Constrained Endpoints

ArXiv Preprint

The world is going through one of the most dangerous pandemics of all time with the rapid spread of the novel coronavirus (COVID-19). According to the World Health Organisation, the most effective way to thwart the transmission of coronavirus is to wear medical face masks. Monitoring the use of face masks in public places has been a challenge because manual monitoring could be unsafe. This paper proposes an architecture for detecting medical face masks for deployment on resource-constrained endpoints having extremely low memory footprints. A small development board with an ARM Cortex-M7 microcontroller clocked at 480 Mhz and having just 496 KB of framebuffer RAM, has been used for the deployment of the model. Using the TensorFlow Lite framework, the model is quantized to further reduce its size. The proposed model is 138 KB post quantization and runs at the inference speed of 30 FPS.

Puranjay Mohan, Aditya Jyoti Paul, Abhay Chirania

2020-11-30

General General

Novel SARS-CoV-2 encoded small RNAs in the passage to humans.

In Bioinformatics (Oxford, England)

MOTIVATION : The Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2) has recently emerged as the responsible for the pandemic outbreak of the coronavirus disease (COVID-19). This virus is closely related to coronaviruses infecting bats and Malayan pangolins, species suspected to be an intermediate host in the passage to humans. Several genomic mutations affecting viral proteins have been identified, contributing to the understanding of the recent animal-to-human transmission. However, the capacity of SARS-CoV-2 to encode functional putative microRNAs (miRNAs) remains largely unexplored.

RESULTS : We have used deep learning to discover 12 candidate stem-loop structures hidden in the viral protein-coding genome. Among the precursors, the expression of eight mature miRNAs-like sequences was confirmed in small RNA-seq data from SARS-CoV-2 infected human cells. Predicted miRNAs are likely to target a subset of human genes of which 109 are transcriptionally deregulated upon infection. Remarkably, 28 of those genes potentially targeted by SARS-CoV-2 miRNAs are down-regulated in infected human cells. Interestingly, most of them have been related to respiratory diseases and viral infection, including several afflictions previously associated with SARS-CoV-1 and SARS-CoV-2. The comparison of SARS-CoV-2 pre-miRNA sequences with those from bat and pangolin coronaviruses suggests that single nucleotide mutations could have helped its progenitors jumping inter-species boundaries, allowing the gain of novel mature miRNAs targeting human mRNAs. Our results suggest that the recent acquisition of novel miRNAs-like sequences in the SARS-CoV-2 genome may have contributed to modulate the transcriptional reprogramming of the new host upon infection.

Merino Gabriela A, Raad Jonathan, Bugnon Leandro A, Yones Cristian, Kamenetzky Laura, Claus Juan, Ariel Federico, Milone Diego H, Stegmayer Georgina

2020-Nov-27

Public Health Public Health

Whether the weather will help us weather the COVID-19 pandemic: Using machine learning to measure twitter users' perceptions.

In International journal of medical informatics ; h5-index 49.0

OBJECTIVE : The potential ability for weather to affect SARS-CoV-2 transmission has been an area of controversial discussion during the COVID-19 pandemic. Individuals' perceptions of the impact of weather can inform their adherence to public health guidelines; however, there is no measure of their perceptions. We quantified Twitter users' perceptions of the effect of weather and analyzed how they evolved with respect to real-world events and time.

MATERIALS AND METHODS : We collected 166,005 English tweets posted between January 23 and June 22, 2020 and employed machine learning/natural language processing techniques to filter for relevant tweets, classify them by the type of effect they claimed, and identify topics of discussion.

RESULTS : We identified 28,555 relevant tweets and estimate that 40.4 % indicate uncertainty about weather's impact, 33.5 % indicate no effect, and 26.1 % indicate some effect. We tracked changes in these proportions over time. Topic modeling revealed major latent areas of discussion.

DISCUSSION : There is no consensus among the public for weather's potential impact. Earlier months were characterized by tweets that were uncertain of weather's effect or claimed no effect; later, the portion of tweets claiming some effect of weather increased. Tweets claiming no effect of weather comprised the largest class by June. Major topics of discussion included comparisons to influenza's seasonality, President Trump's comments on weather's effect, and social distancing.

CONCLUSION : We exhibit a research approach that is effective in measuring population perceptions and identifying misconceptions, which can inform public health communications.

Gupta Marichi, Bansal Aditya, Jain Bhav, Rochelle Jillian, Oak Atharv, Jalali Mohammad S

2020-Nov-10

Individuals’ perceptions, Machine learning, Opinion mining, SARS-CoV-2 transmission, Topic modeling

Pathology Pathology

Identifying Predictors of Psychological Distress During COVID-19: A Machine Learning Approach.

In Frontiers in psychology ; h5-index 92.0

Scientific understanding about the psychological impact of the COVID-19 global pandemic is in its nascent stage. Prior research suggests that demographic factors, such as gender and age, are associated with greater distress during a global health crisis. Less is known about how emotion regulation impacts levels of distress during a pandemic. The present study aimed to identify predictors of psychological distress during the COVID-19 pandemic. Participants (N = 2,787) provided demographics, history of adverse childhood experiences, current coping strategies (use of implicit and explicit emotion regulation), and current psychological distress. The overall prevalence of clinical levels of anxiety, depression, and post-traumatic stress was higher than the prevalence outside a pandemic and was higher than rates reported among healthcare workers and survivors of severe acute respiratory syndrome. Younger participants (<45 years), women, and non-binary individuals reported higher prevalence of symptoms across all measures of distress. A random forest machine learning algorithm was used to identify the strongest predictors of distress. Regression trees were developed to identify individuals at greater risk for anxiety, depression, and post-traumatic stress. Somatization and less reliance on adaptive defense mechanisms were associated with greater distress. These findings highlight the importance of assessing individuals' physical experiences of psychological distress and emotion regulation strategies to help mental health providers tailor assessments and treatment during a global health crisis.

Prout Tracy A, Zilcha-Mano Sigal, Aafjes-van Doorn Katie, Békés Vera, Christman-Cohen Isabelle, Whistler Kathryn, Kui Thomas, Di Giuseppe Mariagrazia

2020

COVID-19 pandemic, anxiety, defense mechanisms, depression, emotion regulation, machine learning, post-traumatic stress, somatization

Radiology Radiology

Machine-learning classification of texture features of portable chest X-ray accurately classifies COVID-19 lung infection.

In Biomedical engineering online

BACKGROUND : The large volume and suboptimal image quality of portable chest X-rays (CXRs) as a result of the COVID-19 pandemic could post significant challenges for radiologists and frontline physicians. Deep-learning artificial intelligent (AI) methods have the potential to help improve diagnostic efficiency and accuracy for reading portable CXRs.

PURPOSE : The study aimed at developing an AI imaging analysis tool to classify COVID-19 lung infection based on portable CXRs.

MATERIALS AND METHODS : Public datasets of COVID-19 (N = 130), bacterial pneumonia (N = 145), non-COVID-19 viral pneumonia (N = 145), and normal (N = 138) CXRs were analyzed. Texture and morphological features were extracted. Five supervised machine-learning AI algorithms were used to classify COVID-19 from other conditions. Two-class and multi-class classification were performed. Statistical analysis was done using unpaired two-tailed t tests with unequal variance between groups. Performance of classification models used the receiver-operating characteristic (ROC) curve analysis.

RESULTS : For the two-class classification, the accuracy, sensitivity and specificity were, respectively, 100%, 100%, and 100% for COVID-19 vs normal; 96.34%, 95.35% and 97.44% for COVID-19 vs bacterial pneumonia; and 97.56%, 97.44% and 97.67% for COVID-19 vs non-COVID-19 viral pneumonia. For the multi-class classification, the combined accuracy and AUC were 79.52% and 0.87, respectively.

CONCLUSION : AI classification of texture and morphological features of portable CXRs accurately distinguishes COVID-19 lung infection in patients in multi-class datasets. Deep-learning methods have the potential to improve diagnostic efficiency and accuracy for portable CXRs.

Hussain Lal, Nguyen Tony, Li Haifang, Abbasi Adeel A, Lone Kashif J, Zhao Zirun, Zaib Mahnoor, Chen Anne, Duong Tim Q

2020-Nov-25

COVID-19, Classification, Feature extraction, Machine learning, Morphological, Texture

Surgery Surgery

Development of a machine learning algorithm to predict intubation among hospitalized patients with COVID-19.

In Journal of critical care ; h5-index 48.0

PURPOSE : The purpose of this study is to develop a machine learning algorithm to predict future intubation among patients diagnosed or suspected with COVID-19.

MATERIALS AND METHODS : This is a retrospective cohort study of patients diagnosed or under investigation for COVID-19. A machine learning algorithm was trained to predict future presence of intubation based on prior vitals, laboratory, and demographic data. Model performance was compared to ROX index, a validated prognostic tool for prediction of mechanical ventilation.

RESULTS : 4087 patients admitted to five hospitals between February 2020 and April 2020 were included. 11.03% of patients were intubated. The machine learning model outperformed the ROX-index, demonstrating an area under the receiver characteristic curve (AUC) of 0.84 and 0.64, and area under the precision-recall curve (AUPRC) of 0.30 and 0.13, respectively. In the Kaplan-Meier analysis, patients alerted by the model were more likely to require intubation during their admission (p < 0.0001).

CONCLUSION : In patients diagnosed or under investigation for COVID-19, machine learning can be used to predict future risk of intubation based on clinical data which are routinely collected and available in clinical setting. Such an approach may facilitate identification of high-risk patients to assist in clinical care.

Arvind Varun, Kim Jun S, Cho Brian H, Geng Eric, Cho Samuel K

2020-Nov-16

COVID-19, Intubation, Machine learning, Prediction, Respiratory distress

General General

Classification of COVID-19 chest X-rays with deep learning: new models or fine tuning?

In Health information science and systems

Background and objectives : Chest X-ray data have been found to be very promising for assessing COVID-19 patients, especially for resolving emergency-department and urgent-care-center overcapacity. Deep-learning (DL) methods in artificial intelligence (AI) play a dominant role as high-performance classifiers in the detection of the disease using chest X-rays. Given many new DL models have been being developed for this purpose, the objective of this study is to investigate the fine tuning of pretrained convolutional neural networks (CNNs) for the classification of COVID-19 using chest X-rays. If fine-tuned pre-trained CNNs can provide equivalent or better classification results than other more sophisticated CNNs, then the deployment of AI-based tools for detecting COVID-19 using chest X-ray data can be more rapid and cost-effective.

Methods : Three pretrained CNNs, which are AlexNet, GoogleNet, and SqueezeNet, were selected and fine-tuned without data augmentation to carry out 2-class and 3-class classification tasks using 3 public chest X-ray databases.

Results : In comparison with other recently developed DL models, the 3 pretrained CNNs achieved very high classification results in terms of accuracy, sensitivity, specificity, precision, F 1 score, and area under the receiver-operating-characteristic curve.

Conclusion : AlexNet, GoogleNet, and SqueezeNet require the least training time among pretrained DL models, but with suitable selection of training parameters, excellent classification results can be achieved without data augmentation by these networks. The findings contribute to the urgent need for harnessing the pandemic by facilitating the deployment of AI tools that are fully automated and readily available in the public domain for rapid implementation.

Pham Tuan D

2021-Dec

Artificial intelligence, COVID-19, Chest X-rays, Classification, Deep learning

General General

Two Stage Transformer Model for COVID-19 Fake News Detection and Fact Checking

ArXiv Preprint

The rapid advancement of technology in online communication via social media platforms has led to a prolific rise in the spread of misinformation and fake news. Fake news is especially rampant in the current COVID-19 pandemic, leading to people believing in false and potentially harmful claims and stories. Detecting fake news quickly can alleviate the spread of panic, chaos and potential health hazards. We developed a two stage automated pipeline for COVID-19 fake news detection using state of the art machine learning models for natural language processing. The first model leverages a novel fact checking algorithm that retrieves the most relevant facts concerning user claims about particular COVID-19 claims. The second model verifies the level of truth in the claim by computing the textual entailment between the claim and the true facts retrieved from a manually curated COVID-19 dataset. The dataset is based on a publicly available knowledge source consisting of more than 5000 COVID-19 false claims and verified explanations, a subset of which was internally annotated and cross-validated to train and evaluate our models. We evaluate a series of models based on classical text-based features to more contextual Transformer based models and observe that a model pipeline based on BERT and ALBERT for the two stages respectively yields the best results.

Rutvik Vijjali, Prathyush Potluri, Siddharth Kumar, Sundeep Teki

2020-11-26

General General

Deep Transfer Learning for COVID-19 Prediction: Case Study for Limited Data Problems.

In Current medical imaging

OBJECTIVE : Automatic prediction of COVID-19 using deep convolution neural networks based pre-trained transfer models and Chest X-ray images.

METHOD : This research employs the advantages of computer vision and medical image analysis to develop an automated model that has the clinical potential for early detection of the disease. Using Deep Learning models, the research aims at evaluating the effectiveness and accuracy of different convolutional neural networks models in the automatic diagnosis of COVID-19 from X-ray images as compared to diagnosis performed by experts in the medical community.

RESULT : Due to the fact that the dataset available for COVID-19 is still limited, the best model to use is the InceptionNetV3. Performance results show that the InceptionNetV3 model yielded the highest accuracy of 98.63% (with data augmentation) and 98.90% (without data augmentation) among the three models designed. However, as the dataset gets bigger, the Inception ResNetV2 and NASNetlarge will do a better job of classification. All the performed networks tend to over-fit when data augmentation is not used, this is due to the small amount of data used for training and validation.

CONCLUSION : A deep transfer learning is proposed to detecting the COVID-19 automatically from chest X-ray by training it with X-ray images gotten from both COVID-19 patients and people with normal chest Xrays. The study is aimed at helping doctors in making decisions in their clinical practice due its high performance and effectiveness, the study also gives an insight to how transfer learning was used to automatically detect the COVID-19.

Albahli Saleh, Albattah Waleed

2020-Nov-23

CNN, Deep transfer learning, X-ray, coronavirus, inceptionetv3, inceptionresnetv2

General General

A Review of Piezoelectric and Magnetostrictive Biosensor Materials for Detection of COVID-19 and Other Viruses.

In Advanced materials (Deerfield Beach, Fla.)

The spread of the severe acute respiratory syndrome coronavirus has changed the lives of people around the world with a huge impact on economies and societies. The development of wearable sensors that can continuously monitor the environment for viruses may become an important research area. Here, the state of the art of research on biosensor materials for virus detection is reviewed. A general description of the principles for virus detection is included, along with a critique of the experimental work dedicated to various virus sensors, and a summary of their detection limitations. The piezoelectric sensors used for the detection of human papilloma, vaccinia, dengue, Ebola, influenza A, human immunodeficiency, and hepatitis B viruses are examined in the first section; then the second part deals with magnetostrictive sensors for the detection of bacterial spores, proteins, and classical swine fever. In addition, progress related to early detection of COVID-19 (coronavirus disease 2019) is discussed in the final section, where remaining challenges in the field are also identified. It is believed that this review will guide material researchers in their future work of developing smart biosensors, which can further improve detection sensitivity in monitoring currently known and future virus threats.

Narita Fumio, Wang Zhenjin, Kurita Hiroki, Li Zhen, Shi Yu, Jia Yu, Soutis Constantinos

2020-Nov-24

Internet of Things, artificial intelligence, biosensors, data analytics, detection properties, electromagneto-mechanical design, machine learning, piezoelectric/magnetostrictive materials, virus

Public Health Public Health

COVID-19 Pneumonia Accurately Detected on Chest Radiographs with Artificial Intelligence.

In Intelligence-based medicine

Purpose : To investigate the diagnostic performance of an Artificial Intelligence (AI) system for detection of COVID-19 in chest radiographs (CXR), and compare results to those of physicians working alone, or with AI support.

Materials and Methods : An AI system was fine-tuned to discriminate confirmed COVID-19 pneumonia, from other viral and bacterial pneumonia and non-pneumonia patients and used to review 302 CXR images from adult patients retrospectively sourced from nine different databases. Fifty-four physicians blind to diagnosis, were invited to interpret images under identical conditions in a test set, and randomly assigned either to receive or not receive support from the AI system. Comparisons were then made between diagnostic performance of physicians working with and without AI support. AI system performance was evaluated using the area under the receiver operating characteristic (AUROC), and sensitivity and specificity of physician performance compared to that of the AI system.

Results : Discrimination by the AI system of COVID-19 pneumonia showed an AUROC curve of 0.96 in the validation and 0.83 in the external test set, respectively. The AI system outperformed physicians in the AUROC overall (70% increase in sensitivity and 1% increase in specificity, p<0.0001). When working with AI support, physicians increased their diagnostic sensitivity from 47% to 61% (p<0.001), although specificity decreased from 79% to 75% (p=0.007).

Conclusions : Our results suggest interpreting chest radiographs (CXR) supported by AI, increases physician diagnostic sensitivity for COVID-19 detection. This approach involving a human-machine partnership may help expedite triaging efforts and improve resource allocation in the current crisis.

Dorr Francisco, Chaves Hernán, Serra María Mercedes, Ramirez Andrés, Costa Martín Elías, Seia Joaquín, Cejas Claudia, Castro Marcelo, Eyheremendy Eduardo, Slezak Diego Fernández, Farez Mauricio F

2020-Nov-19

AI, artificial intelligence, AUPR, area under the precision-recall, AUROC, area under the receiver operating characteristic, Artificial intelligence, COVID-19, CT, computed tomography, CXR, chest radiographs, Chest, DL, deep learning, Diagnostic performance, RT-PCR, real-time reverse transcriptase–polymerase chain reaction, Radiography

General General

Application of deep learning techniques for detection of COVID-19 cases using chest X-ray images: A comprehensive study.

In Biomedical signal processing and control

The emergence of Coronavirus Disease 2019 (COVID-19) in early December 2019 has caused immense damage to health and global well-being. Currently, there are approximately five million confirmed cases and the novel virus is still spreading rapidly all over the world. Many hospitals across the globe are not yet equipped with an adequate amount of testing kits and the manual Reverse Transcription-Polymerase Chain Reaction (RT-PCR) test is time-consuming and troublesome. It is hence very important to design an automated and early diagnosis system which can provide fast decision and greatly reduce the diagnosis error. The chest X-ray images along with emerging Artificial Intelligence (AI) methodologies, in particular Deep Learning (DL) algorithms have recently become a worthy choice for early COVID-19 screening. This paper proposes a DL assisted automated method using X-ray images for early diagnosis of COVID-19 infection. We evaluate the effectiveness of eight pre-trained Convolutional Neural Network (CNN) models such as AlexNet, VGG-16, GoogleNet, MobileNet-V2, SqueezeNet, ResNet-34, ResNet-50 and Inception-V3 for classification of COVID-19 from normal cases. Also, comparative analyses have been made among these models by considering several important factors such as batch size, learning rate, number of epochs, and type of optimizers with an aim to find the best suited model. The models have been validated on publicly available chest X-ray images and the best performance is obtained by ResNet-34 with an accuracy of 98.33%. This study will be useful for researchers to think for the design of more effective CNN based models for early COVID-19 detection.

Nayak Soumya Ranjan, Nayak Deepak Ranjan, Sinha Utkarsh, Arora Vaibhav, Pachori Ram Bilas

2021-Feb

COVID-19, Chest X-ray, Convolutional Neural Networks, Optimization algorithms, SARS-CoV-2

General General

CNN-based transfer learning-BiLSTM network: A novel approach for COVID-19 infection detection.

In Applied soft computing

Coronavirus disease 2019 (COVID-2019), which emerged in Wuhan, China in 2019 and has spread rapidly all over the world since the beginning of 2020, has infected millions of people and caused many deaths. For this pandemic, which is still in effect, mobilization has started all over the world, and various restrictions and precautions have been taken to prevent the spread of this disease. In addition, infected people must be identified in order to control the infection. However, due to the inadequate number of Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests, Chest computed tomography (CT) becomes a popular tool to assist the diagnosis of COVID-19. In this study, two deep learning architectures have been proposed that automatically detect positive COVID-19 cases using Chest CT X-ray images. Lung segmentation (preprocessing) in CT images, which are given as input to these proposed architectures, is performed automatically with Artificial Neural Networks (ANN). Since both architectures contain AlexNet architecture, the recommended method is a transfer learning application. However, the second proposed architecture is a hybrid structure as it contains a Bidirectional Long Short-Term Memories (BiLSTM) layer, which also takes into account the temporal properties. While the COVID-19 classification accuracy of the first architecture is 98.14%, this value is 98.70% in the second hybrid architecture. The results prove that the proposed architecture shows outstanding success in infection detection and, therefore this study contributes to previous studies in terms of both deep architectural design and high classification success.

Aslan Muhammet Fatih, Unlersen Muhammed Fahri, Sabanci Kadir, Durdu Akif

2020-Nov-18

AlexNet, BiLSTM, COVID-19, Hybrid architecture, Transfer learning

General General

Impact of Covid19 on electricity load in Haryana (India).

In International journal of energy research

As it is known that the whole world is battling against the Corona Virus Disease or COVID19 and trying their level best to stop the spread of this pandemic. To avoid the spread, several countries like China, Italy, Spain, America took strict measures like nationwide lockdown or by cordoning off the areas that were suspected of having risks of community spread. Taking cues from the foreign counterparts, the government of India undertook an important decision of nationwide full lockdown on March 25th which was further extended till May 4th, 2020 (47 days-full lockdown). Looking at the current situation government of India pushed the lockdown further with eased curbs, divided the nation into green, orange and red zones, rapid testing of citizens in containment area, mandatory wearing of masks and following social distancing among others. The outbreak of the pandemic, has led to the large economic shock to the world which was never been experienced since decades. Moreover it brought a great uncertainty over the world wide electricity sector as to slow down the spread of the virus, many countries have issued restrictions, including the closure of malls, educational institutions, halting trains, suspending of flights, implemented partial or full lockdowns, insisted work from home to the employees. In this paper, the impact analysis of electricity consumption of state Haryana (India) is done using machine learning conventional algorithms and artificial neural network and electricity load forecasting is done for a week so as to aid the electricity board to know the consumption of the area pre hand and likewise can restrict the electricity production as per requirement. Thus, it will help power system to secure electricity supply and scheduling and reduce wastes since electricity is difficult to store. For this the dataset from regional electricity boards of Haryana that is, Dakshin Haryana Bijli Vitran Nigam and Uttar Haryana Bijli Vitran Nigam were analysed and electricity loads of state were predicted using python programming and as per result analysis it was observed that artificial neural network out performs conventional machine learning models.

Gulati Payal, Kumar Anil, Bhardwaj Raghav

2020-Oct-12

General General

Understanding the temporal evolution of COVID-19 research through machine learning and natural language processing.

In Scientometrics

The outbreak of the novel coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been continuously affecting human lives and communities around the world in many ways, from cities under lockdown to new social experiences. Although in most cases COVID-19 results in mild illness, it has drawn global attention due to the extremely contagious nature of SARS-CoV-2. Governments and healthcare professionals, along with people and society as a whole, have taken any measures to break the chain of transition and flatten the epidemic curve. In this study, we used multiple data sources, i.e., PubMed and ArXiv, and built several machine learning models to characterize the landscape of current COVID-19 research by identifying the latent topics and analyzing the temporal evolution of the extracted research themes, publications similarity, and sentiments, within the time-frame of January-May 2020. Our findings confirm the types of research available in PubMed and ArXiv differ significantly, with the former exhibiting greater diversity in terms of COVID-19 related issues and the latter focusing more on intelligent systems/tools to predict/diagnose COVID-19. The special attention of the research community to the high-risk groups and people with complications was also confirmed.

Ebadi Ashkan, Xi Pengcheng, Tremblay Stéphane, Spencer Bruce, Pall Raman, Wong Alexander

2020-Nov-19

COVID-19 research landscape, Machine learning, Structural topic modeling, Text mining, Topics evolution

General General

Virtual screening of anti-HIV1 compounds against SARS-CoV-2: machine learning modeling, chemoinformatics and molecular dynamics simulation based analysis.

In Scientific reports ; h5-index 158.0

COVID-19 caused by the SARS-CoV-2 is a current global challenge and urgent discovery of potential drugs to combat this pandemic is a need of the hour. 3-chymotrypsin-like cysteine protease (3CLpro) enzyme is the vital molecular target against the SARS-CoV-2. Therefore, in the present study, 1528 anti-HIV1compounds were screened by sequence alignment between 3CLpro of SARS-CoV-2 and avian infectious bronchitis virus (avian coronavirus) followed by machine learning predictive model, drug-likeness screening and molecular docking, which resulted in 41 screened compounds. These 41 compounds were re-screened by deep learning model constructed considering the IC50 values of known inhibitors which resulted in 22 hit compounds. Further, screening was done by structural activity relationship mapping which resulted in two structural clefts. Thereafter, functional group analysis was also done, where cluster 2 showed the presence of several essential functional groups having pharmacological importance. In the final stage, Cluster 2 compounds were re-docked with four different PDB structures of 3CLpro, and their depth interaction profile was analyzed followed by molecular dynamics simulation at 100 ns. Conclusively, 2 out of 1528 compounds were screened as potential hits against 3CLpro which could be further treated as an excellent drug against SARS-CoV-2.

Nand Mahesha, Maiti Priyanka, Joshi Tushar, Chandra Subhash, Pande Veena, Kuniyal Jagdish Chandra, Ramakrishnan Muthannan Andavar

2020-Nov-23

General General

COVID-19 and the epistemology of epidemiological models at the dawn of AI.

In Annals of human biology

The models used to estimate disease transmission, susceptibility and severity determine what epidemiology can (and cannot tell) us about COVID-19. These include: 'model organisms' chosen for their phylogenetic/aetiological similarities; multivariable statistical models to estimate the strength/direction of (potentially causal) relationships between variables (through 'causal inference'), and the (past/future) value of unmeasured variables (through 'classification/prediction'); and a range of modelling techniques to predict beyond the available data (through 'extrapolation'), compare different hypothetical scenarios (through 'simulation'), and estimate key features of dynamic processes (through 'projection'). Each of these models: address different questions using different techniques; involve assumptions that require careful assessment; and are vulnerable to generic and specific biases that can undermine the validity and interpretation of their findings. It is therefore necessary that the models used: can actually address the questions posed; and have been competently applied. In this regard, it is important to stress that extrapolation, simulation and projection cannot offer accurate predictions of future events when the underlying mechanisms (and the contexts involved) are poorly understood and subject to change. Given the importance of understanding such mechanisms/contexts, and the limited opportunity for experimentation during outbreaks of novel diseases, the use of multivariable statistical models to estimate the strength/direction of potentially causal relationships between two variables (and the biases incurred through their misapplication/misinterpretation) warrant particular attention. Such models must be carefully designed to address: 'selection-collider bias', 'unadjusted confounding bias' and 'inferential mediator adjustment bias' - all of which can introduce effects capable of enhancing, masking or reversing the estimated (true) causal relationship between the two variables examined.1 Selection-collider bias occurs when these two variables independently cause a third (the 'collider'), and when this collider determines/reflects the basis for selection in the analysis. It is likely to affect all incompletely representative samples, although its effects will be most pronounced wherever selection is constrained (e.g. analyses focusing on infected/hospitalised individuals). Unadjusted confounding bias disrupts the estimated (true) causal relationship between two variables when: these share one (or more) common cause(s); and when the effects of these causes have not been adjusted for in the analyses (e.g. whenever confounders are unknown/unmeasured). Inferentially similar biases can occur when: one (or more) variable(s) (or 'mediators') fall on the causal path between the two variables examined (i.e. when such mediators are caused by one of the variables and are causes of the other); and when these mediators are adjusted for in the analysis. Such adjustment is commonplace when: mediators are mistaken for confounders; prediction models are mistakenly repurposed for causal inference; or mediator adjustment is used to estimate direct and indirect causal relationships (in a mistaken attempt at 'mediation analysis'). These three biases are central to ongoing and unresolved epistemological tensions within epidemiology. All have substantive implications for our understanding of COVID-19, and the future application of artificial intelligence to 'data-driven' modelling of similar phenomena. Nonetheless, competently applied and carefully interpreted, multivariable statistical models may yet provide sufficient insight into mechanisms and contexts to permit more accurate projections of future disease outbreaks.

Ellison George T H

2020-Sep

Public Health Public Health

Androgen Signaling Regulates SARS-CoV-2 Receptor Levels and Is Associated with Severe COVID-19 Symptoms in Men.

In Cell stem cell

SARS-CoV-2 infection has led to a global health crisis, and yet our understanding of the disease and potential treatment options remains limited. The infection occurs through binding of the virus with angiotensin converting enzyme 2 (ACE2) on the cell membrane. Here, we established a screening strategy to identify drugs that reduce ACE2 levels in human embryonic stem cell (hESC)-derived cardiac cells and lung organoids. Target analysis of hit compounds revealed androgen signaling as a key modulator of ACE2 levels. Treatment with antiandrogenic drugs reduced ACE2 expression and protected hESC-derived lung organoids against SARS-CoV-2 infection. Finally, clinical data on COVID-19 patients demonstrated that prostate diseases, which are linked to elevated androgen, are significant risk factors and that genetic variants that increase androgen levels are associated with higher disease severity. These findings offer insights on the mechanism of disproportionate disease susceptibility in men and identify antiandrogenic drugs as candidate therapeutics for COVID-19.

Samuel Ryan M, Majd Homa, Richter Mikayla N, Ghazizadeh Zaniar, Zekavat Seyedeh Maryam, Navickas Albertas, Ramirez Jonathan T, Asgharian Hosseinali, Simoneau Camille R, Bonser Luke R, Koh Kyung Duk, Garcia-Knight Miguel, Tassetto Michel, Sunshine Sara, Farahvashi Sina, Kalantari Ali, Liu Wei, Andino Raul, Zhao Hongyu, Natarajan Pradeep, Erle David J, Ott Melanie, Goodarzi Hani, Fattahi Faranak

2020-Nov-17

5-alpha reductase inhibitors, ACE2 regulation, COVID-19 risk factors, COVID-19 sex bias, SARS-CoV-2 infection model, deep learning, drug re-purposing, hPSC-based disease modeling, high content screening, virtual drug screen

Radiology Radiology

Deep-learning algorithms for the interpretation of chest radiographs to aid in the triage of COVID-19 patients: A multicenter retrospective study.

In PloS one ; h5-index 176.0

The recent medical applications of deep-learning (DL) algorithms have demonstrated their clinical efficacy in improving speed and accuracy of image interpretation. If the DL algorithm achieves a performance equivalent to that achieved by physicians in chest radiography (CR) diagnoses with Coronavirus disease 2019 (COVID-19) pneumonia, the automatic interpretation of the CR with DL algorithms can significantly reduce the burden on clinicians and radiologists in sudden surges of suspected COVID-19 patients. The aim of this study was to evaluate the efficacy of the DL algorithm for detecting COVID-19 pneumonia on CR compared with formal radiology reports. This is a retrospective study of adult patients that were diagnosed as positive COVID-19 cases based on the reverse transcription polymerase chain reaction among all the patients who were admitted to five emergency departments and one community treatment center in Korea from February 18, 2020 to May 1, 2020. The CR images were evaluated with a publicly available DL algorithm. For reference, CR images without chest computed tomography (CT) scans classified as positive for COVID-19 pneumonia were used given that the radiologist identified ground-glass opacity, consolidation, or other infiltration in retrospectively reviewed CR images. Patients with evidence of pneumonia on chest CT scans were also classified as COVID-19 pneumonia positive outcomes. The overall sensitivity and specificity of the DL algorithm for detecting COVID-19 pneumonia on CR were 95.6%, and 88.7%, respectively. The area under the curve value of the DL algorithm for the detection of COVID-19 with pneumonia was 0.921. The DL algorithm demonstrated a satisfactory diagnostic performance comparable with that of formal radiology reports in the CR-based diagnosis of pneumonia in COVID-19 patients. The DL algorithm may offer fast and reliable examinations that can facilitate patient screening and isolation decisions, which can reduce the medical staff workload during COVID-19 pandemic situations.

Jang Se Bum, Lee Suk Hee, Lee Dong Eun, Park Sin-Youl, Kim Jong Kun, Cho Jae Wan, Cho Jaekyung, Kim Ki Beom, Park Byunggeon, Park Jongmin, Lim Jae-Kwang

2020

Radiology Radiology

DeepCOVID-XR: An Artificial Intelligence Algorithm to Detect COVID-19 on Chest Radiographs Trained and Tested on a Large US Clinical Dataset.

In Radiology ; h5-index 91.0

Background There are characteristic findings of Coronavirus Disease 2019 (COVID-19) on chest imaging. An artificial intelligence (AI) algorithm to detect COVID-19 on chest radiographs might be useful for triage or infection control within a hospital setting, but prior reports have been limited by small datasets and/or poor data quality. Purpose To present DeepCOVID-XR, a deep learning AI algorithm for detecting COVID-19 on chest radiographs, trained and tested on a large clinical dataset. Materials and Methods DeepCOVID-XR is an ensemble of convolutional neural networks to detect COVID-19 on frontal chest radiographs using real-time polymerase chain reaction (RT-PCR) as a reference standard. The algorithm was trained and validated on 14,788 images (4,253 COVID-19 positive) from sites across the Northwestern Memorial Healthcare System from February 2020 to April 2020, then tested on 2,214 images (1,192 COVID-19 positive) from a single hold-out institution. Performance of the algorithm was compared with interpretations from 5 experienced thoracic radiologists on 300 random test images using the McNemar test for sensitivity/specificity and DeLong's test for the area under the receiver operating characteristic curve (AUC). Results A total of 5,853 patients (58±19 years, 3,101 women) were evaluated across datasets. On the entire test set, DeepCOVID-XR's accuracy was 83% with an AUC of 0.90. On 300 random test images (134 COVID-19 positive), DeepCOVID-XR's accuracy was 82% compared to individual radiologists (76%-81%) and the consensus of all 5 radiologists (81%). DeepCOVID-XR had a significantly higher sensitivity (71%) than 1 radiologist (60%, p<0.001) and higher specificity (92%) than 2 radiologists (75%, p<0.001; 84% p=0.009). DeepCOVID-XR's AUC was 0.88 compared to the consensus AUC of 0.85 (p=0.13 for comparison). Using the consensus interpretation as the reference standard, DeepCOVID-XR's AUC was 0.95 (0.92-0.98 95%CI). Conclusion DeepCOVID-XR, an AI algorithm, detected COVID-19 on chest radiographs with performance similar to a consensus of experienced thoracic radiologists. See also the editorial by van Ginneken.

Wehbe Ramsey M, Sheng Jiayue, Dutta Shinjan, Chai Siyuan, Dravid Amil, Barutcu Semih, Wu Yunan, Cantrell Donald R, Xiao Nicholas, Allen Bradley D, MacNealy Gregory A, Savas Hatice, Agrawal Rishi, Parekh Nishant, Katsaggelos Aggelos K

2020-Nov-24

General General

Latent COVID-19 Clusters in Patients with Chronic Respiratory Conditions.

In Studies in health technology and informatics ; h5-index 23.0

The goal of this paper was to apply unsupervised machine learning techniques towards the discovery of latent COVID-19 clusters in patients with chronic lower respiratory diseases (CLRD). Patients who underwent testing for SARS-CoV-2 were identified from electronic medical records. The analytical dataset comprised 2,328 CLRD patients of whom 1,029 were tested COVID-19 positive. We used the factor analysis for mixed data method for preprocessing. It performed principle component analysis on numeric values and multiple correspondence analysis on categorical values which helped convert categorical data into numeric. Cluster analysis was an effective means to both distinguish subgroups of CLRD patients with COVID-19 as well as identify patient clusters which were adversely affected by the infection. Age, comorbidity index and race were important factors for cluster separations. Furthermore, diseases of the circulatory system, the nervous system and sense organs, digestive system, genitourinary system, metabolic diseases and immunity disorders were also important criteria in the resulting cluster analyses.

Cui Wanting, Cabrera Manuel, Finkelstein Joseph

2020-Nov-23

COVID-19, Chronic lower respiratory diseases, cluster analysis

General General

Development and External Validation of a Machine Learning Tool to Rule Out COVID-19 Among Adults in the Emergency Department Using Routine Blood Tests: A Large, Multicenter, Real-World Study.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Conventional diagnosis of COVID-19 with reverse transcription polymerase chain reaction (RT-PCR) testing (hereafter, PCR) is associated with prolonged time to diagnosis and costs of running the test. The SARS-CoV-2 virus might lead to characteristic patterns in levels of widely-available, routine blood tests results that could be identified with machine learning methodologies. Machine learning modalities integrating findings from these common laboratory test results might accelerate ruling out emergency department patients for COVID-19.

OBJECTIVE : We sought to develop (ie, train and internally validate with cross-validation techniques) and externally validate a machine learning model to rule out COVID 19 using only routine blood tests among adults in emergency departments.

METHODS : Using clinical data from emergency departments (EDs) from 66 US hospitals before the pandemic (before the end of December 2019) or during the pandemic (March-July 2020), we included patients aged ≥20 years in the study timeframe. We excluded those with missing laboratory results. Model training used 2,183 PCR-confirmed positive cases from 43 hospitals during the pandemic as positive controls; negative controls were 10,000 pre-pandemic patients from the same hospitals. External validation used 23 hospitals with 1,020 pandemic PCR-positive cases and 171,734 pre-pandemic negative controls. The main outcome was COVID 19 status predicted using same-day routine laboratory results. Model performance was assessed with area under the receiver operating characteristic (AUROC) curve as well as sensitivity, specificity, negative predictive value (NPV).

RESULTS : Of 192,779 patients included in the training, external validation, and sensitivity datasets (median [IQR] age deciles 50.0 [30.0-60.0] years, 40.5% [78,249/192,779] male), AUROC for training and external validation was 0.91 (95% CI, 0.90-0.92). Using a risk score cutoff of 1.0 (out of 100) in the external validation dataset, the model achieved sensitivity of 95.9% and specificity of 41.7%; with a cutoff of 2.0, sensitivity of 92.6% and specificity of 59.9%. At the cutoff of 2, the NPVs at prevalences of 1%, 10%, and 20% were 99.9%, 98.6%, and 97%.

CONCLUSIONS : A machine learning model developed with multicenter clinical data integrating commonly collected ED laboratory data demonstrated high rule-out accuracy for COVID-19 status, and might inform selective use of PCR-based testing.

Plante Timothy B, Blau Aaron, Berg Adrian N, Weinberg Aaron S, Jun Ik E, Tapson Victor F, Kanigan Tanya S, Adib Artur

2020-Nov-19

General General

Mobile Health (mHealth) Viral Diagnostics Enabled with Adaptive Adversarial Learning.

In ACS nano ; h5-index 203.0

Deep-learning (DL)-based image processing has potential to revolutionize the use of smartphones in mobile health (mHealth) diagnostics of infectious diseases. However, the high variability in cellphone image data acquisition and the common need for large amounts of specialist-annotated images for traditional DL model training may preclude generalizability of smartphone-based diagnostics. Here, we employed adversarial neural networks with conditioning to develop an easily reconfigurable virus diagnostic platform that leverages a dataset of smartphone-taken microfluidic chip photos to rapidly generate image classifiers for different target pathogens on-demand. Adversarial learning was also used to augment this real image dataset by generating 16,000 realistic synthetic microchip images, through style generative adversarial networks (StyleGAN). We used this platform, termed smartphone-based pathogen detection resource multiplier using adversarial networks (SPyDERMAN), to accurately detect different intact viruses in clinical samples and to detect viral nucleic acids through integration with CRISPR diagnostics. We evaluated the performance of the system in detecting five different virus targets using 179 patient samples. The generalizability of the system was confirmed by rapid reconfiguration to detect SARS-CoV-2 antigens in nasal swab samples (n = 62) with 100% accuracy. Overall, the SPyDERMAN system may contribute to epidemic preparedness strategies by providing a platform for smartphone-based diagnostics that can be adapted to a given emerging viral agent within days of work.

Shokr Ahmed, Pacheco Luis G C, Thirumalaraju Prudhvi, Kanakasabapathy Manoj Kumar, Gandhi Jahnavi, Kartik Deeksha, Silva Filipe S R, Erdogmus Eda, Kandula Hemanth, Luo Shenglin, Yu Xu G, Chung Raymond T, Li Jonathan Z, Kuritzkes Daniel R, Shafiee Hadi

2020-Nov-23

adversarial learning neural networks, artificial intelligence, clustered regularly interspaced short palindromic repeats, deep learning, diagnostics, severe acute respiratory syndrome coronavirus, smartphones

Radiology Radiology

Abnormal Lung Quantification in Chest CT Images of COVID-19 Patients with Deep Learning and its Application to Severity Prediction.

In Medical physics ; h5-index 59.0

OBJECTIVE : CT provides rich diagnosis and severity information of COVID-19 in clinical practice. However, there is no computerized tool to automatically delineate COVID-19 infection regions in chest CT scans for quantitative assessment in advanced applications such as severity prediction. The aim of this study is to develop a deep learning (DL) based method for automatic segmentation and quantification of infection regions as well as the entire lungs from chest CT scans.

METHODS : The DL-based segmentation method employs the "VB-Net" neural network to segment COVID-19 infection regions in CT scans. The developed DL-based segmentation system is trained by CT scans from 249 COVID-19 patients, and further validated by CT scans from other 300 COVID-19 patients. To accelerate the manual delineation of CT scans for training, a human-involved-model-iterations (HIMI) strategy is also adopted to assist radiologists to refine automatic annotation of each training case. To evaluate the performance of the DL-based segmentation system, three metrics, i.e., Dice similarity coefficient, the differences of volume, and percentage of infection (POI), are calculated between automatic and manual segmentations on the validation set. Then, a clinical study on severity prediction is reported based on the quantitative infection assessment.

RESULTS : The proposed DL-based segmentation system yielded Dice similarity coefficients of 91.6%±10.0% between automatic and manual segmentations, and a mean POI estimation error of 0.3% for the whole lung on the validation dataset. Moreover, compared with the cases with fully manual delineation that often takes hours, the proposed HIMI training strategy can dramatically reduce the delineation time to 4 minutes after 3 iterations of model updating. Besides, the best accuracy of severity prediction was 73.4%±1.3% when the mass of infection (MOI) of multiple lung lobes and bronchopulmonary segments were used as features for severity prediction, indicating the potential clinical application of our quantification technique on severity prediction.

CONCLUSIONS : A DL-based segmentation system has been developed to automatically segment and quantify infection regions in CT scans of COVID-19 patients. Quantitative evaluation indicated high accuracy in automatic infection delineation and severity prediction.

Shan Fei, Gao Yaozong, Wang Jun, Shi Weiya, Shi Nannan, Han Miaofei, Xue Zhong, Shen Dinggang, Shi Yuxin

2020-Nov-22

COVID-19, computed tomography (CT), deep learning, human-involved-model-iterations, infection region segmentation

General General

A Machine Learning-Aided Global Diagnostic and Comparative Tool to Assess Effect of Quarantine Control in COVID-19 Spread.

In Patterns (New York, N.Y.)

We have developed a globally applicable diagnostic COVID-19 model by augmenting the classical SIR epidemiological model with a neural network module. Our model does not rely upon previous epidemics like SARS/MERS and all parameters are optimized via machine learning algorithms used on publicly available COVID-19 data. The model decomposes the contributions to the infection time series to analyze and compare the role of quarantine control policies used in highly affected regions of Europe, North America, South America, and Asia in controlling the spread of the virus. For all continents considered, our results show a generally strong correlation between strengthening of the quarantine controls as learnt by the model and actions taken by the regions' respective governments. In addition, we have hosted our quarantine diagnosis results for the top 70 affected countries worldwide, on a public platform.

Dandekar Raj, Rackauckas Chris, Barbastathis George

2020-Nov-17

COVID-19, epidemiology, machine learning

General General

Ultrasensitive and Selective Detection of SARS-CoV-2 using Thermotropic Liquid Crystals and Image-based Machine Learning.

In Cell reports. Physical science

Rapid, robust virus detection techniques with ultrahigh sensitivity and selectivity are required for the outbreak of the pandemic coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). Here, we report that the femtomolar concentrations of single-stranded ribonucleic acid (ssRNA) of SARS-CoV-2 trigger ordering transitions in liquid crystal (LC) films decorated with cationic surfactant and complementary 15-mer single-stranded deoxyribonucleic acid (ssDNA) probe. More importantly, the sensitivity of the LC to the severe acute respiratory syndrome (SARS) ssRNA, with a 3 base pair-mismatch compared to the SARS-CoV-2 ssRNA, is measured to decrease by seven orders of magnitude, suggesting that the LC ordering transitions depend strongly on the targeted oligonucleotide sequence. Finally, we design a LC-based diagnostic kit and a smartphone-based application (App) to enable automatic detection of SARS-CoV-2 ssRNA, which could be used for reliable self-test of SARS-CoV-2 at home without the need for complex equipment or procedures.

Xu Yang, Rather Adil M, Song Shuang, Fang Jen-Chun, Dupont Robert L, Kara Ufuoma I, Chang Yun, Paulson Joel A, Qin Rongjun, Bao Xiaoping, Wang Xiaoguang

2020-Nov-17

COVID-19, SARS-CoV-2, biosensor, liquid crystals, machine learning, point-of-care detection kit

General General

Designing Futuristic Telemedicine Using Artificial Intelligence and Robotics in the COVID-19 Era.

In Frontiers in public health

Technological innovations such as artificial intelligence and robotics may be of potential use in telemedicine and in building capacity to respond to future pandemics beyond the current COVID-19 era. Our international consortium of interdisciplinary experts in clinical medicine, health policy, and telemedicine have identified gaps in uptake and implementation of telemedicine or telehealth across geographics and medical specialties. This paper discusses various artificial intelligence and robotics-assisted telemedicine or telehealth applications during COVID-19 and presents an alternative artificial intelligence assisted telemedicine framework to accelerate the rapid deployment of telemedicine and improve access to quality and cost-effective healthcare. We postulate that the artificial intelligence assisted telemedicine framework would be indispensable in creating futuristic and resilient health systems that can support communities amidst pandemics.

Bhaskar Sonu, Bradley Sian, Sakhamuri Sateesh, Moguilner Sebastian, Chattu Vijay Kumar, Pandya Shawna, Schroeder Starr, Ray Daniel, Banach Maciej

2020

artificial intelligence, coronavirus disease 2019 (COVID-19), digital medicine, pandemic (COVID-19), robotics, telehealth, telemedicine

General General

Recurrent neural network based prediction of number of COVID-19 cases in India.

In Materials today. Proceedings

COVID-19 has become the most devastating disease of the current century and is pandemic. As per WHO report, there are globally 31,174,627 confirmed cases including 962,613 deaths as of 22nd September,2020. The disease is spreading through outbreaks despite the availability of latest technologies for treatment of patients. In this paper, we proposed a neural network-based prediction of number of cases in India due to COVID-19. Recurrent neural network (RNN) based LSTM is applied on India dataset for prediction. LSTM networks are a type of RNN capable of learning order dependence in sequence forecasting problems. We analyze the performance of the network and then compare it with two parameter reduced variants of LSTM, obtained by elimination of hidden unit signals, bias and input signal. For performance evaluation, we used the MSE measure.

Shyam Sunder Reddy K, Padmanabha Reddy Y C A, Mallikarjuna Rao Ch

2020-Nov-17

COVID-19, LSTM, MSE, Machine learning, Neural Networks, Prediction

General General

Automatic detection of COVID-19 from chest radiographs using deep learning.

In Radiography (London, England : 1995)

INTRODUCTION : The breakdown of a deadly infectious disease caused by a newly discovered coronavirus (named SARS n-CoV2) back in December 2019 has shown no respite to slow or stop in general. This contagious disease has spread across different lengths and breadths of the globe, taking a death toll to nearly 700 k by the start of August 2020. The number is well expected to rise even more significantly. In the absence of a thoroughly tested and approved vaccine, the onus primarily lies on obliging to standard operating procedures and timely detection and isolation of the infected persons. The detection of SARS n-CoV2 has been one of the core concerns during the fight against this pandemic. To keep up with the scale of the outbreak, testing needs to be scaled at par with it. With the conventional PCR testing, most of the countries have struggled to minimize the gap between the scale of outbreak and scale of testing.

METHOD : One way of expediting the scale of testing is to shift to a rigorous computational model driven by deep neural networks, as proposed here in this paper. The proposed model is a non-contact process of determining whether a subject is infected or not and is achieved by using chest radiographs; one of the most widely used imaging technique for clinical diagnosis due to fast imaging and low cost. The dataset used in this work contains 1428 chest radiographs with confirmed COVID-19 positive, common bacterial pneumonia, and healthy cases (no infection). We explored the pre-trained VGG-16 model for classification tasks in this. Transfer learning with fine-tuning was used in this study to train the network on relatively small chest radiographs effectively.

RESULTS : Initial experiments showed that the model achieved promising results and can be significantly used to expedite COVID-19 detection. The experimentation showed an accuracy of 96% and 92.5% in two and three output class cases, respectively.

CONCLUSION : We believe that this study could be used as an initial screening, which can help healthcare professionals to treat the COVID patients by timely detecting better and screening the presence of disease.

IMPLICATION FOR PRACTICE : Its simplicity drives the proposed deep neural network model, the capability to work on small image dataset, the non-contact method with acceptable accuracy is a potential alternative for rapid COVID-19 testing that can be adapted by the medical fraternity considering the criticality of the time along with the magnitudes of the outbreak.

Pandit M K, Banday S A, Naaz R, Chishti M A

2020-Nov-11

COVID, Chest radiographs, Neural networks, Transfer learning

Radiology Radiology

Epicardial adipose tissue is associated with extent of pneumonia and adverse outcomes in patients with COVID-19.

In Metabolism: clinical and experimental

** : Aim We sought to examine the association of epicardial adipose tissue (EAT) quantified on chest computed tomography (CT) with the extent of pneumonia and adverse outcomes in patients with coronavirus disease 2019 (COVID-19).

METHODS : We performed a post-hoc analysis of a prospective international registry comprising 109 consecutive patients (age 64 ± 16 years; 62% male) with laboratory-confirmed COVID-19 and noncontrast chest CT imaging. Using semi-automated software, we quantified the burden (%) of lung abnormalities associated with COVID-19 pneumonia. EAT volume (mL) and attenuation (Hounsfield units) were measured using deep learning software. The primary outcome was clinical deterioration (intensive care unit admission, invasive mechanical ventilation, or vasopressor therapy) or in-hospital death.

RESULTS : In multivariable linear regression analysis adjusted for patient comorbidities, the total burden of COVID-19 pneumonia was associated with EAT volume (β = 10.6, p = 0.005) and EAT attenuation (β = 5.2, p = 0.004). EAT volume correlated with serum levels of lactate dehydrogenase (r = 0.361, p = 0.001) and C-reactive protein (r = 0.450, p < 0.001). Clinical deterioration or death occurred in 23 (21.1%) patients at a median of 3 days (IQR 1-13 days) following the chest CT. In multivariable logistic regression analysis, EAT volume (OR 5.1 [95% CI 1.8-14.1] per doubling p = 0.011) and EAT attenuation (OR 3.4 [95% CI 1.5-7.5] per 5 Hounsfield unit increase, p = 0.003) were independent predictors of clinical deterioration or death, as was total pneumonia burden (OR 2.5, 95% CI 1.4-4.6, p = 0.002), chronic lung disease (OR 1.3 [95% CI 1.1-1.7], p = 0.011), and history of heart failure (OR 3.5 [95% 1.1-8.2], p = 0.037).

CONCLUSIONS : EAT measures quantified from chest CT are independently associated with extent of pneumonia and adverse outcomes in patients with COVID-19, lending support to their use in clinical risk stratification.

Grodecki Kajetan, Lin Andrew, Razipour Aryabod, Cadet Sebastien, McElhinney Priscilla A, Chan Cato, Pressman Barry D, Julien Peter, Maurovich-Horvat Pal, Gaibazzi Nicola, Thakur Udit, Mancini Elisabetta, Agalbato Cecilia, Menè Robert, Parati Gianfranco, Cernigliaro Franco, Nerlekar Nitesh, Torlasco Camilla, Pontone Gianluca, Slomka Piotr J, Dey Damini

2020-Nov-19

COVID-19, Computed tomography, Epicardial adipose tissue, SARS-CoV-2

General General

Automated Quality Assessment of Hand Washing Using Deep Learning

ArXiv Preprint

Washing hands is one of the most important ways to prevent infectious diseases, including COVID-19. Unfortunately, medical staff does not always follow the World Health Organization (WHO) hand washing guidelines in their everyday work. To this end, we present neural networks for automatically recognizing the different washing movements defined by the WHO. We train the neural network on a part of a large (2000+ videos) real-world labeled dataset with the different washing movements. The preliminary results show that using pre-trained neural network models such as MobileNetV2 and Xception for the task, it is possible to achieve >64 % accuracy in recognizing the different washing movements. We also describe the collection and the structure of the above open-access dataset created as part of this work. Finally, we describe how the neural network can be used to construct a mobile phone application for automatic quality control and real-time feedback for medical professionals.

Maksims Ivanovs, Roberts Kadikis, Martins Lulla, Aleksejs Rutkovskis, Atis Elsts

2020-11-23

General General

Target-Centered Drug Repurposing Predictions of Human Angiotensin-Converting Enzyme 2 (ACE2) and Transmembrane Protease Serine Subtype 2 (TMPRSS2) Interacting Approved Drugs for Coronavirus Disease 2019 (COVID-19) Treatment through a Drug-Target Interaction Deep Learning Model.

In Viruses ; h5-index 58.0

Previously, our group predicted commercially available Food and Drug Administration (FDA) approved drugs that can inhibit each step of the replication of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using a deep learning-based drug-target interaction model called Molecule Transformer-Drug Target Interaction (MT-DTI). Unfortunately, additional clinically significant treatment options since the approval of remdesivir are scarce. To overcome the current coronavirus disease 2019 (COVID-19) more efficiently, a treatment strategy that controls not only SARS-CoV-2 replication but also the host entry step should be considered. In this study, we used MT-DTI to predict FDA approved drugs that may have strong affinities for the angiotensin-converting enzyme 2 (ACE2) receptor and the transmembrane protease serine 2 (TMPRSS2) which are essential for viral entry to the host cell. Of the 460 drugs with Kd of less than 100 nM for the ACE2 receptor, 17 drugs overlapped with drugs that inhibit the interaction of ACE2 and SARS-CoV-2 spike reported in the NCATS OpenData portal. Among them, enalaprilat, an ACE inhibitor, showed a Kd value of 1.5 nM against the ACE2. Furthermore, three of the top 30 drugs with strong affinity prediction for the TMPRSS2 are anti-hepatitis C virus (HCV) drugs, including ombitasvir, daclatasvir, and paritaprevir. Notably, of the top 30 drugs, AT1R blocker eprosartan and neuropsychiatric drug lisuride showed similar gene expression profiles to potential TMPRSS2 inhibitors. Collectively, we suggest that drugs predicted to have strong inhibitory potencies to ACE2 and TMPRSS2 through the DTI model should be considered as potential drug repurposing candidates for COVID-19.

Choi Yoonjung, Shin Bonggun, Kang Keunsoo, Park Sungsoo, Beck Bo Ram

2020-Nov-18

ACE2, COVID-19, SARS-CoV-2, TMPRSS2, coronavirus, deep learning, drug repurposing

Internal Medicine Internal Medicine

The effect of IL-6 inhibitors on mortality among hospitalized COVID-19 patients: a multicenter study.

In The Journal of infectious diseases ; h5-index 82.0

BACKGROUND : The effectiveness of interleukin-6 inhibitors (IL6i) in ameliorating Covid-19 disease remains uncertain.

METHODS : We analyzed data for patients aged ≥18 years admitted with a positive SARS-CoV-2 PCR test at four safety-net hospital systems with diverse populations and high rates of medical comorbidities in three different regions of the United States. We used inverse probability of treatment weighting via machine learning for confounding adjustment by demographics, comorbidities, and disease severity markers. We estimated the average treatment effect, the odds of IL6i effect on in-hospital mortality from COVID-19, using a logistic marginal structural model.

RESULTS : Of the 516 patients in this study, 104 (20.1%) received IL6i. The estimate of the average treatment effect adjusted for confounders suggested a 37% reduction in the odds of in-hospital mortality in those who received IL-6i, compared with those who did not, though the confidence interval included the null value of 1 (odds ratio = 0.63, 95% CI: 0.29, 1.38). A sensitivity analysis suggested that potential unmeasured confounding would require a minimum odds ratio of 2.55 to nullify our estimated IL-6i effect size.

CONCLUSIONS : Despite low precision, our findings suggested a relatively large effect size of IL6i in reducing the odds of COVID-19 related in-hospital mortality.

Sinha Pranay, Jafarzadeh S Reza, Assoumou Sabrina A, Bielick Catherine G, Carpenter Bethanne, Garg Shivani, Harleen Sahni, Neogi Tuhina, Nishio Midori Jane, Sagar Manish, Sharp Veronika, Kissin Eugene Y

2020-Nov-20

COVID-19, Cytokine release syndrome, Interleukin 6 inhibitors

General General

Deep learning applications to combat the dissemination of COVID-19 disease: a review.

In European review for medical and pharmacological sciences

Recent Coronavirus (COVID-19) is one of the respiratory diseases, and it is known as fast infectious ability. This dissemination can be decelerated by diagnosing and quarantining patients with COVID-19 at early stages, thereby saving numerous lives. Reverse transcription-polymerase chain reaction (RT-PCR) is known as one of the primary diagnostic tools. However, RT-PCR tests are costly and time-consuming; it also requires specific materials, equipment, and instruments. Moreover, most countries are suffering from a lack of testing kits because of limitations on budget and techniques. Thus, this standard method is not suitable to meet the requirements of fast detection and tracking during the COVID-19 pandemic, which motived to employ deep learning (DL)/convolutional neural networks (CNNs) technology with X-ray and CT scans for efficient analysis and diagnostic. This study provides insight about the literature that discussed the deep learning technology and its various techniques that are recently developed to combat the dissemination of COVID-19 disease.

Alsharif M H, Alsharif Y H, Yahya K, Alomari O A, Albreem M A, Jahid A

2020-Nov

Pathology Pathology

Comparison of deep learning with regression analysis in creating predictive models for SARS-CoV-2 outcomes.

In BMC medical informatics and decision making ; h5-index 38.0

BACKGROUND : Accurately predicting patient outcomes in Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) could aid patient management and allocation of healthcare resources. There are a variety of methods which can be used to develop prognostic models, ranging from logistic regression and survival analysis to more complex machine learning algorithms and deep learning. Despite several models having been created for SARS-CoV-2, most of these have been found to be highly susceptible to bias. We aimed to develop and compare two separate predictive models for death during admission with SARS-CoV-2.

METHOD : Between March 1 and April 24, 2020, 398 patients were identified with laboratory confirmed SARS-CoV-2 in a London teaching hospital. Data from electronic health records were extracted and used to create two predictive models using: (1) a Cox regression model and (2) an artificial neural network (ANN). Model performance profiles were assessed by validation, discrimination, and calibration.

RESULTS : Both the Cox regression and ANN models achieved high accuracy (83.8%, 95% confidence interval (CI) 73.8-91.1 and 90.0%, 95% CI 81.2-95.6, respectively). The area under the receiver operator curve (AUROC) for the ANN (92.6%, 95% CI 91.1-94.1) was significantly greater than that of the Cox regression model (86.9%, 95% CI 85.7-88.2), p = 0.0136. Both models achieved acceptable calibration with Brier scores of 0.13 and 0.11 for the Cox model and ANN, respectively.

CONCLUSION : We demonstrate an ANN which is non-inferior to a Cox regression model but with potential for further development such that it can learn as new data becomes available. Deep learning techniques are particularly suited to complex datasets with non-linear solutions, which make them appropriate for use in conditions with a paucity of prior knowledge. Accurate prognostic models for SARS-CoV-2 can provide benefits at the patient, departmental and organisational level.

Abdulaal Ahmed, Patel Aatish, Charani Esmita, Denny Sarah, Alqahtani Saleh A, Davies Gary W, Mughal Nabeela, Moore Luke S P

2020-11-19

Artificial intelligence, COVID-19, Coronavirus, Machine learning, Prognostication

General General

Effect of heterogeneous risk perception on information diffusion, behavior change, and disease transmission.

In Physical review. E

Motivated by the importance of individual differences in risk perception and behavior change in people's responses to infectious disease outbreaks (particularly the ongoing COVID-19 pandemic), we propose a heterogeneous disease-behavior-information transmission model, in which people's risk of getting infected is influenced by information diffusion, behavior change, and disease transmission. We use both a mean-field approximation and Monte Carlo simulations to analyze the dynamics of the model. Information diffusion influences behavior change by allowing people to be aware of the disease and adopt self-protection and subsequently affects disease transmission by changing the actual infection rate. Results show that (a) awareness plays a central role in epidemic prevention, (b) a reasonable fraction of overreacting nodes are needed in epidemic prevention (c) the basic reproduction number R_{0} has different effects on epidemic outbreak for cases with and without asymptomatic infection, and (d) social influence on behavior change can remarkably decrease the epidemic outbreak size. This research indicates that the media and opinion leaders should not understate the transmissibility and severity of diseases to ensure that people become aware of the disease and adopt self-protection to protect themselves and the whole population.

Ye Yang, Zhang Qingpeng, Ruan Zhongyuan, Cao Zhidong, Xuan Qi, Zeng Daniel Dajun

2020-Oct

Surgery Surgery

Cancer in Lockdown: Impact of the COVID-19 Pandemic on Patients with Cancer.

In The oncologist

The lockdown measures of the ongoing COVID-19 pandemic have disengaged patients with cancer from formal health care settings, leading to an increased use of social media platforms to address unmet needs and expectations. Although remote health technologies have addressed some of the medical needs, the emotional and mental well-being of these patients remain underexplored and underreported. We used a validated artificial intelligence framework to conduct a comprehensive real-time analysis of two data sets of 2,469,822 tweets and 21,800 discussions by patients with cancer during this pandemic. Lung and breast cancer are most prominently discussed, and the most concerns were expressed regarding delayed diagnosis, cancellations, missed treatments, and weakened immunity. All patients expressed significant negative sentiment, with fear being the predominant emotion. Even as some lockdown measures ease, it is crucial that patients with cancer are engaged using social media platforms for real-time identification of issues and the provision of informational and emotional support.

Moraliyage Harsha, Silva Daswin De, Ranasinghe Weranja, Adikari Achini, Alahakoon Damminda, Prasad Raj, Lawrentschuk Nathan, Bolton Damien

2020-Nov-18

Radiology Radiology

Preliminary investigation of relationship between clinical indicators and CT manifestation patterns of COVID-19 pneumonia improvement.

In Journal of thoracic disease ; h5-index 52.0

Background : To retrospectively evaluate several clinical indicators related to the improvement of COVID-19 pneumonia on CT.

Methods : A total of 62 patients with COVID-19 pneumonia were included. The CT scores based on lesion patterns and distributions in serial CT were investigated. The improvement and deterioration of pneumonia was assessed based on the changes of CT scores. Grouped by using the temperature, serum lymphocytes and high sensitivity CRP (hs-CRP) on admission respectively, the CT scores on admission, at peak time and at discharge were evaluated. Correlation analysis was carried out between the time to onset of pneumonia resolution on CT images and the recovery time of temperature, negative conversion of viral nucleic acid, serum lymphocytes and hs-CRP.

Results : The CT scores of the fever group and lymphopenia group were significantly higher than those of normal group on admission, at peak time and at discharge; and the CT scores of normal hs-CRP group were significantly lower than those of the elevated hs-CRP group at peak time and at discharge (P all<0.05). The time to onset of pneumonia resolution on CT image was moderately correlated with negative conversion duration of viral nucleic acid (r =0.501, P<0.05) and the recovery time of hs-CPR (r =0.496, P<0.05).

Conclusions : COVID-19 pneumonia patients with no fever, normal lymphocytes and hs-CRP had mild lesions on admission, and presented with more absorption and fewer pulmonary lesions on discharge. The negative conversion duration of viral nucleic acid and the recovery time of hs-CPR may be the indicator of the pneumonia resolution.

Shi Nannan, Song Fengxiang, Liu Fengjun, Song Pengrui, Lu Yang, Hou Qinguo, Hua Xinyan, Ling Yun, Zhang Jiulong, Huang Chao, Shi Lei, Zhang Zhiyong, Shan Fei, Zhang Qi, Shi Yuxin

2020-Oct

Coronavirus Disease 2019 (COVID-19), computed tomography, pneumonia improvement

Radiology Radiology

CT imaging features of different clinical types of COVID-19 calculated by AI system: a Chinese multicenter study.

In Journal of thoracic disease ; h5-index 52.0

Background : The study is designed to explore the chest CT features of different clinical types of coronavirus disease 2019 (COVID-19) pneumonia based on a Chinese multicenter dataset using an artificial intelligence (AI) system.

Methods : A total of 164 patients confirmed COVID-19 were retrospectively enrolled from 6 hospitals. All patients were divided into the mild type (136 cases) and the severe type (28 cases) according to their clinical manifestations. The total CT severity score and quantitative CT features were calculated by AI pneumonia detection and evaluation system with correction by radiologists. The clinical and CT imaging features of different types were analyzed.

Results : It was observed that patients in the severe type group were older than the mild type group. Round lesions, Fan-shaped lesions, crazy-paving pattern, fibrosis, "white lung", pleural thickening, pleural indentation, mediastinal lymphadenectasis were more common in the CT images of severe patients than in the mild ones. A higher total lung severity score and scores of each lobe were observed in the severe group, with higher scores in bilateral lower lobes of both groups. Further analysis showed that the volume and number of pneumonia lesions and consolidation lesions in overall lung were higher in the severe group, and showed a wider distribution in the lower lobes of bilateral lung in both groups.

Conclusions : Chest CT of patients with severe COVID-19 pneumonia showed more consolidative and progressive lesions. With the assistance of AI, CT could evaluate the clinical severity of COVID-19 pneumonia more precisely and help the early diagnosis and surveillance of the patients.

Hu Xiaofei, Zeng Wenbing, Zhang Yuhan, Zhen Zhiming, Zheng Yalan, Cheng Lin, Wang Xianqi, Luo Haoran, Zhang Shu, Wu Zifeng, Sun Zeyu, Li Xiuli, Cao Yang, Xu Ming, Wang Jian, Chen Wei

2020-Oct

Coronavirus infections, artificial intelligence (AI), pneumonia, viral, severity of illness index, tomography, X-ray computed

Radiology Radiology

Open resource of clinical data from patients with pneumonia for the prediction of COVID-19 outcomes via deep learning.

In Nature biomedical engineering

Data from patients with coronavirus disease 2019 (COVID-19) are essential for guiding clinical decision making, for furthering the understanding of this viral disease, and for diagnostic modelling. Here, we describe an open resource containing data from 1,521 patients with pneumonia (including COVID-19 pneumonia) consisting of chest computed tomography (CT) images, 130 clinical features (from a range of biochemical and cellular analyses of blood and urine samples) and laboratory-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) clinical status. We show the utility of the database for prediction of COVID-19 morbidity and mortality outcomes using a deep learning algorithm trained with data from 1,170 patients and 19,685 manually labelled CT slices. In an independent validation cohort of 351 patients, the algorithm discriminated between negative, mild and severe cases with areas under the receiver operating characteristic curve of 0.944, 0.860 and 0.884, respectively. The open database may have further uses in the diagnosis and management of patients with COVID-19.

Ning Wanshan, Lei Shijun, Yang Jingjing, Cao Yukun, Jiang Peiran, Yang Qianqian, Zhang Jiao, Wang Xiaobei, Chen Fenghua, Geng Zhi, Xiong Liang, Zhou Hongmei, Guo Yaping, Zeng Yulan, Shi Heshui, Wang Lin, Xue Yu, Wang Zheng

2020-Nov-18

General General

A Deep Language-independent Network to analyze the impact of COVID-19 on the World via Sentiment Analysis

ArXiv Preprint

Towards the end of 2019, Wuhan experienced an outbreak of novel coronavirus, which soon spread all over the world, resulting in a deadly pandemic that infected millions of people around the globe. The government and public health agencies followed many strategies to counter the fatal virus. However, the virus severely affected the social and economic lives of the people. In this paper, we extract and study the opinion of people from the top five worst affected countries by the virus, namely USA, Brazil, India, Russia, and South Africa. We propose a deep language-independent Multilevel Attention-based Conv-BiGRU network (MACBiG-Net), which includes embedding layer, word-level encoded attention, and sentence-level encoded attention mechanism to extract the positive, negative, and neutral sentiments. The embedding layer encodes the sentence sequence into a real-valued vector. The word-level and sentence-level encoding is performed by a 1D Conv-BiGRU based mechanism, followed by word-level and sentence-level attention, respectively. We further develop a COVID-19 Sentiment Dataset by crawling the tweets from Twitter. Extensive experiments on our proposed dataset demonstrate the effectiveness of the proposed MACBiG-Net. Also, attention-weights visualization and in-depth results analysis shows that the proposed network has effectively captured the sentiments of the people.

Ashima Yadav, Dinesh Kumar Vishwakarma

2020-11-20

Public Health Public Health

Hate Detection in COVID-19 Tweets in the Arab Region using Deep learning and Topic Modeling.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : The massive scale of social media platforms requires an automatic solution for detecting hate speech. Such solutions will help in reducing the manual analysis of content. Most of the past literature has casted the hate speech detection problem as a supervised text classification task, whether by using classical machine learning methods or, more recently, using deep learning methods. However, previous works investigated this problem in Arabic cyberspace is still limited compared to the published works in English.

OBJECTIVE : This study aims to identify hate-speech posted by Twitter users in the Arab region related to COVID-19 pandemic and discover the main issues discussed among them.

METHODS : We used ArCOV-19 dataset, which is an ongoing collection of Arabic tweets related to the novel Coronavirus COVID-19, starting from January 27, 2020. Tweets were analyzed for hate speech using pretrained Convolutional neural network (CNN) model, and the results of the tweets classification were given a score varied between 0 to 1, with 1 being the most hateful text. We also utilized Non-negative Matrix Factorization (NMF) to discover main issues and topics in hate tweets.

RESULTS : Analysis of hate-speech in Twitter data in the Arab region has identified that the number of non-hate tweets by far exceeded the number of hate tweets, where the percentage of hate tweets in COVID-19 related tweets is 3.2%. It also revealed that the majority of hate tweets (71.4%) are in the low level of hate. This study has identified Saudi Arabia as the highest Arab country in spreading COVID-19 hate tweets during the pandemic. Furthermore, it has shown that the second time period (Mar 1- Mar 30) has the largest number of hate tweets which represents 51.9% of all hate tweets. Conflicting to what was anticipated, in the Arab region, it has been found that the spread of COVID-19 hate-speech in Twitter is weakly related with the dissemination of the pandemic based on Pearson correlation coefficient test (r value is 0.1982). The study has also identified the discussed topics in hate tweets during the pandemic. Analysis of 7 extracted topics showed that 6 of the 7 identified topics involved topics related to hate against China and Iran. Arab users also discussed topics related to political conflicts in Arab region during the COVID-19 pandemic.

CONCLUSIONS : To nations around the world, the COVID-19 pandemic was a serious public health challenge. During COVID-19, frequent use of social media can contribute to spreading hate speech. Online hate speech can have a negative impact on society, and hate speech may have a direct correlation with real hate crimes, which raises the threat of being targeted by hate speech and abusive language. This study is the first to analyze hate speech in the context of Arabic COVID-19 tweets in the Arab region.

Alshalan Raghad, Al-Khalifa Hend, Alsaeed Duaa, Al-Baity Heyam, Alshalan Shahad

2020-Nov-16

General General

An integrated fog and Artificial Intelligence smart health framework to predict and prevent COVID-19.

In Global transitions

Nowadays, COVID-19 is spreading at a rapid rate in almost all the continents of the world. It has already affected many people who are further spreading it day by day. Hence, it is the most essential to alert nearby people to be aware of it due to its communicable behavior. Till May 2020, no vaccine is available for the treatment of this COVID-19, but the existing technologies can be used to minimize its effect. Cloud/fog computing could be used to monitor and control this rapidly spreading infection in a cost-effective and time-saving manner. To strengthen COVID-19 patient prediction, Artificial Intelligence(AI) can be integrated with cloud/fog computing for practical solutions. In this paper, fog assisted the internet of things based quality of service framework is presented to prevent and protect from COVID-19. It provides real-time processing of users' health data to predict the COVID-19 infection by observing their symptoms and immediately generates an emergency alert, medical reports, and significant precautions to the user, their guardian as well as doctors/experts. It collects sensitive information from the hospitals/quarantine shelters through the patient IoT devices for taking necessary actions/decisions. Further, it generates an alert message to the government health agencies for controlling the outbreak of chronic illness and for tanking quick and timely actions.

Singh Prabhdeep, Kaur Rajbir

2020

Artificial intelligence, COVID-19, Cloud/fog computing, Ensemble model, Quality of service framework, Smart city

General General

A systematic review of causal methods enabling predictions under hypothetical interventions

ArXiv Preprint

Background: The methods with which prediction models are usually developed mean that neither the parameters nor the predictions should be interpreted causally. For many applications this is perfectly acceptable. However, when prediction models are used to support decision making, there is often a need for predicting outcomes under hypothetical interventions. Aims: We aimed to identify and compare published methods for developing and validating prediction models that enable risk estimation of outcomes under hypothetical interventions, utilizing causal inference. We aimed to identify the main methodological approaches, their underlying assumptions, targeted estimands, and possible sources of bias. Finally, we aimed to highlight unresolved methodological challenges. Methods: We systematically reviewed literature published by December 2019, considering papers in the health domain that used causal considerations to enable prediction models to be used to evaluate predictions under hypothetical interventions. We included both methodology development studies and applied studies. Results: We identified 4919 papers through database searches and a further 115 papers through manual searches. Of these, 87 papers were retained for full text screening, of which 12 were selected for inclusion. We found papers from both the statistical and the machine learning literature. Most of the identified methods for causal inference from observational data were based on marginal structural models and g-estimation.

Lijing Lin, Matthew Sperrin, David A. Jenkins, Glen P. Martin, Niels Peek

2020-11-19

Public Health Public Health

Information technology in emergency management of COVID-19 outbreak.

In Informatics in medicine unlocked

Emergency management of the emerging infectious disease outbreak is critical for public health threats. Currently, control of the COVID-19 outbreak is an international concern and has become a crucial challenge in many countries. This article reviews significant information technologyIT) applications in emergency management of COVID-19 by considering the prevention/mitigation, preparedness, response, and recovery phases of the crisis. This review was conducted using MEDLINE PubMed), Embase, IEEE, and Google Scholar. Expert opinions were collected to show existence gaps, useful technologies for each phase of emergency management, and future direction. Results indicated that various IT-based systems such as surveillance systems, artificial intelligence, computational methods, Internet of things, remote sensing sensor, online service, and GIS geographic information system) could have different outbreak management applications, especially in response phases. Information technology was applied in several aspects, such as increasing the accuracy of diagnosis, early detection, ensuring healthcare providers' safety, decreasing workload, saving time and cost, and drug discovery. We categorized these applications into four core topics, including diagnosis and prediction, treatment, protection, and management goals, which were confirmed by five experts. Without applying IT, the control and management of the crisis could be difficult on a large scale. For reducing and improving the hazard effect of disaster situations, the role of IT is inevitable. In addition to the response phase, communities should be considered to use IT capabilities in prevention, preparedness, and recovery phases. It is expected that IT will have an influential role in the recovery phase of COVID-19. Providing IT infrastructure and financial support by the governments should be more considered in facilitating IT capabilities.

Asadzadeh Afsoon, Pakkhoo Saba, Saeidabad Mahsa Mirzaei, Khezri Hero, Ferdousi Reza

2020

COVID-19, Disaster, Emergency management, Epidemic, Information technology, Outbreak

General General

Deep Learning for Automated Screening of Tuberculosis from Indian Chest X-rays: Analysis and Update

ArXiv Preprint

Background and Objective: Tuberculosis (TB) is a significant public health issue and a leading cause of death worldwide. Millions of deaths can be averted by early diagnosis and successful treatment of TB patients. Automated diagnosis of TB holds vast potential to assist medical experts in expediting and improving its diagnosis, especially in developing countries like India, where there is a shortage of trained medical experts and radiologists. To date, several deep learning based methods for automated detection of TB from chest radiographs have been proposed. However, the performance of a few of these methods on the Indian chest radiograph data set has been suboptimal, possibly due to different texture of the lungs on chest radiographs of Indian subjects compared to other countries. Thus deep learning for accurate and automated diagnosis of TB on Indian datasets remains an important subject of research. Methods: The proposed work explores the performance of convolutional neural networks (CNNs) for the diagnosis of TB in Indian chest x-ray images. Three different pre-trained neural network models, AlexNet, GoogLenet, and ResNet are used to classify chest x-ray images into healthy or TB infected. The proposed approach does not require any pre-processing technique. Also, other works use pre-trained NNs as a tool for crafting features and then apply standard classification techniques. However, we attempt an end to end NN model based diagnosis of TB from chest x-rays. The proposed visualization tool can also be used by radiologists in the screening of large datasets. Results: The proposed method achieved 93.40% accuracy with 98.60% sensitivity to diagnose TB for the Indian population. Conclusions: The performance of the proposed method is also tested against techniques described in the literature. The proposed method outperforms the state of art on Indian and Shenzhen datasets.

Anushikha Singh, Brejesh Lall, B. K. Panigrahi, Anjali Agrawal, Anurag Agrawal, Balamugesh Thangakunam, DJ Christopher

2020-11-19

General General

Measuring and Preventing COVID-19 Using the SIR Model and Machine Learning in Smart Health Care.

In Journal of healthcare engineering

COVID-19 presents an urgent global challenge because of its contagious nature, frequently changing characteristics, and the lack of a vaccine or effective medicines. A model for measuring and preventing the continued spread of COVID-19 is urgently required to provide smart health care services. This requires using advanced intelligent computing such as artificial intelligence, machine learning, deep learning, cognitive computing, cloud computing, fog computing, and edge computing. This paper proposes a model for predicting COVID-19 using the SIR and machine learning for smart health care and the well-being of the citizens of KSA. Knowing the number of susceptible, infected, and recovered cases each day is critical for mathematical modeling to be able to identify the behavioral effects of the pandemic. It forecasts the situation for the upcoming 700 days. The proposed system predicts whether COVID-19 will spread in the population or die out in the long run. Mathematical analysis and simulation results are presented here as a means to forecast the progress of the outbreak and its possible end for three types of scenarios: "no actions," "lockdown," and "new medicines." The effect of interventions like lockdown and new medicines is compared with the "no actions" scenario. The lockdown case delays the peak point by decreasing the infection and affects the area equality rule of the infected curves. On the other side, new medicines have a significant impact on infected curve by decreasing the number of infected people about time. Available forecast data on COVID-19 using simulations predict that the highest level of cases might occur between 15 and 30 November 2020. Simulation data suggest that the virus might be fully under control only after June 2021. The reproductive rate shows that measures such as government lockdowns and isolation of individuals are not enough to stop the pandemic. This study recommends that authorities should, as soon as possible, apply a strict long-term containment strategy to reduce the epidemic size successfully.

Alanazi Saad Awadh, Kamruzzaman M M, Alruwaili Madallah, Alshammari Nasser, Alqahtani Salman Ali, Karime Ali

2020

General General

SOCAIRE: Forecasting and Monitoring Urban Air Quality in Madrid

ArXiv Preprint

Air quality has become one of the main issues in public health and urban planning management, due to the proven adverse effects of high pollutant concentrations. Considering the mitigation measures that cities all over the world are taking in order to face frequent low air quality episodes, the capability of foreseeing future pollutant concentrations is of great importance. Through this paper, we present SOCAIRE, an operational tool based on a Bayesian and spatiotemporal ensemble of neural and statistical nested models. SOCAIRE integrates endogenous and exogenous information in order to predict and monitor future distributions of the concentration for several pollutants in the city of Madrid. It focuses on modeling each and every available component which might play a role in air quality: past concentrations of pollutants, human activity, numerical pollution estimation, and numerical weather predictions. This tool is currently in operation in Madrid, producing daily air quality predictions for the next 48 hours and anticipating the probability of the activation of the measures included in the city's official air quality \no protocols through probabilistic inferences about compound events.

Rodrigo de Medrano, Víctor de Buen Remiro, José L. Aznarte

2020-11-19

General General

Upper airway gene expression reveals suppressed immune responses to SARS-CoV-2 compared with other respiratory viruses.

In Nature communications ; h5-index 260.0

SARS-CoV-2 infection is characterized by peak viral load in the upper airway prior to or at the time of symptom onset, an unusual feature that has enabled widespread transmission of the virus and precipitated a global pandemic. How SARS-CoV-2 is able to achieve high titer in the absence of symptoms remains unclear. Here, we examine the upper airway host transcriptional response in patients with COVID-19 (n = 93), other viral (n = 41) or non-viral (n = 100) acute respiratory illnesses (ARIs). Compared with other viral ARIs, COVID-19 is characterized by a pronounced interferon response but attenuated activation of other innate immune pathways, including toll-like receptor, interleukin and chemokine signaling. The IL-1 and NLRP3 inflammasome pathways are markedly less responsive to SARS-CoV-2, commensurate with a signature of diminished neutrophil and macrophage recruitment. This pattern resembles previously described distinctions between symptomatic and asymptomatic viral infections and may partly explain the propensity for pre-symptomatic transmission in COVID-19. We further use machine learning to build 27-, 10- and 3-gene classifiers that differentiate COVID-19 from other ARIs with AUROCs of 0.981, 0.954 and 0.885, respectively. Classifier performance is stable across a wide range of viral load, suggesting utility in mitigating false positive or false negative results of direct SARS-CoV-2 tests.

Mick Eran, Kamm Jack, Pisco Angela Oliveira, Ratnasiri Kalani, Babik Jennifer M, Castañeda Gloria, DeRisi Joseph L, Detweiler Angela M, Hao Samantha L, Kangelaris Kirsten N, Kumar G Renuka, Li Lucy M, Mann Sabrina A, Neff Norma, Prasad Priya A, Serpa Paula Hayakawa, Shah Sachin J, Spottiswoode Natasha, Tan Michelle, Calfee Carolyn S, Christenson Stephanie A, Kistler Amy, Langelier Charles

2020-11-17

Radiology Radiology

Deep LF-Net: Semantic Lung Segmentation from Indian Chest Radiographs Including Severely Unhealthy Images

ArXiv Preprint

A chest radiograph, commonly called chest x-ray (CxR), plays a vital role in the diagnosis of various lung diseases, such as lung cancer, tuberculosis, pneumonia, and many more. Automated segmentation of the lungs is an important step to design a computer-aided diagnostic tool for examination of a CxR. Precise lung segmentation is considered extremely challenging because of variance in the shape of the lung caused by health issues, age, and gender. The proposed work investigates the use of an efficient deep convolutional neural network for accurate segmentation of lungs from CxR. We attempt an end to end DeepLabv3+ network which integrates DeepLab architecture, encoder-decoder, and dilated convolution for semantic lung segmentation with fast training and high accuracy. We experimented with the different pre-trained base networks: Resnet18 and Mobilenetv2, associated with the Deeplabv3+ model for performance analysis. The proposed approach does not require any pre-processing technique on chest x-ray images before being fed to a neural network. Morphological operations were used to remove false positives that occurred during semantic segmentation. We construct a CxR dataset of the Indian population that contain healthy and unhealthy CxRs of clinically confirmed patients of tuberculosis, chronic obstructive pulmonary disease, interstitial lung disease, pleural effusion, and lung cancer. The proposed method is tested on 688 images of our Indian CxR dataset including images with severe abnormal findings to validate its robustness. We also experimented on commonly used benchmark datasets such as Japanese Society of Radiological Technology; Montgomery County, USA; and Shenzhen, China for state-of-the-art comparison. The performance of our method is tested against techniques described in the literature and achieved the highest accuracy for lung segmentation on Indian and public datasets.

Anushikha Singh, Brejesh Lall, B. K. Panigrahi, Anjali Agrawal, Anurag Agrawal, DJ Christopher, Balamugesh Thangakunam

2020-11-19

General General

REDIAL-2020: A Suite of Machine Learning Models to Estimate Anti-SARS-CoV-2 Activities.

In ChemRxiv : the preprint server for chemistry

Strategies for drug discovery and repositioning are an urgent need with respect to COVID-19. We developed "REDIAL-2020", a suite of machine learning models for estimating small molecule activity from molecular structure, for a range of SARS-CoV-2 related assays. Each classifier is based on three distinct types of descriptors (fingerprint, physicochemical, and pharmacophore) for parallel model development. These models were trained using high throughput screening data from the NCATS COVID19 portal (https://opendata.ncats.nih.gov/covid19/index.html), with multiple categorical machine learning algorithms. The "best models" are combined in an ensemble consensus predictor that outperforms single models where external validation is available. This suite of machine learning models is available through the DrugCentral web portal (<a href="https://drugdiscovery.utep.edu/redial">http://drugcentral.org/Redial</a>). Acceptable input formats are: drug name, PubChem CID, or SMILES; the output is an estimate of anti-SARS-CoV-2 activities. The web application reports estimated activity across three areas (<i>viral entry</i>, <i>viral replication,</i> and <i>live virus infectivity</i>) spanning six independent models, followed by a similarity search that displays the most similar molecules to the query among experimentally determined data. The ML models have 60% to 74% external predictivity, based on three separate datasets. Complementing the NCATS COVID19 portal, REDIAL-2020 can serve as a rapid online tool for identifying active molecules for COVID-19 treatment. The source code and specific models are available through Github (<a href="https://github.com/sirimullalab/ncats_covid">https://github.com/sirimullalab/</a>redial-2020), or via Docker Hub (https://hub.docker.com/r/sirimullalab/redial-2020) for users preferring a containerized version.

Kc Govinda, Bocci Giovanni, Verma Srijan, Hassan Mahmudulla, Holmes Jayme, Yang Jeremy, Sirimulla Suman, Oprea Tudor I

2020-Sep-16

Artifical Intelligence, COVID-19, Drug Discovery, Drug Repurposing, Machine Learning, Redial, Redial-2020, SARS-CoV-2

General General

Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19.

In ChemRxiv : the preprint server for chemistry

We present a supercomputer-driven pipeline for <i>in-silico</i> drug discovery using enhanced sampling molecular dynamics (MD) and ensemble docking. We also describe preliminary results obtained for 23 systems involving eight protein targets of the proteome of SARS CoV-2. THe MD performed is temperature replica-exchange enhanced sampling, making use of the massively parallel supercomputing on the SUMMIT supercomputer at Oak Ridge National Laboratory, with which more than 1ms of enhanced sampling MD can be generated per day. We have ensemble docked repurposing databases to ten configurations of each of the 23 SARS CoV-2 systems using AutoDock Vina. We also demonstrate that using Autodock-GPU on SUMMIT, it is possible to perform exhaustive docking of one billion compounds in under 24 hours. Finally, we discuss preliminary results and planned improvements to the pipeline, including the use of quantum mechanical (QM), machine learning, and AI methods to cluster MD trajectories and rescore docking poses.

Acharya Atanu, Agarwal Rupesh, Baker Matthew, Baudry Jerome, Bhowmik Debsindhu, Boehm Swen, Byler Kendall, Coates Leighton, Chen Sam Yen-Chi, Cooper Connor J, Demerdash Omar, Daidone Isabella, Eblen John, Ellingson Sally R, Forli Stefano, Glaser Jens, Gumbart James C, Gunnels John, Hernandez Oscar, Irle Stephan, Larkin Jeffery, Lawrence Travis J, LeGrand Scott, Liu Shih-Hsien, Mitchell Julie C, Park Gilchan, Parks Jerry M, Pavlova Anna, Petridis Loukas, Poole Duncan, Pouchard Line, Ramanathan Arvind, Rogers David, Santos-Martins Diogo, Scheinberg Aaron, Sedova Ada, Shen Shawn, Smith Jeremy C, Smith Micholas, Soto Carlos, Tsaris Aristides, Thavappiragasam Mathialakan, Tillack Andreas F, Vermaas Josh V, Vuong Van Quan, Yin Junqi, Yoo Shinjae, Zahran Mai, Zanetti-Polzi Laura

2020-Jul-29

Autodock, COVID-19, Drug Repurposing, Ensemble Docking, High-Throughput Screening, MPro, NSP10, NSP15, NSP16, NSP3, NSP9, Nucelocapsid (N) Protein, PLPro, SARS CoV-2, Spike (S) Protein, Supercomputing, T-REMD

General General

Fighting against COVID-19: A novel deep learning model based on YOLO-v2 with ResNet-50 for medical face mask detection.

In Sustainable cities and society

Deep learning has shown tremendous potential in many real-life applications in different domains. One of these potentials is object detection. Recent object detection which is based on deep learning models has achieved promising results concerning the finding of an object in images. The objective of this paper is to annotate and localize the medical face mask objects in real-life images. Wearing a medical face mask in public areas, protect people from COVID-19 transmission among them. The proposed model consists of two components. The first component is designed for the feature extraction process based on the ResNet-50 deep transfer learning model. While the second component is designed for the detection of medical face masks based on YOLO v2. Two medical face masks datasets have been combined in one dataset to be investigated through this research. To improve the object detection process, mean IoU has been used to estimate the best number of anchor boxes. The achieved results concluded that the adam optimizer achieved the highest average precision percentage of 81% as a detector. Finally, a comparative result with related work has been presented at the end of the research. The proposed detector achieved higher accuracy and precision than the related work.

Loey Mohamed, Manogaran Gunasekaran, Taha Mohamed Hamed N, Khalifa Nour Eldeen M

2020-Nov-12

COVID-19, Deep learning, Medical masked face, ResNet, YOLO

oncology Oncology

Voice perturbations under the stress overload in young individuals: phenotyping and suboptimal health as predictors for cascading pathologies.

In The EPMA journal

Verbal communication is one of the most sophisticated human motor skills reflecting both-the mental and physical health of an individual. Voice parameters and quality changes are usually secondary towards functional and/or structural laryngological alterations under specific systemic processes, syndrome and pathologies. These include but are not restricted to dry mouth and Sicca syndromes, body dehydration, hormonal alterations linked to pubertal, menopausal, and andropausal status, respiratory disorders, gastrointestinal reflux, autoimmune diseases, endocrinologic disorders, underweight versus overweight and obesity, and diabetes mellitus. On the other hand, it is well-established that stress overload is a significant risk factor of cascading pathologies, including but not restricted to neurodegenerative and psychiatric disorders, diabetes mellitus, cardiovascular disease, stroke, and cancers. Our current study revealed voice perturbations under the stress overload as a potentially useful biomarker to identify individuals in suboptimal health conditions who might be strongly predisposed to associated pathologies. Contextually, extended surveys applied in the population might be useful to identify, for example, persons at high risk for respiratory complications under pandemic conditions such as COVID-19. Symptoms of dry mouth syndrome, disturbed microcirculation, altered sense regulation, shifted circadian rhythm, and low BMI were positively associated with voice perturbations under the stress overload. Their functional interrelationships and relevance for cascading associated pathologies are presented in the article. Automated analysis of voice recordings via artificial intelligence (AI) has a potential to derive digital biomarkers. Further, predictive machine learning models should be developed that allows for detecting a suboptimal health condition based on voice recordings, ideally in an automated manner using derived digital biomarkers. Follow-up stratification and monitoring of individuals in suboptimal health conditions are recommended using disease-specific cell-free nucleic acids (ccfDNA, ctDNA, mtDNA, miRNA) combined with metabolic patterns detected in body fluids. Application of the cost-effective targeted prevention within the phase of reversible health damage is recommended based on the individualised patient profiling.

Kunin A, Sargheini N, Birkenbihl C, Moiseeva N, Fröhlich Holger, Golubnitschaja Olga

2020-Nov-12

Artificial intelligence (AI), Association, Biomarker pattern, Body mass index, COVID-19, Circadian rhythm, Disease predisposition, Dry mouth syndrome, Exercise-induced hypoalgesia, Flammer syndrome, Healthcare, High altitude sickness, Hyposalivation, Individualised patient profile, Lifestyle intervention, Machine learning models, Microcirculation, Otorhinolaryngologoical disorders, Pain sensitivity, Pandemic, Phenotyping, Population screening, Predictive preventive personalised medicine, Primary vascular dysregulation, Respiratory complications, Risk assessment, Risk factors, Sense regulation, Sicca syndrome, Stress, survey, Suboptimal health, Thirst, Tinnitus, Underweight, Vasospasm, Voice perturbation, Xerostomia

General General

Towards Using Graph Analytics for Tracking Covid-19.

In Procedia computer science

Graph analytics are now considered the state-of-the-art in many applications of communities detection. The combination between the graph's definition in mathematics and the graphs in computer science as an abstract data structure is the key behind the success of graph-based approaches in machine learning. Based on graphs, several approaches have been developed such as shortest path first (SPF) algorithms, subgraphs extraction, social media analytics, transportation networks, bioinformatic algorithms, etc. While SPF algorithms are widely used in optimization problems, Spectral clustering (SC) algorithms have overcome the limits of the most state-of-art approaches in communities detection. The purpose of this paper is to introduce a graph-based approach of communities detection in the novel coronavirus Covid-19 countries' datasets. The motivation behind this work is to overcome the limitations of multiclass classification, as SC is an unsupervised clustering algorithm, there is no need to predefine the output clusters as a preprocessing step. Our proposed approach is based on a previous contribution on an automatic estimation of the k number of the output clusters. Based on dynamic statistical data for more than 200 countries, each cluster is supposed to group countries having similar behaviors of Covid-19 propagation.

El Mouden Zakariyaa Ait, Taj Rachida Moulay, Jakimi Abdeslam, Hajar Moha

2020

Communities detection, Coronavirus, Covid-19, Graph analytics, Machine learning, Spectral clustering

Radiology Radiology

AI-assisted CT imaging analysis for COVID-19 screening: Building and deploying a medical AI system.

In Applied soft computing

The sudden outbreak of novel coronavirus 2019 (COVID-19) increased the diagnostic burden of radiologists. In the time of an epidemic crisis, we hope artificial intelligence (AI) to reduce physician workload in regions with the outbreak, and improve the diagnosis accuracy for physicians before they could acquire enough experience with the new disease. In this paper, we present our experience in building and deploying an AI system that automatically analyzes CT images and provides the probability of infection to rapidly detect COVID-19 pneumonia. The proposed system which consists of classification and segmentation will save about 30%-40% of the detection time for physicians and promote the performance of COVID-19 detection. Specifically, working in an interdisciplinary team of over 30 people with medical and/or AI background, geographically distributed in Beijing and Wuhan, we are able to overcome a series of challenges (e.g. data discrepancy, testing time-effectiveness of model, data security, etc.) in this particular situation and deploy the system in four weeks. In addition, since the proposed AI system provides the priority of each CT image with probability of infection, the physicians can confirm and segregate the infected patients in time. Using 1,136 training cases (723 positives for COVID-19) from five hospitals, we are able to achieve a sensitivity of 0.974 and specificity of 0.922 on the test dataset, which included a variety of pulmonary diseases.

Wang Bo, Jin Shuo, Yan Qingsen, Xu Haibo, Luo Chuan, Wei Lai, Zhao Wei, Hou Xuexue, Ma Wenshuo, Xu Zhengqing, Zheng Zhuozhao, Sun Wenbo, Lan Lan, Zhang Wei, Mu Xiangdong, Shi Chenxi, Wang Zhongxiao, Lee Jihae, Jin Zijian, Lin Minggui, Jin Hongbo, Zhang Liang, Guo Jun, Zhao Benqi, Ren Zhizhong, Wang Shuhao, Xu Wei, Wang Xinghuan, Wang Jianming, You Zheng, Dong Jiahong

2020-Nov-10

COVID-19, Classification, Deep learning, Medical assistance system, Neural network, Segmentation

General General

Public opinion mining using natural language processing technique for improvisation towards smart city.

In International journal of speech technology

In this digital world integrating smart city concepts, there is a tremendous scope and need for e-governance applications. Now people analyze the opinion of others before purchasing any product, hotel booking, stepping onto restaurants etc. and the respective user share their experience as a feedback towards the service. But there is no e-governance platform to obtain public opinion grievances towards covid19, government new laws, policies etc. With the growing availability and emergence of opinion rich information's, new opportunities and challenges might arise in developing a technology for mining the huge set of public messages, opinions and alert the respective departments to take necessary actions and also nearby ambulances if its related to covid-19. To overcome this pandemic situation a natural language processing based efficient e-governance platform is demandful to detect the corona positive patients and provide transparency on the covid count and also alert the respective health ministry and nearby ambulance based on the user voice inputs. To convert the public voice messages into text, we used Hidden Markov Models (HMMs). To identify respective government department responsible for the respective user voice input, we perform pre-processing, part of speech, unigram, bigram, trigram analysis and fuzzy logic (machine learning technique). After identifying the responsible department, we perform 2 methods, (1) Automatic alert e-mail and message to the government departmental officials and nearby ambulance or covid camp if the user input is related to covis19. (2) Ticketing system for public and government officials monitoring. For experimental results, we used Java based web and mobile application to execute the proposed methodology. Integration of HMM, Fuzzy logic provides promising results.

Leelavathy S, Nithya M

2020-Nov-11

Covid 19, Fuzzy logic, Hidden Markov models, Natural language processing, Speech processing

General General

DeepLMS: a deep learning predictive model for supporting online learning in the Covid-19 era.

In Scientific reports ; h5-index 158.0

Coronavirus (Covid-19) pandemic has imposed a complete shut-down of face-to-face teaching to universities and schools, forcing a crash course for online learning plans and technology for students and faculty. In the midst of this unprecedented crisis, video conferencing platforms (e.g., Zoom, WebEx, MS Teams) and learning management systems (LMSs), like Moodle, Blackboard and Google Classroom, are being adopted and heavily used as online learning environments (OLEs). However, as such media solely provide the platform for e-interaction, effective methods that can be used to predict the learner's behavior in the OLEs, which should be available as supportive tools to educators and metacognitive triggers to learners. Here we show, for the first time, that Deep Learning techniques can be used to handle LMS users' interaction data and form a novel predictive model, namely DeepLMS, that can forecast the quality of interaction (QoI) with LMS. Using Long Short-Term Memory (LSTM) networks, DeepLMS results in average testing Root Mean Square Error (RMSE) [Formula: see text], and average correlation coefficient between ground truth and predicted QoI values [Formula: see text] [Formula: see text], when tested on QoI data from one database pre- and two ones during-Covid-19 pandemic. DeepLMS personalized QoI forecasting scaffolds user's online learning engagement and provides educators with an evaluation path, additionally to the content-related assessment, enriching the overall view on the learners' motivation and participation in the learning process.

Dias Sofia B, Hadjileontiadou Sofia J, Diniz José, Hadjileontiadis Leontios J

2020-11-16

General General

Machine Learning for Mortality Analysis in Patients with COVID-19.

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

This paper analyzes a sample of patients hospitalized with COVID-19 in the region of Madrid (Spain). Survival analysis, logistic regression, and machine learning techniques (both supervised and unsupervised) are applied to carry out the analysis where the endpoint variable is the reason for hospital discharge (home or deceased). The different methods applied show the importance of variables such as age, O2 saturation at Emergency Rooms (ER), and whether the patient comes from a nursing home. In addition, biclustering is used to globally analyze the patient-drug dataset, extracting segments of patients. We highlight the validity of the classifiers developed to predict the mortality, reaching an appreciable accuracy. Finally, interpretable decision rules for estimating the risk of mortality of patients can be obtained from the decision tree, which can be crucial in the prioritization of medical care and resources.

Sánchez-Montañés Manuel, Rodríguez-Belenguer Pablo, Serrano-López Antonio J, Soria-Olivas Emilio, Alakhdar-Mohmara Yasser

2020-Nov-12

COVID-19, feature importance, graphical models, machine learning, survival analysis

Radiology Radiology

An efficient mixture of deep and machine learning models for COVID-19 diagnosis in chest X-ray images.

In PloS one ; h5-index 176.0

A newly emerged coronavirus (COVID-19) seriously threatens human life and health worldwide. In coping and fighting against COVID-19, the most critical step is to effectively screen and diagnose infected patients. Among them, chest X-ray imaging technology is a valuable imaging diagnosis method. The use of computer-aided diagnosis to screen X-ray images of COVID-19 cases can provide experts with auxiliary diagnosis suggestions, which can reduce the burden of experts to a certain extent. In this study, we first used conventional transfer learning methods, using five pre-trained deep learning models, which the Xception model showed a relatively ideal effect, and the diagnostic accuracy reached 96.75%. In order to further improve the diagnostic accuracy, we propose an efficient diagnostic method that uses a combination of deep features and machine learning classification. It implements an end-to-end diagnostic model. The proposed method was tested on two datasets and performed exceptionally well on both of them. We first evaluated the model on 1102 chest X-ray images. The experimental results show that the diagnostic accuracy of Xception + SVM is as high as 99.33%. Compared with the baseline Xception model, the diagnostic accuracy is improved by 2.58%. The sensitivity, specificity and AUC of this model reached 99.27%, 99.38% and 99.32%, respectively. To further illustrate the robustness of our method, we also tested our proposed model on another dataset. Finally also achieved good results. Compared with related research, our proposed method has higher classification accuracy and efficient diagnostic performance. Overall, the proposed method substantially advances the current radiology based methodology, it can be very helpful tool for clinical practitioners and radiologists to aid them in diagnosis and follow-up of COVID-19 cases.

Wang Dingding, Mo Jiaqing, Zhou Gang, Xu Liang, Liu Yajun

2020

Radiology Radiology

Validation of Chest Computed Tomography Artificial Intelligence to Determine the Requirement for Mechanical Ventilation and Risk of Mortality in Hospitalized Coronavirus Disease-19 Patients in a Tertiary Care Center In Mexico City.

In Revista de investigacion clinica; organo del Hospital de Enfermedades de la Nutricion

BACKGROUND : Artificial intelligence (AI) in radiology has improved diagnostic performance and shortened reading times of coronavirus disease 2019 (COVID-19) patients' studies.

OBJECTIVES : The objectives pf the study were to analyze the performance of a chest computed tomography (CT) AI quantitative algorithm for determining the risk of mortality/mechanical ventilation (MV) in hospitalized COVID-19 patients and explore a prognostic multivariate model in a tertiary-care center in Mexico City.

METHODS : Chest CT images of 166 COVID-19 patients hospitalized from April 1 to 20, 2020, were retrospectively analyzed using AI algorithm software. Data were collected from their medical records. We analyzed the diagnostic yield of the relevant CT variables using the area under the ROC curve (area under the curve [AUC]). Optimal thresholds were obtained using the Youden index. We proposed a predictive logistic model for each outcome based on CT AI measures and predetermined laboratory and clinical characteristics.

RESULTS : The highest diagnostic yield of the assessed CT variables for mortality was the percentage of total opacity (threshold >51%; AUC = 0.88, sensitivity = 74%, and specificity = 91%). The AUC of the CT severity score (threshold > 12.5) was 0.88 for MV (sensitivity = 65% and specificity = 92%). The proposed prognostic models include the percentage of opacity and lactate dehydrogenase level for mortality and troponin I and CT severity score for MV requirement.

CONCLUSION : The AI-calculated CT severity score and total opacity percentage showed good diagnostic accuracy for mortality and met MV criteria. The proposed prognostic models using biochemical variables and imaging data measured by AI on chest CT showed good risk classification in our population of hospitalized COVID-19 patients.

Kimura-Sandoval Yukiyoshi, Arévalo-Molina Mary E, Cristancho-Rojas César N, Kimura-Sandoval Yumi, Rebollo-Hurtado Victoria, Licano-Zubiate Mariana, Chapa-Ibargüengoitia Mónica, Muñoz-López Gisela

2020-Nov-17

General General

Innovation and Reform on Technology Empowered Education: The 24th Global Chinese Conference on Computers in Education.

In TechTrends : for leaders in education & training

The 24th Global Chinese Conference on Computers in Education (GCCCE) was held September 12-16, 2020 at Northwest Normal University, Lanzhou, China. The GCCCE adopted a hybrid conference format for the first time, combining traditional face-to-face sessions and online live streaming to reduce the impact from the Covid-19 pandemic. The GCCCE hosted over 300 current presentations, roundtables, and poster sessions in its nine sub-conferences and one English paper session. With a theme of "Innovation and Reform on Technology Empowered Education," the GCCCE organized four keynote speeches, two expert symposiums, six workshops, one teacher forum, and one doctoral consortium. Over 300 attendees were at the GCCCE site in Lanzhou, China. The presentations, roundtables, and posters were live steaming during the conference. The live streaming conference sessions and their video recordings achieved a total of accumulative views over 330,000 times by the end of October 2020 with their viewers from different countries and regions. This report synthesizes the keynote speeches and award-winning presentations at GCCCE to share some highlights with readers.

Hao Jianjiang, Guo Jiong, Wang Charles Xiaoxue

2020-Nov-11

Artificial intelligence, GCCCE 2020, Innovation for education

General General

An evaluation of two commercial deep learning-based information retrieval systems for COVID-19 literature.

In Journal of the American Medical Informatics Association : JAMIA

The COVID-19 pandemic has resulted in a tremendous need for access to the latest scientific information, leading to both corpora for COVID-19 literature and search engines to query such data. While most search engine research is performed in academia with rigorous evaluation, major commercial companies dominate the web search market. Thus, it is expected that commercial pandemic-specific search engines will gain much higher traction than academic alternatives, leading to questions about the empirical performance of these tools. This paper seeks to empirically evaluate two commercial search engines for COVID-19 (Google and Amazon) in comparison with academic prototypes evaluated in the TREC-COVID task. We performed several steps to reduce bias in the manual judgments to ensure a fair comparison of all systems. We find the commercial search engines sizably underperformed those evaluated under TREC-COVID. This has implications for trust in popular health search engines and developing biomedical search engines for future health crises.

Soni Sarvesh, Roberts Kirk

2020-Nov-16

COVID-19, TREC-COVID, coronavirus, information retrieval

General General

Artificial Intelligence and COVID-19: Present State and Future Vision.

In Intelligence-based medicine

The COVID-19 pandemic has lead to catastrophic number of deaths and revealed that much work still remains with data and artificial intelligence. To fully comprehend the dynamics of a pandemic with relevance to artificial intelligence, a primer on global health concepts is first presented. Following this, various aspects of diagnosis and therapy and the relationship to artificial intelligence are presented along with a future projection of an ideal deployment of artificial intelligence in a pandemic. Final thoughts are made about lessons learned and what lies ahead.

Chang Anthony C

2020-Nov-07

Artificial intelligence, COVID-19, Deep learning, Machine learning, Pandemic

General General

KG-COVID-19: a framework to produce customized knowledge graphs for COVID-19 response.

In Patterns (New York, N.Y.)

Integrated, up-to-date data about SARS-CoV-2 and COVID-19 is crucial for the ongoing response to the COVID-19 pandemic by the biomedical research community. While rich biological knowledge exists for SARS-CoV-2 and related viruses (SARS-CoV, MERS-CoV), integrating this knowledge is difficult and time consuming, since much of it is in siloed databases or in textual format. Furthermore, the data required by the research community varies drastically for different tasks-the optimal data for a machine learning task, for example, is much different from the data used to populate a browsable user interface for clinicians. To address these challenges, we created KG-COVID-19, a flexible framework that ingests and integrates heterogeneous biomedical data to produce knowledge graphs (KGs), and applied it to create a KG for COVID-19 response. This KG framework can also be applied to other problems in which siloed biomedical data must be quickly integrated for different research applications, including future pandemics.

Reese Justin T, Unni Deepak, Callahan Tiffany J, Cappelletti Luca, Ravanmehr Vida, Carbon Seth, Shefchek Kent A, Good Benjamin M, Balhoff James P, Fontana Tommaso, Blau Hannah, Matentzoglu Nicolas, Harris Nomi L, Munoz-Torres Monica C, Haendel Melissa A, Robinson Peter N, Joachimiak Marcin P, Mungall Christopher J

2020-Nov-09

Radiology Radiology

Deep learning prediction of likelihood of ICU admission and mortality in COVID-19 patients using clinical variables.

In PeerJ

Background : This study aimed to develop a deep-learning model and a risk-score system using clinical variables to predict intensive care unit (ICU) admission and in-hospital mortality in COVID-19 patients.

Methods : This retrospective study consisted of 5,766 persons-under-investigation for COVID-19 between 7 February 2020 and 4 May 2020. Demographics, chronic comorbidities, vital signs, symptoms and laboratory tests at admission were collected. A deep neural network model and a risk-score system were constructed to predict ICU admission and in-hospital mortality. Prediction performance used the receiver operating characteristic area under the curve (AUC).

Results : The top ICU predictors were procalcitonin, lactate dehydrogenase, C-reactive protein, ferritin and oxygen saturation. The top mortality predictors were age, lactate dehydrogenase, procalcitonin, cardiac troponin, C-reactive protein and oxygen saturation. Age and troponin were unique top predictors for mortality but not ICU admission. The deep-learning model predicted ICU admission and mortality with an AUC of 0.780 (95% CI [0.760-0.785]) and 0.844 (95% CI [0.839-0.848]), respectively. The corresponding risk scores yielded an AUC of 0.728 (95% CI [0.726-0.729]) and 0.848 (95% CI [0.847-0.849]), respectively.

Conclusions : Deep learning and the resultant risk score have the potential to provide frontline physicians with quantitative tools to stratify patients more effectively in time-sensitive and resource-constrained circumstances.

Li Xiaoran, Ge Peilin, Zhu Jocelyn, Li Haifang, Graham James, Singer Adam, Richman Paul S, Duong Tim Q

2020

Coronavirus, Machine learning, Pneumonia, Prediction model, SARS-CoV-2

Radiology Radiology

Deep-learning convolutional neural networks with transfer learning accurately classify COVID-19 lung infection on portable chest radiographs.

In PeerJ

Portable chest X-ray (pCXR) has become an indispensable tool in the management of Coronavirus Disease 2019 (COVID-19) lung infection. This study employed deep-learning convolutional neural networks to classify COVID-19 lung infections on pCXR from normal and related lung infections to potentially enable more timely and accurate diagnosis. This retrospect study employed deep-learning convolutional neural network (CNN) with transfer learning to classify based on pCXRs COVID-19 pneumonia (N = 455) on pCXR from normal (N = 532), bacterial pneumonia (N = 492), and non-COVID viral pneumonia (N = 552). The data was randomly split into 75% training and 25% testing, randomly. A five-fold cross-validation was used for the testing set separately. Performance was evaluated using receiver-operating curve analysis. Comparison was made with CNN operated on the whole pCXR and segmented lungs. CNN accurately classified COVID-19 pCXR from those of normal, bacterial pneumonia, and non-COVID-19 viral pneumonia patients in a multiclass model. The overall sensitivity, specificity, accuracy, and AUC were 0.79, 0.93, and 0.79, 0.85 respectively (whole pCXR), and were 0.91, 0.93, 0.88, and 0.89 (CXR of segmented lung). The performance was generally better using segmented lungs. Heatmaps showed that CNN accurately localized areas of hazy appearance, ground glass opacity and/or consolidation on the pCXR. Deep-learning convolutional neural network with transfer learning accurately classifies COVID-19 on portable chest X-ray against normal, bacterial pneumonia or non-COVID viral pneumonia. This approach has the potential to help radiologists and frontline physicians by providing more timely and accurate diagnosis.

Kikkisetti Shreeja, Zhu Jocelyn, Shen Beiyi, Li Haifang, Duong Tim Q

2020

Chest X-ray, Computed tomography, Coronavirus, Lung infection, Machine learning

General General

The ensemble deep learning model for novel COVID-19 on CT images.

In Applied soft computing

The rapid detection of the novel coronavirus disease, COVID-19, has a positive effect on preventing propagation and enhancing therapeutic outcomes. This article focuses on the rapid detection of COVID-19. We propose an ensemble deep learning model for novel COVID-19 detection from CT images. 2933 lung CT images from COVID-19 patients were obtained from previous publications, authoritative media reports, and public databases. The images were preprocessed to obtain 2500 high-quality images. 2500 CT images of lung tumor and 2500 from normal lung were obtained from a hospital. Transfer learning was used to initialize model parameters and pretrain three deep convolutional neural network models: AlexNet, GoogleNet, and ResNet. These models were used for feature extraction on all images. Softmax was used as the classification algorithm of the fully connected layer. The ensemble classifier EDL-COVID was obtained via relative majority voting. Finally, the ensemble classifier was compared with three component classifiers to evaluate accuracy, sensitivity, specificity, F value, and Matthews correlation coefficient. The results showed that the overall classification performance of the ensemble model was better than that of the component classifier. The evaluation indexes were also higher. This algorithm can better meet the rapid detection requirements of the novel coronavirus disease COVID-19.

Zhou Tao, Lu Huiling, Yang Zaoli, Qiu Shi, Huo Bingqiang, Dong Yali

2020-Nov-06

COVID-19, Deep learning, Ensemble learning, Lung CT images

General General

AI aiding in diagnosing, tracking recovery of COVID-19 using deep learning on Chest CT scans.

In Multimedia tools and applications

Coronavirus (COVID-19) has spread throughout the world, causing mayhem from January 2020 to this day. Owing to its rapidly spreading existence and high death count, the WHO has classified it as a pandemic. Biomedical engineers, virologists, epidemiologists, and people from other medical fields are working to help contain this epidemic as soon as possible. The virus incubates for five days in the human body and then begins displaying symptoms, in some cases, as late as 27 days. In some instances, CT scan based diagnosis has been found to have better sensitivity than RT-PCR, which is currently the gold standard for COVID-19 diagnosis. Lung conditions relevant to COVID-19 in CT scans are ground-glass opacity (GGO), consolidation, and pleural effusion. In this paper, two segmentation tasks are performed to predict lung spaces (segregated from ribcage and flesh in Chest CT) and COVID-19 anomalies from chest CT scans. A 2D deep learning architecture with U-Net as its backbone is proposed to solve both the segmentation tasks. It is observed that change in hyperparameters such as number of filters in down and up sampling layers, addition of attention gates, addition of spatial pyramid pooling as basic block and maintaining the homogeneity of 32 filters after each down-sampling block resulted in a good performance. The proposed approach is assessed using publically available datasets from GitHub and Kaggle. Model performance is evaluated in terms of F1-Score, Mean intersection over union (Mean IoU). It is noted that the proposed approach results in 97.31% of F1-Score and 84.6% of Mean IoU. The experimental results illustrate that the proposed approach using U-Net architecture as backbone with the changes in hyperparameters shows better results in comparison to existing U-Net architecture and attention U-net architecture. The study also recommends how this methodology can be integrated into the workflow of healthcare systems to help control the spread of COVID-19.

Kuchana Maheshwar, Srivastava Amritesh, Das Ronald, Mathew Justin, Mishra Atul, Khatter Kiran

2020-Nov-08

COVID-19, Computed Tomography, Consolidation, Coronavirus, Diagnosis, Ground Glass Opacities (GGO), Hyperparameters, Pleural Effusion, Reverse Transcriptase Polymerase Chain Reaction, Semantic Segmentation, Spatial pyramid pooling, U-Net architecture

Public Health Public Health

How loneliness is talked about in social media during COVID-19 pandemic: Text mining of 4,492 Twitter feeds.

In Journal of psychiatric research ; h5-index 59.0

BACKGROUND : Loneliness is a public health problem that is expected to rise during the COVID-19 pandemic, given the widespread policy of quarantine. The literature is unclear whether loneliness during COVID-19 is similar to those of non-pandemic seasons. This study examined the expression of loneliness on Twitter during COVID-19 pandemic, and identified key areas of loneliness across diverse communities.

METHODS : Twitter was searched for feeds that were:(1) in English; (2) posted from May 1, 2020 to July 1, 2020; (3) posted by individual users (not organisations); and (4) contained the words 'loneliness' and 'COVID-19'. A machine-learning approach (Topic Modeling) identified key topics from the Twitter feeds; Hierarchical Modeling identified overarching themes. Variations in the prevalence of the themes were examined over time and across the number of followers of the Twitter users.

RESULTS : 4492 Twitter feeds were included and classified into 3 themes: (1) Community impact of loneliness during COVID-19; (2) Social distancing during COVID-19 and its effects on loneliness; and (3) Mental health effects of loneliness during COVID-19. The 3 themes demonstrated temporal variations. Particularly in Europe, Theme 1 showed a drastic reduction over time, with a corresponding rise in Theme 3. The themes also varied across number of followers. Highly influential users were more likely to talk about Theme 3 and less about Theme 2.

CONCLUSIONS : The findings reflect close-to-real-time public sentiments on loneliness during the COVID-19 pandemic and demonstrated the potential usefulness of social media to keep tabs on evolving mental health issues. It also provides inspiration for potential interventions to address novel problems-such as loneliness-during COVID-19 pandemic.

Koh Jing Xuan, Liew Tau Ming

2020-Nov-07

COVID-19, Loneliness, Mental health, Natural Language Processing, Social media, Topic modeling, Twitter

Radiology Radiology

Analyzing inter-reader variability affecting deep ensemble learning for COVID-19 detection in chest radiographs.

In PloS one ; h5-index 176.0

Data-driven deep learning (DL) methods using convolutional neural networks (CNNs) demonstrate promising performance in natural image computer vision tasks. However, their use in medical computer vision tasks faces several limitations, viz., (i) adapting to visual characteristics that are unlike natural images; (ii) modeling random noise during training due to stochastic optimization and backpropagation-based learning strategy; (iii) challenges in explaining DL black-box behavior to support clinical decision-making; and (iv) inter-reader variability in the ground truth (GT) annotations affecting learning and evaluation. This study proposes a systematic approach to address these limitations through application to the pandemic-caused need for Coronavirus disease 2019 (COVID-19) detection using chest X-rays (CXRs). Specifically, our contribution highlights significant benefits obtained through (i) pretraining specific to CXRs in transferring and fine-tuning the learned knowledge toward improving COVID-19 detection performance; (ii) using ensembles of the fine-tuned models to further improve performance over individual constituent models; (iii) performing statistical analyses at various learning stages for validating results; (iv) interpreting learned individual and ensemble model behavior through class-selective relevance mapping (CRM)-based region of interest (ROI) localization; and, (v) analyzing inter-reader variability and ensemble localization performance using Simultaneous Truth and Performance Level Estimation (STAPLE) methods. We find that ensemble approaches markedly improved classification and localization performance, and that inter-reader variability and performance level assessment helps guide algorithm design and parameter optimization. To the best of our knowledge, this is the first study to construct ensembles, perform ensemble-based disease ROI localization, and analyze inter-reader variability and algorithm performance for COVID-19 detection in CXRs.

Rajaraman Sivaramakrishnan, Sornapudi Sudhir, Alderson Philip O, Folio Les R, Antani Sameer K

2020

General General

Machine learning in predicting respiratory failure in patients with COVID-19 pneumonia-Challenges, strengths, and opportunities in a global health emergency.

In PloS one ; h5-index 176.0

AIMS : The aim of this study was to estimate a 48 hour prediction of moderate to severe respiratory failure, requiring mechanical ventilation, in hospitalized patients with COVID-19 pneumonia.

METHODS : This was an observational prospective study that comprised consecutive patients with COVID-19 pneumonia admitted to hospital from 21 February to 6 April 2020. The patients' medical history, demographic, epidemiologic and clinical data were collected in an electronic patient chart. The dataset was used to train predictive models using an established machine learning framework leveraging a hybrid approach where clinical expertise is applied alongside a data-driven analysis. The study outcome was the onset of moderate to severe respiratory failure defined as PaO2/FiO2 ratio <150 mmHg in at least one of two consecutive arterial blood gas analyses in the following 48 hours. Shapley Additive exPlanations values were used to quantify the positive or negative impact of each variable included in each model on the predicted outcome.

RESULTS : A total of 198 patients contributed to generate 1068 usable observations which allowed to build 3 predictive models based respectively on 31-variables signs and symptoms, 39-variables laboratory biomarkers and 91-variables as a composition of the two. A fourth "boosted mixed model" included 20 variables was selected from the model 3, achieved the best predictive performance (AUC = 0.84) without worsening the FN rate. Its clinical performance was applied in a narrative case report as an example.

CONCLUSION : This study developed a machine model with 84% prediction accuracy, which is able to assist clinicians in decision making process and contribute to develop new analytics to improve care at high technology readiness levels.

Ferrari Davide, Milic Jovana, Tonelli Roberto, Ghinelli Francesco, Meschiari Marianna, Volpi Sara, Faltoni Matteo, Franceschi Giacomo, Iadisernia Vittorio, Yaacoub Dina, Ciusa Giacomo, Bacca Erica, Rogati Carlotta, Tutone Marco, Burastero Giulia, Raimondi Alessandro, Menozzi Marianna, Franceschini Erica, Cuomo Gianluca, Corradi Luca, Orlando Gabriella, Santoro Antonella, Digaetano Margherita, Puzzolante Cinzia, Carli Federica, Borghi Vanni, Bedini Andrea, Fantini Riccardo, Tabbì Luca, Castaniere Ivana, Busani Stefano, Clini Enrico, Girardis Massimo, Sarti Mario, Cossarizza Andrea, Mussini Cristina, Mandreoli Federica, Missier Paolo, Guaraldi Giovanni

2020

Public Health Public Health

The Relationships of Deteriorating Depression and Anxiety with Longitudinal Behavioral Changes in Google and YouTube Usages among College Students in the United States during COVID-19: Observational Study.

In JMIR mental health

BACKGROUND : Depression and anxiety disorders among the global population are worsened during the coronavirus disease (COVID-19). Yet, current methods for screening these two issues rely on in-person interviews, which can be expensive, time-consuming, blocked by social stigma and quarantines. Meanwhile, how individuals engage with online platforms such as Google Search and YouTube undergoes drastic shifts due to COVID-19 and subsequent lockdowns. Such ubiquitous daily behaviors on online platforms have the potential to capture and correlate with clinically alarming deteriorations in depression and anxiety profiles of users in a non-invasive manner.

OBJECTIVE : The goal of this study is to examine, among college students in the United States, the relationships of deteriorating depression and anxiety conditions with the changes in user behaviors when engaging with Google Search and YouTube during COVID-19.

METHODS : This study recruited a cohort of undergraduate students (N=49) from a U.S. college campus during January 2020 (prior to the pandemic) and measured the anxiety and depression levels of each participant. The anxiety level was assessed via the General Anxiety Disorder-7 (GAD-7). The depression level was assessed via the Patient Health Questionnaire-9 (PHQ-9). This study followed up with the same cohort during May 2020 (during the pandemic), and the anxiety and depression levels were assessed again. The longitudinal Google Search and YouTube history data of all participants were anonymized and collected. From individual-level Google Search and YouTube histories, we developed 5 features that can quantify shifts in online behaviors during the pandemic. We then assessed the correlations of deteriorating depression and anxiety profiles with each of these features. We finally demonstrated the feasibility of utilizing the proposed features to build predictive machine learning models.

RESULTS : Of the 49 participants, 49% (n=24) of them reported an increase in the PHQ-9 depression scores; 53% (n=26) of them reported an increase in the GAD-7 anxiety scores. The results showed that a number of online behavior features were significantly correlated with deteriorations in the PHQ-9 scores (r ranging between -0.37 and 0.75, P.03) and the GAD-7 scores (r ranging between -0.47 and 0.74, P.03). Simple machine learning models are shown to be useful in predicting the change in anxiety and depression scores (MSE ranging between 2.37 and 4.22, R2 ranging between 0.68 and 0.84) with the proposed features.

CONCLUSIONS : The results suggested that deteriorating depression and anxiety conditions have strong correlations with behavioral changes in Google Search and YouTube usages during the COVID-19. Though further studies are required, our results demonstrated the feasibility of utilizing pervasive online data to establish non-invasive surveillance systems for mental health conditions that bypasses many disadvantages of existing screening methods.

CLINICALTRIAL :

Zhang Boyu, Zaman Anis, Silenzio Vincent, Kautz Henry, Hoque Ehsan

2020-Nov-09

General General

Can machine learning optimize the efficiency of the operating room in the era of COVID-19?

In Canadian journal of surgery. Journal canadien de chirurgie

The cancellation of large numbers of surgical procedures because of the coronavirus disease 2019 (COVID-19) pandemic has drastically extended wait lists and negatively affected patient care and experience. As many facilities resume clinical work owing to the currently low burden of disease in our community, we are faced with operative booking protocols and procedures that are not mathematically designed to optimize efficiency. Using a subset of artificial intelligence called "machine learning," we have shown how the use of operating time can be optimized with a custom Python (a high-level programming language) script and an open source machine-learning algorithm, the ORTools software suite from the Google AI division of Alphabet Inc. This allowed the creation of customized models to optimize the efficiency of operating room booking times, which resulted in a reduction in nursing overtime of 21% - a theoretical cost savings of $469 000 over 3 years.

Rozario Natasha, Rozario Duncan

General General

Decoding Asymptomatic COVID-19 Infection and Transmission.

In The journal of physical chemistry letters ; h5-index 129.0

One of the major challenges in controlling the coronavirus disease 2019 (COVID-19) outbreak is its asymptomatic transmission. The pathogenicity and virulence of asymptomatic COVID-19 remain mysterious. On the basis of the genotyping of 75775 SARS-CoV-2 genome isolates, we reveal that asymptomatic infection is linked to SARS-CoV-2 11083G>T mutation (i.e., L37F at nonstructure protein 6 (NSP6)). By analyzing the distribution of 11083G>T in various countries, we unveil that 11083G>T may correlate with the hypotoxicity of SARS-CoV-2. Moreover, we show a global decaying tendency of the 11083G>T mutation ratio indicating that 11083G>T hinders the SARS-CoV-2 transmission capacity. Artificial intelligence, sequence alignment, and network analysis are applied to show that NSP6 mutation L37F may have compromised the virus's ability to undermine the innate cellular defense against viral infection via autophagy regulation. This assessment is in good agreement with our genotyping of the SARS-CoV-2 evolution and transmission across various countries and regions over the past few months.

Wang Rui, Chen Jiahui, Hozumi Yuta, Yin Changchuan, Wei Guo-Wei

2020-Nov-12

General General

Towards the sustainable development of smart cities through mass video surveillance: A response to the COVID-19 pandemic.

In Sustainable cities and society

Sustainable smart city initiatives around the world have recently had great impact on the lives of citizens and brought significant changes to society. More precisely, data-driven smart applications that efficiently manage sparse resources are offering a futuristic vision of smart, efficient, and secure city operations. However, the ongoing COVID-19 pandemic has revealed the limitations of existing smart city deployment; hence; the development of systems and architectures capable of providing fast and effective mechanisms to limit further spread of the virus has become paramount. An active surveillance system capable of monitoring and enforcing social distancing between people can effectively slow the spread of this deadly virus. In this paper, we propose a data-driven deep learning-based framework for the sustainable development of a smart city, offering a timely response to combat the COVID-19 pandemic through mass video surveillance. To implementing social distancing monitoring, we used three deep learning-based real-time object detection models for the detection of people in videos captured with a monocular camera. We validated the performance of our system using a real-world video surveillance dataset for effective deployment.

Shorfuzzaman Mohammad, Hossain M Shamim, Alhamid Mohammed F

2021-Jan

COVID-19 pandemic, Sustainable cities, deep learning, object detection, social distancing, video surveillance

Radiology Radiology

COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images.

In Scientific reports ; h5-index 158.0

The Coronavirus Disease 2019 (COVID-19) pandemic continues to have a devastating effect on the health and well-being of the global population. A critical step in the fight against COVID-19 is effective screening of infected patients, with one of the key screening approaches being radiology examination using chest radiography. It was found in early studies that patients present abnormalities in chest radiography images that are characteristic of those infected with COVID-19. Motivated by this and inspired by the open source efforts of the research community, in this study we introduce COVID-Net, a deep convolutional neural network design tailored for the detection of COVID-19 cases from chest X-ray (CXR) images that is open source and available to the general public. To the best of the authors' knowledge, COVID-Net is one of the first open source network designs for COVID-19 detection from CXR images at the time of initial release. We also introduce COVIDx, an open access benchmark dataset that we generated comprising of 13,975 CXR images across 13,870 patient patient cases, with the largest number of publicly available COVID-19 positive cases to the best of the authors' knowledge. Furthermore, we investigate how COVID-Net makes predictions using an explainability method in an attempt to not only gain deeper insights into critical factors associated with COVID cases, which can aid clinicians in improved screening, but also audit COVID-Net in a responsible and transparent manner to validate that it is making decisions based on relevant information from the CXR images. By no means a production-ready solution, the hope is that the open access COVID-Net, along with the description on constructing the open source COVIDx dataset, will be leveraged and build upon by both researchers and citizen data scientists alike to accelerate the development of highly accurate yet practical deep learning solutions for detecting COVID-19 cases and accelerate treatment of those who need it the most.

Wang Linda, Lin Zhong Qiu, Wong Alexander

2020-11-11

oncology Oncology

Optimizing clinical research procedures in public health emergencies.

In Medicinal research reviews ; h5-index 46.0

Public Health Emergencies of International Concern, such as the coronavirus disease 2019 pandemic, have a devastating impact on an individual and societal level, and there is an urgent need to learn, understand and bridge the therapeutic gap at a time of extreme stress on the patient, health care systems and staff. Well-designed, controlled clinical trials play a crucial role in the discovery of novel diagnostic and management strategies; however, these catastrophic circumstances pose unique challenges in initiating research studies at institutional, national, and international levels, highlighting the importance of a coordinated, collaborative approach. This review discusses key elements necessary to consider for developing clinical trials within a Public Health Emergency setting.

Madariaga Ainhoa, Kasherman Lawrence, Karakasis Katherine, Degendorfer Pamela, Heesters Ann M, Xu Wei, Husain Shahid, Oza Amit M

2020-Nov-11

COVID-19, N-of-1, Public Health Emergency, artificial intelligence, clinical trial, ethics, master protocols, platform studies, randomized trials, umbrella trial

General General

Stigmatization in social media: Documenting and analyzing hate speech for COVID-19 on Twitter.

In Proceedings of the Association for Information Science and Technology. Association for Information Science and Technology

As the COVID-19 pandemic has unfolded, Hate Speech on social media about China and Chinese people has encouraged social stigmatization. For the historical and humanistic purposes, this history-in-the-making needs to be archived and analyzed. Using the query "china+and+coronavirus" to scrape from the Twitter API, we have obtained 3,457,402 key tweets about China relating to COVID-19. In this archive, in which about 40% of the tweets are from the U.S., we identify 25,467 Hate Speech occurrences and analyze them according to lexicon-based emotions and demographics using machine learning and network methods. The results indicate that there are substantial associations between the amount of Hate Speech and demonstrations of sentiments, and state demographics factors. Sentiments of surprise and fear associated with poverty and unemployment rates are prominent. This digital archive and the related analyses are not simply historical, therefore. They play vital roles in raising public awareness and mitigating future crises. Consequently, we regard our research as a pilot study in methods of analysis that might be used by other researchers in various fields.

Fan Lizhou, Yu Huizi, Yin Zhanyuan

2020

COVID‐19, Twitter, coronavirus, hate speech, pandemic

General General

A systematic approach for COVID-19 predictions and parameter estimation.

In Personal and ubiquitous computing

The world is currently facing a pandemic called COVID-19 which has drastically changed our human lifestyle, affecting it badly. The lifestyle and the thought processes of every individual have changed with the current situation. This situation was unpredictable, and it contains a lot of uncertainties. In this paper, the authors have attempted to predict and analyze the disease along with its related issues to determine the maximum number of infected people, the speed of spread, and most importantly, its evaluation using a model-based parameter estimation method. In this research the Susceptible-Infectious-Recovered model with different conditions has been used for the analysis of COVID-19. The effects of lockdown, the light switch method, and parameter variations like contact ratio and reproduction number are also analyzed. The authors have attempted to study and predict the lockdown effect, particularly in India in terms of infected and recovered numbers, which show substantial improvement. A disease-free endemic stability analysis using Lyapunov and LaSalle's method is presented, and novel methods such as the convalescent plasma method and the Who Acquires Infection From Whom method are also discussed, as they are considered to be useful in flattening the curve of COVID-19.

Srivastava Vishal, Srivastava Smriti, Chaudhary Gopal, Al-Turjman Fadi

2020-Nov-06

COVID-19, Convalescent plasma method, Lyapunov-LaSalle, Parameter estimation, Susceptible-Infectious-Recovered (SIR) model, Who Acquires Infection From Whom (WAIFW)

General General

Modeling and forecasting the early evolution of the Covid-19 pandemic in Brazil.

In Scientific reports ; h5-index 158.0

We model and forecast the early evolution of the COVID-19 pandemic in Brazil using Brazilian recent data from February 25, 2020 to March 30, 2020. This early period accounts for unawareness of the epidemiological characteristics of the disease in a new territory, sub-notification of the real numbers of infected people and the timely introduction of social distancing policies to flatten the spread of the disease. We use two variations of the SIR model and we include a parameter that comprises the effects of social distancing measures. Short and long term forecasts show that the social distancing policy imposed by the government is able to flatten the pattern of infection of the COVID-19. However, our results also show that if this policy does not last enough time, it is only able to shift the peak of infection into the future keeping the value of the peak in almost the same value. Furthermore, our long term simulations forecast the optimal date to end the policy. Finally, we show that the proportion of asymptomatic individuals affects the amplitude of the peak of symptomatic infected, suggesting that it is important to test the population.

Bastos Saulo B, Cajueiro Daniel O

2020-11-10

General General

Drugs Repurposing Using QSAR, Docking and Molecular Dynamics for Possible Inhibitors of the SARS-CoV-2 Mpro Protease.

In Molecules (Basel, Switzerland)

Wuhan, China was the epicenter of the first zoonotic transmission of the severe acute respiratory syndrome coronavirus clade 2 (SARS-CoV-2) in December 2019 and it is the causative agent of the novel human coronavirus disease 2019 (COVID-19). Almost from the beginning of the COVID-19 outbreak several attempts were made to predict possible drugs capable of inhibiting the virus replication. In the present work a drug repurposing study is performed to identify potential SARS-CoV-2 protease inhibitors. We created a Quantitative Structure-Activity Relationship (QSAR) model based on a machine learning strategy using hundreds of inhibitor molecules of the main protease (Mpro) of the SARS-CoV coronavirus. The QSAR model was used for virtual screening of a large list of drugs from the DrugBank database. The best 20 candidates were then evaluated in-silico against the Mpro of SARS-CoV-2 by using docking and molecular dynamics analyses. Docking was done by using the Gold software, and the free energies of binding were predicted with the MM-PBSA method as implemented in AMBER. Our results indicate that levothyroxine, amobarbital and ABP-700 are the best potential inhibitors of the SARS-CoV-2 virus through their binding to the Mpro enzyme. Five other compounds showed also a negative but small free energy of binding: nikethamide, nifurtimox, rebimastat, apomine and rebastinib.

Tejera Eduardo, Munteanu Cristian R, López-Cortés Andrés, Cabrera-Andrade Alejandro, Pérez-Castillo Yunierkis

2020-Nov-06

COVID-19, QSAR, SARS-CoV-2, drugs repurposing, molecular dynamics

Radiology Radiology

Accuracy of Conventional and Machine Learning Enhanced Chest Radiography for the Assessment of COVID-19 Pneumonia: Intra-Individual Comparison with CT.

In Journal of clinical medicine

PURPOSE : To evaluate diagnostic accuracy of conventional radiography (CXR) and machine learning enhanced CXR (mlCXR) for the detection and quantification of disease-extent in COVID-19 patients compared to chest-CT.

METHODS : Real-time polymerase chain reaction (rt-PCR)-confirmed COVID-19-patients undergoing CXR from March to April 2020 together with COVID-19 negative patients as control group were retrospectively included. Two independent readers assessed CXR and mlCXR images for presence, disease extent and type (consolidation vs. ground-glass opacities (GGOs) of COVID-19-pneumonia. Further, readers had to assign confidence levels to their diagnosis. CT obtained ≤ 36 h from acquisition of CXR served as standard of reference. Inter-reader agreement, sensitivity for detection and disease extent of COVID-19-pneumonia compared to CT was calculated. McNemar test was used to test for significant differences.

RESULTS : Sixty patients (21 females; median age 61 years, range 38-81 years) were included. Inter-reader agreement improved from good to excellent when mlCXR instead of CXR was used (k = 0.831 vs. k = 0.742). Sensitivity for pneumonia detection improved from 79.5% to 92.3%, however, on the cost of specificity 100% vs. 71.4% (p = 0.031). Overall, sensitivity for the detection of consolidation was higher than for GGO (37.5% vs. 70.4%; respectively). No differences could be found in disease extent estimation between mlCXR and CXR, even though the detection of GGO could be improved. Diagnostic confidence was better on mlCXR compared to CXR (p = 0.013).

CONCLUSION : In line with the current literature, the sensitivity for detection and quantification of COVID-19-pneumonia was moderate with CXR and could be improved when mlCXR was used for image interpretation.

Martini Katharina, Blüthgen Christian, Walter Joan E, Messerli Michael, Nguyen-Kim Thi Dan Linh, Frauenfelder Thomas

2020-Nov-06

critical care, imaging CT/MRI, infection control, pneumonia, respiratory infection, viral infection

Radiology Radiology

Evaluation of Scalability and Degree of Fine-Tuning of Deep Convolutional Neural Networks for COVID-19 Screening on Chest X-ray Images Using Explainable Deep-Learning Algorithm.

In Journal of personalized medicine

According to recent studies, patients with COVID-19 have different feature characteristics on chest X-ray (CXR) than those with other lung diseases. This study aimed at evaluating the layer depths and degree of fine-tuning on transfer learning with a deep convolutional neural network (CNN)-based COVID-19 screening in CXR to identify efficient transfer learning strategies. The CXR images used in this study were collected from publicly available repositories, and the collected images were classified into three classes: COVID-19, pneumonia, and normal. To evaluate the effect of layer depths of the same CNN architecture, CNNs called VGG-16 and VGG-19 were used as backbone networks. Then, each backbone network was trained with different degrees of fine-tuning and comparatively evaluated. The experimental results showed the highest AUC value to be 0.950 concerning COVID-19 classification in the experimental group of a fine-tuned with only 2/5 blocks of the VGG16 backbone network. In conclusion, in the classification of medical images with a limited number of data, a deeper layer depth may not guarantee better results. In addition, even if the same pre-trained CNN architecture is used, an appropriate degree of fine-tuning can help to build an efficient deep learning model.

Lee Ki-Sun, Kim Jae Young, Jeon Eun-Tae, Choi Won Suk, Kim Nan Hee, Lee Ki Yeol

2020-Nov-07

COVID-19, Grad-CAM, chest X-ray, convolutional neural network, deep learning

Radiology Radiology

AI-driven quantification, staging and outcome prediction of COVID-19 pneumonia.

In Medical image analysis

Coronavirus disease 2019 (COVID-19) emerged in 2019 and disseminated around the world rapidly. Computed tomography (CT) imaging has been proven to be an important tool for screening, disease quantification and staging. The latter is of extreme importance for organizational anticipation (availability of intensive care unit beds, patient management planning) as well as to accelerate drug development through rapid, reproducible and quantified assessment of treatment response. Even if currently there are no specific guidelines for the staging of the patients, CT together with some clinical and biological biomarkers are used. In this study, we collected a multi-center cohort and we investigated the use of medical imaging and artificial intelligence for disease quantification, staging and outcome prediction. Our approach relies on automatic deep learning-based disease quantification using an ensemble of architectures, and a data-driven consensus for the staging and outcome prediction of the patients fusing imaging biomarkers with clinical and biological attributes. Highly promising results on multiple external/independent evaluation cohorts as well as comparisons with expert human readers demonstrate the potentials of our approach.

Chassagnon Guillaume, Vakalopoulou Maria, Battistella Enzo, Christodoulidis Stergios, Hoang-Thi Trieu-Nghi, Dangeard Severine, Deutsch Eric, Andre Fabrice, Guillo Enora, Halm Nara, El Hajj Stefany, Bompard Florian, Neveu Sophie, Hani Chahinez, Saab Ines, Campredon Aliénor, Koulakian Hasmik, Bennani Souhail, Freche Gael, Barat Maxime, Lombard Aurelien, Fournier Laure, Monnier Hippolyte, Grand Téodor, Gregory Jules, Nguyen Yann, Khalil Antoine, Mahdjoub Elyas, Brillet Pierre-Yves, Tran Ba Stéphane, Bousson Valérie, Mekki Ahmed, Carlier Robert-Yves, Revel Marie-Pierre, Paragios Nikos

2020-Oct-15

Artifial intelligence, Biomarker discovery, COVID 19 pneumonia, Deep learning, Ensemble methods, Prognosis, Staging

General General

Digital Symptom Checker Usage and Triage: Population-Based Descriptive Study in a Large North American Integrated Health System.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Pressure on the United States (US) healthcare system has been increasing due to a combination of aging populations, rising healthcare expenditures and, most recently, the COVID-19 pandemic. Responses are hindered in part by a reliance on a limited supply of highly trained healthcare professionals, creating a need for scalable technological solutions. Digital symptom checkers are artificial intelligence (AI)-supported software tools that use a conversational "chatbot" format to support rapid diagnosis and consistent triage. The COVID-19 pandemic has brought new attention to these tools, with the need to avoid face-to-face contact and preserve urgent care capacity. However, evidence-based deployment of these chatbots requires an understanding of user demographics and associated triage recommendations generated by a large, general population.

OBJECTIVE : In this study we evaluate the user demographics and levels of triage acuity provided by one symptom checker chatbot deployed in partnership with a large integrated health system in the US.

METHODS : Population-based descriptive study including all online symptom assessments completed on the website and patient portal of the Sutter Health system (24 hospitals in Northern California) from April 24th, 2019 to February 1st, 2020. User demographics were compared to relevant US Census population data.

RESULTS : A total of 26,646 symptom assessments were completed during the study period. Most assessments (17,816/26,646, 66.9%) were completed by female users. Mean user age was 34.3 years (SD: 14.4 years), compared to a median age of 37.3 years of the general population. The most common initial symptom was 'abdominal pain' (2,060/26,646, 7.7%). A substantial portion (12,357/26,646, 46.4%) was completed outside of typical physician office hours. Most users were advised to seek medical care the same day (7,299/26,646, 27.4%) or within 2-3 days (6,301/26,646, 23.6%). Over one quarter of assessments required a high degree of urgency (7,723/26,646, 29.0%).

CONCLUSIONS : Users of the symptom checker chatbot were broadly representative of our patient population, though skewed towards younger and female users. Triage recommendations are comparable to those of nurse-staffed phone triage lines. While the emergence of COVID-19 increases the enthusiasm for remote medical assessment tools, it is important to take an evidence-based approach to their deployment.

CLINICALTRIAL :

Morse Keith E, Ostberg Nicolai P, Jones Veena G, Chan Albert S

2020-Nov-07

General General

COVIDGR dataset and COVID-SDNet methodology for predicting COVID-19 based on Chest X-Ray images.

In IEEE journal of biomedical and health informatics

Currently, Coronavirus disease (COVID-19), one of the most infectious diseases in the 21st century, is diagnosed using RT-PCR testing, CT scans and/or Chest X-Ray (CXR) images. CT (Computed Tomography) scanners and RT-PCR testing are not available in most medical centers and hence in many cases CXR images become the most time/cost effective tool for assisting clinicians in making decisions. Deep learning neural networks have a great potential for building COVID-19 triage systems and detecting COVID-19 patients, especially patients with low severity. Unfortunately, current databases do not allow building such systems as they are highly heterogeneous and biased towards severe cases. This paper is threefold: (i) we demystify the high sensitivities achieved by most recent COVID-19 classification models, (ii) under a close collaboration with Hospital Universitario Clnico San Cecilio, Granada, Spain, we built COVIDGR-1.0, a homogeneous and balanced database that includes all levels of severity, from normal with Positive RT-PCR, Mild, Moderate to Severe. COVIDGR-1.0 contains 426 positive and 426 negative PA (PosteroAnterior) CXR views and (iii) we propose COVID Smart Data based Network (COVID-SDNet) methodology for improving the generalization capacity of COVID-classification models. Our approach reaches good and stable results with an accuracy of 97.72% 0.95%, 86.90% 3.20%, 61.80% 5.49% in severe, moderate and mild COVID-19 severity levels. Our approach could help in the early detection of COVID-19. COVIDGR-1.0 along with the severity level labels are available to the scientific community through this link https://dasci.es/es/transferencia/open-data/covidgr/.

Tabik Siham

2020-Nov-10

General General

Using a simple open-source automated machine learning algorithm to forecast COVID-19 spread: A modelling study.

In Advances in respiratory medicine

INTRODUCTION : Machine learning algorithms have been used to develop prediction models in various infectious and non-infectious settings including interpretation of images in predicting the outcome of diseases. We demonstrate the application of one such simple automated machine learning algorithm to a dataset obtained about COVID-19 spread in South Korea to better understand the disease dynamics.

MATERIAL AND METHODS : Data from 20th January 2020 (when the first case of COVID-19 was detected in South Korea) to 4th March 2020 was accessed from Korea's centre for disease control (KCDC). A future time-series of specified length (taken as 7 days in our study) starting from 5th March 2020 to 11th March 2020 was generated and fed to the model to generate predictions with upper and lower trend bounds of 95% confidence intervals. The model was assessed for its ability to reliably forecast using mean absolute percentage error (MAPE) as the metric.

RESULTS : As on 4th March 2020, 145,541 patients were tested for COVID-19 (in 45 days) in South Korea of which 5166 patients tested positive. The predicted values approximated well with the actual numbers. The difference between predicted and observed values ranged from 4.08% to 12.77% . On average, our predictions differed from actual values by 7.42% (MAPE) over the same period.

CONCLUSION : Open source and automated machine learning tools like Prophet can be applied and are effective in the context of COVID-19 for forecasting spread in naïve communities. It may help countries to efficiently allocate healthcare resources to contain this pandemic.

Asfahan Shahir, Gopalakrishnan Maya, Dutt Naveen, Niwas Ram, Chawla Gopal, Agarwal Mehul, Garg Mahendera Kumar

2020

COVID-19, South Korea, coronavirus, machine learning, pandemic

Public Health Public Health

Heralding the Digitalization of Life in Post-Pandemic East Asian Societies.

In Journal of bioethical inquiry ; h5-index 18.0

Following the outbreak of what would become the COVID-19 pandemic, social distancing measures were quickly introduced across East Asia-including drastic shelter-in-place orders in some cities-drawing on experience with the outbreak of severe acute respiratory syndrome (SARS) almost two decades ago. "Smart City" technologies and other digital tools were quickly deployed for infection control purposes, ranging from conventional thermal scanning cameras to digital tracing in the surveillance of at-risk individuals. Chatbots endowed with artificial intelligence have also been deployed to shift part of healthcare provision away from hospitals and to support a number of programmes for self-management of chronic disease in the community. With the closure of schools and adults working from home, digital technologies have also sustained many aspects of both professional and social life at a pace and scale not considered to be practicable before the outbreak. This paper considers how these new experiences with digital technologies in public health surveillance are spurring digitalization in East Asian societies beyond the conventional public health context. It also considers some of the concerns and challenges that are likely to arise with rapid digitalization, particularly in healthcare.

Ho Calvin Wai-Loon, Caals Karel, Zhang Haihong

2020-Nov-09

Artificial intelligence, COVID-19, Contact tracing, Digital health, Mobile health, Public health surveillance

Radiology Radiology

Deep Learning and Its Role in COVID-19 Medical Imaging.

In Intelligence-based medicine

COVID-19 is one of the greatest global public health challenges in history. COVID-19 is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and is estimated to have an cumulative global case-fatality rate as high as 7.2%[1]. As the SARS-CoV-2 spread across the globe it catalyzed new urgency in building systems to allow rapid sharing and dissemination of data between international healthcare infrastructures and governments in a worldwide effort focused on case tracking/tracing, identifying effective therapeutic protocols, securing healthcare resources, and in drug and vaccine research. In addition to the worldwide efforts to share clinical and routine population health data, there are many large-scale efforts to collect and disseminate medical imaging data, owing to the critical role that imaging has played in diagnosis and management around the world. Given reported false negative rates of the reverse transcriptase polymerase chain reaction (RT-PCR) of up to 61%[2, 3], imaging can be used as an important adjunct or alternative. Furthermore, there has been a shortage of test-kits worldwide and laboratories in many testing sites have struggled to process the available tests within a reasonable time frame. Given these issues surrounding COVID-19, many groups began to explore the benefits of 'big data' processing and algorithms to assist with the diagnosis and therapeutic development of COVID-19.

Desai Sudhen B, Pareek Anuj, Lungren Matthew P

2020-Nov-04

General General

Deep Learning and Medical Image Processing for Coronavirus (COVID-19) Pandemic: A Survey.

In Sustainable cities and society

Since December 2019, the coronavirus disease (COVID-19) outbreak has caused many death cases and affected all sectors of human life. With gradual progression of time, COVID-19 was declared by the world health organization (WHO) as an outbreak, which has imposed a heavy burden on almost all countries, especially ones with weaker health systems and ones with slow responses. In the field of healthcare, deep learning has been implemented in many applications, e.g., diabetic retinopathy detection, lung nodule classification, fetal localization, and thyroid diagnosis. Numerous sources of medical images (e.g., X-ray, CT, and MRI) make deep learning a great technique to combat the COVID-19 outbreak. Motivated by this fact, a large number of research works have been proposed and developed for the initial months of 2020. In this paper, we first focus on summarizing the state-of-the-art research works related to deep learning applications for COVID-19 medical image processing. Then, we provide an overview of deep learning and its applications to healthcare found in the last decade. Next, three use cases in China, Korea, and Canada are also presented to show deep learning applications for COVID-19 medical image processing. Finally, we discuss several challenges and issues related to deep learning implementations for COVID-19 medical image processing, which are expected to drive further studies in controlling the outbreak and controlling the crisis, which results in smart healthy cities.

Bhattacharya Sweta, Reddy Maddikunta Praveen Kumar, Pham Quoc-Viet, Gadekallu Thippa Reddy, Krishnan S Siva Rama, Chowdhary Chiranji Lal, Alazab Mamoun, Piran Md Jalil

2020-Nov-05

Artificial intelligence (AI), Big data, COVID-19, coronavirus pandemic, deep learning, epidemic outbreak, medical image processing

General General

A five-layer deep convolutional neural network with stochastic pooling for chest CT-based COVID-19 diagnosis.

In Machine vision and applications

Till August 17, 2020, COVID-19 has caused 21.59 million confirmed cases in more than 227 countries and territories, and 26 naval ships. Chest CT is an effective way to detect COVID-19. This study proposed a novel deep learning model that can diagnose COVID-19 on chest CT more accurately and swiftly. Based on traditional deep convolutional neural network (DCNN) model, we proposed three improvements: (i) We introduced stochastic pooling to replace average pooling and max pooling; (ii) We combined conv layer with batch normalization layer and obtained the conv block (CB); (iii) We combined dropout layer with fully connected layer and obtained the fully connected block (FCB). Our algorithm achieved a sensitivity of 93.28% ± 1.50%, a specificity of 94.00% ± 1.56%, and an accuracy of 93.64% ± 1.42%, in identifying COVID-19 from normal subjects. We proved using stochastic pooling yields better performance than average pooling and max pooling. We compared different structure configurations and proved our 3CB + 2FCB yields the best performance. The proposed model is effective in detecting COVID-19 based on chest CT images.

Zhang Yu-Dong, Satapathy Suresh Chandra, Liu Shuaiqi, Li Guang-Run

2021

Batch normalization, COVID-19, Convolution block, Deep convolutional neural network, Dropout, Fully connected block, Stochastic pooling

General General

A Computational Model to Predict Consumer Behaviour During COVID-19 Pandemic.

In Computational economics

The knowledge-based economy has drawn increasing attention recently, particularly in online shopping applications where all the transactions and consumer opinions are logged. Machine learning methods could be used to extract implicit knowledge from the logs. Industries and businesses use the knowledge to better understand the consumer behavior, and opportunities and threats correspondingly. The outbreak of coronavirus (COVID-19) pandemic has a great impact on the different aspects of our daily life, in particular, on our shopping behaviour. To predict electronic consumer behaviour could be of valuable help for managers in government, supply chain and retail industry. Although, before coronavirus pandemic we have experienced online shopping, during the disease the number of online shopping increased dramatically. Due to high speed transmission of COVID-19, we have to observe personal and social health issues such as social distancing and staying at home. These issues have direct effect on consumer behaviour in online shopping. In this paper, a prediction model is proposed to anticipate the consumers behaviour using machine learning methods. Five individual classifiers, and their ensembles with Bagging and Boosting are examined on the dataset collected from an online shopping site. The results indicate the model constructed using decision tree ensembles with Bagging achieved the best prediction of consumer behavior with the accuracy of 95.3%. In addition, correlation analysis is performed to determine the most important features influencing the volume of online purchase during coronavirus pandemic.

Safara Fatemeh

2020-Nov-05

Bagging, Boosting, Consumer behavior, Coronavirus disease (COVID-19), E-commerce, Machine learning, Prediction model

General General

LitCovid: an open database of COVID-19 literature.

In Nucleic acids research ; h5-index 217.0

Since the outbreak of the current pandemic in 2020, there has been a rapid growth of published articles on COVID-19 and SARS-CoV-2, with about 10 000 new articles added each month. This is causing an increasingly serious information overload, making it difficult for scientists, healthcare professionals and the general public to remain up to date on the latest SARS-CoV-2 and COVID-19 research. Hence, we developed LitCovid (https://www.ncbi.nlm.nih.gov/research/coronavirus/), a curated literature hub, to track up-to-date scientific information in PubMed. LitCovid is updated daily with newly identified relevant articles organized into curated categories. To support manual curation, advanced machine-learning and deep-learning algorithms have been developed, evaluated and integrated into the curation workflow. To the best of our knowledge, LitCovid is the first-of-its-kind COVID-19-specific literature resource, with all of its collected articles and curated data freely available. Since its release, LitCovid has been widely used, with millions of accesses by users worldwide for various information needs, such as evidence synthesis, drug discovery and text and data mining, among others.

Chen Qingyu, Allot Alexis, Lu Zhiyong

2020-Nov-09

General General

Classification of Severe and Critical COVID-19 Using Deep Learning and Radiomics.

In IEEE journal of biomedical and health informatics

OBJECTIVE : The coronavirus disease 2019 (COVID-19) is rapidly spreading inside China and internationally. We aimed to construct a model integrating information from radiomics and deep learning (DL) features to discriminate critical cases from severe cases of COVID-19 using computed tomography (CT) images.

METHODS : We retrospectively enrolled 217 patients from three centers in China, including 82 patients with severe disease and 135 with critical disease. Patients were randomly divided into a training cohort (n=174) and a test cohort (n=43). We extracted 102 3-dimensional radiomic features from automatically segmented lung volume and selected the significant features. We also developed a 3-dimensional DL network based on center-cropped slices. Using multivariable logistic regression, we then created a merged model based on significant radiomic features and DL scores. We employed the area under the receiver operating characteristic curve (AUC) to evaluate the model's performance. We then conducted cross validation, stratified analysis, survival analysis, and decision curve analysis to evaluate the robustness of our method.

RESULTS : The merged model could distinguish critical patients with AUCs of 0.909 (95% confidence interval [CI]: 0.8590.952) and 0.861 (95% CI: 0.7530.968) in the training and test cohorts, respectively. Stratified analysis indicated that our model was not affected by sex, age, or chronic disease. Moreover, the results of the merged model showed a strong correlation with patient outcomes.

SIGNIFICANCE : A model combining radiomic and DL features of the lung could help distinguish critical cases from severe cases of COVID-19.

Li Cong, Dong Di, Li Liang, Gong Wei, Li Xiaohu, Bai Yan, Wang Meiyun, Hu Zhenhua, Zha Yunfei, Tian Jie

2020-Nov-09

General General

Breakthrough healthcare technologies in the COVID-19 era: a unique opportunity for cardiovascular practitioners and patients.

In Panminerva medica

INTRODUCTION : The coronavirus disease 2019 (COVID-19) pandemic, caused by symptomatic severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) infection, has wreaked havoc globally, challenging the healthcare, economical, technological and social status quo of developing but also developed countries. For instance, the COVID-19 scare has reduced timely hospital admissions for ST-elevation myocardial infarction in Europe and the USA, causing unnecessary deaths and disabilities. While the emergency is still ongoing, enough efforts have been put to study and tackle this condition such that a comprehensive perspective and synthesis on the potential role of breakthrough healthcare technologies is possible. Indeed, current state-of-the-art information technologies can provide a unique opportunity to adapt and adjust to the current healthcare needs associated with COVID-19, either directly or indirectly, and in particular those of cardiovascular patients and practitioners.

EVIDENCE ACQUISITION : We searched several biomedical databases, websites and social media, including PubMed, Medscape, and Twitter, for smartcare approaches suitable for application in the COVID-19 pandemic.

EVIDENCE SYNTHESIS : We retrieved details on several promising avenues for present and future healthcare technologies, capable of substantially reduce the mortality, morbidity, and resource use burden of COVID-19 as well as that of cardiovascular disease. In particular, we have found data supporting the importance of data sharing, model sharing, preprint archiving, social media, medical case sharing, distance learning and continuous medical education, smartphone apps, telemedicine, robotics, big data analysis, machine learning, and deep learning, with the ultimate goal of optimization of individual prevention, diagnosis, tracing, risk-stratification, treatment and rehabilitation.

CONCLUSIONS : We are confident that refinement and command of smartcare technologies will prove extremely beneficial in the short-term, but also dramatically reshape cardiovascular practice and healthcare delivery in the long-term future.

Nudi Raffaele, Campagna Marco, Parma Alessio, Nudi Andrea, Biondi-Zoccai Giuseppe

2020-Nov-09

Radiology Radiology

Using artificial intelligence to assist radiologists in distinguishing COVID-19 from other pulmonary infections.

In Journal of X-ray science and technology

BACKGROUND : Accurate and rapid diagnosis of coronavirus disease (COVID-19) is crucial for timely quarantine and treatment.

PURPOSE : In this study, a deep learning algorithm-based AI model using ResUNet network was developed to evaluate the performance of radiologists with and without AI assistance in distinguishing COVID-19 infected pneumonia patients from other pulmonary infections on CT scans.

METHODS : For model development and validation, a total number of 694 cases with 111,066 CT slides were retrospectively collected as training data and independent test data in the study. Among them, 118 are confirmed COVID-19 infected pneumonia cases and 576 are other pulmonary infections cases (e.g. tuberculosis cases, common pneumonia cases and non-COVID-19 viral pneumonia cases). The cases were divided into training and testing datasets. The independent test was performed by evaluating and comparing the performance of three radiologists with different years of practice experience in distinguishing COVID-19 infected pneumonia cases with and without the AI assistance.

RESULTS : Our final model achieved an overall test accuracy of 0.914 with an area of the receiver operating characteristic (ROC) curve (AUC) of 0.903 in which the sensitivity and specificity are 0.918 and 0.909, respectively. The deep learning-based model then achieved a comparable performance by improving the radiologists' performance in distinguish COVOD-19 from other pulmonary infections, yielding better average accuracy and sensitivity, from 0.941 to 0.951 and from 0.895 to 0.942, respectively, when compared to radiologists without using AI assistance.

CONCLUSION : A deep learning algorithm-based AI model developed in this study successfully improved radiologists' performance in distinguishing COVID-19 from other pulmonary infections using chest CT images.

Yang Yanhong, Lure Fleming Y M, Miao Hengyuan, Zhang Ziqi, Jaeger Stefan, Liu Jinxin, Guo Lin

2020-Nov-02

COVID-19, artificial intelligence (AI), computed tomography (CT), deep learning

General General

Putting the world back to work: an expert system using big data and artificial intelligence in combating the spread of COVID-19 and similar contagious diseases.

In Work (Reading, Mass.)

BACKGROUND : To combat COVID-19, curb the pandemic, and manage containment, governments around the world are turning to data collection and population monitoring for analysis and prediction. The massive data generated through the use of big data and artificial intelligence can play an important role in addressing this unprecedented global health and economic crisis.

OBJECTIVES : The objective of this work is to develop an expert system that combines several solutions to combat COVID-19. The main solution is based on a new developed software called General Guide (GG) application. This expert system allows us to explore, monitor, forecast, and optimize the data collected in order to take an efficient decision to ensure the safety of citizens, forecast, and slow down the spread's rate of COVID-19. It will also facilitate countries' interventions and optimize resources. Moreover, other solutions can be integrated into this expert system, such as the automatic vehicle and passenger sanitizing system equipped with a thermal and smart High Definition (HD) cameras and multi-purpose drones which offer many services. All of these solutions will facilitate lifting COVID-19 restrictions and minimize the impact of this pandemic.

METHODS : The methods used in this expert system will assist in designing and analyzing the model based on big data and artificial intelligence (machine learning). This can enhance countries' abilities and tools in monitoring, combating, and predicting the spread of COVID-19.

RESULTS : The results obtained by this prediction process and the use of the above mentioned solutions will help monitor, predict, generate indicators, and make operational decisions to stop the spread of COVID-19.

CONCLUSIONS : This developed expert system can assist in stopping the spread of COVID-19 globally and putting the world back to work.

Tkatek Said, Belmzoukia Amine, Nafai Said, Abouchabaka Jaafar, Ibnou-Ratib Youssef

2020-Nov-05

Expert system, artificial intelligence, big data, machine learning, prediction, spread of covid-19, work

Public Health Public Health

COVID-19 in China: Risk Factors and R0 Revisited.

In Acta tropica ; h5-index 41.0

The COVID-19 epidemic spread rapidly through China and subsequently proliferated globally leading to a pandemic situation around the globe. Human-to-human transmission, as well as asymptomatic transmission of the infection, have been confirmed. As of April 03, 2020, public health crisis in China due to COVID-19 was potentially under control. We compiled a daily dataset of case counts, mortality, recovery, temperature, population density, and demographic information for each prefecture during the period of January 11 to April 07, 2020. Understanding the characteristics of spatial clustering of the COVID-19 epidemic and R0 is critical in effectively preventing and controlling the ongoing global pandemic. Considering this, the prefectures were grouped based on several relevant features using unsupervised machine learning techniques. Subsequently, we performed a computational analysis utilizing the reported cases in China to estimate the revised R0 among different regions. Finally, our overall research indicates that the impact of temperature and demographic factors on virus transmission may be characterized using a stochastic transmission model. Such predictions will help in prevention planning in an ongoing global pandemic, prioritizing segments of a given community/region for action and providing a visual aid in designing prevention strategies for a specific geographic region. Furthermore, revised estimation and our methodology will aid in improving the human health consequences of COVID-19 elsewhere.

Khan Irtesam Mahmud, Haque Ubydul, Zhang Wenyi, Zafar Sumaira, Wang Yong, He Junyu, Sun Hailong, Lubinda Jailos, Rahman M Sohel

2020-Oct-22

COVID-19, Clustering, Stochastic Transmission Model

General General

Deep Learning Applications to Combat Novel Coronavirus (COVID-19) Pandemic.

In SN computer science

During this global pandemic, researchers around the world are trying to find out innovative technology for a smart healthcare system to combat coronavirus. The evidence of deep learning applications on the past epidemic inspires the experts by giving a new direction to control this outbreak. The aim of this paper is to discuss the contributions of deep learning at several scales including medical imaging, disease tracing, analysis of protein structure, drug discovery, and virus severity and infectivity to control the ongoing outbreak. A progressive search of the database related to the applications of deep learning was executed on COVID-19. Further, a comprehensive review is done using selective information by assessing the different perspectives of deep learning. This paper attempts to explore and discuss the overall applications of deep learning on multiple dimensions to control novel coronavirus (COVID-19). Though various studies are conducted using deep learning algorithms, there are still some constraints and challenges while applying for real-world problems. The ongoing progress in deep learning contributes to handle coronavirus infection and plays an effective role to develop appropriate solutions. It is expected that this paper would be a great help for the researchers who would like to contribute to the development of remedies for this current pandemic in this area.

Asraf Amanullah, Islam Md Zabirul, Haque Md Rezwanul, Islam Md Milon

2020

COVID-19, Deep learning, Diagnosis, Novel coronavirus, Pandemic

General General

A deep learning-based social distance monitoring framework for COVID-19.

In Sustainable cities and society

The ongoing COVID-19 corona virus outbreak has caused a global disaster with its deadly spreading. Due to the absence of effective remedial agents and the shortage of immunizations against the virus, population vulnerability increases. In the current situation, as there are no vaccines available; therefore, social distancing is thought to be an adequate precaution (norm) against the spread of the pandemic virus. The risks of virus spread can be minimized by avoiding physical contact among people. The purpose of this work is, therefore, to provide a deep learning platform for social distance tracking using an overhead perspective. The framework uses the YOLOv3 object recognition paradigm to identify humans in video sequences. The transfer learning methodology is also implemented to increase the accuracy of the model. In this way, the detection algorithm uses a pre-trained algorithm that is connected to an extra trained layer using an overhead human data set. The detection model identifies peoples using detected bounding box information. Using the Euclidean distance, the detected bounding box centroid's pairwise distances of people are determined. To estimate social distance violations between people, we used an approximation of physical distance to pixel and set a threshold. A violation threshold is established to evaluate whether or not the distance value breaches the minimum social distance threshold. In addition, a tracking algorithm is used to detect individuals in video sequences such that the person who violates/crosses the social distance threshold is also being tracked. Experiments are carried out on different video sequences to test the efficiency of the model. Findings indicate that the developed framework successfully distinguishes individuals who walk too near and breaches/violates social distances; also, the transfer learning approach boosts the overall efficiency of the model. The accuracy of 92% and 98% achieved by the detection model without and with transfer learning, respectively. The tracking accuracy of the model is 95%.

Ahmed Imran, Ahmad Misbah, Rodrigues Joel J P C, Jeon Gwanggil, Din Sadia

2020-Nov-01

COVID-19, Deep learning, Overhead view, Person detection, Social distancing, Transfer learning, YOLOv3

General General

InstaCovNet-19: A deep learning classification model for the detection of COVID-19 patients using Chest X-ray.

In Applied soft computing

Recently, the whole world became infected by the newly discovered coronavirus (COVID-19). SARS-CoV-2, or widely known as COVID-19, has proved to be a hazardous virus severely affecting the health of people. It causes respiratory illness, especially in people who already suffer from other diseases. Limited availability of test kits as well as symptoms similar to other diseases such as pneumonia has made this disease deadly, claiming the lives of millions of people. Artificial intelligence models are found to be very successful in the diagnosis of various diseases in the biomedical field In this paper, an integrated stacked deep convolution network InstaCovNet-19 is proposed. The proposed model makes use of various pre-trained models such as ResNet101, Xception, InceptionV3, MobileNet, and NASNet to compensate for a relatively small amount of training data. The proposed model detects COVID-19 and pneumonia by identifying the abnormalities caused by such diseases in Chest X-ray images of the person infected. The proposed model achieves an accuracy of 99.08% on 3 class (COVID-19, Pneumonia, Normal) classification while achieving an accuracy of 99.53% on 2 class (COVID, NON-COVID) classification. The proposed model achieves an average recall, F1 score, and precision of 99%, 99%, and 99%, respectively on ternary classification, while achieving a 100% precision and a recall of 99% on the binary class., while achieving a 100% precision and a recall of 99% on the COVID class. InstaCovNet-19's ability to detect COVID-19 without any human intervention at an economical cost with high accuracy can benefit humankind greatly in this age of Quarantine.

Gupta Anunay, Anjum Gupta, Shreyansh Katarya

2020-Oct-29

COVID-19, Convolution network, InstaCovNet-19, Integrated stacking, Pneumonia

Radiology Radiology

A one-year hospital-based prospective COVID-19 open-cohort in the Eastern Mediterranean region: The Khorshid COVID Cohort (KCC) study.

In PloS one ; h5-index 176.0

The COVID-19 is rapidly scattering worldwide, and the number of cases in the Eastern Mediterranean Region is rising. Thus, there is a need for immediate targeted actions. We designed a longitudinal study in a hot outbreak zone to analyze the serial findings between infected patients for detecting temporal changes from February 2020. In a hospital-based open-cohort study, patients are followed from admission until one year from their discharge (the 1st, 4th, 12th weeks, and the first year). The patient recruitment phase finished at the end of August 2020, and the follow-up continues by the end of August 2021. The measurements included demographic, socio-economics, symptoms, health service diagnosis and treatment, contact history, and psychological variables. The signs improvement, death, length of stay in hospital were considered primary, and impaired pulmonary function and psychotic disorders were considered main secondary outcomes. Moreover, clinical symptoms and respiratory functions are being determined in such follow-ups. Among the first 600 COVID-19 cases, 490 patients with complete information (39% female; the average age of 57±15 years) were analyzed. Seven percent of these patients died. The three main leading causes of admission were: fever (77%), dry cough (73%), and fatigue (69%). The most prevalent comorbidities between COVID-19 patients were hypertension (35%), diabetes (28%), and ischemic heart disease (14%). The percentage of primary composite endpoints (PCEP), defined as death, the use of mechanical ventilation, or admission to an intensive care unit was 18%. The Cox Proportional-Hazards Model for PCEP indicated the following significant risk factors: Oxygen saturation < 80% (HR = 6.3; [CI 95%: 2.5,15.5]), lymphopenia (HR = 3.5; [CI 95%: 2.2,5.5]), Oxygen saturation 80%-90% (HR = 2.5; [CI 95%: 1.1,5.8]), and thrombocytopenia (HR = 1.6; [CI 95%: 1.1,2.5]). This long-term prospective Cohort may support healthcare professionals in the management of resources following this pandemic.

Sami Ramin, Soltaninejad Forogh, Amra Babak, Naderi Zohre, Haghjooy Javanmard Shaghayegh, Iraj Bijan, Haji Ahmadi Somayeh, Shayganfar Azin, Dehghan Mehrnegar, Khademi Nilufar, Sadat Hosseini Nastaran, Mortazavi Mojgan, Mansourian Marjan, Mañanas Miquel Angel, Marateb Hamid Reza, Adibi Peyman

2020

General General

Impact of non-pharmaceutical interventions on the incidence of respiratory infections during the COVID-19 outbreak in Korea: a nationwide surveillance study.

In Clinical infectious diseases : an official publication of the Infectious Diseases Society of America

BACKGROUND : Many countries have implemented non-pharmaceutical interventions (NPIs) to slow the spread of coronavirus disease 2019 (COVID-19). We aimed to determine whether NPIs led to the decline in the incidences of respiratory infections.

METHODS : We conducted a retrospective, ecological study using a nationwide notifiable diseases database and a respiratory virus sample surveillance collected from January 2016 through July 2020 in the Republic of Korea. Intervention period was defined as February-July 2020, when the government implemented NPIs nationwide. Observed incidences in the intervention period were compared to the predicted incidences by autoregressive integrated moving average model and the 4-year mean cumulative incidences (CuIs) in the same months of the pre-intervention period.

RESULTS : Five infectious diseases met the inclusion criteria: chickenpox, mumps, invasive pneumococcal disease, scarlet fever, and pertussis. The incidences of chickenpox and mumps during the intervention period were significantly lower than the prediction model. The CuIs of chickenpox and mumps were 36.4% (95% CI, 23.9-76.3) and 63.4% (95% CI, 48.0-93.3) of the predicted values. Subgroup analysis showed that the decrease in the incidence was universal for chickenpox, while mumps showed a marginal reduction among those aged <18 years, but not in adults. The incidence of respiratory viruses was significantly lower than both the predicted incidence (19.5%; 95% CI, 11.8-55.4%) and the 4-year mean CuIs in the pre-intervention period (24.5%; P<0.001).

CONCLUSIONS : The implementation of NPIs was associated with a significant reduction in the incidences of several respiratory infections in Korea.

Huh Kyungmin, Jung Jaehun, Hong Jinwook, Kim MinYoung, Ahn Jong Gyun, Kim Jong-Hun, Kang Ji-Man

2020-Nov-05

COVID-19, South Korea, non-pharmaceutical intervention, respiratory infection, social distancing

Public Health Public Health

Individual Perceived Stress Mediates Psychological Distress in Medical Workers During COVID-19 Epidemic Outbreak in Wuhan.

In Neuropsychiatric disease and treatment

Background : Since the novel coronavirus disease (COVID-19) outbreak in Wuhan, thousands of medical workers have been dispatched to support Wuhan against the virus. The purpose of this study was to identify the independent risk factors for psychological distress in order to develop a more effective strategy and precise evidence-based psychological intervention for medical workers.

Methods : This multisite cross-sectional survey recruited doctors and nurses from local and nonlocal medical teams working at 16 hospitals in Wuhan to complete this online survey from February to March, 2020. Psychological status was evaluated through Perceived Stress Scales (PSS), Patient Health Questionnaire-9 (PHQ-9), General Anxiety Disorder Scale (GAD-7) and Acute Stress Disorder Scale (ASDS).

Results : Of 966 participants, the prevalence of stress (95.9%), depression (46.0%) and anxiety (39.3%) were high. Local medical workers exhibited even higher scores of PSS, PHQ-9, GAD-7 and ASDS than those from outside Hubei (P<0.001). Females had more severe perceived stress, depression and anxiety than males (P<0.001). Multiple logistic regression showed that perceived stress is associated with increased odds of depression (OR=1.413; 95% CI: 1.338-1.493; P<0.001) and anxiety (OR=1.515; 95% CI: 1.407-1.631; P<0.001).

Conclusion : Our findings demonstrated a high prevalence of stress, depression, anxiety and acute distress among medical workers on the front-line during the COVID-19 outbreak in Wuhan. The level of psychological impact may be mediated by individual perceptions of stressful events.

Zhang Chen, Peng Daihui, Lv Lu, Zhuo Kaiming, Yu Kai, Shen Tian, Xu Yifeng, Wang Zhen

2020

COVID-19, Wuhan, anxiety, depression, stress

Internal Medicine Internal Medicine

An artificial intelligence-based first-line defence against COVID-19: digitally screening citizens for risks via a chatbot.

In Scientific reports ; h5-index 158.0

To combat the pandemic of the coronavirus disease 2019 (COVID-19), numerous governments have established phone hotlines to prescreen potential cases. These hotlines have struggled with the volume of callers, leading to wait times of hours or, even, an inability to contact health authorities. Symptoma is a symptom-to-disease digital health assistant that can differentiate more than 20,000 diseases with an accuracy of more than 90%. We tested the accuracy of Symptoma to identify COVID-19 using a set of diverse clinical cases combined with case reports of COVID-19. We showed that Symptoma can accurately distinguish COVID-19 in 96.32% of clinical cases. When considering only COVID-19 symptoms and risk factors, Symptoma identified 100% of those infected when presented with only three signs. Lastly, we showed that Symptoma's accuracy far exceeds that of simple "yes-no" questionnaires widely available online. In summary, Symptoma provides unparalleled accuracy in systematically identifying cases of COVID-19 while also considering over 20,000 other diseases. Furthermore, Symptoma allows free text input, furthered with disease-specific follow up questions, in 36 languages. Combined, these results and accessibility give Symptoma the potential to be a key tool in the global fight against COVID-19. The Symptoma predictor is freely available online at https://www.symptoma.com .

Martin Alistair, Nateqi Jama, Gruarin Stefanie, Munsch Nicolas, Abdarahmane Isselmou, Zobel Marc, Knapp Bernhard

2020-Nov-04

General General

A review on drug repurposing applicable to COVID-19.

In Briefings in bioinformatics

Drug repurposing involves the identification of new applications for existing drugs at a lower cost and in a shorter time. There are different computational drug-repurposing strategies and some of these approaches have been applied to the coronavirus disease 2019 (COVID-19) pandemic. Computational drug-repositioning approaches applied to COVID-19 can be broadly categorized into (i) network-based models, (ii) structure-based approaches and (iii) artificial intelligence (AI) approaches. Network-based approaches are divided into two categories: network-based clustering approaches and network-based propagation approaches. Both of them allowed to annotate some important patterns, to identify proteins that are functionally associated with COVID-19 and to discover novel drug-disease or drug-target relationships useful for new therapies. Structure-based approaches allowed to identify small chemical compounds able to bind macromolecular targets to evaluate how a chemical compound can interact with the biological counterpart, trying to find new applications for existing drugs. AI-based networks appear, at the moment, less relevant since they need more data for their application.

Dotolo Serena, Marabotti Anna, Facchiano Angelo, Tagliaferri Roberto

2020-Nov-05

AI, COVID-19, drug repurposing, molecular docking, network-based approaches, new therapies

General General

Streamlining follicular monitoring during controlled ovarian stimulation: a data-driven approach to efficient IVF care in the new era of social distancing.

In Human reproduction (Oxford, England)

STUDY QUESTION : What is the optimal follicular tracking strategy for controlled ovarian stimulation (COS) in order to minimise face-to-face interactions?

SUMMARY ANSWER : As data from follicular tracking scans on Days 5, 6 or 7 of stimulation are the most useful to accurately predict trigger timing and risk of over-response, scans on these days should be prioritised if streamlined monitoring is necessary.

WHAT IS KNOWN ALREADY : British Fertility Society guidance for centres restarting ART following coronavirus disease 2019 (COVID-19) pandemic-related shutdowns recommends reducing the number of patient visits for monitoring during COS. Current evidence on optimal monitoring during ovarian stimulation is sparse, and protocols vary significantly. Small studies of simplifying IVF therapy by minimising monitoring have reported no adverse effects on outcomes, including live birth rate. There are opportunities to learn from the adaptations necessary during these extraordinary times to improve the efficiency of IVF care in the longer term.

STUDY DESIGN, SIZE, DURATION : A retrospective database analysis of 9294 ultrasound scans performed during monitoring of 2322 IVF cycles undertaken by 1875 women in a single centre was performed. The primary objective was to identify when in the IVF cycle the data obtained from ultrasound are most predictive of both oocyte maturation trigger timing and an over-response to stimulation. If a reduced frequency of clinic visits is needed due to COVID-19 precautions, prioritising attendance for monitoring scans on the most predictive cycle days may be prudent.

PARTICIPANTS/MATERIALS, SETTING, METHODS : The study comprised anonymised retrospective database analysis of IVF/ICSI cycles at a tertiary referral IVF centre. Machine learning models are used in combining demographic and follicular tracking data to predict cycle oocyte maturation trigger timing and over-response. The primary outcome was the day or days in cycle from which scan data yield optimal model prediction performance statistics. The model for predicting trigger day uses patient age, number of follicles at baseline scan and follicle count by size for the current scan. The model to predict over-response uses age and number of follicles of a given size.

MAIN RESULTS AND THE ROLE OF CHANCE : The earliest cycle day for which our model has high accuracy to predict both trigger day and risk of over-response is stimulation Day 5. The Day 5 model to predict trigger date has a mean squared error 2.16 ± 0.12 and to predict over-response an area under the receiver operating characteristic curve 0.91 ± 0.01.

LIMITATIONS, REASONS FOR CAUTION : This is a retrospective single-centre study and the results may not be generalisable to centres using different treatment protocols. The results are derived from modelling, and further clinical validation studies will verify the accuracy of the model.

WIDER IMPLICATIONS OF THE FINDINGS : Follicular tracking starting at Day 5 of stimulation may help to streamline the amount of monitoring required in COS. Previous small studies have shown that minimal monitoring protocols did not adversely impact outcomes. If IVF can safely be made less onerous on the clinic's resources and patient's time, without compromising success, this could help to reduce burden-related treatment drop-out.

STUDY FUNDING/COMPETING INTEREST(S) : F.P.C. acknowledges funding from the NIHR Applied Research Collaboration Wessex. The authors declare they have no competing interests in relation to this work.

TRIAL REGISTRATION NUMBER : N/A.

Robertson I, Chmiel F P, Cheong Y

2020-Nov-04

machine learning, oocyte maturation, ovarian stimulation, over-response, trigger, ultrasound

Radiology Radiology

CT and clinical assessment in asymptomatic and pre-symptomatic patients with early SARS-CoV-2 in outbreak settings.

In European radiology ; h5-index 62.0

OBJECTIVES : The early infection dynamics of patients with SARS-CoV-2 are not well understood. We aimed to investigate and characterize associations between clinical, laboratory, and imaging features of asymptomatic and pre-symptomatic patients with SARS-CoV-2.

METHODS : Seventy-four patients with RT-PCR-proven SARS-CoV-2 infection were asymptomatic at presentation. All were retrospectively identified from 825 patients with chest CT scans and positive RT-PCR following exposure or travel risks in outbreak settings in Japan and China. CTs were obtained for every patient within a day of admission and were reviewed for infiltrate subtypes and percent with assistance from a deep learning tool. Correlations of clinical, laboratory, and imaging features were analyzed and comparisons were performed using univariate and multivariate logistic regression.

RESULTS : Forty-eight of 74 (65%) initially asymptomatic patients had CT infiltrates that pre-dated symptom onset by 3.8 days. The most common CT infiltrates were ground glass opacities (45/48; 94%) and consolidation (22/48; 46%). Patient body temperature (p < 0.01), CRP (p < 0.01), and KL-6 (p = 0.02) were associated with the presence of CT infiltrates. Infiltrate volume (p = 0.01), percent lung involvement (p = 0.01), and consolidation (p = 0.043) were associated with subsequent development of symptoms.

CONCLUSIONS : COVID-19 CT infiltrates pre-dated symptoms in two-thirds of patients. Body temperature elevation and laboratory evaluations may identify asymptomatic patients with SARS-CoV-2 CT infiltrates at presentation, and the characteristics of CT infiltrates could help identify asymptomatic SARS-CoV-2 patients who subsequently develop symptoms. The role of chest CT in COVID-19 may be illuminated by a better understanding of CT infiltrates in patients with early disease or SARS-CoV-2 exposure.

KEY POINTS : • Forty-eight of 74 (65%) pre-selected asymptomatic patients with SARS-CoV-2 had abnormal chest CT findings. • CT infiltrates pre-dated symptom onset by 3.8 days (range 1-5). • KL-6, CRP, and elevated body temperature identified patients with CT infiltrates. Higher infiltrate volume, percent lung involvement, and pulmonary consolidation identified patients who developed symptoms.

Varble Nicole, Blain Maxime, Kassin Michael, Xu Sheng, Turkbey Evrim B, Amalou Amel, Long Dilara, Harmon Stephanie, Sanford Thomas, Yang Dong, Xu Ziyue, Xu Daguang, Flores Mona, An Peng, Carrafiello Gianpaolo, Obinata Hirofumi, Mori Hitoshi, Tamura Kaku, Malayeri Ashkan A, Holland Steven M, Palmore Tara, Sun Kaiyuan, Turkbey Baris, Wood Bradford J

2020-Nov-04

Asymptomatic infections, Pneumonia, SARS-CoV, Virus shedding

General General

Modelling the spread of SARS-CoV-2 pandemic - Impact of lockdowns & interventions.

In The Indian journal of medical research

Background & objectives : To handle the current COVID-19 pandemic in India, multiple strategies have been applied and implemented to slow down the virus transmission. These included clinical management of active cases, rapid development of treatment strategies, vaccines computational modelling and statistical tools to name a few. This article presents a mathematical model for a time series prediction and analyzes the impact of the lockdown.

Methods : Several existing mathematical models were not able to account for asymptomatic patients, with limited testing capability at onset and no data on serosurveillance. In this study, a new model was used which was developed on lines of susceptible-asymptomatic-infected-recovered (SAIR) to assess the impact of the lockdown and make predictions on its future course. Four parameters were used, namely β, γ, η and ε. β measures the likelihood of the susceptible person getting infected, and γ denotes recovery rate of patients. The ratio β/γ is denoted by R0 (basic reproduction number).

Results : The disease spread was reduced due to initial lockdown. An increase in γ reflects healthcare and hospital services, medications and protocols put in place. In Delhi, the predictions from the model were corroborated with July and September serosurveys, which showed antibodies in 23.5 and 33 per cent population, respectively.

Interpretation & conclusions : The SAIR model has helped understand the disease better. If the model is correct, we may have reached herd immunity with about 380 million people already infected. However, personal protective measures remain crucial. If there was no lockdown, the number of active infections would have peaked at close to 14.7 million, resulted in more than 2.6 million deaths, and the peak would have arrived by June 2020. The number of deaths with the current trends may be less than 0.2 million.

Agrawal Manindra, Kanitkar Madhuri, Vidyasagar M

2020-Nov-04

General General

Identifying propaganda from online social networks during COVID-19 using machine learning techniques.

In International journal of information technology : an official journal of Bharati Vidyapeeth's Institute of Computer Applications and Management

COVID-19, affected the entire world because of its non-availability of vaccine. Due to social distancing online social networks are massively used in pandemic times. Information is being shared enormously without knowing the authenticity of the source. Propaganda is one of the type of information that is shared deliberately for gaining political and religious influence. It is the systematic and deliberate way of shaping opinion and influencing thoughts of a person for achieving the desired intention of a propagandist. Various propagandistic messages are being shared during COVID-19 about the deadly virus. We extracted data from twitter using its application program interface (API), Annotation is being performed manually. Hybrid feature engineering is performed for choosing the most relevant features.The binary classification of tweets is being performed with the help of machine learning algorithms. Decision tree gives better results among all other algorithms. For better results feature engineering may be improved and deep learning can be used for classification task.

Khanday Akib Mohi Ud Din, Khan Qamar Rayees, Rabani Syed Tanzeel

2020-Oct-29

COVID-19, Decision tree, Machine learning, Online social networks, Propaganda

Radiology Radiology

The study of automatic machine learning base on radiomics of non-focus area in the first chest CT of different clinical types of COVID-19 pneumonia.

In Scientific reports ; h5-index 158.0

To explore the possibility of predicting the clinical types of Corona-Virus-Disease-2019 (COVID-19) pneumonia by analyzing the non-focus area of the lung in the first chest CT image of patients with COVID-19 by using automatic machine learning (Auto-ML). 136 moderate and 83 severe patients were selected from the patients with COVID-19 pneumonia. The clinical and laboratory data were collected for statistical analysis. The texture features of the Non-focus area of the first chest CT of patients with COVID-19 pneumonia were extracted, and then the classification model of the first chest CT of COVID-19 pneumonia was constructed by using these texture features based on the Auto-ML method of radiomics, The area under curve(AUC), true positive rate(TPR), true negative rate (TNR), positive predictive value(PPV) and negative predictive value (NPV) of the operating characteristic curve (ROC) were used to evaluate the accuracy of the first chest CT image classification model in patients with COVID-19 pneumonia. The TPR, TNR, PPV, NPV and AUC of the training cohort and test cohort of the moderate group and the control group, the severe group and the control group, the moderate group and the severe group were all greater than 95% and 0.95 respectively. The non-focus area of the first CT image of COVID-19 pneumonia has obvious difference in different clinical types. The AUTO-ML classification model of Radiomics based on this difference can be used to predict the clinical types of COVID-19 pneumonia.

Tan Hui-Bin, Xiong Fei, Jiang Yuan-Liang, Huang Wen-Cai, Wang Ye, Li Han-Han, You Tao, Fu Ting-Ting, Lu Ran, Peng Bi-Wen

2020-Nov-03

Public Health Public Health

Emergency response to the COVID-19 pandemic using digital health technologies: practical experience of a tertiary hospital in China.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : The outbreak of the coronavirus disease 2019 (COVID-19) has caused a continuing global pandemic. Hospitals are integral in the control and prevention of COVID-19 but are met with numerous challenges in the midst of the epidemic.

OBJECTIVE : Our study aimed to introduce the practical experience of design and implementation, as well as the preliminary results, of an online COVID-19 service platform from a tertiary hospital in China.

METHODS : The online COVID-19 service platform was deployed within the healthcare system of the Guangdong Second Provincial General Hospital-Internet Hospital, a program function which provides online medical services for both public individuals and lay-healthcare workers. The focal functions of this system include COVID-19 automated screening, related symptoms monitoring, online consultation, psychological support, and it also serves as a COVID-19 knowledge hub. The design and process for each function were introduced. The platform services usage data were collected and represented by three periods: the pre-epidemic period (2019.12.22~2020.1.22, 32days), the controlled period (2020.1.23~2020.3.31, 69days), and the post-epidemic period (2020.4.1~2020.6.30, 91days).

RESULTS : By the end of June 2020, the COVID-19 automated screening and symptoms monitoring system had been used by 96,642 people for 161,884 and 7,795,194 person-times. The number of general online consultation service per-day scaled up from 30 visits in pre-epidemic period to 122 visits during the controlled period, and dropped to 73 visits in the post-epidemic period. The psychological counseling program served 636 clients during the epidemic period. For people who used the COVID-19 automated screening service, overall, 160,916 (99.40%) of the users were classified under the no risk category. 464 (0.29%) of the people were categorized under the medium to high risk class, and 12 people (0.01%) were recommended for further COVID-19 testing and treatment. Among the 96,642 individuals who used the COVID-19 related symptoms monitoring service, 6,696 (6.9%) were symptomatic at some points during monitoring period. Fever was the most frequently reported symptom, with 2684 (40%) of the people having had this symptom. Cough and sore throat, with 1,657 (25%) and 1,622 (24%) people respectively, were also relatively frequently reported among the symptomatic clients.

CONCLUSIONS : The online COVID-19 service platform exhibited as a role model for using digital health technologies to respond to the COVID-19 pandemic from a tertiary hospital in China. The digital solutions of COVID-19 automated screening, daily symptoms monitoring, online care service, and knowledge propagation have plausible acceptability and feasibility for complementing offline hospital services and facilitating disease control and prevention.

CLINICALTRIAL :

Lian Wanmin, Wen Li, Zhou Qiru, Zhu Weijie, Duan Wenzhou, Xiao Xiongzhi, Mhungu Florence, Huang Wenchen, Li Chongchong, Cheng Weibin, Tian Junzhang

2020-Oct-29

General General

Sentiment analysis and its applications in fighting COVID-19 and infectious diseases: A systematic review.

In Expert systems with applications

The COVID-19 pandemic caused by the novel coronavirus SARS-CoV-2 occurred unexpectedly in China in December 2019. Tens of millions of confirmed cases and more than hundreds of thousands of confirmed deaths are reported worldwide according to the World Health Organisation. News about the virus is spreading all over social media websites. Consequently, these social media outlets are experiencing and presenting different views, opinions and emotions during various outbreak-related incidents. For computer scientists and researchers, big data are valuable assets for understanding people's sentiments regarding current events, especially those related to the pandemic. Therefore, analysing these sentiments will yield remarkable findings. To the best of our knowledge, previous related studies have focused on one kind of infectious disease. No previous study has examined multiple diseases via sentiment analysis. Accordingly, this research aimed to review and analyse articles about the occurrence of different types of infectious diseases, such as epidemics, pandemics, viruses or outbreaks, during the last 10 years, understand the application of sentiment analysis and obtain the most important literature findings. Articles on related topics were systematically searched in five major databases, namely, ScienceDirect, PubMed, Web of Science, IEEE Xplore and Scopus, from 1 January 2010 to 30 June 2020. These indices were considered sufficiently extensive and reliable to cover our scope of the literature. Articles were selected based on our inclusion and exclusion criteria for the systematic review, with a total of n = 28 articles selected. All these articles were formed into a coherent taxonomy to describe the corresponding current standpoints in the literature in accordance with four main categories: lexicon-based models, machine learning-based models, hybrid-based models and individuals. The obtained articles were categorised into motivations related to disease mitigation, data analysis and challenges faced by researchers with respect to data, social media platforms and community. Other aspects, such as the protocol being followed by the systematic review and demographic statistics of the literature distribution, were included in the review. Interesting patterns were observed in the literature, and the identified articles were grouped accordingly. This study emphasised the current standpoint and opportunities for research in this area and promoted additional efforts towards the understanding of this research field.

Alamoodi A H, Zaidan B B, Zaidan A A, Albahri O S, Mohammed K I, Malik R Q, Almahdi E M, Chyad M A, Tareq Z, Albahri A S, Hameed Hamsa, Alaa Musaab

2020-Oct-28

COVID-19, Disease mitigation, Epidemic, Infectious disease, Opinion mining, Pandemic, Sentiment analysis

General General

Hypertension management in 2030: a kaleidoscopic view.

In Journal of human hypertension

The last decade has witnessed the healthcare system going paperless with increased use of electronic healthcare records. Artificial intelligence tools including smartphones and smart watches have changed the landscape of day-to-day lives. Digitisation, decentralisation of healthcare and empowerment of allied healthcare providers and patients themselves have made shared clinical decision-making a reality. The year 2020 quickly turned into an unprecedented time in our lives with the entry of COVID-19. Amidst a pandemic, healthcare systems rapidly adapted and transformed, and changes that otherwise would have taken a decade, took a mere few weeks (Webster, Lancet 395:1180-1, 2020). This essay reviews evidence of transformation in the realm of hypertension management, namely diagnosis, lifestyle changes, therapeutics and prevention of hypertension at both individual and population levels, and presents an extrapolation of how this transformation might shape the next decade.

Kulkarni Spoorthy

2020-Nov-02

Radiology Radiology

Effectiveness of Streptococcus Pneumoniae Urinary Antigen Testing in Decreasing Mortality of COVID-19 Co-Infected Patients: A Clinical Investigation.

In Medicina (Kaunas, Lithuania)

BACKGROUND AND OBJECTIVES : Streptococcus pneumoniae urinary antigen (u-Ag) testing has recently gained attention in the early diagnosis of severe and critical acute respiratory syndrome coronavirus-2/pneumococcal co-infection. The aim of this study is to assess the effectiveness of Streptococcus pneumoniae u-Ag testing in coronavirus disease 2019 (COVID-19) patients, in order to assess whether pneumococcal co-infection is associated with different mortality rate and hospital stay in these patients.

MATERIALS AND METHODS : Charts, protocols, mortality, and hospitalization data of a consecutive series of COVID-19 patients admitted to a tertiary hospital in northern Italy during COVID-19 outbreak were retrospectively reviewed. All patients underwent Streptococcus pneumoniae u-Ag testing to detect an underlying pneumococcal co-infection. Covid19+/u-Ag+ and Covid19+/u-Ag- patients were compared in terms of overall survival and length of hospital stay using chi-square test and survival analysis.

RESULTS : Out of 575 patients with documented pneumonia, 13% screened positive for the u-Ag test. All u-Ag+ patients underwent treatment with Ceftriaxone and Azithromycin or Levofloxacin. Lopinavir/Ritonavir or Darunavir/Cobicistat were added in 44 patients, and hydroxychloroquine and low-molecular-weight heparin (LMWH) in 47 and 33 patients, respectively. All u-Ag+ patients were hospitalized. Mortality was 15.4% and 25.9% in u-Ag+ and u-Ag- patients, respectively (p = 0.09). Survival analysis showed a better prognosis, albeit not significant, in u-Ag+ patients. Median hospital stay did not differ among groups (10 vs. 9 days, p = 0.71).

CONCLUSIONS : The routine use of Streptococcus pneumoniae u-Ag testing helped to better target antibiotic therapy with a final trend of reduction in mortality of u-Ag+ COVID-19 patients having a concomitant pneumococcal infection. Randomized trials on larger cohorts are necessary in order to draw definitive conclusion.

Desai Antonio, Santonocito Orazio Giuseppe, Caltagirone Giuseppe, Kogan Maria, Ghetti Federica, Donadoni Ilaria, Porro Francesca, Savevski Victor, Poretti Dario, Ciccarelli Michele, Martinelli Boneschi Filippo, Voza Antonio

2020-Oct-29

COVID-19, SARS-CoV-2, Streptococcus pneumoniae, antibodies, bacterial infection 2

Surgery Surgery

Metabolomics Profiling of Critically Ill Coronavirus Disease 2019 Patients: Identification of Diagnostic and Prognostic Biomarkers.

In Critical care explorations

Objectives : Coronavirus disease 2019 continues to spread rapidly with high mortality. We performed metabolomics profiling of critically ill coronavirus disease 2019 patients to understand better the underlying pathologic processes and pathways, and to identify potential diagnostic/prognostic biomarkers.

Design : Blood was collected at predetermined ICU days to measure the plasma concentrations of 162 metabolites using both direct injection-liquid chromatography-tandem mass spectrometry and proton nuclear magnetic resonance.

Setting : Tertiary-care ICU and academic laboratory.

Subjects : Patients admitted to the ICU suspected of being infected with severe acute respiratory syndrome coronavirus 2, using standardized hospital screening methodologies, had blood samples collected until either testing was confirmed negative on ICU day 3 (coronavirus disease 2019 negative) or until ICU day 10 if the patient tested positive (coronavirus disease 2019 positive).

Interventions : None.

Measurements and Main Results : Age- and sex-matched healthy controls and ICU patients that were either coronavirus disease 2019 positive or coronavirus disease 2019 negative were enrolled. Cohorts were well balanced with the exception that coronavirus disease 2019 positive patients suffered bilateral pneumonia more frequently than coronavirus disease 2019 negative patients. Mortality rate for coronavirus disease 2019 positive ICU patients was 40%. Feature selection identified the top-performing metabolites for identifying coronavirus disease 2019 positive patients from healthy control subjects and was dominated by increased kynurenine and decreased arginine, sarcosine, and lysophosphatidylcholines. Arginine/kynurenine ratio alone provided 100% classification accuracy between coronavirus disease 2019 positive patients and healthy control subjects (p = 0.0002). When comparing the metabolomes between coronavirus disease 2019 positive and coronavirus disease 2019 negative patients, kynurenine was the dominant metabolite and the arginine/kynurenine ratio provided 98% classification accuracy (p = 0.005). Feature selection identified creatinine as the top metabolite for predicting coronavirus disease 2019-associated mortality on both ICU days 1 and 3, and both creatinine and creatinine/arginine ratio accurately predicted coronavirus disease 2019-associated death with 100% accuracy (p = 0.01).

Conclusions : Metabolomics profiling with feature classification easily distinguished both healthy control subjects and coronavirus disease 2019 negative patients from coronavirus disease 2019 positive patients. Arginine/kynurenine ratio accurately identified coronavirus disease 2019 status, whereas creatinine/arginine ratio accurately predicted coronavirus disease 2019-associated death. Administration of tryptophan (kynurenine precursor), arginine, sarcosine, and/or lysophosphatidylcholines may be considered as potential adjunctive therapies.

Fraser Douglas D, Slessarev Marat, Martin Claudio M, Daley Mark, Patel Maitray A, Miller Michael R, Patterson Eric K, O’Gorman David B, Gill Sean E, Wishart David S, Mandal Rupasri, Cepinskas Gediminas

2020-Oct

biomarker, coronavirus disease 2019, diagnoses, intensive care unit, metabolomics, prognoses

General General

Quantitative Structure-Activity Relationship Machine Learning Models and their Applications for Identifying Viral 3CLpro- and RdRp-Targeting Compounds as Potential Therapeutics for COVID-19 and Related Viral Infections.

In ACS omega

In response to the ongoing COVID-19 pandemic, there is a worldwide effort being made to identify potential anti-SARS-CoV-2 therapeutics. Here, we contribute to these efforts by building machine-learning predictive models to identify novel drug candidates for the viral targets 3 chymotrypsin-like protease (3CLpro) and RNA-dependent RNA polymerase (RdRp). Chemist-curated training sets of substances were assembled from CAS data collections and integrated with curated bioassay data. The best-performing classification models were applied to screen a set of FDA-approved drugs and CAS REGISTRY substances that are similar to, or associated with, antiviral agents. Numerous substances with potential activity against 3CLpro or RdRp were found, and some were validated by published bioassay studies and/or by their inclusion in upcoming or ongoing COVID-19 clinical trials. This study further supports that machine learning-based predictive models may be used to assist the drug discovery process for COVID-19 and other diseases.

Ivanov Julian, Polshakov Dmitrii, Kato-Weinstein Junko, Zhou Qiongqiong, Li Yingzhu, Granet Roger, Garner Linda, Deng Yi, Liu Cynthia, Albaiu Dana, Wilson Jeffrey, Aultman Christopher

2020-Oct-27

Public Health Public Health

Conspiracy in the time of corona: automatic detection of emerging COVID-19 conspiracy theories in social media and the news.

In Journal of computational social science

Rumors and conspiracy theories thrive in environments of low confidence and low trust. Consequently, it is not surprising that ones related to the COVID-19 pandemic are proliferating given the lack of scientific consensus on the virus's spread and containment, or on the long-term social and economic ramifications of the pandemic. Among the stories currently circulating in US-focused social media forums are ones suggesting that the 5G telecommunication network activates the virus, that the pandemic is a hoax perpetrated by a global cabal, that the virus is a bio-weapon released deliberately by the Chinese, or that Bill Gates is using it as cover to launch a broad vaccination program to facilitate a global surveillance regime. While some may be quick to dismiss these stories as having little impact on real-world behavior, recent events including the destruction of cell phone towers, racially fueled attacks against Asian Americans, demonstrations espousing resistance to public health orders, and wide-scale defiance of scientifically sound public mandates such as those to wear masks and practice social distancing, countermand such conclusions. Inspired by narrative theory, we crawl social media sites and news reports and, through the application of automated machine-learning methods, discover the underlying narrative frameworks supporting the generation of rumors and conspiracy theories. We show how the various narrative frameworks fueling these stories rely on the alignment of otherwise disparate domains of knowledge, and consider how they attach to the broader reporting on the pandemic. These alignments and attachments, which can be monitored in near real time, may be useful for identifying areas in the news that are particularly vulnerable to reinterpretation by conspiracy theorists. Understanding the dynamics of storytelling on social media and the narrative frameworks that provide the generative basis for these stories may also be helpful for devising methods to disrupt their spread.

Shahsavari Shadi, Holur Pavan, Wang Tianyi, Tangherlini Timothy R, Roychowdhury Vwani

2020-Oct-28

4Chan, 5G, Bill Gates, Bio-weapons, COVID-19, China, Conspiracy theories, Corona virus, Data visualization, Machine learning, Narrative, Networks, News, Reddit, Rumor, Social media

Public Health Public Health

Discovery of Potential Flavonoid Inhibitors Against COVID-19 3CL Proteinase Based on Virtual Screening Strategy.

In Frontiers in molecular biosciences

The outbreak of 2019 novel coronavirus (COVID-19) has caused serious threat to public health. Discovery of new anti-COVID-19 drugs is urgently needed. Fortunately, the crystal structure of COVID-19 3CL proteinase was recently resolved. The proteinase has been identified as a promising target for drug discovery in this crisis. Here, a dataset including 2030 natural compounds was screened and refined based on the machine learning and molecular docking. The performance of six machine learning (ML) methods of predicting active coronavirus inhibitors had achieved satisfactory accuracy, especially, the AUC (Area Under ROC Curve) scores with fivefold cross-validation of Logistic Regression (LR) reached up to 0.976. Comprehensive ML prediction and molecular docking results accounted for the compound Rutin, which was approved by NMPA (National Medical Products Administration), exhibited the best AUC and the most promising binding affinity compared to other compounds. Therefore, Rutin might be a promising agent in anti-COVID-19 drugs development.

Xu Zhongren, Yang Lixiang, Zhang Xinghao, Zhang Qiling, Yang Zhibin, Liu Yuanhao, Wei Shuang, Liu Wukun

2020

COVID-19 3CL proteinase, flavonoids, machine learning, molecular docking, rutin, virtual screening

General General

Comparative study of ANN and Fuzzy classifier for forecasting Electrical activity of Heart to diagnose Covid-19.

In Materials today. Proceedings

Covid-19 is a dangerous communicable virus which lets down the world economy. Severe respiratory syndrome SARS-COV-2 leads to Corona Virus Disease (COVID-19) and has the capability of transmission through human-to-human and surface-to-human transmission leads the world to catastrophic phase. Computational system based biological signal analysis helps medical officers in handling COVID-19 tasks like ECG monitoring at Intensive care, fatal ventricular fibrillation, etc., This paper is on diagnosing heart dysfunctions such as tachycardia, bradycardia, ventricular fibrillation, cardiac arrhythmia using fuzzy relations and artificial intelligence algorithm. In this study, the heart pulse base signal and features like spectral entropy, largest lyapunov exponent, Poincare plot and detrended fluctuation analysis are extracted and presented for classification purpose. The RR intervals of Poincare plot summarize RR time series obtained from an ECG in one picture, and a time interval quantities derives information duration of HRV. This analysis eases the prediction of heart rate fluctuation due to Covid or other heart disorders. The better accuracy level in diagnosing heart pulse irregularity using Artificial Neural network(ANN) is an integer value (0 to 4)but for Fuzzy Classifier, it is 0.8 to 0.9.The processing time for analyzing heart dysfunctionalties is 0.05s using ANN which is far better than Fuzzy classifier.

Nivethitha T, Kumar Palanisamy Satheesh, Mohanaprakash K, Jeevitha K

2020-Oct-27

Covid-19, HRV signal, first return maps, lyapunov exponent, spectral entropy

General General

Stopping the COVID-19 Pandemic: A Review on the Advances of Diagnosis, Treatment, and Control Measures.

In Journal of pathogens

With the continued spread of COVID-19 across the world, rapid diagnostic tools, readily available respurposable drugs, and prompt containment measures to control the SARS-CoV-2 infection are of paramount importance. Examples of recent advances in diagnostic tests are CRISPR technology, IgG assay, spike protein detection, and use of artificial intelligence. The gold standard reverse transcription polymerase chain (RT-PCR) has also been upgraded with point-of-care rapid tests. Supportive treatment, mechanical ventilation, and extracorporeal membrane oxygenation (ECMO) remain the primary choice, while therapeutic options include antivirals, antiparasitics, anti-inflammatories, interferon, convalescent plasma, monoclonal antibody, hyperimmunoglobulin, RNAi, and mesenchymal stem cell therapy. Different types of vaccines such as RNA, DNA, and lentiviral, inactivated, and viral vector are in clinical trials. Moreover, rapidly deployable and easy-to-transport innovative vaccine delivery systems are also in development. As countries have started easing down on the lockdown measures, the chance for a second wave of infection demands strict and rational control policies to keep fatalities minimized. An improved understanding of the advances in diagnostic tools, treatments, vaccines, and control measures for COVID-19 can provide references for further research and aid better containment strategies.

Siam Md Hasanul Banna, Nishat Nahida Hannan, Ahmed Ahsan, Hossain Mohammad Sorowar

2020

General General

Lower Circulating Interferon-Gamma Is a Risk Factor for Lung Fibrosis in COVID-19 Patients.

In Frontiers in immunology ; h5-index 100.0

Cytokine storm resulting from SARS-CoV-2 infection is one of the leading causes of acute respiratory distress syndrome (ARDS) and lung fibrosis. We investigated the effect of inflammatory molecules to identify any marker that is related to lung fibrosis in coronavirus disease 2019 (COVID-19). Seventy-six COVID-19 patients who were admitted to Youan Hospital between January 21 and March 20, 2020 and recovered were recruited for this study. Pulmonary fibrosis, represented as fibrotic volume on chest CT images, was computed by an artificial intelligence (AI)-assisted program. Plasma samples were collected from the participants shortly after admission, to measure the basal inflammatory molecules levels. At discharge, fibrosis was present in 46 (60.5%) patients whose plasma interferon-γ (IFN-γ) levels were twofold lower than those without fibrosis (p > 0.05). The multivariate-adjusted logistic regression analysis demonstrated the inverse association risk of having lung fibrosis and basal circulating IFN-γ levels with an estimate of 0.43 (p = 0.02). Per the 1-SD increase of basal IFN-γ level in circulation, the fibrosis volume decreased by 0.070% (p = 0.04) at the discharge of participants. The basal circulating IFN-γ levels were comparable with c-reactive protein in the discrimination of the occurrence of lung fibrosis among COVID-19 patients at discharge, unlike circulating IL-6 levels. In conclusion, these data indicate that decreased circulating IFN-γ is a risk factor of lung fibrosis in COVID-19.

Hu Zhong-Jie, Xu Jia, Yin Ji-Ming, Li Li, Hou Wei, Zhang Li-Li, Zhou Zhen, Yu Yi-Zhou, Li Hong-Jun, Feng Ying-Mei, Jin Rong-Hua

2020

COVID-19, IFN-γ, SRAS-CoV-2, artificial intelligence 2, inflammation, pulmonary fibrosis

General General

A deep transfer learning model with classical data augmentation and CGAN to detect COVID-19 from chest CT radiography digital images.

In Neural computing & applications

The Coronavirus disease 2019 (COVID-19) is the fastest transmittable virus caused by severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2). The detection of COVID-19 using artificial intelligence techniques and especially deep learning will help to detect this virus in early stages which will reflect in increasing the opportunities of fast recovery of patients worldwide. This will lead to release the pressure off the healthcare system around the world. In this research, classical data augmentation techniques along with Conditional Generative Adversarial Nets (CGAN) based on a deep transfer learning model for COVID-19 detection in chest CT scan images will be presented. The limited benchmark datasets for COVID-19 especially in chest CT images are the main motivation of this research. The main idea is to collect all the possible images for COVID-19 that exists until the very writing of this research and use the classical data augmentations along with CGAN to generate more images to help in the detection of the COVID-19. In this study, five different deep convolutional neural network-based models (AlexNet, VGGNet16, VGGNet19, GoogleNet, and ResNet50) have been selected for the investigation to detect the Coronavirus-infected patient using chest CT radiographs digital images. The classical data augmentations along with CGAN improve the performance of classification in all selected deep transfer models. The outcomes show that ResNet50 is the most appropriate deep learning model to detect the COVID-19 from limited chest CT dataset using the classical data augmentation with testing accuracy of 82.91%, sensitivity 77.66%, and specificity of 87.62%.

Loey Mohamed, Manogaran Gunasekaran, Khalifa Nour Eldeen M

2020-Oct-26

CGAN, COVID-19, Deep transfer learning, SARS-CoV-2

Public Health Public Health

ARIMA models for predicting the end of COVID-19 pandemic and the risk of second rebound.

In Neural computing & applications

Globally, many research works are going on to study the infectious nature of COVID-19 and every day we learn something new about it through the flooding of the huge data that are accumulating hourly rather than daily which instantly opens hot research avenues for artificial intelligence researchers. However, the public's concern by now is to find answers for two questions; (1) When this COVID-19 pandemic will be over? and (2) After coming to its end, will COVID-19 return again in what is known as a second rebound of the pandemic? In this work, we developed a predictive model that can estimate the expected period that the virus can be stopped and the risk of the second rebound of COVID-19 pandemic. Therefore, we have considered the SARIMA model to predict the spread of the virus on several selected countries and used it for predicting the COVID-19 pandemic life cycle and its end. The study can be applied to predict the same for other countries as the nature of the virus is the same everywhere. The proposed model investigates the statistical estimation of the slowdown period of the pandemic which is extracted based on the concept of normal distribution. The advantages of this study are that it can help governments to act and make sound decisions and plan for future so that the anxiety of the people can be minimized and prepare the mentality of people for the next phases of the pandemic. Based on the experimental results and simulation, the most striking finding is that the proposed algorithm shows the expected COVID-19 infections for the top countries of the highest number of confirmed cases will be manifested between Dec-2020 and  Apr-2021. Moreover, our study forecasts that there may be a second rebound of the pandemic in a year time if the currently taken precautions are eased completely. We have to consider the uncertain nature of the current COVID-19 pandemic and the growing inter-connected and complex world, that are ultimately demanding flexibility, robustness and resilience to cope with the unexpected future events and scenarios.

Malki Zohair, Atlam El-Sayed, Ewis Ashraf, Dagnew Guesh, Alzighaibi Ahmad Reda, ELmarhomy Ghada, Elhosseini Mostafa A, Hassanien Aboul Ella, Gad Ibrahim

2020-Oct-23

AIC, ARIMA models, COVID-19 pandemic, Infection control, Prediction, SARIMA, Second rebound

General General

Recommendations for Bayesian hierarchical model specifications for case-control studies in mental health

ArXiv Preprint

Hierarchical model fitting has become commonplace for case-control studies of cognition and behaviour in mental health. However, these techniques require us to formalise assumptions about the data-generating process at the group level, which may not be known. Specifically, researchers typically must choose whether to assume all subjects are drawn from a common population, or to model them as deriving from separate populations. These assumptions have profound implications for computational psychiatry, as they affect the resulting inference (latent parameter recovery) and may conflate or mask true group-level differences. To test these assumptions we ran systematic simulations on synthetic multi-group behavioural data from a commonly used multi-armed bandit task (reinforcement learning task). We then examined recovery of group differences in latent parameter space under the two commonly used generative modelling assumptions: (1) modelling groups under a common shared group-level prior (assuming all participants are generated from a common distribution, and are likely to share common characteristics); (2) modelling separate groups based on symptomatology or diagnostic labels, resulting in separate group-level priors. We evaluated the robustness of these approaches to variations in data quality and prior specifications on a variety of metrics. We found that fitting groups separately (assumptions 2), provided the most accurate and robust inference across all conditions. Our results suggest that when dealing with data from multiple clinical groups, researchers should analyse patient and control groups separately as it provides the most accurate and robust recovery of the parameters of interest.

Vincent Valton, Toby Wise, Oliver J. Robinson

2020-11-03

Internal Medicine Internal Medicine

A Call for Antimicrobial Stewardship in Patients with COVID-19: A Nationwide Cohort Study in Korea.

In Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases

Of 6,871 patients hospitalized with COVID-19 in Korea, 35.21% were prescribed antibiotics, and 22.16% received antibiotics against either methicillin-resistant Staphylococcus aureus or Pseudomonas aeruginosa. There is an urgent need for antimicrobial stewardship for COVID-19 to prevent the collateral damage associated with antibiotic overuse.

Shin Dong Hoon, Kang Minsun, Song Kyoung-Ho, Jung Jaehun, Kim Eu Suk, Kim Hong Bin

2020-Oct-30

Antimicrobial stewardship, COVID-19, Nationwide study, anti-MRSA or anti-pseudomonal, antibiotics

General General

Functional and druggability analysis of the SARS-CoV-2 proteome.

In European journal of pharmacology ; h5-index 57.0

The infectious coronavirus disease (COVID-19) pandemic, caused by the coronavirus SARS-CoV-2, appeared in December 2019 in Wuhan, China, and has spread worldwide. As of today, more than 38 million people have been infected and over 1 million fatalities. With the purpose of contributing to the development of effective therapeutics, we performed an in silico determination of binding hot-spots and an assessment of their druggability within the complete SARS-CoV-2 proteome. All structural, non-structural, and accessory proteins have been studied, and whenever experimental structural data of SARS-CoV-2 proteins were not available, homology models were built based on solved SARS-CoV structures. Several potential allosteric or protein-protein interaction druggable sites on different viral targets were identified, knowledge that could be used to expand current drug discovery endeavors beyond the currently explored cysteine proteases and the polymerase complex. It is our hope that this study will support the efforts of the scientific community both in understanding the molecular determinants of this disease and in widening the repertoire of viral targets in the quest for repurposed or novel drugs against COVID-19.

Cavasotto Claudio N, Lamas Maximiliano Sánchez, Maggini Julián

2020-Oct-30

Binding sites, COVID-19, Coronavirus, Drug discovery, Druggability, SARS-CoV-2

General General

Predicting the animal hosts of coronaviruses from compositional biases of spike protein and whole genome sequences through machine learning

bioRxiv Preprint

The COVID-19 pandemic has demonstrated the serious potential for novel zoonotic coronaviruses to emerge and cause major outbreaks. The immediate animal origin of the causative virus, SARS-CoV-2, remains unknown, a notoriously challenging task for emerging disease investigations. Coevolution with hosts leads to specific evolutionary signatures within viral genomes that can inform likely animal origins. We obtained a set of 650 spike protein and 511 whole genome nucleotide sequences from 225 and 187 viruses belonging to the family Coronaviridae, respectively. We then trained random forest models independently on genome composition biases of spike protein and whole genome sequences, including dinucleotide and codon usage biases in order to predict animal host (of nine possible categories, including human). In hold-one-out cross-validation, predictive accuracy on unseen coronaviruses consistently reached ~73%, indicating evolutionary signal in spike proteins to be just as informative as whole genome sequences. However, different composition biases were informative in each case. Applying optimised random forest models to classify human sequences of MERS-CoV and SARS-CoV revealed evolutionary signatures consistent with their recognised intermediate hosts (camelids, carnivores), while human sequences of SARS-CoV-2 were predicted as having bat hosts (suborder Yinpterochiroptera), supporting bats as the suspected origins of the current pandemic. In addition to phylogeny, variation in genome composition can act as an informative approach to predict emerging virus traits as soon as sequences are available. More widely, this work demonstrates the potential in combining genetic resources with machine learning algorithms to address long-standing challenges in emerging infectious diseases.

Brierley, L.; Fowler, A.

2020-11-02

oncology Oncology

Digital triage: Novel strategies for population health management in response to the COVID-19 pandemic.

In Healthcare (Amsterdam, Netherlands)

The COVID-19 pandemic has created unique challenges for the U.S. healthcare system due to the staggering mismatch between healthcare system capacity and patient demand. The healthcare industry has been a relatively slow adopter of digital innovation due to the conventional belief that humans need to be at the center of healthcare delivery tasks. However, in the setting of the COVID-19 pandemic, artificial intelligence (AI) may be used to carry out specific tasks such as pre-hospital triage and enable clinicians to deliver care at scale. Recognizing that the majority of COVID-19 cases are mild and do not require hospitalization, Partners HealthCare (now Mass General Brigham) implemented a digitally-automated pre-hospital triage solution to direct patients to the appropriate care setting before they showed up at the emergency department and clinics, which would otherwise consume resources, expose other patients and staff to potential viral transmission, and further exacerbate supply-and-demand mismatching. Although the use of AI has been well-established in other industries to optimize supply and demand matching, the introduction of AI to perform tasks remotely that were traditionally performed in-person by clinical staff represents a significant milestone in healthcare operations strategy.

Lai Lucinda, Wittbold Kelley A, Dadabhoy Farah Z, Sato Rintaro, Landman Adam B, Schwamm Lee H, He Shuhan, Patel Rajesh, Wei Nancy, Zuccotti Gianna, Lennes Inga T, Medina Danika, Sequist Thomas D, Bomba Garrett, Keschner Yonatan G, Zhang Haipeng Mark

2020-Oct-26

Artificial intelligence, COVID-19, Chatbot, Digital health, Pandemic, Triage

Radiology Radiology

Dual-branch combination network (DCN): Towards accurate diagnosis and lesion segmentation of COVID-19 using CT images.

In Medical image analysis

The recent global outbreak and spread of coronavirus disease (COVID-19) makes it an imperative to develop accurate and efficient diagnostic tools for the disease as medical resources are getting increasingly constrained. Artificial intelligence (AI)-aided tools have exhibited desirable potential; for example, chest computed tomography (CT) has been demonstrated to play a major role in the diagnosis and evaluation of COVID-19. However, developing a CT-based AI diagnostic system for the disease detection has faced considerable challenges, which is mainly due to the lack of adequate manually-delineated samples for training, as well as the requirement of sufficient sensitivity to subtle lesions in the early infection stages. In this study, we developed a dual-branch combination network (DCN) for COVID-19 diagnosis that can simultaneously achieve individual-level classification and lesion segmentation. To focus the classification branch more intensively on the lesion areas, a novel lesion attention module was developed to integrate the intermediate segmentation results. Furthermore, to manage the potential influence of different imaging parameters from individual facilities, a slice probability mapping method was proposed to learn the transformation from slice-level to individual-level classification. We conducted experiments on a large dataset of 1202 subjects from ten institutes in China. The results demonstrated that 1) the proposed DCN attained a classification accuracy of 96.74% on the internal dataset and 92.87% on the external validation dataset, thereby outperforming other models; 2) DCN obtained comparable performance with fewer samples and exhibited higher sensitivity, especially in subtle lesion detection; and 3) DCN provided good interpretability on the loci of infection compared to other deep models due to its classification guided by high-level semantic information. An online CT-based diagnostic platform for COVID-19 derived from our proposed framework is now available.

Gao Kai, Su Jianpo, Jiang Zhongbiao, Zeng Ling-Li, Feng Zhichao, Shen Hui, Rong Pengfei, Xu Xin, Qin Jian, Yang Yuexiang, Wang Wei, Hu Dewen

2020-Oct-08

Attention, COVID-19, CT image, Combined segmentation and classification

Public Health Public Health

Plasma Proteomics Identify Biomarkers and Pathogenesis of COVID-19.

In Immunity ; h5-index 136.0

The coronavirus disease 2019 (COVID-19) pandemic is a global public health crisis. However, little is known about the pathogenesis and biomarkers of COVID-19. Here, we profiled host responses to COVID-19 by performing plasma proteomics of a cohort of COVID-19 patients, including non-survivors and survivors recovered from mild or severe symptoms, and uncovered numerous COVID-19-associated alterations of plasma proteins. We developed a machine-learning-based pipeline to identify 11 proteins as biomarkers and a set of biomarker combinations, which were validated by an independent cohort and accurately distinguished and predicted COVID-19 outcomes. Some of the biomarkers were further validated by enzyme-linked immunosorbent assay (ELISA) using a larger cohort. These markedly altered proteins, including the biomarkers, mediate pathophysiological pathways, such as immune or inflammatory responses, platelet degranulation and coagulation, and metabolism, that likely contribute to the pathogenesis. Our findings provide valuable knowledge about COVID-19 biomarkers and shed light on the pathogenesis and potential therapeutic targets of COVID-19.

Shu Ting, Ning Wanshan, Wu Di, Xu Jiqian, Han Qiangqiang, Huang Muhan, Zou Xiaojing, Yang Qingyu, Yuan Yang, Bie Yuanyuan, Pan Shangwen, Mu Jingfang, Han Yang, Yang Xiaobo, Zhou Hong, Li Ruiting, Ren Yujie, Chen Xi, Yao Shanglong, Qiu Yang, Zhang Ding-Yu, Xue Yu, Shang You, Zhou Xi

2020-Oct-20

COVID-19, SARS-CoV-2, biomarkers, plasma, proteomics

Cardiology Cardiology

Intricate interplay between Covid-19 and cardiovascular diseases.

In Reviews in medical virology

Covid-19 disease can involve any organ system leading to myriad manifestations and complications. Cardiovascular manifestations are being increasingly recognised with the improved understanding of the disease. Acute coronary syndrome, myocarditis, arrhythmias, cardiomyopathy; heart failure and thromboembolic disease have all been described. The elderly and those with prior cardiac diseases are at an increased risk of mortality. Overlapping symptomatology, ability of drugs to cause QTc interval (start of Q wave to the end of T wave) prolongation on electrocardiogram and arrhythmias, potential drug interactions, the need to recognise patients requiring urgent definitive management and provide necessary bedside interventions without increasing the risk of nosocomial spread have made the management challenging. In the background of a pandemic, non-Covid-19 cardiac patients are affected by delayed treatment and nosocomial exposure. Triaging using telemedicine and artificial intelligence along with utilization of bedside rapid diagnostic tests to detect Covid-19 could prove helpful in this aspect.

Keri Vishakh C, Hooda Amit, Kodan Parul, R L Brunda, Jorwal Pankaj, Wig Naveet

2020-Oct-31

Covid-19, cardiovascular diseases, extrapulmonary, hydroxychloroquine, myocarditis, stress cardiomyopathy

Public Health Public Health

Epidemic analysis of COVID -19 in Italy based on spatiotemporal geographic information and Google Trends.

In Transboundary and emerging diseases ; h5-index 40.0

Since the first two novel coronavirus cases appeared in January of 2020, the outbreak of the COVID-19 epidemic seriously threatens the public health of Italy. In this article, the distribution characteristics and spreading of COVID-19 in various regions of Italy were analyzed by heat maps. Meanwhile, spatial autocorrelation, spatio-temporal clustering analysis, and kernel density method were also applied to analyze the spatial clustering of COVID-19. The results showed that the Italian epidemic has a temporal trend and spatial aggregation. The epidemic was concentrated in northern Italy and gradually spread to other regions. Finally, the Google trends index of the COVID-19 epidemic was further employed to build a prediction model combined with machine learning algorithms. Using the Adaboost algorithm for single-factor modeling. It shows that these six features (Mask, Pneumonia, Thermometer, ISS, Disinfection, Disposable gloves) of the AUC values are all greater than 0.9, indicating that these features have a large contribution to the prediction model. It's also implied that the public's attention to the epidemic is increasing as well as the awareness of the need for protective measures. This increased awareness of the epidemic will prompt the public to pay more attention to protective measures, thereby reducing the risk of coronavirus infection.

Niu Bing, Liang Ruirui, Zhang Shuwen, Zhang Hui, Qu Xiaosheng, Su Qiang, Zheng Linfeng, Chen Qin

2020-Oct-31

GIS, Google Trends, coronavirus, epidemic, machine learning

Surgery Surgery

Machine learning prediction for mortality of patients diagnosed with COVID-19: a nationwide Korean cohort study.

In Scientific reports ; h5-index 158.0

The rapid spread of COVID-19 has resulted in the shortage of medical resources, which necessitates accurate prognosis prediction to triage patients effectively. This study used the nationwide cohort of South Korea to develop a machine learning model to predict prognosis based on sociodemographic and medical information. Of 10,237 COVID-19 patients, 228 (2.2%) died, 7772 (75.9%) recovered, and 2237 (21.9%) were still in isolation or being treated at the last follow-up (April 16, 2020). The Cox proportional hazards regression analysis revealed that age > 70, male sex, moderate or severe disability, the presence of symptoms, nursing home residence, and comorbidities of diabetes mellitus (DM), chronic lung disease, or asthma were significantly associated with increased risk of mortality (p ≤ 0.047). For machine learning, the least absolute shrinkage and selection operator (LASSO), linear support vector machine (SVM), SVM with radial basis function kernel, random forest (RF), and k-nearest neighbors were tested. In prediction of mortality, LASSO and linear SVM demonstrated high sensitivities (90.7% [95% confidence interval: 83.3, 97.3] and 92.0% [85.9, 98.1], respectively) and specificities (91.4% [90.3, 92.5] and 91.8%, [90.7, 92.9], respectively) while maintaining high specificities > 90%, as well as high area under the receiver operating characteristics curves (0.963 [0.946, 0.979] and 0.962 [0.945, 0.979], respectively). The most significant predictors for LASSO included old age and preexisting DM or cancer; for RF they were old age, infection route (cluster infection or infection from personal contact), and underlying hypertension. The proposed prediction model may be helpful for the quick triage of patients without having to wait for the results of additional tests such as laboratory or radiologic studies, during a pandemic when limited medical resources must be wisely allocated without hesitation.

An Chansik, Lim Hyunsun, Kim Dong-Wook, Chang Jung Hyun, Choi Yoon Jung, Kim Seong Woo

2020-Oct-30

Internal Medicine Internal Medicine

Comparison of antiviral effect for mild-to-moderate COVID-19 cases between lopinavir/ritonavir versus hydroxychloroquine: A nationwide propensity score-matched cohort study.

In International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases

OBJECTIVES : We aimed to compare the antiviral effect of hydroxychloroquine (HCQ) and lopinavir/ritonavir (LPV/r) in patients with COVID-19.

METHODS : Nationwide retrospective case-control study was conducted to compare the effect of HCQ and LPV/r on viral shedding duration among patients with mild-to-moderate COVID-19 using the reimbursement data of National Health Insurance Service. After propensity score matching (PSM), multivariate analysis was conducted to determine statistically significant risk factors associated with prolonged viral shedding.

RESULTS : Overall, 4,197 patients with mild-to-moderate COVID-19 were included. Patients were categorized into three groups: LPV/r (n = 1,268), HCQ (n = 801), and standard care without HCQ or LPV/r (controls, n = 2128). The median viral shedding duration was 23 (IQR 17-32), 23 (IQR 16-32), and 18 (IQR 12-25) days in the LPV/r, HCQ, and control groups, respectively. Even after PSM, the viral shedding duration was not significantly different between LPV/r and HCQ groups: 23 (IQR, 17-32) days versus 23 (IQR, 16-32) days. On multivariate analysis, old age, malignancy, steroid use, and concomitant pneumonia were statistically significant risk factors for prolonged viral shedding.

CONCLUSION : The viral shedding duration was similar between HCQ and LPV/r treatment groups. There was no benefit in improving viral clearance compared to the control group.

Choi Min Joo, Kang Minsun, Shin So Youn, Noh Ji Yun, Cheong Hee Jin, Kim Woo Joo, Jung Jaehun, Song Joon Young

2020-Oct-27

COVID-19, Hydroxychloroquine, Lopinavir, Ritonavir, SARS-CoV-2

Public Health Public Health

Transforming vaccine development.

In Seminars in immunology

The urgency to develop vaccines against Covid-19 is putting pressure on the long and expensive development timelines that are normally required for development of lifesaving vaccines. There is a unique opportunity to take advantage of new technologies, the smart and flexible design of clinical trials, and evolving regulatory science to speed up vaccine development against Covid-19 and transform vaccine development altogether.

Black Steve, Bloom David E, Kaslow David C, Pecetta Simone, Rappuoli Rino

2020-Oct-27

Adjuvants, COVID-19, Human genetics, Machine learning, Platform technologies, Real world evidence, Regulatory convergence, Smart clinical trials, Systems biology, Vaccine, Vaccine development, Vaccine discovery, Vaccines safety

Radiology Radiology

Computed tomography semi-automated lung volume quantification in SARS-CoV-2-related pneumonia.

In European radiology ; h5-index 62.0

OBJECTIVES : To evaluate a semi-automated segmentation and ventilated lung quantification on chest computed tomography (CT) to assess lung involvement in patients affected by SARS-CoV-2. Results were compared with clinical and functional parameters and outcomes.

METHODS : All images underwent quantitative analyses with a dedicated workstation using a semi-automatic lung segmentation software to compute ventilated lung volume (VLV), Ground-glass opacity (GGO) volume (GGO-V), and consolidation volume (CONS-V) as absolute volume and as a percentage of total lung volume (TLV). The ratio between CONS-V, GGO-V, and VLV (CONS-V/VLV and GGO-V/VLV, respectively), TLV (CONS-V/TLV, GGO-V/TLV, and GGO-V + CONS-V/TLV respectively), and the ratio between VLV and TLV (VLV/TLV) were calculated.

RESULTS : A total of 108 patients were enrolled. GGO-V/TLV significantly correlated with WBC (r = 0.369), neutrophils (r = 0.446), platelets (r = 0.182), CRP (r = 0.190), PaCO2 (r = 0.176), HCO3- (r = 0.284), and PaO2/FiO2 (P/F) values (r = - 0.344). CONS-V/TLV significantly correlated with WBC (r = 0.294), neutrophils (r = 0.300), lymphocytes (r = -0.225), CRP (r = 0.306), PaCO2 (r = 0.227), pH (r = 0.162), HCO3- (r = 0.394), and P/F (r = - 0.419) values. Statistically significant differences between CONS-V, GGO-V, GGO-V/TLV, CONS-V/TLV, GGO-V/VLV, CONS-V/VLV, GGO-V + CONS-V/TLV, VLV/TLV, CT score, and invasive ventilation by ET were found (all p < 0.05).

CONCLUSION : The use of quantitative semi-automated algorithm for lung CT elaboration effectively correlates the severity of SARS-CoV-2-related pneumonia with laboratory parameters and the need for invasive ventilation.

KEY POINTS : • Pathological lung volumes, expressed both as GGO-V and as CONS-V, can be considered a useful tool in SARS-CoV-2-related pneumonia. • All lung volumes, expressed themselves and as ratio with TLV and VLV, correlate with laboratory data, in particular C-reactive protein and white blood cell count. • All lung volumes correlate with patient's outcome, in particular concerning invasive ventilation.

Ippolito Davide, Ragusi Maria, Gandola Davide, Maino Cesare, Pecorelli Anna, Terrani Simone, Peroni Marta, Giandola Teresa, Porta Marco, Talei Franzesi Cammillo, Sironi Sandro

2020-Oct-30

Artificial intelligence, Computed tomography, X-ray, Infection, coronavirus, Lung volume measurements, Pneumonia

General General

Reading List: Select Healthcare Transformation Library 2.0.

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

Reading List: Select Healthcare Transformation Library 2.0 represents a broad-based, annotated, general reading list for students of health care innovation. The books were drawn from the 5,000-book private home library of Ronald S. Weinstein, MD, President Emeritus of the American Telemedicine Association. Weinstein is a lifelong book collector with special interests in the history of medical innovation and poetry. A Massachusetts General Hospital-trained pathologist and inductee into the US Distance Learning Association's Hall of Fame, he is known as a pioneer in telemedicine and the "father of telepathology" for his invention, patenting, and commercialization of telepathology, a subspecialty of telemedicine that is a billion-dollar worldwide industry today. This Reading List: Select Healthcare Transformation Library 2.0 consists of 41 books divided into 10 sections: (1) Human Intelligence, Behavior, and Creativity; (2) Societal Revolutions; (3) Innovation; (4) Healthcare System Transformations; (5) Education; (6) Transformational Technologies-Part 1 (AI, Automation, and Robotics); (7) Transformational Technologies-Part 2 (Telemedicine and Telehealth); (8) Digital Medicine; (9) Healthcare Transformation Implementation; and (10) COVID-19 Pandemic as an Innovation Accelerator.

Weinstein Ronald S, Holcomb Michael J

2020-Oct-30

artificial intelligence, connected health, digital medicine, e-health, health care transformation, innovation accelerator, robotics, telehealth, telemedicine

General General

Correlates of Health-Protective Behavior During the Initial Days of the COVID-19 Outbreak in Norway.

In Frontiers in psychology ; h5-index 92.0

The coronavirus outbreak manifested in Norway in March 2020. It was met with a combination of mandatory changes (closing of public institutions) and recommended changes (hygiene behavior, physical distancing). It has been emphasized that health-protective behavior such as increased hygiene or physical distancing are able to slow the spread of infections and flatten the curve. Drawing on previous health-psychological studies during the outbreak of various pandemics, we investigated psychological and demographic factors predicting the adoption and engagement in health-protective behavior and changes in such behavior, attitudes, and emotions over time. We recruited a non-representative sample of Norwegians (n = 8676) during a 15-day period (March 12-26 2020) at the beginning of the COVID-19 outbreak in Norway. Employing both traditional methods and exploratory machine learning, we replicated earlier findings that engagement in health-protective behavior is associated with specific demographic characteristics. Further, we observed that increased media exposure, perceiving measures as effective, and perceiving the outbreak as serious was positively related to engagement in health-protective behavior. We also found indications that hygiene and physical distancing behaviors were related to somewhat different psychological and demographic factors. Over the sampling period, reported engagement in physical distancing increased, while experienced concern or fear declined. Contrary to previous studies, we found no or only small positive predictions by confidence in authorities, knowledge about the outbreak, and perceived individual risk, while all of those variables were rather high. These findings provide guidance for health communications or interventions targeting the adoption of health-protective behaviors in order to diminish the spread of COVID-19.

Zickfeld Janis H, Schubert Thomas W, Herting Anders Kuvaas, Grahe Jon, Faasse Kate

2020

COVID-19, Norway, concern, coronavirus, health protective behavior, perceived risk

General General

Identifying and Ranking Common COVID-19 Symptoms from Arabic Twitter.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : A massive amount of COVID-19 related data is generated everyday by Twitter users. Self-reports of COVID-19 symptoms on Twitter can reveal a great deal about the disease and its prevalence in the community. In particular, self-reports can be used as a valuable resource to learn more about the common symptoms and whether their order of appearance differs among different groups in the community. With sufficient available data, this has the potential of developing a COVID-19 risk-assessment system that is tailored toward specific group of people.

OBJECTIVE : The aim of this study is to identify the most common symptoms reported by COVID-19 patients in the Arabic language and order the symptoms appearance based on the collected data.

METHODS : We searched the Arabic content of Twitter for personal reports of COVID-19 symptoms from March 1st to May 27th, 2020. We identified 463 Arabic users who had tweeted testing positive for COVID-19 and extracted the symptoms they publicly associated with COVID-19. Furthermore, we asked them directly through personal messages to opt in and rank the appearance of the first three symptoms they had experienced right before (or after) diagnosed with COVID-19. Finally, we tracked their Twitter timeline to identify additional symptoms that were mentioned within ±5 days from the day of tweeting having COVID-19. In summary, a list of 270 COVID-19 reports were collected and symptoms were (at least partially) ranked from early to late.

RESULTS : The collected reports contained 893 symptoms originated from 74% (n=201) male and 26% (n=69) female Twitter users. The majority (82%) of the tracked users were living in Saudi Arabia (46%) and Kuwait (36%). Furthermore, 13% (n=36) of the collected reports were asymptomatic. Out of the users with symptoms (n=234), 66% (n=180) provided a chronological order of appearance for at least three symptoms. Fever 59% (n=139), Headache 43% (n=101), and Anosmia 39% (n=91) were found to be the top three symptoms mentioned by the reports. They count also for the top-3 common first symptoms in a way that 28% (n=65) said their COVID journey started with a Fever, 15% (n=34) with a Headache and 12% (n=28) with Anosmia. Out of the Saudi symptomatic reported cases (n=110), the most common three symptoms were Fever 59% (n=65), Anosmia 42% (n=46), and Headache 38% (n=42).

CONCLUSIONS : This study identified the most common symptoms of COVID-19 from Arabic tweets. These symptoms can be further analyzed in clinical setting and may be incorporated in a real-time COVID-19 risk estimator based on the users' tweets.

CLINICALTRIAL :

Alanazi Eisa, Alashaikh Abdulaziz, Alqurashi Sarah, Alanazi Aued

2020-Oct-26

Public Health Public Health

Twitter discussions and emotions about COVID-19 pandemic: a machine learning approach.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Public response to the COVID-19 pandemic is important to be measured. Twitter data are an important source for the infodemiology study of public response monitoring.

OBJECTIVE : The objective of the study is to examine coronavirus disease (COVID-19) related discussions, concerns, and sentiments that emerged from tweets posted by Twitter users.

METHODS : We analyze 4 million Twitter messages related to the COVID-19 pandemic using a list of 20 hashtags such as "coronavirus," "COVID-19," "quarantine" from March 7 to April 21 in 2020. We use a machine learning approach, Latent Dirichlet Allocation (LDA), to identify popular unigram, bigrams, salient topics and themes, and sentiments in the collected tweets.

RESULTS : Popular unigrams include "virus," "lockdown," and "quarantine." Popular bigrams include "COVID-19," "stay home," "corona virus," "social distancing," and "new cases." We identify 13 discussion topics and categorize them into 5 different themes, such as "public health measures to slow the spread of COVID-19," "social stigma associated with COVID-19," "coronavirus news cases and deaths," "COVID-19 in the United States," and "coronavirus cases in the rest of the world." Across all identified topics, the dominant sentiments for the spread of coronavirus are anticipation that measures that can be taken, followed by a mixed feeling of trust, anger, and fear for different topics. The public reveals a significant feeling of fear when discussing the coronavirus new cases and deaths than other topics.

CONCLUSIONS : The study shows that Twitter data and machine learning approaches can be leveraged for infodemiology study by studying the evolving public discussions and sentiments during the COVID-19. As the situation evolves rapidly, several topics are consistently dominant on Twitter, such as "the confirmed cases and death rates," "preventive measures," "health authorities and government policies," "COVID-19 stigma," and "negative psychological reactions (e.g., fear)." Real-time monitoring and assessment of the Twitter discussions and concerns can be promising for public health emergency responses and planning. Already emerged pandemic fear, stigma, and mental health concerns may continue to influence public trust when there occurs a second wave of COVID-19 or a new surge of the imminent pandemic.

CLINICALTRIAL :

Xue Jia, Chen Junxiang, Hu Ran, Chen Chen, Zheng Chengda, Su Yue, Zhu Tingshao

2020-Oct-28

General General

Don't sugar coat the COVID (only the vasculature).

In Biomedical journal

This issue of the Biomedical Journal acquaints us with the compelling hypothesis that the vascular glycocalyx lies at the intersection of severe COVID-19 risk factors and damages, and the ways used by artificial intelligence to predict interactions between SARS-CoV-2 and human proteins. Furthermore, we explore the antiviral potential of valinomycin and the long list of COVID-19-related clinical trials, and learn how (not) to fix a broken femoral head. Last but not least, we get to enjoy the tale of the cellular oxygen-sensing system as well as the role of the host complement system during Leptospira infection, and learn that SARS-CoV-2 can sometimes come with a pathogenic plus one.

Häfner Sophia Julia

2020-Oct-10

COVID-19, Femoral head fracture, Glycocalyx, Hypoxia-inducible factor, Valinomycin

Cardiology Cardiology

Predictive Accuracy of COVID-19 World Health Organization (WHO) Severity Classification and Comparison with a Bayesian-Method-Based Severity Score (EPI-SCORE).

In Pathogens (Basel, Switzerland)

Assess the predictive accuracy of the WHO COVID-19 severity classification on COVID-19 hospitalized patients. The secondary aim was to compare its predictive power with a new prediction model, named COVID-19 EPI-SCORE, based on a Bayesian network analysis. Methods: We retrospectively analyzed a population of 295 COVID-19 RT-PCR positive patients hospitalized at Epicura Hospital Center, Belgium, admitted between March 1st and April 30th, 2020. Results: Our cohort's median age was 73 (62-83) years, and the female proportion was 43%. All patients were classified following WHO severity classification at admission. In total, 125 (42.4%) were classified as Moderate, 69 (23.4%) as Severe, and 101 (34.2%) as Critical. Death proportions through these three classes were 11.2%, 33.3%, and 67.3%, respectively, and the proportions of critically ill patients (dead or needed Invasive Mechanical Ventilation) were 11.2%, 34.8%, and 83.2%, respectively. A Bayesian network analysis was used to create a model to analyze predictive accuracy of the WHO severity classification and to create the EPI-SCORE. The six variables that have been automatically selected by our machine learning algorithm were the WHO severity classification, acute kidney injury, age, Lactate Dehydrogenase Levels (LDH), lymphocytes and activated prothrombin time (aPTT). Receiver Operation Characteristic (ROC) curve indexes hereby obtained were 83.8% and 91% for the models based on WHO classification only and our EPI-SCORE, respectively. Conclusions: Our study shows that the WHO severity classification is reliable in predicting a severe outcome among COVID-19 patients. The addition to this classification of a few clinical and laboratory variables as per our COVID-19 EPI-SCORE has demonstrated to significantly increase its accuracy.

de Terwangne Christophe, Laouni Jabber, Jouffe Lionel, Lechien Jerome R, Bouillon Vincent, Place Sammy, Capulzini Lucio, Machayekhi Shahram, Ceccarelli Antonia, Saussez Sven, Sorgente Antonio, Epibase Team On Behalf Of

2020-Oct-24

COVID-19, SARS-COV-2, WHO, classification, coronavirus, prediction, score, severity

General General

COVI-AgentSim: an Agent-based Model for Evaluating Methods of Digital Contact Tracing

ArXiv Preprint

The rapid global spread of COVID-19 has led to an unprecedented demand for effective methods to mitigate the spread of the disease, and various digital contact tracing (DCT) methods have emerged as a component of the solution. In order to make informed public health choices, there is a need for tools which allow evaluation and comparison of DCT methods. We introduce an agent-based compartmental simulator we call COVI-AgentSim, integrating detailed consideration of virology, disease progression, social contact networks, and mobility patterns, based on parameters derived from empirical research. We verify by comparing to real data that COVI-AgentSim is able to reproduce realistic COVID-19 spread dynamics, and perform a sensitivity analysis to verify that the relative performance of contact tracing methods are consistent across a range of settings. We use COVI-AgentSim to perform cost-benefit analyses comparing no DCT to: 1) standard binary contact tracing (BCT) that assigns binary recommendations based on binary test results; and 2) a rule-based method for feature-based contact tracing (FCT) that assigns a graded level of recommendation based on diverse individual features. We find all DCT methods consistently reduce the spread of the disease, and that the advantage of FCT over BCT is maintained over a wide range of adoption rates. Feature-based methods of contact tracing avert more disability-adjusted life years (DALYs) per socioeconomic cost (measured by productive hours lost). Our results suggest any DCT method can help save lives, support re-opening of economies, and prevent second-wave outbreaks, and that FCT methods are a promising direction for enriching BCT using self-reported symptoms, yielding earlier warning signals and a significantly reduced spread of the virus per socioeconomic cost.

Prateek Gupta, Tegan Maharaj, Martin Weiss, Nasim Rahaman, Hannah Alsdurf, Abhinav Sharma, Nanor Minoyan, Soren Harnois-Leblanc, Victor Schmidt, Pierre-Luc St. Charles, Tristan Deleu, Andrew Williams, Akshay Patel, Meng Qu, Olexa Bilaniuk, Gaétan Marceau Caron, Pierre Luc Carrier, Satya Ortiz-Gagné, Marc-Andre Rousseau, David Buckeridge, Joumana Ghosn, Yang Zhang, Bernhard Schölkopf, Jian Tang, Irina Rish, Christopher Pal, Joanna Merckx, Eilif B. Muller, Yoshua Bengio

2020-10-30

General General

Country-level pandemic risk and preparedness classification based on COVID-19 data: A machine learning approach.

In PloS one ; h5-index 176.0

In this work we present a three-stage Machine Learning strategy to country-level risk classification based on countries that are reporting COVID-19 information. A K% binning discretisation (K = 25) is used to create four risk groups of countries based on the risk of transmission (coronavirus cases per million population), risk of mortality (coronavirus deaths per million population), and risk of inability to test (coronavirus tests per million population). The four risk groups produced by K% binning are labelled as 'low', 'medium-low', 'medium-high', and 'high'. Coronavirus-related data are then removed and the attributes for prediction of the three types of risk are given as the geopolitical and demographic data describing each country. Thus, the calculation of class label is based on coronavirus data but the input attributes are country-level information regardless of coronavirus data. The three four-class classification problems are then explored and benchmarked through leave-one-country-out cross validation to find the strongest model, producing a Stack of Gradient Boosting and Decision Tree algorithms for risk of transmission, a Stack of Support Vector Machine and Extra Trees for risk of mortality, and a Gradient Boosting algorithm for the risk of inability to test. It is noted that high risk for inability to test is often coupled with low risks for transmission and mortality, therefore the risk of inability to test should be interpreted first, before consideration is given to the predicted transmission and mortality risks. Finally, the approach is applied to more recent risk levels to data from September 2020 and weaker results are noted due to the growth of international collaboration detracting useful knowledge from country-level attributes which suggests that similar machine learning approaches are more useful prior to situations later unfolding.

Bird Jordan J, Barnes Chloe M, Premebida Cristiano, Ekárt Anikó, Faria Diego R

2020

Pathology Pathology

Capsule endoscopy - Recent developments and future directions.

In Expert review of gastroenterology & hepatology

** : Introduction: Capsule endoscopy (CE) is an established modality in the diagnostic algorithm of small bowel (SB) pathology. Its use has expanded for investigation of upper and lower gastrointestinal diseases with similar prototypes.

AREAS COVERED : This review covers the role and recent advances of CE, as a non-invasive investigative tool.

EXPERT OPINION : The use of upper gastrointestinal CE is useful in patients who require surveillance for varices particularly in the current era of the COVID 19 pandemic. It has also shown high accuracy in the detection of upper gastrointestinal haemorrhage in patients presenting to the emergency department with a suspicion of haemorrhage. Findings on CE help to guide further management by device assisted enteroscopy. The data on colon CE suggests comparable diagnostic accuracy to colonoscopy for polyp detection, however more evidence is required in the high risk group. Crohn's CE has become an integral part of the management of patients with Crohn's disease offering a comparative assessment tool post escalation of therapy. Artificial intelligence within CE, has demonstrated similar if not better diagnostic yield compared to the human with a significantly shorter reading time. Artificial intelligence is likely to be in-built within CE reading platforms over the next few years minimising reporting time and human error.

Zammit Chetcuti Stefania, Sidhu Reena

2020-Oct-28

Crohn’s capsule endoscopy, artificial intelligence, colon capsule endoscopy, magnetically controlled upper gastrointestinal capsule, small bowel capsule endoscopy

General General

Retrospective analysis of the accuracy of predicting the alert level of COVID-19 in 202 countries using Google Trends and machine learning.

In Journal of global health

Background : Internet search engine data, such as Google Trends, was shown to be correlated with the incidence of COVID-19, but only in several countries. We aim to develop a model from a small number of countries to predict the epidemic alert level in all the countries worldwide.

Methods : The "interest over time" and "interest by region" Google Trends data of Coronavirus, pneumonia, and six COVID symptom-related terms were searched. The daily incidence of COVID-19 from 10 January to 23 April 2020 of 202 countries was retrieved from the World Health Organization. Three alert levels were defined. Ten weeks' data from 20 countries were used for training with machine learning algorithms. The features were selected according to the correlation and importance. The model was then tested on 2830 samples of 202 countries.

Results : Our model performed well in 154 (76.2%) countries, of which each had no more than four misclassified samples. In these 154 countries, the accuracy was 0.8133, and the kappa coefficient was 0.6828. While in all 202 countries, the accuracy was 0.7527, and the kappa coefficient was 0.5841. The proposed algorithm based on Random Forest Classification and nine features performed better compared to other machine learning methods and the models with different numbers of features.

Conclusions : Our result suggested that the model developed from 20 countries with Google Trends data and Random Forest Classification can be applied to predict the epidemic alert levels of most countries worldwide.

Peng Yuanyuan, Li Cuilian, Rong Yibiao, Chen Xinjian, Chen Haoyu

2020-Dec

General General

Forecasting spread of COVID-19 using Google Trends: A hybrid GWO-Deep learning approach.

In Chaos, solitons, and fractals

The recent outbreak of COVID-19 has brought the entire world to a standstill. The rapid pace at which the virus has spread across the world is unprecedented. The sheer number of infected cases and fatalities in such a short period of time has overwhelmed medical facilities across the globe. The rapid pace of the spread of the novel coronavirus makes it imperative that its' spread be forecasted well in advance in order to plan for eventualities. An accurate early forecasting of the number of cases would certainly assist governments and various other organizations to strategize and prepare for the newly infected cases, well in advance. In this work, a novel method of forecasting the future cases of infection, based on the study of data mined from the internet search terms of people in the affected region, is proposed. The study utilizes relevant Google Trends of specific search terms related to COVID-19 pandemic along with European Centre for Disease prevention and Control (ECDC) data on COVID-19 spread, to forecast the future trends of daily new cases, cumulative cases and deaths for India, USA and UK. For this purpose, a hybrid GWO-LSTM model is developed, where the network parameters of Long Short Term Memory (LSTM) network are optimized using Grey Wolf Optimizer (GWO). The results of the proposed model are compared with the baseline models including Auto Regressive Integrated Moving Average (ARIMA), and it is observed that the proposed model achieves much better results in forecasting the future trends of the spread of infection. Using the proposed hybrid GWO-LSTM model incorporating online big data from Google Trends, a reduction in Mean Absolute Percentage Error (MAPE) values for forecasting results to the extent of about 98% have been observed. Further, reduction in MAPE by 74% for models incorporating Google Trends was observed, thus, confirming the efficacy of utilizing public sentiments in terms of search frequencies of relevant terms online, in forecasting pandemic numbers.

Prasanth Sikakollu, Singh Uttam, Kumar Arun, Tikkiwal Vinay Anand, Chong Peter H J

2020-Oct-22

Auto Regressive Integrated Moving Average (ARIMA), COVID-19, Deep Learning, Forecasting, Google Trends, Grey Wolf Optimization (GWO), Long Short Term Memory (LSTM), Optimization, Pandemic

General General

Artificial intelligence in medicine and the disclosure of risks.

In AI & society

This paper focuses on the use of 'black box' AI in medicine and asks whether the physician needs to disclose to patients that even the best AI comes with the risks of cyberattacks, systematic bias, and a particular type of mismatch between AI's implicit assumptions and an individual patient's background situation. Pace current clinical practice, I argue that, under certain circumstances, these risks do need to be disclosed. Otherwise, the physician either vitiates a patient's informed consent or violates a more general obligation to warn him about potentially harmful consequences. To support this view, I argue, first, that the already widely accepted conditions in the evaluation of risks, i.e. the 'nature' and 'likelihood' of risks, speak in favour of disclosure and, second, that principled objections against the disclosure of these risks do not withstand scrutiny. Moreover, I also explain that these risks are exacerbated by pandemics like the COVID-19 crisis, which further emphasises their significance.

Kiener Maximilian

2020-Oct-22

Artificial intelligence, COVID-19, Informed consent, Medical disclosure, Risks

General General

Assessing concerns for the economic consequence of the COVID-19 response and mental health problems associated with economic vulnerability and negative economic shock in Italy, Spain, and the United Kingdom.

In PloS one ; h5-index 176.0

Many different countries have been under lockdown or extreme social distancing measures to control the spread of COVID-19. The potentially far-reaching side effects of these measures have not yet been fully understood. In this study we analyse the results of a multi-country survey conducted in Italy (N = 3,504), Spain (N = 3,524) and the United Kingdom (N = 3,523), with two separate analyses. In the first analysis, we examine the elicitation of citizens' concerns over the downplaying of the economic consequences of the lockdown during the COVID-19 pandemic. We control for Social Desirability Bias through a list experiment included in the survey. In the second analysis, we examine the data from the same survey to predict the level of stress, anxiety and depression associated with being economically vulnerable and having been affected by a negative economic shock. To accomplish this, we have used a prediction algorithm based on machine learning techniques. To quantify the size of this affected population, we compare its magnitude with the number of people affected by COVID-19 using measures of susceptibility, vulnerability and behavioural change collected in the same questionnaire. We find that the concern for the economy and for "the way out" of the lockdown is diffuse and there is evidence of minor underreporting. Additionally, we estimate that around 42.8% of the populations in the three countries are at high risk of stress, anxiety, and depression, based on their level of economic vulnerability and their exposure to a negative economic shock.

Codagnone Cristiano, Bogliacino Francesco, Gómez Camilo, Charris Rafael, Montealegre Felipe, Liva Giovanni, Lupiáñez-Villanueva Francisco, Folkvord Frans, Veltri Giuseppe A

2020

Internal Medicine Internal Medicine

Easy-to-use machine learning model predicting prognosis of COVID-19 patients.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Prioritizing patients in need of intensive care is necessary to reduce the mortality rate during the global pandemic of the coronavirus disease 2019 (COVID-19). Although several scoring methods have been introduced, many require laboratory or radiographic findings that may not be easily available in certain situations.

OBJECTIVE : The purpose of this study was to develop a machine learning model that predicts the need for intensive care for COVID-19 patients with easily providable characteristics, limited to baseline demographics, comorbidities, and symptoms.

METHODS : A retrospective study was performed using a nationwide cohort in South Korea. Patients admitted to 100 hospitals from January 25th, 2020 to June 3rd, 2020 were included. Patient information was collected retrospectively by the attending physicians in each hospital and uploaded to an online case report form. Variables that could be easily provided were extracted. The variables were age, sex, smoking history, body temperature, comorbidities, activities of daily living, and symptoms. The primary outcome was the need for intensive care, defined as admission to the intensive care unit, use of extracorporeal life support, mechanical ventilation, vasopressors, or death within 30 days of hospitalization. Patients admitted until March 20th were included in the derivation group to develop prediction models using an automated machine learning technique. The models were externally validated in patients admitted after March 21st, 2020. The machine learning model with the best discrimination performance was selected and compared against CURB-65 using the area under the receiver operating characteristic curve (AUROC).

RESULTS : A total of 4787 patients were included in the analysis, of which 3294 were assigned to the derivation group and 1493 to the validation group. Among the 4787 patients, 460 (9.6%) patients needed intensive care. Of the 55 machine learning models developed, the XGBoost model revealed the highest discrimination performance. The AUROC of the XGBoost model was 0.897 (95% CI 0.877-0.917) for the derivation group and 0.885 (95% CI 0.855-0.915) for the validation group. Both the AUROCs were superior to those of CURB-65, which were 0.836 (95% CI 0.825-0.847) and 0.843 (95% CI 0.829-0.857), respectively.

CONCLUSIONS : We developed a machine learning model comprising simple patient-provided characteristics, which can efficiently predict the need for intensive care among COVID-19 patients.

CLINICALTRIAL :

Kim Hyung-Jun, Han Deokjae, Kim Jeong-Han, Kim Daehyun, Ha Beomman, Seog Woong, Lee Yeon-Kyeng, Lim Dosang, Hong Sung Ok, Park Mi-Jin, Heo JoonNyung

2020-Oct-25

General General

Abusers indoors and coronavirus outside: an examination of public discourse about COVID-19 and family violence on Twitter.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Family violence (including IPV/domestic violence, child abuse, elder abuse) is the hidden pandemics during the COVID-19. The rates of family violence are rising fast. Women and children are disproportionately affected and vulnerable during the pandemic.

OBJECTIVE : This study aims to provide a large-scale analysis of public discourse mentioning family violence and the COVID-19 pandemic on Twitter.

METHODS : We analyzed one million Tweets related to family violence and COVID-19 from April 12 to July 16, 2020, for this study. We used the machine learning approach, Latent Dirichlet Allocation, and identified salient themes, topics, and representative Twitter examples.

RESULTS : We extracted nine themes from what people are saying about family violence, and the COVID-19 pandemic, including (1) Increased vulnerability: COVID-19 and family violence (e.g., rising rates, hotline calls increased, murder & homicide); (2) the types of family violence (e.g., child abuse, domestic violence, sexual abuse) and (3) forms of family violence (e.g., physical aggression, coercive control); (4) risk factors of family violence (e.g., alcohol abuse, financial constraints, gun, quarantine); (5) victims of family violence (e.g., LGBTQ, women, and women of color, children); (6) social services for family violence (e.g., hotlines, social workers, confidential services, shelters, funding); (7) law enforcement response (e.g., 911 calls, police arrest, protective orders, abuse reports); (8) Social movement/ awareness (e.g., support victims, raise awareness); and (9) domestic violence-related news (e.g., Tara Reade, Melissa Derosa).

CONCLUSIONS : This study overcomes the limitation of existing scholarship that lacks data for consequences of COVID-19 on family violence. We contribute to understanding family violence during the pandemic by providing surveillance in Tweets, which is essential to identifying potentially useful policy programs in offering targeted support for victims and survivors and preparing for the next wave.

Xue Jia, Chen Junxiang, Chen Chen, Hu Ran, Zhu Tingshao

2020-Oct-26

General General

A Deep Learning Prognosis Model Help Alert for COVID-19 Patients at High-Risk of Death: A Multi-center Study.

In IEEE journal of biomedical and health informatics

Since its outbreak in December 2019, the persistent coronavirus disease (COVID-19) became a global health emergency. It is imperative to develop a prognostic tool to identify high-risk patients and assist in the formulation of treatment plans. We retrospectively collected 366 severe or critical COVID-19 patients from four centers, including 70 patients who died within 14 days (labeled as high-risk patients) since their initial CT scan and 296 who survived more than 14 days or were cured (labeled as low-risk patients). We developed a 3D densely connected convolutional neural network (termed De-COVID19-Net) to predict the probability of COVID-19 patients belonging to the high-risk or low-risk group, combining CT and clinical information. The area under the curve (AUC) and other evaluation techniques were used to assess our model. The De-COVID19-Net yielded an AUC of 0.952 (95% confidence interval, 0.928-0.977) on the training set and 0.943 (0.904-0.981) on the test set. The stratified analyses indicated that our model's performance is independent of age, sex, and with/without chronic diseases. The Kaplan-Meier analysis revealed that our model could significantly categorize patients into high-risk and low-risk groups (p < 0.001). In conclusion, De-COVID19-Net can non-invasively predict whether a patient will die shortly based on the patient's initial CT scan with an impressive performance, which indicated that it could be used as a potential prognosis tool to alert high-risk patients and intervene in advance.

Meng Lingwei, Dong Di, Li Liang, Niu Meng, Bai Yan, Wang Meiyun, Qiu Xiaoming, Zha Yunfei, Tian Jie

2020-Oct-27

General General

A potential treatment for COVID-19 based on modal characteristics and dynamic responses analysis of 2019-nCoV.

In Nonlinear dynamics

The 2019-nCoV is ravaging the world, taking lots of lives, and it is emergent to find a solution to deal with this novel pneumonia. This paper provides a potential treatment for COVID-19 utilizing resonance to destroy the infection ability of 2019-nCoV. Firstly, the geometry size of 2019-nCoV is scaled up by 10,000 times. The additional mass is used to represent the effect of the fluid around a spike protein. The finite element analysis (FEA) is used to study the modal characteristics of the tuned 2019-nCoV model and mistuned 2019-nCoV model in blood, respectively. Based on FEA, the lumped parameter mechanical model of 2019-nCoV is established. Then, the dynamic responses of mistuned 2019-nCoV are investigated through harmonic response and dynamical analysis. Finally, a potential method utilizing 360° sweep excitation to cure COVID-19 is put forward.

Yao Minghui, Wang Hongbo

2020-Oct-21

2019-nCoV, Dynamic responses, Modal characteristics, Potential treatments

General General

COSMO-RS-Based Descriptors for the Machine Learning-Enabled Screening of Nucleotide Analogue Drugs against SARS-CoV-2.

In The journal of physical chemistry letters ; h5-index 129.0

Chemical similarity-based approaches employed to repurpose or develop new treatments for emerging diseases, such as COVID-19, correlates molecular structure-based descriptors of drugs with those of a physiological counterpart or clinical phenotype. We propose novel descriptors based on a COSMO-RS (short for conductor-like screening model for real solvents) σ-profiles for enhanced drug screening enabled by machine learning (ML). The descriptors' performance is hereby illustrated for nucleotide analogue drugs that inhibit the ribonucleic acid-dependent ribonucleic acid polymerase, key to viral transcription and genome replication. The COSMO-RS-based descriptors account for both chemical reactivity and structure, and are more effective for ML-based screening than fingerprints based on molecular structure and simple physical/chemical properties. The descriptors are evaluated using principal component analysis, an unsupervised ML technique. Our results correlate with the active monophosphate forms of the leading drug remdesivir and the prospective drug EIDD-2801 with nucleotides, followed by other promising drugs, and are superior to those from molecular structure-based descriptors and molecular docking. The COSMO-RS-based descriptors could help accelerate drug discovery for the treatment of emerging diseases.

Gusarov Sergey, Stoyanov Stanislav R

2020-Oct-26

General General

Bots and online hate during the COVID-19 pandemic: case studies in the United States and the Philippines.

In Journal of computational social science

Online hate speech represents a serious problem exacerbated by the ongoing COVID-19 pandemic. Although often anchored in real-world social divisions, hate speech in cyberspace may also be fueled inorganically by inauthentic actors like social bots. This work presents and employs a methodological pipeline for assessing the links between hate speech and bot-driven activity through the lens of social cybersecurity. Using a combination of machine learning and network science tools, we empirically characterize Twitter conversations about the pandemic in the United States and the Philippines. Our integrated analysis reveals idiosyncratic relationships between bots and hate speech across datasets, highlighting different network dynamics of racially charged toxicity in the US and political conflicts in the Philippines. Most crucially, we discover that bot activity is linked to higher hate in both countries, especially in communities which are denser and more isolated from others. We discuss several insights for probing issues of online hate speech and coordinated disinformation, especially through a global approach to computational social science.

Uyheng Joshua, Carley Kathleen M

2020-Oct-20

Bots, COVID-19, Hate speech, Information maneuvers, Social cybersecurity

General General

Ensemble learning model for diagnosing COVID-19 from routine blood tests.

In Informatics in medicine unlocked

Background and objectives : The pandemic of novel coronavirus disease 2019 (COVID-19) has severely impacted human society with a massive death toll worldwide. There is an urgent need for early and reliable screening of COVID-19 patients to provide better and timely patient care and to combat the spread of the disease. In this context, recent studies have reported some key advantages of using routine blood tests for initial screening of COVID-19 patients. In this article, first we present a review of the emerging techniques for COVID-19 diagnosis using routine laboratory and/or clinical data. Then, we propose ERLX which is an ensemble learning model for COVID-19 diagnosis from routine blood tests.

Method : The proposed model uses three well-known diverse classifiers, extra trees, random forest and logistic regression, which have different architectures and learning characteristics at the first level, and then combines their predictions by using a second level extreme gradient boosting (XGBoost) classifier to achieve a better performance. For data preparation, the proposed methodology employs a KNNImputer algorithm to handle null values in the dataset, isolation forest (iForest) to remove outlier data, and a synthetic minority oversampling technique (SMOTE) to balance data distribution. For model interpretability, features importance are reported by using the SHapley Additive exPlanations (SHAP) technique.

Results : The proposed model was trained and evaluated by using a publicly available data set from Albert Einstein Hospital in Brazil, which consisted of 5644 data samples with 559 confirmed COVID-19 cases. The ensemble model achieved outstanding performance with an overall accuracy of 99.88% [95% CI: 99.6-100], AUC of 99.38% [95% CI: 97.5-100], a sensitivity of 98.72% [95% CI: 94.6-100] and a specificity of 99.99% [95% CI: 99.99-100].

Discussion : The proposed model revealed better performance when compared against existing state-of-the-art studies (Banerjee et al., 2020; de Freitas Barbosa et al., 2020; de Moraes Batista et al., 2020; Soares et al., 2020) [3,22,56,71] for the same set of features employed by them. As compared to the best performing Bayes Net model (de Freitas Barbosa et al., 2020) [22] average accuracy of 95.159%, ERLX achieved an average accuracy of 99.94%. In comparison with AUC of 85% reported by the SVM model (de Moraes Batista et al., 2020) [56], ERLX obtained AUC of 99.77% in addition to improvements in sensitivity, and specificity. As compared with ER-COV model (Soares et al., 2020) [71] average sensitivity of 70.25% and specificity of 85.98%, ERLX model achieved sensitivity of 99.47% and specificity of 99.99%. The ERLX model obtained a considerably higher score as compared with ANN model (Banerjee et al., 2020) [3] in all performance metrics. Therefore, the model presented is robust and can be deployed for reliable early and rapid screening of COVID-19 patients.

AlJame Maryam, Ahmad Imtiaz, Imtiaz Ayyub, Mohammed Ameer

2020

COVID-19, Diagnostic model, Ensemble, Machine learning, Routine blood tests

General General

Phytochemicals from Selective Plants Have Promising Potential against SARS-CoV-2: Investigation and Corroboration through Molecular Docking, MD Simulations, and Quantum Computations.

In BioMed research international ; h5-index 102.0

Coronaviruses have been reported previously due to their association with the severe acute respiratory syndrome (SARS). After SARS, these viruses were known to be causing Middle East respiratory syndrome (MERS) and caused 35% evanescence amid victims pursuing remedial care. Nowadays, beta coronaviruses, members of Coronaviridae, family order Nidovirales, have become subjects of great importance due to their latest pandemic originating from Wuhan, China. The virus named as human-SARS-like coronavirus-2 contains four structural as well as sixteen nonstructural proteins encoded by single-stranded ribonucleic acid of positive polarity. As there is no vaccine available to treat the infection caused by these viruses, there is a dire need for taking necessary steps against this virus. Herein, we have targeted two nonstructural proteins of SARS-CoV-2, namely, methyltransferase (nsp16) and helicase (nsp13), respectively, due to their substantial activity in viral pathogenesis. A total of 2035 compounds were analyzed for their pharmacokinetics and pharmacological properties. The screened 108 compounds were docked against both targeted proteins and were compared with previously reported known compounds. Compounds with high binding affinity were analyzed for their reactivity through DFT analysis, and binding was analyzed using molecular dynamics simulations. Through the analyses performed in this study, it is concluded that EryvarinM, Silydianin, Osajin, and Raddeanine can be considered potential inhibitors for MTase, while TomentodiplaconeB, Osajin, Sesquiterpene Glycoside, Rhamnetin, and Silydianin for helicase after these compounds are validated thoroughly using in vitro and in vivo protocols.

Kousar Kafila, Majeed Arshia, Yasmin Farkhanda, Hussain Waqar, Rasool Nouman

2020

General General

Individual-Level Fatality Prediction of COVID-19 Patients Using AI Methods.

In Frontiers in public health

The global covid-19 pandemic puts great pressure on medical resources worldwide and leads healthcare professionals to question which individuals are in imminent need of care. With appropriate data of each patient, hospitals can heuristically predict whether or not a patient requires immediate care. We adopted a deep learning model to predict fatality of individuals tested positive given the patient's underlying health conditions, age, sex, and other factors. As the allocation of resources toward a vulnerable patient could mean the difference between life and death, a fatality prediction model serves as a valuable tool to healthcare workers in prioritizing resources and hospital space. The models adopted were evaluated and refined using the metrics of accuracy, specificity, and sensitivity. After data preprocessing and training, our model is able to predict whether a covid-19 confirmed patient is likely to be dead or not, given their information and disposition. The metrics between the different models are compared. Results indicate that the deep learning model outperforms other machine learning models to solve this rare event prediction problem.

Li Yun, Horowitz Melanie Alfonzo, Liu Jiakang, Chew Aaron, Lan Hai, Liu Qian, Sha Dexuan, Yang Chaowei

2020

COVID-19, deep learning, fatality prediction, machine learning, pandemic, rare event

General General

A deep learning model and machine learning methods for the classification of potential coronavirus treatments on a single human cell.

In Journal of nanoparticle research : an interdisciplinary forum for nanoscale science and technology

Coronavirus pandemic is burdening healthcare systems around the world to the full capacity they can accommodate. There is an overwhelming need to find a treatment for this virus as early as possible. Computer algorithms and deep learning can participate positively by finding a potential treatment for SARS-CoV-2. In this paper, a deep learning model and machine learning methods for the classification of potential coronavirus treatments on a single human cell will be presented. The dataset selected in this work is a subset of the publicly online datasets available on RxRx.ai. The objective of this research is to automatically classify a single human cell according to the treatment type and the treatment concentration level. A DCNN model and a methodology are proposed throughout this work. The methodical idea is to convert the numerical features from the original dataset to the image domain and then fed them up into a DCNN model. The proposed DCNN model consists of three convolutional layers, three ReLU layers, three pooling layers, and two fully connected layers. The experimental results show that the proposed DCNN model for treatment classification (32 classes) achieved 98.05% in testing accuracy if it is compared with classical machine learning such as support vector machine, decision tree, and ensemble. In treatment concentration level prediction, the classical machine learning (ensemble) algorithm achieved 98.5% in testing accuracy while the proposed DCNN model achieved 98.2%. The performance metrics strengthen the obtained results from the conducted experiments for the accuracy of treatment classification and treatment concentration level prediction.

Khalifa Nour Eldeen M, Taha Mohamed Hamed N, Manogaran Gunasekaran, Loey Mohamed

2020

COVID-19, Classical machine learning, Deep transfer learning

General General

Triple-view Convolutional Neural Networks for COVID-19 Diagnosis with Chest X-ray

ArXiv Preprint

The Coronavirus Disease 2019 (COVID-19) is affecting increasingly large number of people worldwide, posing significant stress to the health care systems. Early and accurate diagnosis of COVID-19 is critical in screening of infected patients and breaking the person-to-person transmission. Chest X-ray (CXR) based computer-aided diagnosis of COVID-19 using deep learning becomes a promising solution to this end. However, the diverse and various radiographic features of COVID-19 make it challenging, especially when considering each CXR scan typically only generates one single image. Data scarcity is another issue since collecting large-scale medical CXR data set could be difficult at present. Therefore, how to extract more informative and relevant features from the limited samples available becomes essential. To address these issues, unlike traditional methods processing each CXR image from a single view, this paper proposes triple-view convolutional neural networks for COVID-19 diagnosis with CXR images. Specifically, the proposed networks extract individual features from three views of each CXR image, i.e., the left lung view, the right lung view and the overall view, in three streams and then integrate them for joint diagnosis. The proposed network structure respects the anatomical structure of human lungs and is well aligned with clinical diagnosis of COVID-19 in practice. In addition, the labeling of the views does not require experts' domain knowledge, which is needed by many existing methods. The experimental results show that the proposed method achieves state-of-the-art performance, especially in the more challenging three class classification task, and admits wide generality and high flexibility.

Jianjia Zhang

2020-10-27

General General

CGNet: A graph-knowledge embedded convolutional neural network for detection of pneumonia.

In Information processing & management

Pneumonia is a global disease that causes high children mortality. The situation has even been worsening by the outbreak of the new coronavirus named COVID-19, which has killed more than 983,907 so far. People infected by the virus would show symptoms like fever and coughing as well as pneumonia as the infection progresses. Timely detection is a public consensus achieved that would benefit possible treatments and therefore contain the spread of COVID-19. X-ray, an expedient imaging technique, has been widely used for the detection of pneumonia caused by COVID-19 and some other virus. To facilitate the process of diagnosis of pneumonia, we developed a deep learning framework for a binary classification task that classifies chest X-ray images into normal and pneumonia based on our proposed CGNet. In our CGNet, there are three components including feature extraction, graph-based feature reconstruction and classification. We first use the transfer learning technique to train the state-of-the-art convolutional neural networks (CNNs) for binary classification while the trained CNNs are used to produce features for the following two components. Then, by deploying graph-based feature reconstruction, we, therefore, combine features through the graph to reconstruct features. Finally, a shallow neural network named GNet, a one layer graph neural network, which takes the combined features as the input, classifies chest X-ray images into normal and pneumonia. Our model achieved the best accuracy at 0.9872, sensitivity at 1 and specificity at 0.9795 on a public pneumonia dataset that includes 5,856 chest X-ray images. To evaluate the performance of our proposed method on detection of pneumonia caused by COVID-19, we also tested the proposed method on a public COVID-19 CT dataset, where we achieved the highest performance at the accuracy of 0.99, specificity at 1 and sensitivity at 0.98, respectively.

Yu Xiang, Wang Shui-Hua, Zhang Yu-Dong

2021-Jan

COVID-19, Chest X-ray images, Feature reconstruction, Graph, Transfer learning

Radiology Radiology

On the diminishing return of labeling clinical reports

ArXiv Preprint

Ample evidence suggests that better machine learning models may be steadily obtained by training on increasingly larger datasets on natural language processing (NLP) problems from non-medical domains. Whether the same holds true for medical NLP has by far not been thoroughly investigated. This work shows that this is indeed not always the case. We reveal the somehow counter-intuitive observation that performant medical NLP models may be obtained with small amount of labeled data, quite the opposite to the common belief, most likely due to the domain specificity of the problem. We show quantitatively the effect of training data size on a fixed test set composed of two of the largest public chest x-ray radiology report datasets on the task of abnormality classification. The trained models not only make use of the training data efficiently, but also outperform the current state-of-the-art rule-based systems by a significant margin.

Jean-Baptiste Lamare, Tobi Olatunji, Li Yao

2020-10-27

Public Health Public Health

Impact of systematic factors on the outbreak outcome of novel coronavirus disease (COVID-19) in China.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : The novel coronavirus disease (COVID-19) spread world widely and caused a new pandemic. The Chinese government took strong intervention measures in the early stage of the epidemic, including strict travel-ban and social distancing policies. Prioritizing contribution of different factors is important for precise prevention and control of infectious diseases. Here, we proposed a novel framework for resolving this question and applied it to data from China.

OBJECTIVE : To systematically reveal factors and their contribution to the control of COVID-19 in China, both at the national and city level.

METHODS : Daily COVID-19 cases and related multidimensional data, including travel-related, medical, socioeconomic, environmental, and Influenza-like illness factors, from 343 cities in China were collected. Correlation analysis and interpretable machine learning algorithm were used to explore the quantitative contribution of different factors on either new cases or growth rate of COVID-19 for the epidemic period from January 17 to February 29, 2020.

RESULTS : Many factors considered in this study are correlated to the spread of COVID-19 in China. Overall, travel-related population movements are the main contributing factors for both new cases and growth rate of COVID-19 in China, and the contributions are as high as 77% and 41%, respectively. There is a clear lag effect for travel related factors (previous vs current week: 45% vs 32% for new cases, and 21% vs 20% for growth rate). The contribution for travel from non-Wuhan regions is non-ignorable (12% and 26% for new cases and growth rate), especially for the growth rate (rank first as a single factor). City flow, a measure of control strength, contributes 16% and 7% to new cases and growth rate. Socioeconomic factors also play important roles in the growth rate of COVID-19 in China with 28% contribution. Other factors, including medical, environmental and influenza-like illness ones, also contribute to new cases and growth rate of COVID-19 in China. Based on the analysis for individual city, compared to Beijing, population flow from Wuhan and internal flow within the city are driving factors for more new cases in Wenzhou, while for Chongqing the contribution is mainly from population flow from Hubei beyond Wuhan. The higher growth rate for Wenzhou is driven by its population-related factors.

CONCLUSIONS : Many factors contributed to the outbreak outcome of COVID-19 in China. Travel-related population movement was the main driving factor with strong lag effect, and population movement from non-Wuhan regions is a non-ignorable hidden variable. For the growth rate, more factors were involved, including the socioeconomic ones that contributed more than a quarter. Those differential effects for various factors, along with city-level specificity, emphasize the importance of targeted and precise strategies for outbreak control of current COVID-19 crisis and other future infectious diseases.

CLINICALTRIAL :

Cao Zicheng, Tang Feng, Chen Cai, Zhang Chi, Guo Yichen, Lin Ruizhen, Huang Zhihong, Teng Yi, Xie Ting, Xu Yutian, Song Yanxin, Wu Feng, Dong Peipei, Luo Ganfeng, Jiang Yawen, Zou Huachun, Chen Yao-Qing, Sun Litao, Shu Yuelong, Du Xiangjun

2020-Oct-22

General General

Identifying novel factors associated with COVID-19 transmission and fatality using the machine learning approach.

In The Science of the total environment

The COVID-19 virus has infected more than 38 million people and resulted in more than one million deaths worldwide as of October 14, 2020. By using the logistic regression model, we identified novel critical factors associated with COVID19 cases, death, and case fatality rates in 154 countries and in the 50 U.S. states. Among numerous factors associated with COVID-19 risk, economic inequality enhanced the risk of COVID-19 transmission. The per capita hospital beds correlated negatively with COVID-19 deaths. Blood types B and AB were protective factors for COVID-19 risk, while blood type A was a risk factor. The prevalence of HIV and influenza and pneumonia was associated with reduced COVID-19 risk. Increased intake of vegetables, edible oil, protein, vitamin D, and vitamin K was associated with reduced COVID-19 risk, while increased intake of alcohol was associated with increased COVID-19 risk. Other factors included age, sex, temperature, humidity, social distancing, smoking, health investment, urbanization level, and race. High temperature is a more compelling factor mitigating COVID-19 transmission than low temperature. Our comprehensive identification of the factors affecting COVID-19 transmission and fatality may provide new insights into the COVID-19 pandemic and advise effective strategies for preventing and migrating COVID-19 spread.

Li Mengyuan, Zhang Zhilan, Cao Wenxiu, Liu Yijing, Du Beibei, Chen Canping, Liu Qian, Uddin Md Nazim, Jiang Shanmei, Chen Cai, Zhang Yue, Wang Xiaosheng

2020-Oct-13

COVID-19 fatality, COVID-19 transmission, Machine learning, Protective factor, Risk factor

Internal Medicine Internal Medicine

Impact of obesity, fasting plasma glucose level, blood pressure, and renal function on the severity of COVID-19: a matter of sexual dimorphism?

In Diabetes research and clinical practice ; h5-index 50.0

AIMS : This study aimed to assess whether body mass index (BMI), fasting plasma glucose (FPG) levels, blood pressure (BP), and kidney function were associated with the risk of severe disease or death in patients with COVID-19.

METHODS : Data on candidate risk factors were extracted from patients' last checkup records. Propensity score-matched cohorts were constructed, and logistic regression models were used to adjust for age, sex, and comorbidities. The primary outcome was death or severe COVID-19, defined as requiring supplementary oxygen or higher ventilatory support.

RESULTS : Among 7,649 patients with confirmed COVID-19, 2,231 (29.2%) received checkups and Severe COVID-19 occurred in 307 patients (13.8%). A BMI of 25.0-29.9 was associated with the outcome among women (aOR, 2.29; 95% CI,: 1.41-3.73) and patients aged 50-69 years (aOR, 1.64; 95% CI, 1.06-2.54). An FPG ≥126 mg/dL was associated with poor outcomes in women (aOR, 2.06; 95% CI, 1.13-3.77) but not in men. Similarly, estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2 was a risk factor in women (aOR, 3.46; 95% CI, 1.71-7.01) and patients aged <70 years.

CONCLUSIONS : The effects of BMI, FPG, and eGFR on outcomes associated with COVID-19 were prominent in women but not in men.

Huh Kyungmin, Lee Rugyeom, Ji Wonjun, Kang Minsun, Cheol Hwang In, Ho Lee Dae, Jung Jaehun

2020-Oct-20

COVID-19, diabetes, dyslipidemia, hypertension, obesity, outcome

General General

Factors affecting COVID-19 infected and death rates inform lockdown-related policymaking.

In PloS one ; h5-index 176.0

BACKGROUND : After claiming nearly five hundred thousand lives globally, the COVID-19 pandemic is showing no signs of slowing down. While the UK, USA, Brazil and parts of Asia are bracing themselves for the second wave-or the extension of the first wave-it is imperative to identify the primary social, economic, environmental, demographic, ethnic, cultural and health factors contributing towards COVID-19 infection and mortality numbers to facilitate mitigation and control measures.

METHODS : We process several open-access datasets on US states to create an integrated dataset of potential factors leading to the pandemic spread. We then apply several supervised machine learning approaches to reach a consensus as well as rank the key factors. We carry out regression analysis to pinpoint the key pre-lockdown factors that affect post-lockdown infection and mortality, informing future lockdown-related policy making.

FINDINGS : Population density, testing numbers and airport traffic emerge as the most discriminatory factors, followed by higher age groups (above 40 and specifically 60+). Post-lockdown infected and death rates are highly influenced by their pre-lockdown counterparts, followed by population density and airport traffic. While healthcare index seems uncorrelated with mortality rate, principal component analysis on the key features show two groups: states (1) forming early epicenters and (2) experiencing strong second wave or peaking late in rate of infection and death. Finally, a small case study on New York City shows that days-to-peak for infection of neighboring boroughs correlate better with inter-zone mobility than the inter-zone distance.

INTERPRETATION : States forming the early hotspots are regions with high airport or road traffic resulting in human interaction. US states with high population density and testing tend to exhibit consistently high infected and death numbers. Mortality rate seems to be driven by individual physiology, preexisting condition, age etc., rather than gender, healthcare facility or ethnic predisposition. Finally, policymaking on the timing of lockdowns should primarily consider the pre-lockdown infected numbers along with population density and airport traffic.

Roy Satyaki, Ghosh Preetam

2020

General General

Implementation of Convolutional Neural Network Approach for COVID-19 Disease Detection.

In Physiological genomics

In this paper two novel, powerful and robust Convolutional Neural Network (CNN) architectures are designed and proposed for two different classification tasks using publicly available datasets. The first architecture is able to decide whether a given chest X-ray image of a patient contains COVID-19 or not with 98.92% average accuracy. The second CNN architecture is able to divide a given chest X-ray image of a patient into three classes (COVID-19 vs. Normal vs. Pneumonia) with 98.27% average accuracy. The hyper-parameters of the both CNN models are automatically determined using Grid Search. Experimental results on large clinical datasets show the effectiveness of the proposed architectures and demonstrate that the proposed algorithms can overcome disadvantages mentioned above. Moreover, the proposed CNN models are fully-automatic in terms of not requiring the extraction of diseased tissue; which is a great improvement of available automatic methods in the literature. To the best of author's knowledge, this study is the first study to detect COVID-19 disease from given chest X-ray images, using CNN whose hyper parameters are automatically determined by the Grid Search. Another important contribution of this study is that it is the first CNN based COVID-19 chest X-ray image classification study which uses the largest possible clinical dataset. A total of 1524 COVID-19, 1527 pneumonia and 1524 normal X-ray images are collected. It is aimed to collect the largest number of COVID-19 X-ray images that exist in the literature until the writing of this research paper.

Irmak Emrah

2020-Oct-23

COVID-19 detection, Convolutional neural network, Deep learning, Image classification, Medical Image

Radiology Radiology

Decoding COVID-19 pneumonia: comparison of deep learning and radiomics CT image signatures.

In European journal of nuclear medicine and molecular imaging ; h5-index 66.0

PURPOSE : High-dimensional image features that underlie COVID-19 pneumonia remain opaque. We aim to compare feature engineering and deep learning methods to gain insights into the image features that drive CT-based for COVID-19 pneumonia prediction, and uncover CT image features significant for COVID-19 pneumonia from deep learning and radiomics framework.

METHODS : A total of 266 patients with COVID-19 and other viral pneumonia with clinical symptoms and CT signs similar to that of COVID-19 during the outbreak were retrospectively collected from three hospitals in China and the USA. All the pneumonia lesions on CT images were manually delineated by four radiologists. One hundred eighty-four patients (n = 93 COVID-19 positive; n = 91 COVID-19 negative; 24,216 pneumonia lesions from 12,001 CT image slices) from two hospitals from China served as discovery cohort for model development. Thirty-two patients (17 COVID-19 positive, 15 COVID-19 negative; 7883 pneumonia lesions from 3799 CT image slices) from a US hospital served as external validation cohort. A bi-directional adversarial network-based framework and PyRadiomics package were used to extract deep learning and radiomics features, respectively. Linear and Lasso classifiers were used to develop models predictive of COVID-19 versus non-COVID-19 viral pneumonia.

RESULTS : 120-dimensional deep learning image features and 120-dimensional radiomics features were extracted. Linear and Lasso classifiers identified 32 high-dimensional deep learning image features and 4 radiomics features associated with COVID-19 pneumonia diagnosis (P < 0.0001). Both models achieved sensitivity > 73% and specificity > 75% on external validation cohort with slight superior performance for radiomics Lasso classifier. Human expert diagnostic performance improved (increase by 16.5% and 11.6% in sensitivity and specificity, respectively) when using a combined deep learning-radiomics model.

CONCLUSIONS : We uncover specific deep learning and radiomics features to add insight into interpretability of machine learning algorithms and compare deep learning and radiomics models for COVID-19 pneumonia that might serve to augment human diagnostic performance.

Wang Hongmei, Wang Lu, Lee Edward H, Zheng Jimmy, Zhang Wei, Halabi Safwan, Liu Chunlei, Deng Kexue, Song Jiangdian, Yeom Kristen W

2020-Oct-23

AI interpretability, CT chest, Coronavirus disease 2019 pneumonia, Explainable AI, Machine learning

Radiology Radiology

Integrative analysis for COVID-19 patient outcome prediction.

In Medical image analysis

While image analysis of chest computed tomography (CT) for COVID-19 diagnosis has been intensively studied, little work has been performed for image-based patient outcome prediction. Management of high-risk patients with early intervention is a key to lower the fatality rate of COVID-19 pneumonia, as a majority of patients recover naturally. Therefore, an accurate prediction of disease progression with baseline imaging at the time of the initial presentation can help in patient management. In lieu of only size and volume information of pulmonary abnormalities and features through deep learning based image segmentation, here we combine radiomics of lung opacities and non-imaging features from demographic data, vital signs, and laboratory findings to predict need for intensive care unit (ICU) admission. To our knowledge, this is the first study that uses holistic information of a patient including both imaging and non-imaging data for outcome prediction. The proposed methods were thoroughly evaluated on datasets separately collected from three hospitals, one in the United States, one in Iran, and another in Italy, with a total 295 patients with reverse transcription polymerase chain reaction (RT-PCR) assay positive COVID-19 pneumonia. Our experimental results demonstrate that adding non-imaging features can significantly improve the performance of prediction to achieve AUC up to 0.884 and sensitivity as high as 96.1%, which can be valuable to provide clinical decision support in managing COVID-19 patients. Our methods may also be applied to other lung diseases including but not limited to community acquired pneumonia. The source code of our work is available at https://github.com/DIAL-RPI/COVID19-ICUPrediction.

Chao Hanqing, Fang Xi, Zhang Jiajin, Homayounieh Fatemeh, Arru Chiara D, Digumarthy Subba R, Babaei Rosa, Mobin Hadi K, Mohseni Iman, Saba Luca, Carriero Alessandro, Falaschi Zeno, Pasche Alessio, Wang Ge, Kalra Mannudeep K, Yan Pingkun

2020-Oct-13

Artificial intelligence, COVID-19, Chest CT, Outcome prediction

General General

CLINICAL CHARACTERISTICS AND PROGNOSTIC FACTORS FOR ICU ADMISSION OF PATIENTS WITH COVID-19: A RETROSPECTIVE STUDY USING MACHINE LEARNING AND NATURAL LANGUAGE PROCESSING.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : There remain many unknowns regarding the onset and clinical course of the ongoing COVID-19 pandemic. We used a combination of classic epidemiological methods, natural language processing (NLP), and machine learning (for predictive modeling), to analyse the electronic health records (EHRs) of patients with COVID-19.

OBJECTIVE : Our primary objectives are to describe the clinical characteristics and determine the factors that predict ICU admission of patients with COVID-19. These are aimed at better understanding the real-world epidemiology of the disease using a well-defined population.

METHODS : We explored the unstructured free text in the EHRs within the SESCAM Healthcare Network (Castilla La-Mancha, Spain) from the entire population with available EHRs (1,364,924 patients) from January 1st to March 29th, 2020. We extracted related clinical information upon diagnosis, progression and outcome for all COVID-19 cases.

RESULTS : A total of 10,504 patients with a clinical or PCR-confirmed diagnosis of COVID-19 were identified, 52.5% males, with age of 58.2±19.7 years. Upon admission, the most common symptoms were cough, fever, and dyspnoea, but all in less than half of cases. Overall, 6% of hospitalized patients required ICU admission. Using a machine-learning, data-driven algorithm we identified that a combination of age, fever, and tachypnoea was the most parsimonious predictor of ICU admission: those younger than 56 years, without tachypnoea, and temperature <39ºC, (or >39ºC without respiratory crackles), were free of ICU admission. On the contrary, COVID-19 patients aged 40 to 79 years were likely to be admitted to the ICU if they had tachypnoea and delayed their visit to the ER after being seen in primary care.

CONCLUSIONS : Our results show that a combination of easily obtainable clinical variables (age, fever, and tachypnoea with/without respiratory crackles) predicts which COVID-19 patients require ICU admission.

CLINICALTRIAL :

Izquierdo Jose Luis, Ancochea Julio, Soriano Joan B

2020-Oct-20

General General

Telemonitoring Parkinson's disease using machine learning by combining tremor and voice analysis.

In Brain informatics

BACKGROUND : With the growing number of the aged population, the number of Parkinson's disease (PD) affected people is also mounting. Unfortunately, due to insufficient resources and awareness in underdeveloped countries, proper and timely PD detection is highly challenged. Besides, all PD patients' symptoms are neither the same nor they all become pronounced at the same stage of the illness. Therefore, this work aims to combine more than one symptom (rest tremor and voice degradation) by collecting data remotely using smartphones and detect PD with the help of a cloud-based machine learning system for telemonitoring the PD patients in the developing countries.

METHOD : This proposed system receives rest tremor and vowel phonation data acquired by smartphones with built-in accelerometer and voice recorder sensors. The data are primarily collected from diagnosed PD patients and healthy people for building and optimizing machine learning models that exhibit higher performance. After that, data from newly suspected PD patients are collected, and the trained algorithms are evaluated to detect PD. Based on the majority-vote from those algorithms, PD-detected patients are connected with a nearby neurologist for consultation. Upon receiving patients' feedback after being diagnosed by the neurologist, the system may update the model by retraining using the latest data. Also, the system requests the detected patients periodically to upload new data to track their disease progress.

RESULT : The highest accuracy in PD detection using offline data was [Formula: see text] from voice data and [Formula: see text] from tremor data when used separately. In both cases, k-nearest neighbors (kNN) gave the highest accuracy over support vector machine (SVM) and naive Bayes (NB). The application of maximum relevance minimum redundancy (MRMR) feature selection method showed that by selecting different feature sets based on the patient's gender, we could improve the detection accuracy. This study's novelty is the application of ensemble averaging on the combined decisions generated from the analysis of voice and tremor data. The average accuracy of PD detection becomes [Formula: see text] when ensemble averaging was performed on majority-vote from kNN, SVM, and NB.

CONCLUSION : The proposed system can detect PD using a cloud-based system for computation, data preserving, and regular monitoring of voice and tremor samples captured by smartphones. Thus, this system can be a solution for healthcare authorities to ensure the older population's accessibility to a better medical diagnosis system in the developing countries, especially in the pandemic situation like COVID-19, when in-person monitoring is minimal.

Sajal Md Sakibur Rahman, Ehsan Md Tanvir, Vaidyanathan Ravi, Wang Shouyan, Aziz Tipu, Mamun Khondaker Abdullah Al

2020-Oct-22

Accelerometer, Machine-learning, Parkinson’s, Telemonitoring, Tremor

General General

Modelling of COVID-19 Morbidity in Russia.

In Studies in health technology and informatics ; h5-index 23.0

The outbreak of COVID-19 has led to a crucial change in ordinary healthcare approaches. In comparison with emergencies re-allocation of resources for a long period of time is required and the peak utilization of the resources is also hard to predict. Furthermore, the epidemic models do not provide reliable information of the development of the pandemic's development, so it creates a high load on the healthcare systems with unforeseen duration. To predict morbidity of the novel COVID-19, we used records covering the time period from 01-03-2020 to 25-05-2020 and include sophisticated information of the morbidity in Russia. Total of 45238 patients were analyzed. The predictive model was developed as a combination of Holt and Holt-Winter models with Gradient boosting Regression. As we can see from the table 2, the models demonstrated a very good performance on the test data set. The forecast is quite reliable, however, due to the many uncertainties, only a real-world data can prove the correctness of the forecast.

Kopanitsa Georgy, Metsker Oleg, Yakovlev Alexey, Fedorenko Alexey, Zvartau Nadezhda

2020-Sep-04

COVID-19, Russia, forecast, machine learning, morbidity

General General

Survival Analysis of COVID-19 Patients in Russia Using Machine Learning.

In Studies in health technology and informatics ; h5-index 23.0

The current pandemic can likely have several waves and will require a major effort to save lives and provide optimal treatment. The efficient clinical resource planning and efficient treatment require identification of risk groups and specific clinical features of the patients. In this study we develop analyze mortality for COVID19 patients in Russia. We identify comorbidities and risk factors for different groups of patients including cardiovascular diseases and therapy. In the study we used a Russian national COVID registry, that provides sophisticated information about all the COVID-19 patients in Russia. To analyze Features importance for the mortality we have calculated Shapley values for the "mortality" class and ANN hidden layer coefficients for patient lifetime. We calculated the distribution of days spent in hospital before death to show how many days a patient occupies a bed depending on the age and the severity of the disease to allow optimal resource planning and enable age-based risk assessment. Predictors of the days spent in hospital were calculated using Pearson correlation coefficient. Decisions trees were developed to classify the patients into the groups and reveal the lethality factors.

Metsker Oleg, Kopanitsa Georgy, Yakovlev Alexey, Veronika Karlina, Zvartau Nadezhda

2020-Sep-04

COVID-19, Russia, mortality, risk factors

General General

A Review of Deep Learning Methods for Irregularly Sampled Medical Time Series Data

ArXiv Preprint

Irregularly sampled time series (ISTS) data has irregular temporal intervals between observations and different sampling rates between sequences. ISTS commonly appears in healthcare, economics, and geoscience. Especially in the medical environment, the widely used Electronic Health Records (EHRs) have abundant typical irregularly sampled medical time series (ISMTS) data. Developing deep learning methods on EHRs data is critical for personalized treatment, precise diagnosis and medical management. However, it is challenging to directly use deep learning models for ISMTS data. On the one hand, ISMTS data has the intra-series and inter-series relations. Both the local and global structures should be considered. On the other hand, methods should consider the trade-off between task accuracy and model complexity and remain generality and interpretability. So far, many existing works have tried to solve the above problems and have achieved good results. In this paper, we review these deep learning methods from the perspectives of technology and task. Under the technology-driven perspective, we summarize them into two categories - missing data-based methods and raw data-based methods. Under the task-driven perspective, we also summarize them into two categories - data imputation-oriented and downstream task-oriented. For each of them, we point out their advantages and disadvantages. Moreover, we implement some representative methods and compare them on four medical datasets with two tasks. Finally, we discuss the challenges and opportunities in this area.

Chenxi Sun, Hongda Shen, Moxian Song, Hongyan Li

2020-10-23

Public Health Public Health

Predicting Perceived Stress Related to the Covid-19 Outbreak through Stable Psychological Traits and Machine Learning Models.

In Journal of clinical medicine

The global SARS-CoV-2 outbreak and subsequent lockdown had a significant impact on people's daily lives, with strong implications for stress levels due to the threat of contagion and restrictions to freedom. Given the link between high stress levels and adverse physical and mental consequences, the COVID-19 pandemic is certainly a global public health issue. In the present study, we assessed the effect of the pandemic on stress levels in N = 2053 Italian adults, and characterized more vulnerable individuals on the basis of sociodemographic features and stable psychological traits. A set of 18 psycho-social variables, generalized regressions, and predictive machine learning approaches were leveraged. We identified higher levels of perceived stress in the study sample relative to Italian normative values. Higher levels of distress were found in women, participants with lower income, and participants living with others. Higher rates of emotional stability and self-control, as well as a positive coping style and internal locus of control, emerged as protective factors. Predictive learning models identified participants with high perceived stress, with a sensitivity greater than 76%. The results suggest a characterization of people who are more vulnerable to experiencing high levels of stress during the COVID-19 pandemic. This characterization may contribute to early and targeted intervention strategies.

Flesia Luca, Monaro Merylin, Mazza Cristina, Fietta Valentina, Colicino Elena, Segatto Barbara, Roma Paolo

2020-Oct-19

COVID-19, coping, mental health, personality, public health, stress

General General

Tele-operative Robotic Lung Ultrasound Scanning Platform for Triage of COVID-19 Patients

ArXiv Preprint

Novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a pandemic of epic proportions and a global response to prepare health systems worldwide is of utmost importance. In addition to its cost-effectiveness in a resources-limited setting, lung ultrasound (LUS) has emerged as a rapid noninvasive imaging tool for the diagnosis of COVID-19 infected patients. Concerns surrounding LUS include the disparity of infected patients and healthcare providers, relatively small number of physicians and sonographers capable of performing LUS, and most importantly, the requirement for substantial physical contact between the patient and operator, increasing the risk of transmission. Mitigation of the spread of the virus is of paramount importance. A 2-dimensional (2D) tele-operative robotic platform capable of performing LUS in for COVID-19 infected patients may be of significant benefit. The authors address the aforementioned issues surrounding the use of LUS in the application of COVID- 19 infected patients. In addition, first time application, feasibility and safety were validated in three healthy subjects, along with 2D image optimization and comparison for overall accuracy. Preliminary results demonstrate that the proposed platform allows for successful acquisition and application of LUS in humans.

Ryosuke Tsumura, John W. Hardin, Keshav Bimbraw, Olushola S. Odusanya, Yihao Zheng, Jeffrey C. Hill, Beatrice Hoffmann, Winston Soboyejo, Haichong K. Zhang

2020-10-23

Radiology Radiology

An improved multivariate model that distinguishes COVID-19 from seasonal flu and other respiratory diseases.

In Aging ; h5-index 49.0

COVID-19 shared many symptoms with seasonal flu, and community-acquired pneumonia (CAP) Since the responses to COVID-19 are dramatically different, this multicenter study aimed to develop and validate a multivariate model to accurately discriminate COVID-19 from influenza and CAP. Three independent cohorts from two hospitals (50 in discovery and internal validation sets, and 55 in the external validation cohorts) were included, and 12 variables such as symptoms, blood tests, first reverse transcription-polymerase chain reaction (RT-PCR) results, and chest CT images were collected. An integrated multi-feature model (RT-PCR, CT features, and blood lymphocyte percentage) established with random forest algorism showed the diagnostic accuracy of 92.0% (95% CI: 73.9 - 99.1) in the training set, and 96. 6% (95% CI: 79.6 - 99.9) in the internal validation cohort. The model also performed well in the external validation cohort with an area under the receiver operating characteristic curve of 0.93 (95% CI: 0.79 - 1.00), an F1 score of 0.80, and a Matthews correlation coefficient (MCC) of 0.76. In conclusion, the developed multivariate model based on machine learning techniques could be an efficient tool for COVID-19 screening in nonendemic regions with a high rate of influenza and CAP in the post-COVID-19 era.

Guo Xing, Li Yanrong, Li Hua, Li Xueqin, Chang Xu, Bai Xuemei, Song Zhanghong, Li Junfeng, Li Kefeng

2020-Oct-21

COVID-19, diagnostic model, influenza, multi-feature, random forest

General General

Risk estimation of SARS-CoV-2 transmission from bluetooth low energy measurements.

In NPJ digital medicine

Digital contact tracing approaches based on Bluetooth low energy (BLE) have the potential to efficiently contain and delay outbreaks of infectious diseases such as the ongoing SARS-CoV-2 pandemic. In this work we propose a machine learning based approach to reliably detect subjects that have spent enough time in close proximity to be at risk of being infected. Our study is an important proof of concept that will aid the battery of epidemiological policies aiming to slow down the rapid spread of COVID-19.

Sattler Felix, Ma Jackie, Wagner Patrick, Neumann David, Wenzel Markus, Schäfer Ralf, Samek Wojciech, Müller Klaus-Robert, Wiegand Thomas

2020

Computer science, Risk factors, Viral infection

General General

Analysis of pedestrian activity before and during COVID-19 lockdown, using webcam time-lapse from Cracow and machine learning.

In PeerJ

At the turn of February and March 2020, COVID-19 pandemic reached Europe. Many countries, including Poland imposed lockdown as a method of securing social distance between potentially infected. Stay-at-home orders and movement control within public space not only affected the touristm industry, but also the everyday life of the inhabitants. The hourly time-lapse from four HD webcams in Cracow (Poland) are used in this study to estimate how pedestrian activity changed during COVID-19 lockdown. The collected data covers the period from 9 June 2016 to 19 April 2020 and comes from various urban zones. One zone is tourist, one is residential and two are mixed. In the first stage of the analysis, a state-of-the-art machine learning algorithm (YOLOv3) is used to detect people. Additionally, a non-standard application of the YOLO method is proposed, oriented to the images from HD webcams. This approach (YOLOtiled) is less prone to pedestrian detection errors with the only drawback being the longer computation time. Splitting the HD image into smaller tiles increases the number of detected pedestrians by over 50%. In the second stage, the analysis of pedestrian activity before and during the COVID-19 lockdown is conducted for hourly, daily and weekly averages. Depending on the type of urban zone, the number of pedestrians decreased from 33% in residential zones to 85% in tourist zones located in the Old Town. The presented method allows for more efficient detection and counting of pedestrians from HD time-lapse webcam images compared to SSD, YOLOv3 and Faster R-CNN. The result of the research is a published database with the detected number of pedestrians from the four-year observation period for four locations in Cracow.

Szczepanek Robert

2020

COVID-19, Cracow, Data science, Database, OpenCV, Pedestrian counting, People detection, Webcam, YOLOv3

Pathology Pathology

Transcriptional and proteomic insights into the host response in fatal COVID-19 cases.

In Proceedings of the National Academy of Sciences of the United States of America

Coronavirus disease 2019 (COVID-19), the global pandemic caused by SARS-CoV-2, has resulted thus far in greater than 933,000 deaths worldwide; yet disease pathogenesis remains unclear. Clinical and immunological features of patients with COVID-19 have highlighted a potential role for changes in immune activity in regulating disease severity. However, little is known about the responses in human lung tissue, the primary site of infection. Here we show that pathways related to neutrophil activation and pulmonary fibrosis are among the major up-regulated transcriptional signatures in lung tissue obtained from patients who died of COVID-19 in Wuhan, China. Strikingly, the viral burden was low in all samples, which suggests that the patient deaths may be related to the host response rather than an active fulminant infection. Examination of the colonic transcriptome of these patients suggested that SARS-CoV-2 impacted host responses even at a site with no obvious pathogenesis. Further proteomics analysis validated our transcriptome findings and identified several key proteins, such as the SARS-CoV-2 entry-associated protease cathepsins B and L and the inflammatory response modulator S100A8/A9, that are highly expressed in fatal cases, revealing potential drug targets for COVID-19.

Wu Meng, Chen Yaobing, Xia Han, Wang Changli, Tan Chin Yee, Cai Xunhui, Liu Yufeng, Ji Fenghu, Xiong Peng, Liu Ran, Guan Yuanlin, Duan Yaqi, Kuang Dong, Xu Sanpeng, Cai Hanghang, Xia Qin, Yang Dehua, Wang Ming-Wei, Chiu Isaac M, Cheng Chao, Ahern Philip P, Liu Liang, Wang Guoping, Surana Neeraj K, Xia Tian, Kasper Dennis L

2020-Oct-20

COVID-19, NETosis, SARS-CoV-2, fibrosis, neutrophil

Radiology Radiology

A model based on CT radiomic features for predicting RT-PCR becoming negative in coronavirus disease 2019 (COVID-19) patients.

In BMC medical imaging

BACKGROUND : Coronavirus disease 2019 (COVID-19) has emerged as a global pandemic. According to the diagnosis and treatment guidelines of China, negative reverse transcription-polymerase chain reaction (RT-PCR) is the key criterion for discharging COVID-19 patients. However, repeated RT-PCR tests lead to medical waste and prolonged hospital stays for COVID-19 patients during the recovery period. Our purpose is to assess a model based on chest computed tomography (CT) radiomic features and clinical characteristics to predict RT-PCR negativity during clinical treatment.

METHODS : From February 10 to March 10, 2020, 203 mild COVID-19 patients in Fangcang Shelter Hospital were retrospectively included (training: n = 141; testing: n = 62), and clinical characteristics were collected. Lung abnormalities on chest CT images were segmented with a deep learning algorithm. CT quantitative features and radiomic features were automatically extracted. Clinical characteristics and CT quantitative features were compared between RT-PCR-negative and RT-PCR-positive groups. Univariate logistic regression and Spearman correlation analyses identified the strongest features associated with RT-PCR negativity, and a multivariate logistic regression model was established. The diagnostic performance was evaluated for both cohorts.

RESULTS : The RT-PCR-negative group had a longer time interval from symptom onset to CT exams than the RT-PCR-positive group (median 23 vs. 16 days, p < 0.001). There was no significant difference in the other clinical characteristics or CT quantitative features. In addition to the time interval from symptom onset to CT exams, nine CT radiomic features were selected for the model. ROC curve analysis revealed AUCs of 0.811 and 0.812 for differentiating the RT-PCR-negative group, with sensitivity/specificity of 0.765/0.625 and 0.784/0.600 in the training and testing datasets, respectively.

CONCLUSION : The model combining CT radiomic features and clinical data helped predict RT-PCR negativity during clinical treatment, indicating the proper time for RT-PCR retesting.

Cai Quan, Du Si-Yao, Gao Si, Huang Guo-Liang, Zhang Zheng, Li Shu, Wang Xin, Li Pei-Ling, Lv Peng, Hou Gang, Zhang Li-Na

2020-Oct-20

COVID-19, Computed tomography, Quantitative, RT-PCR, Radiomics

General General

Comparison of ARIMA, ETS, NNAR and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy

ArXiv Preprint

Coronavirus disease (COVID-19) is a severe ongoing novel pandemic that has emerged in Wuhan, China, in December 2019. As of October 13, the outbreak has spread rapidly across the world, affecting over 38 million people, and causing over 1 million deaths. In this article, I analysed several time series forecasting methods to predict the spread of COVID-19 second wave in Italy, over the period after October 13, 2020. I used an autoregressive model (ARIMA), an exponential smoothing state space model (ETS), a neural network autoregression model (NNAR), and the following hybrid combinations of them: ARIMA-ETS, ARIMA-NNAR, ETS-NNAR, and ARIMA-ETS-NNAR. About the data, I forecasted the number of patients hospitalized with mild symptoms, and in intensive care units (ICU). The data refer to the period February 21, 2020-October 13, 2020 and are extracted from the website of the Italian Ministry of Health (www.salute.gov.it). The results show that i) the hybrid models, except for ARIMA-ETS, are better at capturing the linear and non-linear epidemic patterns, by outperforming the respective single models; and ii) the number of COVID-19-related hospitalized with mild symptoms and in ICU will rapidly increase in the next weeks, by reaching the peak in about 50-60 days, i.e. in mid-December 2020, at least. To tackle the upcoming COVID-19 second wave, on one hand, it is necessary to hire healthcare workers and implement sufficient hospital facilities, protective equipment, and ordinary and intensive care beds; and on the other hand, it may be useful to enhance social distancing by improving public transport and adopting the double-shifts schooling system, for example.

Gaetano Perone

2020-10-22

General General

Application of tele-podiatry in diabetic foot management: A series of illustrative cases.

In Diabetes & metabolic syndrome

BACKGROUND AND AIMS : Telemedicine had been proposed as a tool to manage diabetes, but its role in management of diabetic foot ulcer is still evolving. The COVID-19 pandemic and related social restrictions have necessitated the use of telemedicine in the management of diabetic foot disease (tele-podiatry), particularly of patients classified as low-risk.

MATERIALS AND METHODS : We present a report of three cases of varied diabetic foot problems assessed during the present pandemic using different forms of telemedicine for triaging, management of low-risk cases and for follow-up.

RESULTS : Tele-podiatry was effective in the management of low-risk subjects with diabetic foot ulcer, and also useful in referral of high-risk subjects for hospital/clinic visit, facilitating proper management. It also helped in the follow-up of the cases.

CONCLUSION : Telemedicine is a good screening tool for diagnosing and managing low-risk subjects with diabetic foot problems, and also enables a triaging system for deciding on hospital visits and hospitalization. Telemedicine offers several benefits in the management of diabetic foot disease, although it also has some limitations. Based on our experience during the pandemic, we recommend its judicious use in the triaging of patients of diabetic foot disease and management of low-risk cases. Future innovation in technology and artificial intelligence may help in better tele-podiatry care in the time to come.

Kavitha Karakkattu V, Deshpande Shailesh R, Pandit Anil P, Unnikrishnan Ambika G

2020-Oct-11

Diabetes mellitus, Diabetic foot triaging, Pandemic, Tele-podiatry, Telemedicine

General General

Development, evaluation, and validation of machine learning models for COVID-19 detection based on routine blood tests.

In Clinical chemistry and laboratory medicine ; h5-index 46.0

Objectives The rRT-PCR test, the current gold standard for the detection of coronavirus disease (COVID-19), presents with known shortcomings, such as long turnaround time, potential shortage of reagents, false-negative rates around 15-20%, and expensive equipment. The hematochemical values of routine blood exams could represent a faster and less expensive alternative. Methods Three different training data set of hematochemical values from 1,624 patients (52% COVID-19 positive), admitted at San Raphael Hospital (OSR) from February to May 2020, were used for developing machine learning (ML) models: the complete OSR dataset (72 features: complete blood count (CBC), biochemical, coagulation, hemogasanalysis and CO-Oxymetry values, age, sex and specific symptoms at triage) and two sub-datasets (COVID-specific and CBC dataset, 32 and 21 features respectively). 58 cases (50% COVID-19 positive) from another hospital, and 54 negative patients collected in 2018 at OSR, were used for internal-external and external validation. Results We developed five ML models: for the complete OSR dataset, the area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.83 to 0.90; for the COVID-specific dataset from 0.83 to 0.87; and for the CBC dataset from 0.74 to 0.86. The validations also achieved good results: respectively, AUC from 0.75 to 0.78; and specificity from 0.92 to 0.96. Conclusions ML can be applied to blood tests as both an adjunct and alternative method to rRT-PCR for the fast and cost-effective identification of COVID-19-positive patients. This is especially useful in developing countries, or in countries facing an increase in contagions.

Cabitza Federico, Campagner Andrea, Ferrari Davide, Di Resta Chiara, Ceriotti Daniele, Sabetta Eleonora, Colombini Alessandra, De Vecchi Elena, Banfi Giuseppe, Locatelli Massimo, Carobene Anna

2020-Oct-20

COVID-19, SARS-CoV-2, blood laboratory tests, complete blood count, gradient boosted decision tree, machine learning

General General

How the COVID-19 pandemic favored the adoption of digital technologies in healthcare: a systematic review of early scientific literature.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : The COVID-19 pandemic is favoring the digital transition in many industries and in the society as a whole. Healthcare responded to the first phase of the pandemic through the rapid adoption of digital solutions and advanced technology tools.

OBJECTIVE : The aim of this study is to describe which digital solutions have been reported in the early scientific literature to mitigate the impact of COVID-19 on individuals and health systems.

METHODS : We conducted a systematic review of COVID-19 early literature (from January 1, 2020 to April 30, 2020) searching MEDLINE and medRxiv with terms considered adequate to find relevant literature on the use of digital technologies in response to the pandemic. We extracted study characteristics such as paper title, journal, publication date, and categorized the retrieved papers by type of technology, and patient needs addressed. We built a scoring rubric by cross-classifying the patient needs with the type of technology. We also extracted information and classified each technology reported by the selected articles according to healthcare system targets, grade of innovation, and scalability to other geographical areas.

RESULTS : The search identified 269 articles, of which 124 full-text articles were assessed and included in the review after screening. Of selected articles, most of them addressed the use of digital technologies for diagnosis, surveillance and prevention. We report that digital solutions and innovative technologies have mainly been proposed for the diagnosis of COVID-19. In particular, within the reviewed articles we identified numerous suggestions on the use of artificial-intelligence-powered tools for the diagnosis and screening of COVID-19. Digital technologies are useful also for prevention and surveillance measures, for example through contact-tracing apps or monitoring of internet searches and social media usage. Fewer scientific contributions address the use of digital technologies for lifestyle empowerment or patient engagement.

CONCLUSIONS : In the field of diagnosis, digital solutions that integrate with the traditional methods, such as AI-based diagnostic algorithms based both on imaging and/or clinical data, seem promising. As for surveillance, digital apps have already proven their effectiveness, but problems related to privacy and usability remain. For other patient needs, several solutions have been proposed using, for example, telemedicine or telehealth tools. These have long been available, but perhaps this historical moment could actually favor their definitive large-scale adoption. It is worth taking advantage of the push given by the crisis and important to keep track of the digital solutions proposed today to implement tomorrow's best practices and models of care, and to adopt at least some of the solutions proposed in the scientific literature, especially in those national health systems which in recent years proved to be particularly resistant to the digital transition.

CLINICALTRIAL :

Golinelli Davide, Boetto Erik, Carullo Gherardo, Nuzzolese Andrea Giovanni, Landini Maria Paola, Fantini Maria Pia

2020-Sep-15

General General

MCCS: a novel recognition pattern-based method for fast track discovery of anti-SARS-CoV-2 drugs.

In Briefings in bioinformatics

Given the scale and rapid spread of the coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, or 2019-nCoV), there is an urgent need to identify therapeutics that are effective against COVID-19 before vaccines are available. Since the current rate of SARS-CoV-2 knowledge acquisition via traditional research methods is not sufficient to match the rapid spread of the virus, novel strategies of drug discovery for SARS-CoV-2 infection are required. Structure-based virtual screening for example relies primarily on docking scores and does not take the importance of key residues into consideration, which may lead to a significantly higher incidence rate of false-positive results. Our novel in silico approach, which overcomes these limitations, can be utilized to quickly evaluate FDA-approved drugs for repurposing and combination, as well as designing new chemical agents with therapeutic potential for COVID-19. As a result, anti-HIV or antiviral drugs (lopinavir, tenofovir disoproxil, fosamprenavir and ganciclovir), antiflu drugs (peramivir and zanamivir) and an anti-HCV drug (sofosbuvir) are predicted to bind to 3CLPro in SARS-CoV-2 with therapeutic potential for COVID-19 infection by our new protocol. In addition, we also propose three antidiabetic drugs (acarbose, glyburide and tolazamide) for the potential treatment of COVID-19. Finally, we apply our new virus chemogenomics knowledgebase platform with the integrated machine-learning computing algorithms to identify the potential drug combinations (e.g. remdesivir+chloroquine), which are congruent with ongoing clinical trials. In addition, another 10 compounds from CAS COVID-19 antiviral candidate compounds dataset are also suggested by Molecular Complex Characterizing System with potential treatment for COVID-19. Our work provides a novel strategy for the repurposing and combinations of drugs in the market and for prediction of chemical candidates with anti-COVID-19 potential.

Feng Zhiwei, Chen Maozi, Xue Ying, Liang Tianjian, Chen Hui, Zhou Yuehan, Nolin Thomas D, Smith Randall B, Xie Xiang-Qun

2020-Oct-20

COVID-19, MCCS, drug combination, drug repurposing, residue energy contribution

General General

Advanced Machine Learning-Based Analytics on COVID-19 Data Using Generative Adversarial Networks.

In Materials today. Proceedings

The domain of medical diagnosis and predictive analytics is one of the key domains of research with enormous dimensions whereby the diseases of different types can be predicted. Nowadays, there is a huge panic of impact and rapid mutation of the COVID-19 virus impression. The world is getting affected by this virus to a huge extent and there is no vaccine developed so far. India is also having more than 10,000 patients with than 300 deceased. The global human community is having around 20 lacs of Coronavirus patients. The Generative Adversarial Network (GAN) is the contemporary high-performance approach in which the use of advanced neural networks is done for the cavernous analytics of the images and multimedia data. In this research work, the analytics of key points from medical images of the COVID-19 dataset is to be presented using which the diagnosis and predictions can be done for the patients. The GANs are used for the generation, transformation as well as presentation of the dataset and key points using advanced deep learning models which can analyze the patterns in the medical images including X-Ray, CT Scan, and many others. Using such approaches with the integration of GANs, the overall predictive analytics can be made high performance aware as compared to the classical neural networks with multiple layers. In this research manuscript, the inscription of work is projected on the benchmark datasets with the advanced scripting so that the predictive mining and knowledge discovery can be done effectively with more accuracy.

Vijay Kumar Janga, Harshavardhan A, Bhukya Hanumanthu, Krishna Prasad A V

2020-Oct-14

COVID-19 Data Analytics, GAN, Generative Adversarial Network, Generative Adversarial Network in Medical Diagnosis

General General

Numerical simulation of the novel coronavirus spreading.

In Expert systems with applications

The COVID-19 virus outbreak has affected most of the world in 2020. This paper deals with artificial intelligence (AI) methods that can address the problem of predicting scale, dynamics and sensitivity of the outbreak to preventive actions undertaken with a view to combatting the epidemic. In our study, we developed a cellular automata (CA) model for simulating the COVID-19 disease spreading. The enhanced infectious disease dynamics  S E I R (Susceptible, Exposed, Infectious, and Recovered) model was applied to estimate the epidemic trends in Poland, France, and Spain. We introduced new parameters into the simulation framework which reflect the statistically confirmed dependencies such as age-dependent death probability, a different definition of the contact rate and enhanced parameters reflecting population mobility. To estimate key epidemiological measures and to predict possible dynamics of the disease, we juxtaposed crucial CA framework parameters to the reported COVID-19 values, e.g. length of infection, mortality rates and the reproduction number. Moreover, we used real population density and age structures of the studied epidemic populations. The model presented allows for the examination of the effectiveness of preventive actions and their impact on the spreading rate and the duration of the disease. It also shows the influence of structure and behavior of the populations studied on key epidemic parameters, such as mortality and infection rates. Although our results are critically dependent on the assumptions underpinning our model and there is considerable uncertainty associated with the outbreaks at such an early epidemic stage, the obtained simulation results seem to be in general agreement with the observed behavior of the real COVID-19 disease, and our numerical framework can be effectively used to analyze the dynamics and efficacy of epidemic containment methods.

Medrek M, Pastuszak Z

2021-Mar-15

Cellular automata, Epidemic spread model, Mathematical model, Novel coronavirus, SEIR model

Public Health Public Health

Evaluating the plausible application of advanced machine learnings in exploring determinant factors of present pandemic: A case for continent specific COVID-19 analysis.

In The Science of the total environment

Coronavirus disease, a novel severe acute respiratory syndrome (SARS COVID-19), has become a global health concern due to its unpredictable nature and lack of adequate medicines. Machine Learning (ML) models could be effective in identifying the most critical factors which are responsible for the overall fatalities caused by COVID-19. The functional capabilities of ML models in epidemiological research, especially for COVID-19, are not substantially explored. To bridge this gap, this study has adopted two advanced ML models, viz. Random Forest (RF) and Gradient Boosted Machine (GBM), to perform the regression modelling and provide subsequent interpretation. Five successive steps were followed to carry out the analysis: (1) identification of relevant key explanatory variables; (2) application of data dimensionality reduction for eliminating redundant information; (3) utilizing ML models for measuring relative influence (RI) of the explanatory variables; (4) evaluating interconnections between and among the key explanatory variables and COVID-19 case and death counts; (5) time series analysis for examining the rate of incidences of COVID-19 cases and deaths. Among the explanatory variables considered in this study, air pollution, migration, economy, and demographic factor were found to be the most significant controlling factors. Since a very limited research is available to discuss the superiority of ML models for identifying the key determinants of COVID-19, this study could be a reference for future public health research. Additionally, all the models and data used in this study are open source and freely available, thereby, reproducibility and scientific replication will be achievable easily.

Chakraborti Suman, Maiti Arabinda, Pramanik Suvamoy, Sannigrahi Srikanta, Pilla Francesco, Banerjee Anushna, Das Dipendra Nath

2020-Oct-06

Air pollution, COVID-19, Machine learning, Pandemic, Relative importance, Socioeconomic

General General

High Tech, High Risk: Tech Ethics Lessons for the COVID-19 Pandemic Response.

In Patterns (New York, N.Y.)

The COVID-19 pandemic has, in a matter of a few short months, drastically reshaped society around the world. Because of the growing perception of machine learning as a technology capable of addressing large problems at scale, machine learning applications have been seen as desirable interventions in mitigating the risks of the pandemic disease. However, machine learning, like many tools of technocratic governance, is deeply implicated in the social production and distribution of risk and the role of machine learning in the production of risk must be considered as engineers and other technologists develop tools for the current crisis. This paper describes the coupling of machine learning and the social production of risk, generally, and in pandemic responses specifically. It goes on to describe the role of risk management in the effort to institutionalize ethics in the technology industry and how such efforts can benefit from a deeper understanding of the social production of risk through machine learning.

Moss Emanuel, Metcalf Jacob

2020-Oct-09

General General

Designing low-cost, accurate cervical screening strategies that take into account COVID-19: a role for self-sampled HPV typing2.

In Infectious agents and cancer

Background : We propose an economical cervical screening research and implementation strategy designed to take into account the typically slow natural history of cervical cancer and the severe but hopefully temporary impact of COVID-19. The commentary introduces the practical validation of some critical components of the strategy, described in three manuscripts detailing recent project results in Asia and Africa.The main phases of a cervical screening program are 1) primary screening of women in the general population, 2) triage testing of the small minority of women that screen positive to determine need for treatment, and 3) treatment of triage-positive women thought to be at highest risk of precancer or even cancer. In each phase, attention must now be paid to safety in relation to SARS-CoV-2 transmission. The new imperatives of the COVID-19 pandemic support self-sampled HPV testing as the primary cervical screening method. Most women can be reassured for several years by a negative test performed on a self-sample collected at home, without need of clinic visit and speculum examination. The advent of relatively inexpensive, rapid and accurate HPV DNA testing makes it possible to return screening results from self-sampling very soon after specimen collection, minimizing loss to follow-up. Partial HPV typing provides important risk stratification useful for triage of HPV-positive women. A second "triage" test is often useful to guide management. In lower-resource settings, visual inspection with acetic acid (VIA) is still proposed but it is inaccurate and poorly reproducible, misclassifying the risk stratification gained by primary HPV testing. A deep-learning based approach to recognizing cervical precancer, adaptable to a smartphone camera, is being validated to improve VIA performance. The advent and approval of thermal ablation permits quick, affordable and safe, immediate treatment at the triage clinic of the majority of HPV-positive, triage-positive women.

Conclusions : Overall, only a small percentage of women in cervical screening programs need to attend the hospital clinic for a surgical procedure, particularly when screening is targeted to the optimal age range for detection of precancer rather than older ages with decreased visual screening performance and higher risks of hard-to-treat outcomes including invasive cancer.

Ajenifuja Kayode Olusegun, Belinson Jerome, Goldstein Andrew, Desai Kanan T, de Sanjose Silvia, Schiffman Mark

2020

COVID-19, Cervical screening, HPV, Self-sampling, Triage

General General

Assessing countries' performances against COVID-19 via WSIDEA and machine learning algorithms.

In Applied soft computing

The COVID-19 pandemic, which first spread to the People of Republic of China and then to other countries in a short time, affected the whole world by infecting millions of people and have been increasing its impact day by day. Hundreds of researchers in many countries are in search of a solution to end up this pandemic. This study aims to contribute to the literature by performing detailed analyses via a new three-staged framework constructed based on data envelopment analysis and machine learning algorithms to assess the performances of 142 countries against the COVID-19 outbreak. Particularly, clustering analyses were made using k-means and hierarchic clustering methods. Subsequently, efficiency analysis of countries were performed by a novel model, the weighted stochastic imprecise data envelopment analysis. Finally, parameters were analyzed with decision tree and random forest algorithms. Results have been analyzed in detail, and the classification of countries are determined by providing the most influential parameters. The analysis showed that the optimum number of clusters for 142 countries is three. In addition, while 20 countries out of 142 countries were fully effective, 36% of them were found to be effective at a rate of 90%. Finally, it has been observed that the data such as GDP, smoking rates, and the rate of diabetes patients do not affect the effectiveness level of the countries.

Aydin Nezir, Yurdakul Gökhan

2020-Dec

COVID-19, Clustering, Machine learning, Weighted stochastic imprecise data envelopment analysis

General General

Comparing the accuracy of several network-based COVID-19 prediction algorithms.

In International journal of forecasting

Researchers from various scientific disciplines have attempted to forecast the spread of the Coronavirus Disease 2019 (COVID-19). The proposed epidemic prediction methods range from basic curve fitting methods and traffic interaction models to machine-learning approaches. If we combine all these approaches, we obtain the Network Inference-based Prediction Algorithm (NIPA). In this paper, we analyse a diverse set of COVID-19 forecast algorithms, including several modifications of NIPA. Among the diverse set of algorithms that we evaluated, original NIPA performs best on forecasting the spread of COVID-19 in Hubei, China and in the Netherlands. In particular, we show that network-based forecasting is superior to any other forecasting algorithm.

Achterberg Massimo A, Prasse Bastian, Ma Long, Trajanovski Stojan, Kitsak Maksim, Van Mieghem Piet

2020-Oct-09

Bayesian methods, Epidemiology, Forecast accuracy, Machine learning methods, Network inference, SIR model, Time series methods

Cardiology Cardiology

Usefulness of machine learning in COVID-19 for the detection and prognosis of cardiovascular complications.

In Reviews in cardiovascular medicine

Since January 2020, coronavirus disease 2019 (COVID-19) has rapidly become a global concern, and its cardiovascular manifestations have highlighted the need for fast, sensitive and specific tools for early identification and risk stratification. Machine learning is a software solution with the ability to analyze large amounts of data and make predictions without prior programming. When faced with new problems with unique challenges as evident in the COVID-19 pandemic, machine learning can offer solutions that are not apparent on the surface by sifting quickly through massive quantities of data and making associations that may have been missed. Artificial intelligence is a broad term that encompasses different tools, including various types of machine learning and deep learning. Here, we review several cardiovascular applications of machine learning and artificial intelligence and their potential applications to cardiovascular diagnosis, prognosis, and therapy in COVID-19 infection.

Zimmerman Allison, Kalra Dinesh

2020-Sep-30

COVID-19, artificial intelligence, cardiovascular, machine learning

Pathology Pathology

Characterizing Deep Gaussian Processes via Nonlinear Recurrence Systems

ArXiv Preprint

Recent advances in Deep Gaussian Processes (DGPs) show the potential to have more expressive representation than that of traditional Gaussian Processes (GPs). However, there exists a pathology of deep Gaussian processes that their learning capacities reduce significantly when the number of layers increases. In this paper, we present a new analysis in DGPs by studying its corresponding nonlinear dynamic systems to explain the issue. Existing work reports the pathology for the squared exponential kernel function. We extend our investigation to four types of common stationary kernel functions. The recurrence relations between layers are analytically derived, providing a tighter bound and the rate of convergence of the dynamic systems. We demonstrate our finding with a number of experimental results.

Anh Tong, Jaesik Choi

2020-10-19

General General

Explainable Automated Fact-Checking for Public Health Claims

ArXiv Preprint

Fact-checking is the task of verifying the veracity of claims by assessing their assertions against credible evidence. The vast majority of fact-checking studies focus exclusively on political claims. Very little research explores fact-checking for other topics, specifically subject matters for which expertise is required. We present the first study of explainable fact-checking for claims which require specific expertise. For our case study we choose the setting of public health. To support this case study we construct a new dataset PUBHEALTH of 11.8K claims accompanied by journalist crafted, gold standard explanations (i.e., judgments) to support the fact-check labels for claims. We explore two tasks: veracity prediction and explanation generation. We also define and evaluate, with humans and computationally, three coherence properties of explanation quality. Our results indicate that, by training on in-domain data, gains can be made in explainable, automated fact-checking for claims which require specific expertise.

Neema Kotonya, Francesca Toni

2020-10-19

General General

Efficient Estimation and Evaluation of Prediction Rules in Semi-Supervised Settings under Stratified Sampling

ArXiv Preprint

In many contemporary applications, large amounts of unlabeled data are readily available while labeled examples are limited. There has been substantial interest in semi-supervised learning (SSL) which aims to leverage unlabeled data to improve estimation or prediction. However, current SSL literature focuses primarily on settings where labeled data is selected randomly from the population of interest. Non-random sampling, while posing additional analytical challenges, is highly applicable to many real world problems. Moreover, no SSL methods currently exist for estimating the prediction performance of a fitted model under non-random sampling. In this paper, we propose a two-step SSL procedure for evaluating a prediction rule derived from a working binary regression model based on the Brier score and overall misclassification rate under stratified sampling. In step I, we impute the missing labels via weighted regression with nonlinear basis functions to account for nonrandom sampling and to improve efficiency. In step II, we augment the initial imputations to ensure the consistency of the resulting estimators regardless of the specification of the prediction model or the imputation model. The final estimator is then obtained with the augmented imputations. We provide asymptotic theory and numerical studies illustrating that our proposals outperform their supervised counterparts in terms of efficiency gain. Our methods are motivated by electronic health records (EHR) research and validated with a real data analysis of an EHR-based study of diabetic neuropathy.

Jessica Gronsbell, Molei Liu, Lu Tian, Tianxi Cai

2020-10-19

General General

Knowledge Graph-based Question Answering with Electronic Health Records

ArXiv Preprint

Question Answering (QA) on Electronic Health Records (EHR), namely EHR QA, can work as a crucial milestone towards developing an intelligent agent in healthcare. EHR data are typically stored in a relational database, which can also be converted to a Directed Acyclic Graph (DAG), allowing two approaches for EHR QA: Table-based QA and Knowledge Graph-based QA. We hypothesize that the graph-based approach is more suitable for EHR QA as graphs can represent relations between entities and values more naturally compared to tables, which essentially require JOIN operations. To validate our hypothesis, we first construct EHR QA datasets based on MIMIC-III, where the same question-answer pairs are represented in SQL (table-based) and SPARQL (graph-based), respectively. We then test a state-of-the-art EHR QA model on both datasets where the model demonstrated superior QA performance on the SPARQL version. Finally, we open-source both MIMICSQL* and MIMIC-SPARQL* to encourage further EHR QA research in both direction

Junwoo Park, Youngwoo Cho, Haneol Lee, Jaegul Choo, Edward Choi

2020-10-19

General General

A Reinforcement Learning Approach to Health Aware Control Strategy

Mediterranean Conference on Control and Automation (MED). IEEE, 2019, Jul 2019, Akko, Israel

Health-aware control (HAC) has emerged as one of the domains where control synthesis is sought based upon the failure prognostics of system/component or the Remaining Useful Life (RUL) predictions of critical components. The fact that mathematical dynamic (transition) models of RUL are rarely available, makes it difficult for RUL information to be incorporated into the control paradigm. A novel framework for health aware control is presented in this paper where reinforcement learning based approach is used to learn an optimal control policy in face of component degradation by integrating global system transition data (generated by an analytical model that mimics the real system) and RUL predictions. The RUL predictions generated at each step, is tracked to a desired value of RUL. The latter is integrated within a cost function which is maximized to learn the optimal control. The proposed method is studied using simulation of a DC motor and shaft wear.

Mayank Shekhar Jha, Philippe Weber, Didier Theilliol, Jean-Christophe Ponsart, Didier Maquin

2020-10-19

General General

IoT Platform for COVID-19 Prevention and Control: A Survey

ArXiv Preprint

As a result of the worldwide transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), coronavirus disease 2019 (COVID-19) has evolved into an unprecedented pandemic. Currently, with unavailable pharmaceutical treatments and vaccines, this novel coronavirus results in a great impact on public health, human society, and global economy, which is likely to last for many years. One of the lessons learned from the COVID-19 pandemic is that a long-term system with non-pharmaceutical interventions for preventing and controlling new infectious diseases is desirable to be implemented. Internet of things (IoT) platform is preferred to be utilized to achieve this goal, due to its ubiquitous sensing ability and seamless connectivity. IoT technology is changing our lives through smart healthcare, smart home, and smart city, which aims to build a more convenient and intelligent community. This paper presents how the IoT could be incorporated into the epidemic prevention and control system. Specifically, we demonstrate a potential fog-cloud combined IoT platform that can be used in the systematic and intelligent COVID-19 prevention and control, which involves five interventions including COVID-19 Symptom Diagnosis, Quarantine Monitoring, Contact Tracing \& Social Distancing, COVID-19 Outbreak Forecasting, and SARS-CoV-2 Mutation Tracking. We investigate and review the state-of-the-art literatures of these five interventions to present the capabilities of IoT in countering against the current COVID-19 pandemic or future infectious disease epidemics.

Yudi Dong, Yu-Dong Yao

2020-10-15

General General

M3Lung-Sys: A Deep Learning System for Multi-Class Lung Pneumonia Screening from CT Imaging.

In IEEE journal of biomedical and health informatics

To counter the outbreak of COVID-19, the accurate diagnosis of suspected cases plays a crucial role in timely quarantine, medical treatment, and preventing the spread of the pandemic. Considering the limited training cases and resources (e.g, time and budget), we propose a Multi-task Multi-slice Deep Learning System ( M3Lung-Sys) for multi-class lung pneumonia screening from CT imaging, which only consists of two 2D CNN networks, i.e., slice- and patient-level classification networks. The former aims to seek the feature representations from abundant CT slices instead of limited CT volumes, and for the overall pneumonia screening, the latter one could recover the temporal information by feature refinement and aggregation between different slices. In addition to distinguish COVID-19 from Healthy, H1N1, and CAP cases, our M3Lung-Sys also be able to locate the areas of relevant lesions, without any pixel-level annotation. To further demonstrate the effectiveness of our model, we conduct extensive experiments on a chest CT imaging dataset with a total of 734 patients (251 healthy people, 245 COVID-19 patients, 105 H1N1 patients, and 133 CAP patients). The quantitative results with plenty of metrics indicate the superiority of our proposed model on both slice- and patient-level classification tasks. More importantly, the generated lesion location maps make our system interpretable and more valuable to clinicians.

Qian Xuelin, Fu Huazhu, Shi Weiya, Chen Tao, Fu Yanwei, Shan Fei, Xue Xiangyang

2020-Oct-13

Radiology Radiology

Clinical and laboratory data, radiological structured report findings and quantitative evaluation of lung involvement on baseline chest CT in COVID-19 patients to predict prognosis.

In La Radiologia medica

OBJECTIVE : To evaluate by means of regression models the relationships between baseline clinical and laboratory data and lung involvement on baseline chest CT and to quantify the thoracic disease using an artificial intelligence tool and a visual scoring system to predict prognosis in patients with COVID-19 pneumonia.

MATERIALS AND METHODS : This study included 103 (41 women and 62 men; 68.8 years of mean age-range, 29-93 years) with suspicious COVID-19 viral infection evaluated by reverse transcription real-time fluorescence polymerase chain reaction (RT-PCR) test. All patients underwent CT examinations at the time of admission in addition to clinical and laboratory findings recording. All chest CT examinations were reviewed using a structured report. Moreover, using an artificial intelligence tool we performed an automatic segmentation on CT images based on Hounsfield unit to calculate residual healthy lung parenchyma, ground-glass opacities (GGO), consolidations and emphysema volumes for both right and left lungs. Two expert radiologists, in consensus, attributed at the CT pulmonary disease involvement a severity score using a scale of 5 levels; the score was attributed for GGO and consolidation for each lung, and then, an overall radiological severity visual score was obtained summing the single score. Univariate and multivariate regression analysis was performed.

RESULTS : Symptoms and comorbidities did not show differences statistically significant in terms of patient outcome. Instead, SpO2 was significantly lower in patients hospitalized in critical conditions or died while age, HS CRP, leukocyte count, neutrophils, LDH, d-dimer, troponin, creatinine and azotemia, ALT, AST and bilirubin values were significantly higher. GGO and consolidations were the main CT patterns (a variable combination of GGO and consolidations was found in 87.8% of patients). CT COVID-19 disease was prevalently bilateral (77.6%) with peripheral distribution (74.5%) and multiple lobes localizations (52.0%). Consolidation, emphysema and residual healthy lung parenchyma volumes showed statistically significant differences in the three groups of patients based on outcome (patients discharged at home, patients hospitalized in stable conditions and patient hospitalized in critical conditions or died) while GGO volume did not affect the patient's outcome. Moreover, the overall radiological severity visual score (cutoff ≥ 8) was a predictor of patient outcome. The highest value of R-squared (R2 = 0.93) was obtained by the model that combines clinical/laboratory findings at CT volumes. The highest accuracy was obtained by clinical/laboratory and CT findings model with a sensitivity, specificity and accuracy, respectively, of 88%, 78% and 81% to predict discharged/stable patients versus critical/died patients.

CONCLUSION : In conclusion, both CT visual score and computerized software-based quantification of the consolidation, emphysema and residual healthy lung parenchyma on chest CT images were independent predictors of outcome in patients with COVID-19 pneumonia.

Salvatore Cappabianca, Roberta Fusco, Angela de Lisio, Cesare Paura, Alfredo Clemente, Giuliano Gagliardi, Giulio Lombardi, Giuliana Giacobbe, Maria Russo Gaetano, Paola Belfiore Maria, Fabrizio Urraro, Roberta Grassi, Beatrice Feragalli, Vittorio Miele

2020-Oct-12

COVID-19, Chest CT, Outcome, Regression model

Radiology Radiology

CT Quantification and Machine-learning Models for Assessment of Disease Severity and Prognosis of COVID-19 Patients.

In Academic radiology

OBJECTIVE : This study was to investigate the CT quantification of COVID-19 pneumonia and its impacts on the assessment of disease severity and the prediction of clinical outcomes in the management of COVID-19 patients.

MATERIALS METHODS : Ninety-nine COVID-19 patients who were confirmed by positive nucleic acid test (NAT) of RT-PCR and hospitalized from January 19, 2020 to February 19, 2020 were collected for this retrospective study. All patients underwent arterial blood gas test, routine blood test, chest CT examination, and physical examination on admission. In addition, follow-up clinical data including the disease severity, clinical treatment, and clinical outcomes were collected for each patient. Lung volume, lesion volume, nonlesion lung volume (NLLV) (lung volume - lesion volume), and fraction of nonlesion lung volume (%NLLV) (nonlesion lung volume / lung volume) were quantified in CT images by using two U-Net models trained for segmentation of lung and COVID-19 lesions in CT images. Furthermore, we calculated 20 histogram textures for lesions volume and NLLV, respectively. To investigate the validity of CT quantification in the management of COVID-19, we built random forest (RF) models for the purpose of classification and regression to assess the disease severity (Moderate, Severe, and Critical) and to predict the need and length of ICU stay, the duration of oxygen inhalation, hospitalization, sputum NAT-positive, and patient prognosis. The performance of RF classifiers was evaluated using the area under the receiver operating characteristic curves (AUC) and that of RF regressors using the root-mean-square error.

RESULTS : Patients were classified into three groups of disease severity: moderate (n = 25), severe (n = 47) and critical (n = 27), according to the clinical staging. Of which, a total of 32 patients, 1 (1/25) moderate, 6 (6/47) severe, and 25 critical (25/27), respectively, were admitted to ICU. The median values of ICU stay were 0, 0, and 12 days, the duration of oxygen inhalation 10, 15, and 28 days, the hospitalization 12, 16, and 28 days, and the sputum NAT-positive 8, 9, and 13 days, in three severity groups, respectively. The clinical outcomes were complete recovery (n = 3), partial recovery with residual pulmonary damage (n = 80), prolonged recovery (n = 15), and death (n = 1). The %NLLV in three severity groups were 92.18 ± 9.89%, 82.94 ± 16.49%, and 66.19 ± 24.15% with p value <0.05 among each two groups. The AUCs of RF classifiers using hybrid models were 0.927 and 0.929 in classification of moderate vs (severe + critical), and severe vs critical, respectively, which were significantly higher than either radiomics models or clinical models (p < 0.05). The root-mean-square errors of RF regressors were 0.88 weeks for prediction of duration of hospitalization (mean: 2.60 ± 1.01 weeks), 0.92 weeks for duration of oxygen inhalation (mean: 2.44 ± 1.08 weeks), 0.90 weeks for duration of sputum NAT-positive (mean: 1.59 ± 0.98 weeks), and 0.69 weeks for stay of ICU (mean: 1.32 ± 0.67 weeks), respectively. The AUCs for prediction of ICU treatment and prognosis (partial recovery vs prolonged recovery) were 0.945 and 0.960, respectively.

CONCLUSION : CT quantification and machine-learning models show great potentials for assisting decision-making in the management of COVID-19 patients by assessing disease severity and predicting clinical outcomes.

Cai Wenli, Liu Tianyu, Xue Xing, Luo Guibo, Wang Xiaoli, Shen Yihong, Fang Qiang, Sheng Jifang, Chen Feng, Liang Tingbo

2020-Sep-21

COVID-19, Computed tomography, Machine-learning, Novel coronavirus pneumonia, Quantitative image analysis

Radiology Radiology

Development of a quantitative segmentation model to assess the effect of comorbidity on patients with COVID-19.

In European journal of medical research

BACKGROUND : The coronavirus disease 2019 (COVID-19) has brought a global disaster. Quantitative lesions may provide the radiological evidence of the severity of pneumonia and further to assess the effect of comorbidity on patients with COVID-19.

METHODS : 294 patients with COVID-19 were enrolled from February, 24, 2020 to June, 1, 2020 from six centers. Multi-task Unet network was used to segment the whole lung and lesions from chest CT images. This deep learning method was pre-trained in 650 CT images (550 in primary dataset and 100 in test dataset) with COVID-19 or community-acquired pneumonia and Dice coefficients in test dataset were calculated. 50 CT scans of 50 patients (15 with comorbidity and 35 without comorbidity) were random selected to mark lesions manually. The results will be compared with the automatic segmentation model. Eight quantitative parameters were calculated based on the segmentation results to evaluate the effect of comorbidity on patients with COVID-19.

RESULTS : Quantitative segmentation model was proved to be effective and accurate with all Dice coefficients more than 0.85 and all accuracies more than 0.95. Of the 294 patients, 52 (17.7%) patients were reported having at least one comorbidity; 14 (4.8%) having more than one comorbidity. Patients with any comorbidity were older (P < 0.001), had longer incubation period (P < 0.001), were more likely to have abnormal laboratory findings (P < 0.05), and be in severity status (P < 0.001). More lesions (including larger volume of lesion, consolidation, and ground-glass opacity) were shown in patients with any comorbidity than patients without comorbidity (all P < 0.001). More lesions were found on CT images in patients with more comorbidities. The median volumes of lesion, consolidation, and ground-glass opacity in diabetes mellitus group were largest among the groups with single comorbidity that had the incidence rate of top three.

CONCLUSIONS : Multi-task Unet network can make quantitative CT analysis of lesions to assess the effect of comorbidity on patients with COVID-19, further to provide the radiological evidence of the severity of pneumonia. More lesions (including GGO and consolidation) were found in CT images of cases with comorbidity. The more comorbidities patients have, the more lesions CT images show.

Zhang Cui, Yang Guangzhao, Cai Chunxian, Xu Zhihua, Wu Hai, Guo Youmin, Xie Zongyu, Shi Hengfeng, Cheng Guohua, Wang Jian

2020-Oct-12

COVID-19, Comorbidity, Deep learning, X-ray computed tomography

General General

"Individualized learning in a course with a tight schedule".

In Procedia computer science

The article presents a solution supporting individualised learning in courses with a tight schedule. Such courses pose additional organisational challenges and require appropriate tools. The presented solution is based on an Intelligent Tutoring System immersed in repository of e-learning content, which enables selection of content immediately before its provision to the student instead of at the beginning of a course. Thanks to this, the system, having identified the student's needs, is able to make available the most suitable repository content at a given stage of education. The flexibility of the system is guaranteed by modularisation of content and its logical division using the UCTS taxonomy. The content has been described by means of concepts arranged according to the specificity of the domain to which the resources belong in order to ensure that the ITS is able to select relevant content. The proposed solution was used to set up an Applications of Fuzzy Logic course, which was part of an Artificial Intelligence class. The course was conducted within a very limited time frame resulting from the COVID-19 epidemic.

Marciniak Jacek, Szczepański Marcin

2020

Intelligent Tutoring Systems, content repositories, individualized learning

General General

MH-COVIDNet: Diagnosis of COVID-19 using Deep Neural Networks and Meta-heuristic-based Feature Selection on X-ray Images.

In Biomedical signal processing and control

COVID-19 is a disease that causes symptoms in the lungs and causes deaths around the world. Studies are ongoing for the diagnosis and treatment of this disease, which is defined as a pandemic. Early diagnosis of this disease is important for human life. This process is progressing rapidly with diagnostic studies based on deep learning. Therefore, to contribute to this field, a deep learning-based approach that can be used for early diagnosis of the disease is proposed in our study. In this approach, a data set consisting of 3 classes of COVID19, normal and pneumonia lung X-ray images was created, with each class containing 364 images. Pre-processing was performed using the image contrast enhancement algorithm on the prepared data set and a new data set was obtained. Feature extraction was completed from this data set with deep learning models such as AlexNet, VGG19, GoogleNet, and ResNet. For the selection of the best potential features, two metaheuristic algorithms of binary particle swarm optimization and binary gray wolf optimization were used. After combining the features obtained in the feature selection of the enhancement data set, they were classified using SVM. The overall accuracy of the proposed approach was obtained as 99.38%. The results obtained by verification with two different metaheuristic algorithms proved that the approach we propose can help experts during COVID-19 diagnostic studies.

Canayaz Murat

2020-Oct-06

BGWO, BPSO, COVID-19, deep learning models, pneumonia

General General

A novel comparative study for detection of Covid-19 on CT lung images using texture analysis, machine learning, and deep learning methods.

In Multimedia tools and applications

The Covid-19 virus outbreak that emerged in China at the end of 2019 caused a huge and devastating effect worldwide. In patients with severe symptoms of the disease, pneumonia develops due to Covid-19 virus. This causes intense involvement and damage in lungs. Although the emergence of the disease occurred a short time ago, many literature studies have been carried out in which these effects of the disease on the lungs were revealed by the help of lung CT imaging. In this study, 1.396 lung CT images in total (386 Covid-19 and 1.010 Non-Covid-19) were subjected to automatic classification. In this study, Convolutional Neural Network (CNN), one of the deep learning methods, was used which suggested automatic classification of CT images of lungs for early diagnosis of Covid-19 disease. In addition, k-Nearest Neighbors (k-NN) and Support Vector Machine (SVM) was used to compare the classification successes of deep learning with machine learning. Within the scope of the study, a 23-layer CNN architecture was designed and used as a classifier. Also, training and testing processes were performed for Alexnet and Mobilenetv2 CNN architectures as well. The classification results were also calculated for the case of increasing the number of images used in training for the first 23-layer CNN architecture by 5, 10, and 20 times using data augmentation methods. To reveal the effect of the change in the number of images in the training and test clusters on the results, two different training and testing processes, 2-fold and 10-fold cross-validation, were performed and the results of the study were calculated. As a result, thanks to these detailed calculations performed within the scope of the study, a comprehensive comparison of the success of the texture analysis method, machine learning, and deep learning methods in Covid-19 classification from CT images was made. The highest mean sensitivity, specificity, accuracy, F-1 score, and AUC values obtained as a result of the study were 0,9197, 0,9891, 0,9473, 0,9058, 0,9888; respectively for 2-fold cross-validation, and they were 0,9404, 0,9901, 0,9599, 0,9284, 0,9903; respectively for 10-fold cross-validation.

Yasar Huseyin, Ceylan Murat

2020-Oct-06

Convolutional neural networks (CNN), Covid-19, Deep learning, Lung CT classification, Machine learning, Texture analysis methods

General General

Isfahan and Covid-19: Deep Spatiotemporal Representation.

In Chaos, solitons, and fractals

The coronavirus COVID-19 is affecting 213 countries and territories around the world. Iran was one of the first affected countries by this virus. Isfahan, as the third most populated province of Iran, experienced a noticeable epidemic. The prediction of epidemic size, peak value, and peak time can help policymakers in correct decisions. In this study, deep learning is selected as a powerful tool for forecasting this epidemic in Isfahan. A combination of effective Social Determinant of Health (SDH) and the occurrences of COVID-19 data are used as spatiotemporal input by using time-series information from different locations. Different models are utilized, and the best performance is found to be for a tailored type of long short-term memory (LSTM). This new method incorporates mutual effect of all classes (confirmed/ death / recovered) in predication process. The future trajectory of the outbreak in Isfahan is forecasted with the proposed model. The paper demonstrates the positive effect of adding SDHs in pandemic prediction. Furthermore, the effectiveness of different SDHs is discussed, and the most effective terms are introduced. The method expresses high ability in both short- and long- term forecasting of the outbreak. The model proves that in predicting one class (like the number of confirmed cases), the effect of other accompanying numbers (like death and recovered cases) cannot be ignored. In conclusion, the superiorities of this model (particularity the long term predication ability) turn it into a reliable tool for helping the health decision makers.

Kafieh Rahele, Saeedizadeh Narges, Arian Roya, Amini Zahra, Serej Nasim Dadashi, Vaezi Atefeh, Javanmard Shaghayegh Haghjooy

2020-Oct-05

COVID-19, Isfahan, deep learning, predication

General General

Applications of Artificial Intelligence in Battling Against Covid-19: A Literature Review.

In Chaos, solitons, and fractals

Colloquially known as coronavirus, the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2), that causes CoronaVirus Disease 2019 (COVID-19), has become a matter of grave concern for every country around the world. The rapid growth of the pandemic has wreaked havoc and prompted the need for immediate reactions to curb the effects. To manage the problems, many research in a variety of area of science have started studying the issue. Artificial Intelligence is among the area of science that has found great applications in tackling the problem in many aspects. Here, we perform an overview on the applications of AI in a variety of fields including diagnosis of the disease via different types of tests and symptoms, monitoring patients, identifying severity of a patient, processing covid-19 related imaging tests, epidemiology, pharmaceutical studies, etc. The aim of this paper is to perform a comprehensive survey on the applications of AI in battling against the difficulties the outbreak has caused. Thus we cover every way that AI approaches have been employed and to cover all the research until the writing of this paper. We try organize the works in a way that overall picture is comprehensible. Such a picture, although full of details, is very helpful in understand where AI sits in current pandemonium. We also tried to conclude the paper with ideas on how the problems can be tackled in a better way and provide some suggestions for future works.

Tayarani-N Mohammad-H

2020-Oct-03

Artificial Intelligence, Artificial Neural Networks, Convolutional Neural Networks, Coronavirus, Covid-19, Deep Learning, Deep Neural Networks, Drug discovery, Epidemiology, Evolutionary Algorithms, Machine Learning, SARS-CoV-2, Vaccine Development

General General

A time series-based statistical approach for outbreak spread forecasting: Application of COVID-19 in Greece.

In Expert systems with applications

The aim of this paper is the generation of a time-series based statistical data-driven procedure in order to track an outbreak. At first are used univariate time series models in order to predict the evolution of the reported cases. Moreover, are considered combinations of the models in order to provide more accurate and robust results. Additionally, statistical probability distributions are considered in order to generate future scenarios. Final step is the build and use of an epidemiological model (tSIR) and the calculation of an epidemiological ratio (R0) for estimating the termination of the outbreak. The time series models include Exponential Smoothing and ARIMA approaches from the classical models, also Feed-Forward Artificial Neural Networks and Multivariate Adaptive Regression Splines from the machine learning toolbox. Combinations include simple mean, Newbolt-Granger and Bates-Granger approaches. Finally, the tSIR model and the R0 ratio are used for estimating the spread and the reversion of the pandemic. The suggested procedure is used to track the COVID-19 epidemic in Greece. This epidemic has appeared in China in December 2019 and has been widespread since then to all over the world. Greece is the center of this empirical study as is considered an early successful paradigm of resistance against the virus.

Katris Christos

2021-Mar-15

Classical time series models, Forecast combinations, Machine learning approaches, Time series forecasting, tSIR epidemiological model

General General

A light CNN for detecting COVID-19 from CT scans of the chest.

In Pattern recognition letters

Computer Tomography (CT) imaging of the chest is a valid diagnosis tool to detect COVID-19 promptly and to control the spread of the disease. In this work we propose a light Convolutional Neural Network (CNN) design, based on the model of the SqueezeNet, for the efficient discrimination of COVID-19 CT images with respect to other community-acquired pneumonia and/or healthy CT images. The architecture allows to an accuracy of 85.03% with an improvement of about 3.2% in the first dataset arrangement and of about 2.1% in the second dataset arrangement. The obtained gain, though of low entity, can be really important in medical diagnosis and, in particular, for Covid-19 scenario. Also the average classification time on a high-end workstation, 1.25 s, is very competitive with respect to that of more complex CNN designs, 13.41 s, witch require pre-processing. The proposed CNN can be executed on medium-end laptop without GPU acceleration in 7.81 s: this is impossible for methods requiring GPU acceleration. The performance of the method can be further improved with efficient pre-processing strategies for witch GPU acceleration is not necessary.

Polsinelli Matteo, Cinque Luigi, Placidi Giuseppe

2020-Dec

CNN, COVID-19, Deep Learning, Pattern Recognition

Radiology Radiology

Radiology in the News: A Content Analysis of Radiology-Related Information Retrieved From Google Alerts.

In Current problems in diagnostic radiology

INTRODUCTION : Radiology topics receive substantial online media attention, with prior studies focusing on social media platform coverage. We used Google Alerts, a content change detection and notification service, to prospectively analyze new radiology-related content appearing on the internet.

MATERIALS AND METHODS : An automated notification was created on Google Alerts for the search term "radiology," sending the user emails with up to 3 new links daily. All links from November 2019 through April 2020 were assessed by 2 of 3 independent raters using a coding system to classify the content source and primary topic of discussion. The top 5 primary topics were retrospectively evaluated to identify prevalent subcategories. Content viewing restrictions were documented.

RESULTS : 526 links were accessed. The majority (68%) of links were created by non-radiology lay press, followed by radiology-related lay press (28%), university-based publications (2%), and professional society websites (2%). The primary topic of these links most frequently related to market trends (28%), promotional material (20%), COVID-19 (13%), artificial intelligence (8%), and new technology or equipment (5%). 15% of links discussed a topic sourced from another article, such as a peer-reviewed journal, though only 2 linked directly to the journal itself. 8% of links had content viewing restrictions.

CONCLUSION : New radiology content was largely disseminated via non-radiology news sources; radiologists should therefore ensure their research and viewpoints are presented in these outlets. Google Alerts may be a useful tool to stay abreast of the most current public radiology subject matters, especially during these times of social isolation and rapidly evolving clinical practice.

Munawar Kamran, Sugi Mark D, Prabhu Vinay

2020-Oct-08

General General

Enhancing the Identification of Cyberbullying through Participant Roles

ArXiv Preprint

Cyberbullying is a prevalent social problem that inflicts detrimental consequences to the health and safety of victims such as psychological distress, anti-social behaviour, and suicide. The automation of cyberbullying detection is a recent but widely researched problem, with current research having a strong focus on a binary classification of bullying versus non-bullying. This paper proposes a novel approach to enhancing cyberbullying detection through role modeling. We utilise a dataset from ASKfm to perform multi-class classification to detect participant roles (e.g. victim, harasser). Our preliminary results demonstrate promising performance including 0.83 and 0.76 of F1-score for cyberbullying and role classification respectively, outperforming baselines.

Gathika Ratnayaka, Thushari Atapattu, Mahen Herath, Georgia Zhang, Katrina Falkner

2020-10-13

General General

Monitoring the Impact of Air Quality on the COVID-19 Fatalities in Delhi, India: Using Machine Learning Techniques.

In Disaster medicine and public health preparedness

OBJECTIVE : The focus of this study is to monitor the effect of lockdown on the various air pollutants due to COVID-19 pandemic and identify the ones that affect COVID-19 fatalities so that measures to control the pollution could be enforced.

METHODS : Various machine learning techniques: Decision Trees, Linear Regression and Random Forest have been applied to correlate air pollutants and COVID-19 fatalities in Delhi. Furthermore, a comparison between the concentration of various air pollutants and the air quality index during lockdown period and last two years 2018 and 2019 has been presented.

RESULTS : From the experimental work, it has been observed that the pollutants Ozone and Toluene have increased during the lockdown period. It has also been deduced that the pollutants that may impact the mortalities due to COVID-19 are Ozone, NH3, NO2, and PM10.

CONCLUSIONS : The novel corona virus has led to environmental restoration due to lockdown. However, there is a need to impose measures to control Ozone pollution as there has been a significant increase in its concentration and it also impacts the COVID-19 mortality rate.

Sethi Jasleen Kaur, Mittal Mamta

2020-Oct-12

Air Pollutants, COVID-19, Decision Trees, Linear Regression, Machine Learning, Random Forest

General General

COVID-19 Imaging Data Privacy by Federated Learning Design: A Theoretical Framework

ArXiv Preprint

To address COVID-19 healthcare challenges, we need frequent sharing of health data, knowledge and resources at a global scale. However, in this digital age, data privacy is a big concern that requires the secure embedding of privacy assurance into the design of all technological solutions that use health data. In this paper, we introduce differential privacy by design (dPbD) framework and discuss its embedding into the federated machine learning system. To limit the scope of our paper, we focus on the problem scenario of COVID-19 imaging data privacy for disease diagnosis by computer vision and deep learning approaches. We discuss the evaluation of the proposed design of federated machine learning systems and discuss how differential privacy by design (dPbD) framework can enhance data privacy in federated learning systems with scalability and robustness. We argue that scalable differentially private federated learning design is a promising solution for building a secure, private and collaborative machine learning model such as required to combat COVID19 challenge.

Anwaar Ulhaq, Oliver Burmeister

2020-10-13

General General

A combined approach of MALDI-TOF Mass Spectrometry and multivariate analysis as a potential tool for the detection of SARS-CoV-2 virus in nasopharyngeal swabs.

In Journal of virological methods

Coronavirus disease 2019, known as COVID-19, is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The early, sensitive and specific detection of SARS-CoV-2 virus is widely recognized as the critical point in responding to the ongoing outbreak. Currently, the diagnosis is based on molecular real time RT-PCR techniques, although their implementation is being threatened due to the extraordinary demand for supplies worldwide. That is why the development of alternative and / or complementary tests becomes so relevant. Here, we exploit the potential of mass spectrometry technology combined with machine learning algorithms, for the detection of COVID-19 positive and negative protein profiles directly from nasopharyngeal swabs samples. According to the preliminary results obtained, accuracy = 67.66%, sensitivity = 61.76%, specificity = 71.72%, and although these parameters still need to be improved to be used as a screening technique, mass spectrometry-based methods coupled with multivariate analysis showed that it is an interesting tool that deserves to be explored as a complementary diagnostic approach due to the low cost and fast performance. However, further steps, such as the analysis of a large number of samples, should be taken in consideration to determine the applicability of the method developed.

Rocca María Florencia, Zintgraff Jonathan Cristian, Dattero María Elena, Santos Leonardo Silva, Ledesma Martín, Vay Carlos, Prieto Mónica, Benedetti Estefanía, Avaro Martín, Russo Mara, Nachtigall Fabiane Manke, Baumeister Elsa

2020-Oct-09

COVID-19, MALDI-TOF, Mass spectrometry, SARS-CoV-2, machine learning

General General

Severity and Consolidation Quantification of COVID-19 from CT Images Using Deep Learning Based on Hybrid Weak Labels.

In IEEE journal of biomedical and health informatics

Early and accurate diagnosis of Coronavirus disease (COVID-19) is essential for patient isolation and contact tracing so that the spread of infection can be limited. Computed tomography (CT) can provide important information in COVID-19, especially for patients with moderate to severe disease as well as those with worsening cardiopulmonary status. As an automatic tool, deep learning methods can be utilized to perform semantic segmentation of affected lung regions, which is important to establish disease severity and prognosis prediction. Both the extent and type of pulmonary opacities help assess disease severity. However, manually pixel-level multi-class labelling is time-consuming, subjective, and non-quantitative. In this work, we proposed a hybrid weak label-based deep learning method that utilize both the manually annotated pulmonary opacities from COVID-19 pneumonia and the patient-level disease-type information available from the clinical report. A UNet was firstly trained with semantic labels to segment the total infected region. It was used to initialize another UNet, which was trained to segment the consolidations with patient-level information using the Expectation-Maximization (EM) algorithm. To demonstrate the performance of the proposed method, multi-institutional CT datasets from Iran, Italy, South Korea, and the United States were utilized. Results show that our proposed method can predict the infected regions as well as the consolidation regions with a good correlation to human annotation.

Wu Dufan, Gong Kuang, Arru Chiara, Homayounieh Fatemeh, Bizzo Bernardo, Buch Varun, Ren Hui, Kim Kyungsang, Neumark Nir, Tak Won Young, Kang Min Kyu, Carriero Alessandro, Saba Luca, Dayan Ittai, Masjedi Mahsa, Babaei Rosa, Kalra Mannudeep K, Li Quanzheng

2020-Oct-12

Public Health Public Health

A national fight against COVID-19: lessons and experiences from China.

In Australian and New Zealand journal of public health

OBJECTIVE : This paper aims to review the public health measures and actions taken during the fight against COVID-19 in China, to generate a model for prevention and control public health emergency by summarising the lessons and experiences gained.

METHODS : This paper adopts a widely accepted qualitative research and coding method to form an analysis on word materials.

RESULTS : Although Chinese CDC didn't work effectively in the early stages on risk identification and warning, China was able to respond quickly and successfully to this medical emergency after the initial shock of the awareness of a novel epidemic with a swift implementation of national-scale health emergency management.

CONCLUSIONS : The success in fighting against COVID-19 in China can be attributed to: 1) adaptable governance to changing situations; 2) culture of moral compliance with rules; 3) trusted collaboration between government and people; 4) an advanced technical framework ABCD+5G (A-Artificial intelligence; B-Block chain; C-Cloud computing; D-Big data). Implications for public health: This paper constructs a conceptual model for pandemic management based on the lessons and experiences of fighting COVID-19 in China. It provides insights for pandemic control and public emergency management in similar context.

Wang Lixia, Yan Beibei, Boasson Vigdis

2020-Oct-12

ABCD+5G, COVID-19, emergency management, pandemic, public health emergencies

General General

Zero-Shot Learning and its Applications from Autonomous Vehicles to COVID-19 Diagnosis: A Review.

In Intelligence-based medicine

The challenge of learning a new concept, object, or a new medical disease recognition without receiving any examples beforehand is called Zero-Shot Learning (ZSL). One of the major issues in deep learning based methodologies such as in Medical Imaging and other real-world applications is the requirement of large annotated datasets prepared by clinicians or experts to train the model. ZSL is known for having minimal human intervention by relying only on previously known or trained concepts plus currently existing auxiliary information. This is ever-growing research for the cases where we have very limited or no annotated datasets available and the detection / recognition system has human-like characteristics in learning new concepts. This makes the ZSL applicable in many real-world scenarios, from unknown object detection in autonomous vehicles to medical imaging and unforeseen diseases such as COVID-19 Chest X-Ray (CXR) based diagnosis. In this review paper, we introduce a novel and broaden solution called Few / one-shot learning, and present the definition of the ZSL problem as an extreme case of the few-shot learning. We review over fundamentals and the challenging steps of Zero-Shot Learning, including state-of-the-art categories of solutions, as well as our recommended solution, motivations behind each approach, their advantages over each category to guide both clinicians and AI researchers to proceed with the best techniques and practices based on their applications. Inspired from different settings and extensions, we then review through different datasets inducing medical and non-medical images, the variety of splits, and the evaluation protocols proposed so far. Finally, we discuss the recent applications and future directions of ZSL. We aim to convey a useful intuition through this paper towards the goal of handling complex learning tasks more similar to the way humans learn. We mainly focus on two applications in the current modern yet challenging era: coping with an early and fast diagnosis of COVID-19 cases, and also encouraging the readers to develop other similar AI-based automated detection / recognition systems using ZSL.

Rezaei Mahdi, Shahidi Mahsa

2020-Oct-02

Autonomous Vehicles, COVID-19 Pandemic, Chest X-Ray (CXR), Deep Learning, Machine Learning, SARS-CoV-2, Semantic Embedding, Supervised Annotation, Zero-Shot Learning

General General

Redesigning COVID 19 Care with Network Medicine and Machine Learning: A review.

In Mayo Clinic proceedings. Innovations, quality & outcomes

Emerging evidence regarding COVID 19 highlights the role of individual resistance and immune function in both susceptibility to infection as well as severity of disease. Multiple factors influence the response of the human host when exposed to viral pathogens. Influencing an individual's susceptibility to infection include such factors as nutritional status, physical and psychosocial stressors, obesity, protein calorie malnutrition, emotional resilience, single nucleotide polymorphisms (SNPs), environmental toxins-including air pollution and first- and second-hand tobacco smoke, sleep habits, sedentary lifestyle, drug-induced nutritional deficiencies and drug-induced immunomodulatory effects, availability of nutrient dense food and empty calories. This review examines the network of interacting co-factors that influence the host-pathogen relationship, which in turn determine one's susceptibility to viral infections like COVID 19. It then evaluates the role of machine learning, including predictive analytics and random forest modeling, to help clinicians assess patients' risk of developing active infection and devise a comprehensive approach to prevention and treatment.

Halamka John, Cerrato Paul, Perlman Adam

2020-Oct-05

Radiology Radiology

Deep learning for automatic quantification of lung abnormalities in COVID-19 patients: first experience and correlation with clinical parameters.

In European journal of radiology open

Rationale and objectives : To demonstrate the first experience of a deep learning-based algorithm for automatic quantification of lung parenchymal abnormalities in chest CT of COVID-19 patients and to correlate quantitative results with clinical and laboratory parameters.

Materials and methods : We retrospectively included 60 consecutive patients (mean age, 61 ± 12 years; 18 females) with proven COVID-19 infection undergoing chest CT between March and May 2020. Clinical and laboratory data (within 24 hours before/after chest CT) were recorded. Prototype software using a deep learning algorithm was applied for automatic segmentation and quantification of lung opacities. Percentage of opacity (PO, ground-glass and consolidations) and percentage of high opacity (PHO, consolidations), were defined as 100 times the volume of segmented abnormalities divided by the volume of the lung mask.

Results : Automatic CT analysis of the lung was feasible in all patients (n = 60). The median time to accomplish automatic evaluation was 120 s (IQR: 118-128 s). In four cases (7%), manual corrections were necessary. Patients with need for mechanical ventilation had a significantly higher PO (median 44%, IQR: 23-58% versus 13%, IQR: 10-24%; p = 0.001) and PHO (median: 11%, IQR: 6-21% versus 3%, IQR: 2-7%, p = 0.002) compared to those without. The PO and PHO moderately correlated with c-reactive protein (r = 0.49-0.60, both p < 0.001) and leucocyte count (r = 0.30-0.40, both p = 0.05). PO had a negative correlation with SO2 (r=-0.50, p = 0.001).

Conclusion : Preliminary experience indicates the feasibility of a rapid, automatic quantification tool of lung parenchymal abnormalities in COVID-19 patients using deep learning, with results correlating with laboratory and clinical parameters.

Mergen Victor, Kobe Adrian, Blüthgen Christian, Euler André, Flohr Thomas, Frauenfelder Thomas, Alkadhi Hatem, Eberhard Matthias

2020-Oct-06

COVID-19, computed tomography, deep learning, lung infection

General General

Mortality prediction model for the triage of COVID-19, pneumonia, and mechanically ventilated ICU patients: A retrospective study.

In Annals of medicine and surgery (2012)

Rationale : Prediction of patients at risk for mortality can help triage patients and assist in resource allocation.

Objectives : Develop and evaluate a machine learning-based algorithm which accurately predicts mortality in COVID-19, pneumonia, and mechanically ventilated patients.

Methods : Retrospective study of 53,001 total ICU patients, including 9166 patients with pneumonia and 25,895 mechanically ventilated patients, performed on the MIMIC dataset. An additional retrospective analysis was performed on a community hospital dataset containing 114 patients positive for SARS-COV-2 by PCR test. The outcome of interest was in-hospital patient mortality.

Results : When trained and tested on the MIMIC dataset, the XGBoost predictor obtained area under the receiver operating characteristic (AUROC) values of 0.82, 0.81, 0.77, and 0.75 for mortality prediction on mechanically ventilated patients at 12-, 24-, 48-, and 72- hour windows, respectively, and AUROCs of 0.87, 0.78, 0.77, and 0.734 for mortality prediction on pneumonia patients at 12-, 24-, 48-, and 72- hour windows, respectively. The predictor outperformed the qSOFA, MEWS and CURB-65 risk scores at all prediction windows. When tested on the community hospital dataset, the predictor obtained AUROCs of 0.91, 0.90, 0.86, and 0.87 for mortality prediction on COVID-19 patients at 12-, 24-, 48-, and 72- hour windows, respectively, outperforming the qSOFA, MEWS and CURB-65 risk scores at all prediction windows.

Conclusions : This machine learning-based algorithm is a useful predictive tool for anticipating patient mortality at clinically useful timepoints, and is capable of accurate mortality prediction for mechanically ventilated patients as well as those diagnosed with pneumonia and COVID-19.

Ryan Logan, Lam Carson, Mataraso Samson, Allen Angier, Green-Saxena Abigail, Pellegrini Emily, Hoffman Jana, Barton Christopher, McCoy Andrea, Das Ritankar

2020-Nov

Artificial intelligence, COVID-19, Machine learning, Mortality prediction, SARS-CoV-2

General General

Alexa, What classes do I have today? The use of Artificial Intelligence via Smart Speakers in Education.

In Procedia computer science

Looking back to the rumours from the early 2000's, when the world of technology bloomed together with the curiosity towards what was next to come, by 2020, robots should have assisted and supported almost every task from our daily life. While this may seem as a Sci-Fi movie scenario, it is partially a tangible reality, that we quickly got used to, thanks to the introduction of smart speakers. As the world changes, so does the future of our students. In this respects, the evolution of the technology comes up with specific environments for educational purpose. Building smart learning environments supported by e-learning platforms is an important area of research in education domain within our days. The evolution of these smart learning environments is justified by some events (Covid19) that force students to learn remotely. The paper proposes a software application component using Alexa smart speaker, that integrates different services (Amazon Web Services, Microsoft Services) for a proper virtual environment platform, for both students and teachers. It addresses the main concerns of the current educational system, and provides a smart solution through the use of Artificial Intelligence based tools. The proposed approach not only achieves unifying data and knowledge-share mechanisms in a remotely mode, but it brings also a good learning experience, increasing the effectiveness and the efficiency of the learning process.

Şerban Camelia, Todericiu Ioana-Alexandra

2020

artificial intelligence, e-learning, smart speakers, virtual learning environments

General General

Machine learning for coronavirus covid-19 detection from chest x-rays.

In Procedia computer science

At the end of 2019, a new form of Coronavirus, called COVID-19, has widely spread in the world. To quickly screen patients with the aim to detect this new form of pulmonary disease, in this paper we propose a method aimed to automatically detect the COVID-19 disease by analysing medical images. We exploit supervised machine learning techniques building a model considering a data-set freely available for research purposes of 85 chest X-rays. The experiment shows the effectiveness of the proposed method in the discrimination between the COVID-19 disease and other pulmonary diseases.

Brunese Luca, Martinelli Fabio, Mercaldo Francesco, Santone Antonella

2020

COVID-19, Coronavirus, artificial intelligence, machine learning, medical images, x-ray

Cardiology Cardiology

Digital cardiovascular care in COVID-19 pandemic: A potential alternative?

In Journal of cardiac surgery ; h5-index 21.0

BACKGROUND : Cardiovascular patients are at increased risk of acquiring coronavirus disease 2019 (COVID-19) infection while their visit to healthcare facilities. There is a need for alternative tools for optimal monitoring and management of cardiovascular patients in the present pandemic situation. Digital health care may prove to be a new revolutionary tool to protect cardiovascular patients from coronavirus disease by avoiding routine visits to health care facilities that are already overwhelmed with COVID-19 patients.

METHODS : To evaluate the role of digital health care in the present era of the COVID-19 pandemic, we have reviewed the published literature on digital health services providing cardiovascular care.

RESULTS AND CONCLUSION : Digital health including telemedicine services, robotic telemedicine carts, use of artificial intelligence and machine learning, use of digital gadgets like smartwatches and web-based applications may be a safe alternative for the management of cardiovascular patients in the present pandemic situation.

Kaushik Atul, Patel Surendra, Dubey Kalika

2020-Oct-10

COVID-19 pandemic, artificial intelligence, cardiovascular care, digital health, telemedicine

General General

Unsupervised explainable AI for simultaneous molecular evolutionary study of forty thousand SARS-CoV-2 genomes

bioRxiv Preprint

Unsupervised AI (artificial intelligence) can obtain novel knowledge from big data without particular models or prior knowledge and is highly desirable for unveiling hidden features in big data. SARS-CoV-2 poses a serious threat to public health and one important issue in characterizing this fast-evolving virus is to elucidate various aspects of their genome sequence changes. We previously established unsupervised AI, a BLSOM (batch-learning SOM), which can analyze five million genomic sequences simultaneously. The present study applied the BLSOM to the oligonucleotide compositions of forty thousand SARS-CoV-2 genomes. While only the oligonucleotide composition was given, the obtained clusters of genomes corresponded primarily to known main clades and internal divisions in the main clades. Since the BLSOM is explainable AI, it reveals which features of the oligonucleotide composition are responsible for clade clustering. The BLSOM has powerful image display capabilities and enables efficient knowledge discovery about viral evolutionary processes.

Ikemura, T.; Wada, K.; Wada, Y.; Iwasaki, Y.; Abe, T.

2020-10-12

General General

Prediction of COVID-19 Severity from Chest CT and Laboratory Measurements: Evaluation of a Machine Learning Approach.

In JMIR medical informatics ; h5-index 23.0

BACKGROUND : Most of mortality of COVID-19 were from severe patients.

OBJECTIVE : Effective treatment of these severe cases remains a challenge due to a lack of early detection.

METHODS : A total set of 27 severe and 151 non-severe clinical and computerized tomography (CT) records from 46 COVID-19 patients (10 severe, 36 non-severe) was collected for building the model. Using a recently published convolutional neural network (CNN), we managed to extract features from CT images. A machine learning model which combines these features with clinical laboratory results was also trained.

RESULTS : Herein, we presented a prediction model, combining the radiological outcome with the clinical biochemical indexes, to identify the severe cases. The prediction model yields a cross-validated AUROC score of 0.93 and F1 score of 0.89, which improved 6% and 15%, respectively, from the models with laboratory tests features only. In addition, we developed a statistical model for forecasting severity based on patients' laboratory tests results before turning into severe cases, with an AUROC score of 0.81.

CONCLUSIONS : To our knowledge, this is the first report on predicting COVID-19 patient's severity progression, as well as severity forecasting, through a combination analysis of laboratory tests and CT images.

CLINICALTRIAL :

Zhu Fang, Li Daowei, Zhang Qiang, Tan Yue, Yue Yuanyi, Bai Yuhan, Li Jimeng, Li Jiahang, Feng Xinghuo, Chen Shiyu, Xu Youjun, Xiao Si-Yu, Sun Muyan, Li Xiaona

2020-Sep-21

Radiology Radiology

Dynamic evaluation of lung involvement during coronavirus disease-2019 (COVID-19) with quantitative lung CT.

In Emergency radiology

PURPOSE : To identify and quantify lung changes associated with coronavirus disease-2019 (COVID-19) with quantitative lung CT during the disease.

METHODS : This retrospective study reviewed COVID-19 patients who underwent multiple chest CT scans during their disease course. Quantitative lung CT was used to determine the nature and volume of lung involvement. A semi-quantitative scoring system was also used to evaluate lung lesions.

RESULTS : This study included eighteen cases (4 cases in mild type, 10 cases in moderate type, 4 cases in severe type, and without critical type cases) with confirmed COVID-19. Patients had a mean hospitalized period of 24.1 ± 7.1 days (range: 14-38 days) and underwent an average CT scans of 3.9 ± 1.6 (range: 2-8). The total volumes of lung abnormalities reached a peak of 8.8 ± 4.1 days (range: 2-14 days). The ground-glass opacity (GGO) volume percentage was higher than the consolidative opacity (CO) volume percentage on the first CT examination (Z = 2.229, P = 0.026), and there was no significant difference between the GGO volume percentage and that of CO at the peak stage (Z = - 0.628, P = 0.53). The volume percentage of lung involvement identified by AI demonstrated a strong correlation with the total CT scores at each stage (r = 0.873, P = 0.0001).

CONCLUSIONS : Quantitative lung CT can automatically identify the nature of lung involvement and quantify the dynamic changes of lung lesions on CT during COVID-19. For patients who recovered from COVID-19, GGO was the predominant imaging feature on the initial CT scan, while GGO and CO were the main appearances at peak stage.

Ma Chun, Wang Xiao-Ling, Xie Dong-Mei, Li Yu-Dan, Zheng Yong-Ji, Zhang Hai-Bing, Ming Bing

2020-Oct-10

Artificial intelligence, Coronavirus, Lung, Pneumonia, Tomography, X-ray, viral

General General

A comprehensive study on classification of COVID-19 on computed tomography with pretrained convolutional neural networks.

In Scientific reports ; h5-index 158.0

The use of imaging data has been reported to be useful for rapid diagnosis of COVID-19. Although computed tomography (CT) scans show a variety of signs caused by the viral infection, given a large amount of images, these visual features are difficult and can take a long time to be recognized by radiologists. Artificial intelligence methods for automated classification of COVID-19 on CT scans have been found to be very promising. However, current investigation of pretrained convolutional neural networks (CNNs) for COVID-19 diagnosis using CT data is limited. This study presents an investigation on 16 pretrained CNNs for classification of COVID-19 using a large public database of CT scans collected from COVID-19 patients and non-COVID-19 subjects. The results show that, using only 6 epochs for training, the CNNs achieved very high performance on the classification task. Among the 16 CNNs, DenseNet-201, which is the deepest net, is the best in terms of accuracy, balance between sensitivity and specificity, [Formula: see text] score, and area under curve. Furthermore, the implementation of transfer learning with the direct input of whole image slices and without the use of data augmentation provided better classification rates than the use of data augmentation. Such a finding alleviates the task of data augmentation and manual extraction of regions of interest on CT images, which are adopted by current implementation of deep-learning models for COVID-19 classification.

Pham Tuan D

2020-Oct-09

Radiology Radiology

Development and evaluation of an artificial intelligence system for COVID-19 diagnosis.

In Nature communications ; h5-index 260.0

Early detection of COVID-19 based on chest CT enables timely treatment of patients and helps control the spread of the disease. We proposed an artificial intelligence (AI) system for rapid COVID-19 detection and performed extensive statistical analysis of CTs of COVID-19 based on the AI system. We developed and evaluated our system on a large dataset with more than 10 thousand CT volumes from COVID-19, influenza-A/B, non-viral community acquired pneumonia (CAP) and non-pneumonia subjects. In such a difficult multi-class diagnosis task, our deep convolutional neural network-based system is able to achieve an area under the receiver operating characteristic curve (AUC) of 97.81% for multi-way classification on test cohort of 3,199 scans, AUC of 92.99% and 93.25% on two publicly available datasets, CC-CCII and MosMedData respectively. In a reader study involving five radiologists, the AI system outperforms all of radiologists in more challenging tasks at a speed of two orders of magnitude above them. Diagnosis performance of chest x-ray (CXR) is compared to that of CT. Detailed interpretation of deep network is also performed to relate system outputs with CT presentations. The code is available at https://github.com/ChenWWWeixiang/diagnosis_covid19 .

Jin Cheng, Chen Weixiang, Cao Yukun, Xu Zhanwei, Tan Zimeng, Zhang Xin, Deng Lei, Zheng Chuansheng, Zhou Jie, Shi Heshui, Feng Jianjiang

2020-Oct-09

General General

Machine learning techniques for sequence-based prediction of viral-host interactions between SARS-CoV-2 and human proteins.

In Biomedical journal

BACKGROUND : COVID-19 (Coronavirus Disease-19), a disease caused by the SARS-CoV-2 virus, has been declared as a pandemic by the World Health Organization on March 11, 2020. Over 15 million people have already been affected worldwide by COVID-19, resulting in more than 0.6 million deaths. Protein-protein interactions (PPIs) play a key role in the cellular process of SARS-CoV-2 virus infection in the human body. Recently a study has reported some SARS-CoV-2 proteins that interact with several human proteins while many potential interactions remain to be identified.

METHOD : In this article, various machine learning models are built to predict the PPIs between the virus and human proteins that are further validated using biological experiments. The classification models are prepared based on different sequence-based features of human proteins like amino acid composition, pseudo amino acid composition, and conjoint triad.

RESULT : We have built an ensemble voting classifier using SVMRadial, SVMPolynomial, and Random Forest technique that gives a greater accuracy, precision, specificity, recall, and F1 score compared to all other models used in the work. A total of 1326 potential human target proteins of SARS-CoV-2 have been predicted by the proposed ensemble model and validated using gene ontology and KEGG pathway enrichment analysis. Several repurposable drugs targeting the predicted interactions are also reported.

CONCLUSION : This study may encourage the identification of potential targets for more effective anti-COVID drug discovery.

Dey Lopamudra, Chakraborty Sanjay, Mukhopadhyay Anirban

2020-Sep-03

COVID-19, Classifier ensemble, Machine learning, Protein–protein interaction, SARS-CoV-2, Supervised classification

General General

Evaluation of a genetic risk score for severity of COVID-19 using human chromosomal-scale length variation.

In Human genomics

INTRODUCTION : The course of COVID-19 varies from asymptomatic to severe in patients. The basis for this range in symptoms is unknown. One possibility is that genetic variation is partly responsible for the highly variable response. We evaluated how well a genetic risk score based on chromosomal-scale length variation and machine learning classification algorithms could predict severity of response to SARS-CoV-2 infection.

METHODS : We compared 981 patients from the UK Biobank dataset who had a severe reaction to SARS-CoV-2 infection before 27 April 2020 to a similar number of age-matched patients drawn for the general UK Biobank population. For each patient, we built a profile of 88 numbers characterizing the chromosomal-scale length variability of their germ line DNA. Each number represented one quarter of the 22 autosomes. We used the machine learning algorithm XGBoost to build a classifier that could predict whether a person would have a severe reaction to COVID-19 based only on their 88-number classification.

RESULTS : We found that the XGBoost classifier could differentiate between the two classes at a significant level (p = 2 · 10-11) as measured against a randomized control and (p = 3 · 10-14) as measured against the expected value of a random guessing algorithm (AUC = 0.5). However, we found that the AUC of the classifier was only 0.51, too low for a clinically useful test.

CONCLUSION : Genetics play a role in the severity of COVID-19, but we cannot yet develop a useful genetic test to predict severity.

Toh Christopher, Brody James P

2020-Oct-09

COVID-19, Genetic risk score, Machine learning, UK biobank

General General

Prognostic Assessment of COVID-19 in ICU by Machine Learning Methods: A Retrospective Study.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Patients with coronavirus disease (COVID-19) in ICU have a high mortality rate, and how to early assess the prognosis and carry out precise treatment is of great significance.

OBJECTIVE : To use machine learning to construct a model for the analysis of risk factors and prediction of death among ICU patients with COVID-19.

METHODS : In this retrospective study, 123 COVID-19 patients inthe ICU of Vulcan Hill Hospital were selected from the database, and data were randomly divided into a training data set (n = 98) and test data set (n = 25) with a 4:1 ratio. Significance tests, analysis of correlation and factor analysis were used to screen the 100 potential risk factors individually. Conventional logistic regression methods and four machine learning algorithms were used to construct the risk prediction model for the prognosis of COVID-19 patients in ICU. Performance of these machine learning models was measured by the area under the receiver operating characteristic curve (AUC). Model interpretation and model evaluation of the risk prediction model, such as calibration curve, SHAP, LIME, etc., were performed to ensure its stability and reliability.The outcome is based on the ICU death recorded from the database.

RESULTS : Layer-by-layer screening of 100 potential risk factors finallyrevealed 8 important risk factors that were included in the risk prediction model: lymphocyte percentage (LYM%), prothrombin time (PT), lactate dehydrogenase (LDH), total bilirubin (T-Bil), percentage of eosinophils (EOS%), creatinine(Cr), neutrophil percentage (NEUT%), albumin (ALB) level. Finally, eXtreme Gradient Boosting (XGBoost) established by 8 important risk factors showed the best recognition ability in the training set of 5-fold cross validation (AUC=0.86) and the verification queue (AUC=0.92). The calibration curve showed that the risk predicted by the model was in good agreement with the actual risk. In addition, using SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) algorithms, feature interpretation and sample prediction interpretation algorithms of the XGBoost black box model were implemented. Additionally, the model has been translated into an online risk calculator that is freely available for the public usage ( http://114.251.235.51:1226/index).

CONCLUSIONS : The 8 factors XGBoost model predicts risk of death in ICU patients with COVID-19 well,which initially demonstrates stability and can be used effectively to predict COVID-19 prognosis in ICU patients.

CLINICALTRIAL :

Pan Pan, Li Yichao, Xiao Yongjiu, Han Bingchao, Su Mingliang, Li Yansheng, Zhang Siqi, Jiang Dapeng, Chen Xia, Zhou Fuquan, Ma Ling, Bao Pengtao, Su Longxiang, Xie Lixin

2020-Oct-08

General General

Computer-aided prediction and design of IL-6 inducing peptides: IL-6 plays a crucial role in COVID-19.

In Briefings in bioinformatics

Interleukin 6 (IL-6) is a pro-inflammatory cytokine that stimulates acute phase responses, hematopoiesis and specific immune reactions. Recently, it was found that the IL-6 plays a vital role in the progression of COVID-19, which is responsible for the high mortality rate. In order to facilitate the scientific community to fight against COVID-19, we have developed a method for predicting IL-6 inducing peptides/epitopes. The models were trained and tested on experimentally validated 365 IL-6 inducing and 2991 non-inducing peptides extracted from the immune epitope database. Initially, 9149 features of each peptide were computed using Pfeature, which were reduced to 186 features using the SVC-L1 technique. These features were ranked based on their classification ability, and the top 10 features were used for developing prediction models. A wide range of machine learning techniques has been deployed to develop models. Random Forest-based model achieves a maximum AUROC of 0.84 and 0.83 on training and independent validation dataset, respectively. We have also identified IL-6 inducing peptides in different proteins of SARS-CoV-2, using our best models to design vaccine against COVID-19. A web server named as IL-6Pred and a standalone package has been developed for predicting, designing and screening of IL-6 inducing peptides (https://webs.iiitd.edu.in/raghava/il6pred/).

Dhall Anjali, Patiyal Sumeet, Sharma Neelam, Usmani Salman Sadullah, Raghava Gajendra P S

2020-Oct-09

COVID-19, Interleukin 6 (IL-6), computer-aided prediction, machine learning, pro-inflammatory cytokine

General General

Predicting Coronavirus Disease 2019 Infection Risk and Related Risk Drivers in Nursing Homes: A Machine Learning Approach.

In Journal of the American Medical Directors Association

OBJECTIVE : Inform coronavirus disease 2019 (COVID-19) infection prevention measures by identifying and assessing risk and possible vectors of infection in nursing homes (NHs) using a machine-learning approach.

DESIGN : This retrospective cohort study used a gradient boosting algorithm to evaluate risk of COVID-19 infection (ie, presence of at least 1 confirmed COVID-19 resident) in NHs.

SETTING AND PARTICIPANTS : The model was trained on outcomes from 1146 NHs in Massachusetts, Georgia, and New Jersey, reporting COVID-19 case data on April 20, 2020. Risk indices generated from the model using data from May 4 were prospectively validated against outcomes reported on May 11 from 1021 NHs in California.

METHODS : Model features, pertaining to facility and community characteristics, were obtained from a self-constructed dataset based on multiple public and private sources. The model was assessed via out-of-sample area under the receiver operating characteristic curve (AUC), sensitivity, and specificity in the training (via 10-fold cross-validation) and validation datasets.

RESULTS : The mean AUC, sensitivity, and specificity of the model over 10-fold cross-validation were 0.729 [95% confidence interval (CI) 0.690‒0.767], 0.670 (95% CI 0.477‒0.862), and 0.611 (95% CI 0.412‒0.809), respectively. Prospective out-of-sample validation yielded similar performance measures (AUC 0.721; sensitivity 0.622; specificity 0.713). The strongest predictors of COVID-19 infection were identified as the NH's county's infection rate and the number of separate units in the NH; other predictors included the county's population density, historical Centers of Medicare and Medicaid Services cited health deficiencies, and the NH's resident density (in persons per 1000 square feet). In addition, the NH's historical percentage of non-Hispanic white residents was identified as a protective factor.

CONCLUSIONS AND IMPLICATIONS : A machine-learning model can help quantify and predict NH infection risk. The identified risk factors support the early identification and management of presymptomatic and asymptomatic individuals (eg, staff) entering the NH from the surrounding community and the development of financially sustainable staff testing initiatives in preventing COVID-19 infection.

Sun Christopher L F, Zuccarelli Eugenio, Zerhouni El Ghali A, Lee Jason, Muller James, Scott Karen M, Lujan Alida M, Levi Retsef

2020-Aug-27

COVID-19, Nursing homes, health policy, infection prevention, long-term care facility, machine-learning, risk modeling

General General

Digital phenotyping to enhance substance use treatment during the COVID-19 pandemic: Viewpoint.

In JMIR mental health

The COVID-19 pandemic has required transitioning many clinical addiction treatment programs to telephonic or virtual visits. Novel solutions are needed to enhance substance use treatment during a time when many patients are disconnected from clinical care and social supports. Digital phenotyping, which leverages the unique functionality of smartphones sensors (GPS, social behavior, and typing patterns), can buttress clinical treatment in a remote, scalable fashion. Specifically, digital phenotyping has the potential to improve relapse prediction and intervention, relapse detection, and overdose intervention. Digital phenotyping may enhance relapse prediction through coupling machine learning algorithms with the enormous wealth of collected behavioral data. Activity based analysis in real time potentially can be used to prevent relapse by warning substance users when they approach locational triggers such as bars or liquor stores. Wearable devices detect when someone has relapsed to substances through measuring physiological changes such as electrodermal activity and locomotion. Despite its initial promise, privacy, security and barriers to access are important issues to address.

Hsu Michael, Ahern David K, Suzuki Joji

2020-Sep-25

Radiology Radiology

Using Artificial Intelligence for COVID-19 Chest X-ray Diagnosis.

In Federal practitioner : for the health care professionals of the VA, DoD, and PHS

Background : Coronavirus disease-19 (COVID-19), caused by a novel member of the coronavirus family, is a respiratory disease that rapidly reached pandemic proportions with high morbidity and mortality. In only a few months, it has had a dramatic impact on society and world economies. COVID-19 has presented numerous challenges to all aspects of health care, including reliable methods for diagnosis, treatment, and prevention. Initial efforts to contain the spread of the virus were hampered by the time required to develop reliable diagnostic methods. Artificial intelligence (AI) is a rapidly growing field of computer science with many applications for health care. Machine learning is a subset of AI that uses deep learning with neural network algorithms. It can recognize patterns and achieve complex computational tasks often far quicker and with increased precision than can humans.

Methods : In this article, we explore the potential for the simple and widely available chest X-ray (CXR) to be used with AI to diagnose COVID-19 reliably. Microsoft CustomVision is an automated image classification and object detection system that is a part of Microsoft Azure Cognitive Services. We utilized publicly available CXR images for patients with COVID-19 pneumonia, pneumonia from other etiologies, and normal CXRs as a dataset to train Microsoft CustomVision.

Results : Our trained model overall demonstrated 92.9% sensitivity (recall) and positive predictive value (precision), with results for each label showing sensitivity and positive predictive value at 94.8% and 98.9% for COVID-19 pneumonia, 89% and 91.8% for non-COVID-19 pneumonia, 95% and 88.8% for normal lung. We then validated the program using CXRs of patients from our institution with confirmed COVID-19 diagnoses along with non-COVID-19 pneumonia and normal CXRs. Our model performed with 100% sensitivity, 95% specificity, 97% accuracy, 91% positive predictive value, and 100% negative predictive value.

Conclusions : We have used a readily available, commercial platform to demonstrate the potential of AI to assist in the successful diagnosis of COVID-19 pneumonia on CXR images. The findings have implications for screening and triage, initial diagnosis, monitoring disease progression, and identifying patients at increased risk of morbidity and mortality. Based on the data, a website was created to demonstrate how such technologies could be shared and distributed to others to combat entities such as COVID-19 moving forward.

Borkowski Andrew A, Viswanadhan Narayan A, Thomas L Brannon, Guzman Rodney D, Deland Lauren A, Mastorides Stephen M

2020-Sep

General General

Potential limitations in COVID-19 machine learning due to data source variability: a case study in the nCov2019 dataset.

In Journal of the American Medical Informatics Association : JAMIA

OBJECTIVE : Lack of representative COVID-19 data is a bottleneck for reliable and generalizable machine learning. Data sharing is insufficient without data quality, where source variability plays an important role. We showcase and discuss potential biases from data source variability for COVID-19 machine learning.

MATERIALS AND METHODS : We used the publicly available nCov2019 dataset, including patient level data from several countries. We aimed to the discovery and classification of severity subgroups using symptoms and comorbidities.

RESULTS : Cases from the two countries with the highest prevalence were divided into separate subgroups with distinct severity manifestations. This variability can reduce the representativeness of training data with respect the model target populations and increase model complexity at risk of overfitting.

CONCLUSION : Data source variability is a potential contributor to bias in distributed research networks. We call for systematic assessment and reporting of data source variability and data quality in COVID-19 data sharing, as key information for reliable and generalizable machine learning.

Sáez Carlos, Romero Nekane, Conejero J Alberto, García-Gómez Juan M

2020-Oct-07

COVID-19, biases, data quality, data sharing, dataset shift, distributed research networks, heterogeneity, machine learning, multi site data, variability

General General

Feasibility of Asynchronous and Automated Telemedicine in Otolaryngology: Prospective Cross-Sectional Study.

In JMIR medical informatics ; h5-index 23.0

BACKGROUND : COVID-19 often causes respiratory symptoms, making otolaryngology offices one of the most susceptible places for community transmission of the virus. Thus, telemedicine may benefit both patients and physicians.

OBJECTIVE : This study aims to explore the feasibility of telemedicine for the diagnosis of all otologic disease types.

METHODS : A total of 177 patients were prospectively enrolled, and the patient's clinical manifestations with otoendoscopic images were written in the electrical medical records. Asynchronous diagnoses were made for each patient to assess Top-1 and Top-2 accuracy, and we selected 20 cases to conduct a survey among four different otolaryngologists to assess the accuracy, interrater agreement, and diagnostic speed. We also constructed an experimental automated diagnosis system and assessed Top-1 accuracy and diagnostic speed.

RESULTS : Asynchronous diagnosis showed Top-1 and Top-2 accuracies of 77.40% and 86.44%, respectively. In the selected 20 cases, the Top-2 accuracy of the four otolaryngologists was on average 91.25% (SD 7.50%), with an almost perfect agreement between them (Cohen kappa=0.91). The automated diagnostic model system showed 69.50% Top-1 accuracy. Otolaryngologists could diagnose an average of 1.55 (SD 0.48) patients per minute, while the machine learning model was capable of diagnosing on average 667.90 (SD 8.3) patients per minute.

CONCLUSIONS : Asynchronous telemedicine in otology is feasible owing to the reasonable Top-2 accuracy when assessed by experienced otolaryngologists. Moreover, enhanced diagnostic speed while sustaining the accuracy shows the possibility of optimizing medical resources to provide expertise in areas short of physicians.

Cha Dongchul, Shin Seung Ho, Kim Jungghi, Eo Tae Seong, Na Gina, Bae Seong Hoon, Jung Jinsei, Kim Sung Huhn, Moon In Seok, Choi Jae Young, Park Yu Rang

2020-Sep-22

General General

Machine Learning to Predict Mortality and Critical Events in COVID-19 Positive New York City Patients: A Cohort Study.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Coronavirus disease 2019 (COVID-19) has infected millions of patients worldwide and has been responsible for several hundred thousand fatalities. This has necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods for achieving this are lacking.

OBJECTIVE : We analyze Electronic Health Records from COVID-19 positive hospitalized patients admitted to the Mount Sinai Health System in New York City (NYC). We present machine learning models for making predictions about the hospital course over clinically meaningful time horizons based on patient characteristics at admission. We assess performance of these models at multiple hospitals and time points.

METHODS : We utilized XGBoost and baseline comparator models, for predicting in-hospital mortality and critical events at time windows of 3, 5, 7 and 10 days from admission. Our study population included harmonized electronic health record (EHR) data from five hospitals in NYC for 4,098 COVID-19+ patients admitted from March 15, 2020 to May 22, 2020. Models were first trained on patients from a single hospital (N=1514) before or on May 1, externally validated on patients from four other hospitals (N=2201) before or on May 1, and prospectively validated on all patients after May 1 (N=383). Finally, we establish model interpretability to identify and rank variables that drive model predictions.

RESULTS : On cross-validation, the XGBoost classifier outperformed baseline models, with area under the receiver operating characteristic curve (AUC-ROC) for mortality at 0.89 at 3 days, 0.85 at 5 and 7 days, and 0.84 at 10 days; XGBoost also performed well for critical event prediction with AUC-ROC of 0.80 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. In external validation, XGBoost achieved an AUC-ROC of 0.88 at 3 days, 0.86 at 5 days, 0.86 at 7 days, and 0.84 at 10 days for mortality prediction. Similarly, XGBoost achieved an AUC-ROC of 0.78 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. Trends in performance on prospective validation sets were similar. At 7 days, acute kidney injury on admission, elevated LDH, tachypnea, and hyperglycemia were the strongest drivers of critical event prediction, while higher age, anion gap, and C-reactive protein were the strongest drivers for mortality prediction.

CONCLUSIONS : We trained and validated (both externally and prospectively) machine-learning models for mortality and critical events at different time horizons. These models identify at-risk patients, as well as uncover underlying relationships predicting outcomes.

CLINICALTRIAL :

Vaid Akhil, Somani Sulaiman, Russak Adam J, De Freitas Jessica K, Chaudhry Fayzan F, Paranjpe Ishan, Johnson Kipp W, Lee Samuel J, Miotto Riccardo, Richter Felix, Zhao Shan, Beckmann Noam D, Naik Nidhi, Kia Arash, Timsina Prem, Lala Anuradha, Paranjpe Manish, Golden Eddye, Danieletto Matteo, Singh Manbir, Meyer Dara, O’Reilly Paul F, Huckins Laura, Kovatch Patricia, Finkelstein Joseph, Freeman Robert M, Argulian Edgar, Kasarskis Andrew, Percha Bethany, Aberg Judith A, Bagiella Emilia, Horowitz Carol R, Murphy Barbara, Nestler Eric J, Schadt Eric E, Cho Judy H, Cordon-Cardo Carlos, Fuster Valentin, Charney Dennis S, Reich David L, Bottinger Erwin P, Levin Matthew A, Narula Jagat, Fayad Zahi A, Just Allan C, Charney Alexander W, Nadkarni Girish N, Glicksberg Benjamin

2020-Oct-02

General General

Increased risk of COVID-19 infection and mortality in people with mental disorders: analysis from electronic health records in the United States.

In World psychiatry : official journal of the World Psychiatric Association (WPA)

Concerns have been expressed that persons with a pre-existing mental disorder may represent a population at increased risk for COVID-19 infection and with a higher likelihood of adverse outcomes of the infection, but there is no systematic research evidence in this respect. This study assessed the impact of a recent (within past year) diagnosis of a mental disorder - including attention-deficit/hyperactivity disorder (ADHD), bipolar disorder, depression and schizophrenia - on the risk for COVID-19 infection and related mortality and hospitalization rates. We analyzed a nation-wide database of electronic health records of 61 million adult patients from 360 hospitals and 317,000 providers, across 50 states in the US, up to July 29, 2020. Patients with a recent diagnosis of a mental disorder had a significantly increased risk for COVID-19 infection, an effect strongest for depression (adjusted odds ratio, AOR=7.64, 95% CI: 7.45-7.83, p<0.001) and schizophrenia (AOR=7.34, 95% CI: 6.65-8.10, p<0.001). Among patients with a recent diagnosis of a mental disorder, African Americans had higher odds of COVID-19 infection than Caucasians, with the strongest ethnic disparity for depression (AOR=3.78, 95% CI: 3.58-3.98, p<0.001). Women with mental disorders had higher odds of COVID-19 infection than males, with the strongest gender disparity for ADHD (AOR=2.03, 95% CI: 1.73-2.39, p<0.001). Patients with both a recent diagnosis of a mental disorder and COVID-19 infection had a death rate of 8.5% (vs. 4.7% among COVID-19 patients with no mental disorder, p<0.001) and a hospitalization rate of 27.4% (vs. 18.6% among COVID-19 patients with no mental disorder, p<0.001). These findings identify individuals with a recent diagnosis of a mental disorder as being at increased risk for COVID-19 infection, which is further exacerbated among African Americans and women, and as having a higher frequency of some adverse outcomes of the infection. This evidence highlights the need to identify and address modifiable vulnerability factors for COVID-19 infection and to prevent delays in health care provision in this population.

Wang QuanQiu, Xu Rong, Volkow Nora D

2020-Oct-07

ADHD, COVID‐19, access to care, bipolar disorder, depression, discrimination, ethnic disparity, gender disparity, hospitalization, mental disorders, mortality, risk of infection, schizophrenia

General General

Issues associated with deploying CNN transfer learning to detect COVID-19 from chest X-rays.

In Physical and engineering sciences in medicine

Covid-19 first occurred in Wuhan, China in December 2019. Subsequently, the virus spread throughout the world and as of June 2020 the total number of confirmed cases are above 4.7 million with over 315,000 deaths. Machine learning algorithms built on radiography images can be used as a decision support mechanism to aid radiologists to speed up the diagnostic process. The aim of this work is to conduct a critical analysis to investigate the applicability of convolutional neural networks (CNNs) for the purpose of COVID-19 detection in chest X-ray images and highlight the issues of using CNN directly on the whole image. To accomplish this task, we use 12-off-the-shelf CNN architectures in transfer learning mode on 3 publicly available chest X-ray databases together with proposing a shallow CNN architecture in which we train it from scratch. Chest X-ray images are fed into CNN models without any preprocessing to replicate researches used chest X-rays in this manner. Then a qualitative investigation performed to inspect the decisions made by CNNs using a technique known as class activation maps (CAM). Using CAMs, one can map the activations contributed to the decision of CNNs back to the original image to visualize the most discriminating region(s) on the input image. We conclude that CNN decisions should not be taken into consideration, despite their high classification accuracy, until clinicians can visually inspect and approve the region(s) of the input image used by CNNs that lead to its prediction.

Majeed Taban, Rashid Rasber, Ali Dashti, Asaad Aras

2020-Oct-06

COVID-19, Class activation maps, Convolutional neural network, Coronavirus, Deep learning

Public Health Public Health

Machine learning based early warning system enables accurate mortality risk prediction for COVID-19.

In Nature communications ; h5-index 260.0

Soaring cases of coronavirus disease (COVID-19) are pummeling the global health system. Overwhelmed health facilities have endeavored to mitigate the pandemic, but mortality of COVID-19 continues to increase. Here, we present a mortality risk prediction model for COVID-19 (MRPMC) that uses patients' clinical data on admission to stratify patients by mortality risk, which enables prediction of physiological deterioration and death up to 20 days in advance. This ensemble model is built using four machine learning methods including Logistic Regression, Support Vector Machine, Gradient Boosted Decision Tree, and Neural Network. We validate MRPMC in an internal validation cohort and two external validation cohorts, where it achieves an AUC of 0.9621 (95% CI: 0.9464-0.9778), 0.9760 (0.9613-0.9906), and 0.9246 (0.8763-0.9729), respectively. This model enables expeditious and accurate mortality risk stratification of patients with COVID-19, and potentially facilitates more responsive health systems that are conducive to high risk COVID-19 patients.

Gao Yue, Cai Guang-Yao, Fang Wei, Li Hua-Yi, Wang Si-Yuan, Chen Lingxi, Yu Yang, Liu Dan, Xu Sen, Cui Peng-Fei, Zeng Shao-Qing, Feng Xin-Xia, Yu Rui-Di, Wang Ya, Yuan Yuan, Jiao Xiao-Fei, Chi Jian-Hua, Liu Jia-Hao, Li Ru-Yuan, Zheng Xu, Song Chun-Yan, Jin Ning, Gong Wen-Jian, Liu Xing-Yu, Huang Lei, Tian Xun, Li Lin, Xing Hui, Ma Ding, Li Chun-Rui, Ye Fei, Gao Qing-Lei

2020-10-06

General General

Reference ontology and database annotation of the COVID-19 Open Research Dataset (CORD-19)

bioRxiv Preprint

The COVID-19 Open Research Dataset (CORD-19) was released in March 2020 to allow the machine learning and wider research community to develop techniques to answer scientific questions on COVID-19. The data set consists of a large collection of scientific literature, including over 100,000 full text papers. Annotating training data to normalise variability in biological entities can improve the performance of downstream analysis and interpretation. To facilitate and enhance the use of the CORD-19 data in these applications, in late March 2020 we performed a comprehensive annotation process using named entity recognition tool, TERMite, along with a number of large reference ontologies and vocabularies including domains of genes, proteins, drugs and virus strains. The additional annotation has identified and tagged over 45 million entities within the corpus made up of 62,746 unique biomedical entities. The latest updated version of the annotated data, as well as older versions, is made openly available under GPL-2.0 License for the community to use at: https://github.com/SciBiteLabs/CORD19

Giles, O.; Huntley, R.; Karlsson, A.; Lomax, J.; Malone, J.

2020-10-07

General General

M3Lung-Sys: A Deep Learning System for Multi-Class Lung Pneumonia Screening from CT Imaging

ArXiv Preprint

To counter the outbreak of COVID-19, the accurate diagnosis of suspected cases plays a crucial role in timely quarantine, medical treatment, and preventing the spread of the pandemic. Considering the limited training cases and resources (e.g, time and budget), we propose a Multi-task Multi-slice Deep Learning System (M3Lung-Sys) for multi-class lung pneumonia screening from CT imaging, which only consists of two 2D CNN networks, i.e., slice- and patient-level classification networks. The former aims to seek the feature representations from abundant CT slices instead of limited CT volumes, and for the overall pneumonia screening, the latter one could recover the temporal information by feature refinement and aggregation between different slices. In addition to distinguish COVID-19 from Healthy, H1N1, and CAP cases, our M 3 Lung-Sys also be able to locate the areas of relevant lesions, without any pixel-level annotation. To further demonstrate the effectiveness of our model, we conduct extensive experiments on a chest CT imaging dataset with a total of 734 patients (251 healthy people, 245 COVID-19 patients, 105 H1N1 patients, and 133 CAP patients). The quantitative results with plenty of metrics indicate the superiority of our proposed model on both slice- and patient-level classification tasks. More importantly, the generated lesion location maps make our system interpretable and more valuable to clinicians.

Xuelin Qian, Huazhu Fu, Weiya Shi, Tao Chen, Yanwei Fu, Fei Shan, Xiangyang Xue

2020-10-07

General General

Clinical Characteristics and Outcomes of Severe and Critical Patients With 2019 Novel Coronavirus Disease (COVID-19) in Wenzhou: A Retrospective Study.

In Frontiers in medicine

Information about severe cases of 2019 novel coronavirus disease (COVID-19) infection is scarce. The aim of this study was to report the clinical characteristics and outcomes of severe and critical patients with confirmed COVID-19 infection in Wenzhou city. In this single-centered, retrospective cohort study, we consecutively enrolled 37 RT-PCR confirmed positive severe or critical patients from January 28 to February 16, 2020 in a tertiary hospital. Outcomes were followed up until 28-day mortality. Fifteen severe and 22 critical adult patients with the COVID-19 infection were included. Twenty-six (68.4%) were men. Echocardiography data results suggest that normal or increased cardiac output and diastolic dysfunction are the most common manifestations. Compared with severe patients, critical patients were older, more likely to exhibit low platelet counts and high blood urea nitrogen, and were in hospital for longer. Most patients had organ dysfunction during hospitalization, including 11 (29.7%) with ARDS, 8 (21.6%) with acute kidney injury, 17 (45.9%) with acute cardiac injury, and 33 (89.2%) with acute liver dysfunction. Eighteen (48.6%) patients were treated with high-flow ventilation, 9 (13.8%) with non-invasive ventilation, 10 (15.4%) with invasive mechanical ventilation, 7 (18.9%) with prone position ventilation, 6 (16.2%) with extracorporeal membrane oxygenation (ECMO), and 3 (8.1%) with renal replacement therapy. Only 1 (2.7%) patient had died in the 28-day follow up in our study. All patients had bilateral infiltrates on their chest CT scan. Twenty-one (32.3%) patients presented ground glass opacity (GGO) with critical patients more localized in the periphery and the center. The mortality of critical patients with the COVID-19 infection is low in our study. Cardiac function was enhanced in the early stage and less likely to develop into acute cardiac injury, but most patients suffered with acute liver injury. The CT imaging presentations of COVID-19 in critical patients were more likely with consolidation and bilateral lung involvement.

Qian Song-Zan, Hong Wan-Dong, Lingjie-Mao Chenfeng-Lin, Zhendong-Fang Pan

2020

COVID-19, critically ill, infection, outcome, severity

Radiology Radiology

The Performance of Deep Neural Networks in Differentiating Chest X-Rays of COVID-19 Patients From Other Bacterial and Viral Pneumonias.

In Frontiers in medicine

Chest radiography is a critical tool in the early detection, management planning, and follow-up evaluation of COVID-19 pneumonia; however, in smaller clinics around the world, there is a shortage of radiologists to analyze large number of examinations especially performed during a pandemic. Limited availability of high-resolution computed tomography and real-time polymerase chain reaction in developing countries and regions of high patient turnover also emphasizes the importance of chest radiography as both a screening and diagnostic tool. In this paper, we compare the performance of 17 available deep learning algorithms to help identify imaging features of COVID19 pneumonia. We utilize an existing diagnostic technology (chest radiography) and preexisting neural networks (DarkNet-19) to detect imaging features of COVID-19 pneumonia. Our approach eliminates the extra time and resources needed to develop new technology and associated algorithms, thus aiding the front-line healthcare workers in the race against the COVID-19 pandemic. Our results show that DarkNet-19 is the optimal pre-trained neural network for the detection of radiographic features of COVID-19 pneumonia, scoring an overall accuracy of 94.28% over 5,854 X-ray images. We also present a custom visualization of the results that can be used to highlight important visual biomarkers of the disease and disease progression.

Elgendi Mohamed, Nasir Muhammad Umer, Tang Qunfeng, Fletcher Richard Ribon, Howard Newton, Menon Carlo, Ward Rabab, Parker William, Nicolaou Savvas

2020

artificial intelligence, chest X-ray radiography, convolutional neural networks, corona virus, image classification, neural network, transfer learning

Public Health Public Health

Using Nominal Group Technique to Elucidate a COVID-19 Research Agenda for Maternal and Child Health (MCH) Populations.

In International journal of MCH and AIDS

As the global impact of the COVID-19 pandemic continues to evolve, robust data describing its effect on maternal and child health (MCH) remains limited. The aim of this study was to elucidate an agenda for COVID-19 research with particular focus on its impact within MCH populations. This was achieved using the Nominal Group Technique through which researchers identified and ranked 12 research topics across various disciplines relating to MCH in the setting of COVID-19. Proposed research topics included vaccine development, genomics, and artificial intelligence among others. The proposed research priorities could serve as a template for a vigorous COVID-19 research agenda by the NIH and other national funding agencies in the US.

Ikedionwu Chioma A, Dongarwar Deepa, Yusuf Korede K, Maiyegun Sitratullah O, Ibrahimi Sahra, Salihu Hamisu M

2020

Artificial intelligence, Big data, COVID-19, Coronavirus, MCH, Maternal and child health, Pandemics

General General

The investigation of multiresolution approaches for chest X-ray image based COVID-19 detection.

In Health information science and systems

COVID-19 is a novel virus, which has a fast spreading rate, and now it is seen all around the world. The case and death numbers are increasing day by day. Some tests have been used to determine the COVID-19. Chest X-ray and chest computerized tomography (CT) are two important imaging tools for determination and monitoring of COVID-19. And new methods have been searching for determination of the COVID-19. In this paper, the investigation of various multiresolution approaches in detection of COVID-19 is carried out. Chest X-ray images are used as input to the proposed approach. As recent trend in machine learning shifts toward the deep learning, we would like to show that the traditional methods such as multiresolution approaches are still effective. To this end, the well-known multiresolution approaches namely Wavelet, Shearlet and Contourlet transforms are used to decompose the chest X-ray images and the entropy and the normalized energy approaches are employed for feature extraction from the decomposed chest X-ray images. Entropy and energy features are generally accompanied with the multiresolution approaches in texture recognition applications. The extreme learning machines (ELM) classifier is considered in the classification stage of the proposed study. A dataset containing 361 different COVID-19 chest X-ray images and 200 normal (healthy) chest X-ray images are used in the experimental works. The performance evaluation is carried out by employing various metric namely accuracy, sensitivity, specificity and precision. As deep learning is mentioned, a comparison between proposed multiresolution approaches and deep learning approaches is also carried out. To this end, deep feature extraction and fine-tuning of pretrained convolutional neural networks (CNNs) are considered. For deep feature extraction, pretrained, ResNet50 model is employed. For classification of the deep features, the Support Vector Machines (SVM) classifier is used. The ResNet50 model is also used in the fine-tuning. The experimental works show that multiresolution approaches produced better performance than the deep learning approaches. Especially, Shearlet transform outperformed at all. 99.29% accuracy score is obtained by using Shearlet transform.

Ismael Aras M, Şengür Abdulkadir

2020-Dec

COVID-19, Chest X-ray images, Contourlet, Deep learning, Multiresolution approaches, Shearlet, Wavelet

General General

Review on Diagnosis of COVID-19 from Chest CT Images Using Artificial Intelligence.

In Computational and mathematical methods in medicine

The COVID-19 diagnostic approach is mainly divided into two broad categories, a laboratory-based and chest radiography approach. The last few months have witnessed a rapid increase in the number of studies use artificial intelligence (AI) techniques to diagnose COVID-19 with chest computed tomography (CT). In this study, we review the diagnosis of COVID-19 by using chest CT toward AI. We searched ArXiv, MedRxiv, and Google Scholar using the terms "deep learning", "neural networks", "COVID-19", and "chest CT". At the time of writing (August 24, 2020), there have been nearly 100 studies and 30 studies among them were selected for this review. We categorized the studies based on the classification tasks: COVID-19/normal, COVID-19/non-COVID-19, COVID-19/non-COVID-19 pneumonia, and severity. The sensitivity, specificity, precision, accuracy, area under the curve, and F1 score results were reported as high as 100%, 100%, 99.62, 99.87%, 100%, and 99.5%, respectively. However, the presented results should be carefully compared due to the different degrees of difficulty of different classification tasks.

Ozsahin Ilker, Sekeroglu Boran, Musa Musa Sani, Mustapha Mubarak Taiwo, Uzun Ozsahin Dilber

2020

General General

Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers-A study to show how popularity is affecting accuracy in social media.

In Applied soft computing

COVID-19 originally known as Corona VIrus Disease of 2019, has been declared as a pandemic by World Health Organization (WHO) on 11th March 2020. Unprecedented pressures have mounted on each country to make compelling requisites for controlling the population by assessing the cases and properly utilizing available resources. The rapid number of exponential cases globally has become the apprehension of panic, fear and anxiety among people. The mental and physical health of the global population is found to be directly proportional to this pandemic disease. The current situation has reported more than twenty four million people being tested positive worldwide as of 27th August, 2020 Therefore, it's the need of the hour to implement different measures to safeguard the countries by demystifying the pertinent facts and information. This paper aims to bring out the fact that tweets containing all handles related to COVID-19 and WHO have been unsuccessful in guiding people around this pandemic outbreak appositely. This study analyses two types of tweets gathered during the pandemic times. In one case, around twenty three thousand most re-tweeted tweets within the time span from1st Jan 2019 to 23rd March 2020 have been analysed and observation says that the maximum number of the tweets portrays neutral or negative sentiments. On the other hand, a dataset containing 226668 tweets collected within the time span between December 2019 and May 2020 have been analysed which contrastingly show that there were a maximum number of positive and neutral tweets tweeted by netizens. The research demonstrates that though people have tweeted mostly positive regarding COVID-19, yet netizens were busy engrossed in re-tweeting the negative tweets and that no useful words could be found in WordCloud or computations using word frequency in tweets. The claims have been validated through a proposed model using deep learning classifiers with admissible accuracy up to 81%. Apart from these the authors have proposed the implementation of a Gaussian membership function based fuzzy rule base to correctly identify sentiments from tweets. The accuracy for the said model yields up to a permissible rate of 79%.

Chakraborty Koyel, Bhatia Surbhi, Bhattacharyya Siddhartha, Platos Jan, Bag Rajib, Hassanien Aboul Ella

2020-Sep-28

00-01, 99-00, COVID-19, Deep learning, Emotional intelligence, Fuzzy rule, Gaussian membership function, Sentiment analysis, Tweets, WHO

General General

Health is the Motive and Digital is the Instrument.

In Journal of the Indian Institute of Science

The coronavirus crisis has seen an unprecedented response from India and the world. If the viral outbreak has exposed gross inadequacies in the healthcare systems of nations both rich and poor, it has stirred a digital healthcare revolution that has been building since the past decade. We have seen how this new era of digital health evolved over the years since healthcare started getting increasingly unaffordable in the western countries forcing a relook in their strategies to explosion of digital innovations in mobile telephony and applications, internet, wearable devices, artificial intelligence, robotics, big data and genomics. The single biggest trigger for the digital shift has indeed been the COVID-19 pandemic this year, more so in India with astonishing response from the private enterprise and the proactive push from the government so evident. However, the full potential of this digital revolution cannot be realized as long as core structural reforms in public healthcare do not take place along with significant boost in digital infrastructure. The way digital technologies have helped facilitate strategy and response to the global pandemic and with predictions of more zoonotic outbreaks impending in the coming years, it has become imperative for the world to increasingly adopt and integrate digital innovations to make healthcare more accessible, interconnected and affordable.

Seethalakshmi S, Nandan Rahul

2020-Sep-27

General General

Deep Learning Approaches for COVID-19 Detection Based on Chest X-ray Images.

In Expert systems with applications

COVID-19 is a novel virus that causes infection in both the upper respiratory tract and the lungs. The numbers of cases and deaths have increased on a daily basis on the scale of a global pandemic. Chest X-ray images have proven useful for monitoring various lung diseases and have recently been used to monitor the COVID-19 disease. In this paper, deep-learning-based approaches, namely deep feature extraction, fine-tuning of pretrained convolutional neural networks (CNN), and end-to-end training of a developed CNN model, have been used in order to classify COVID-19 and normal (healthy) chest X-ray images. For deep feature extraction, pretrained deep CNN models (ResNet18, ResNet50, ResNet101, VGG16, and VGG19) were used. For classification of the deep features, the Support Vector Machines (SVM) classifier was used with various kernel functions, namely Linear, Quadratic, Cubic, and Gaussian. The aforementioned pretrained deep CNN models were also used for the fine-tuning procedure. A new CNN model is proposed in this study with end-to-end training. A dataset containing 180 COVID-19 and 200 normal (healthy) chest X-ray images was used in the study's experimentation. Classification accuracy was used as the performance measurement of the study. The experimental works reveal that deep learning shows potential in the detection of COVID-19 based on chest X-ray images. The deep features extracted from the ResNet50 model and SVM classifier with the Linear kernel function produced a 94.7% accuracy score, which was the highest among all the obtained results. The achievement of the fine-tuned ResNet50 model was found to be 92.6%, whilst end-to-end training of the developed CNN model produced a 91.6% result. Various local texture descriptors and SVM classifications were also used for performance comparison with alternative deep approaches; the results of which showed the deep approaches to be quite efficient when compared to the local texture descriptors in the detection of COVID-19 based on chest X-ray images.

Ismael Aras M, Şengür Abdulkadir

2020-Sep-28

COVID-19, chest X-ray images, convolutional neural networks, deep learning, local texture descriptors

Internal Medicine Internal Medicine

Development and Validation of the Quick COVID-19 Severity Index: A Prognostic Tool for Early Clinical Decompensation.

In Annals of emergency medicine ; h5-index 53.0

STUDY OBJECTIVE : The goal of this study is to create a predictive, interpretable model of early hospital respiratory failure among emergency department (ED) patients admitted with coronavirus disease 2019 (COVID-19).

METHODS : This was an observational, retrospective, cohort study from a 9-ED health system of admitted adult patients with severe acute respiratory syndrome coronavirus 2 (COVID-19) and an oxygen requirement less than or equal to 6 L/min. We sought to predict respiratory failure within 24 hours of admission as defined by oxygen requirement of greater than 10 L/min by low-flow device, high-flow device, noninvasive or invasive ventilation, or death. Predictive models were compared with the Elixhauser Comorbidity Index, quick Sequential [Sepsis-related] Organ Failure Assessment, and the CURB-65 pneumonia severity score.

RESULTS : During the study period, from March 1 to April 27, 2020, 1,792 patients were admitted with COVID-19, 620 (35%) of whom had respiratory failure in the ED. Of the remaining 1,172 admitted patients, 144 (12.3%) met the composite endpoint within the first 24 hours of hospitalization. On the independent test cohort, both a novel bedside scoring system, the quick COVID-19 Severity Index (area under receiver operating characteristic curve mean 0.81 [95% confidence interval {CI} 0.73 to 0.89]), and a machine-learning model, the COVID-19 Severity Index (mean 0.76 [95% CI 0.65 to 0.86]), outperformed the Elixhauser mortality index (mean 0.61 [95% CI 0.51 to 0.70]), CURB-65 (0.50 [95% CI 0.40 to 0.60]), and quick Sequential [Sepsis-related] Organ Failure Assessment (0.59 [95% CI 0.50 to 0.68]). A low quick COVID-19 Severity Index score was associated with a less than 5% risk of respiratory decompensation in the validation cohort.

CONCLUSION : A significant proportion of admitted COVID-19 patients progress to respiratory failure within 24 hours of admission. These events are accurately predicted with bedside respiratory examination findings within a simple scoring system.

Haimovich Adrian D, Ravindra Neal G, Stoytchev Stoytcho, Young H Patrick, Wilson Francis P, van Dijk David, Schulz Wade L, Taylor R Andrew

2020-Oct

General General

COVIDomaly: A Deep Convolutional Autoencoder Approach for Detecting Early Cases of COVID-19

ArXiv Preprint

As of September 2020, the COVID-19 pandemic continues to devastate the health and well-being of the global population. With more than 33 million confirmed cases and over a million deaths, global health organizations are still a long way from fully containing the pandemic. This pandemic has raised serious questions about the emergency preparedness of health agencies, not only in terms of treatment of an unseen disease, but also in identifying its early symptoms. In the particular case of COVID-19, several studies have indicated that chest radiography images of the infected patients show characteristic abnormalities. However, at the onset of a given pandemic, such as COVID-19, there may not be sufficient data for the affected cases to train models for their robust detection. Hence, supervised classification is ill-posed for this problem because the time spent in collecting large amounts of infected peoples' data could lead to the loss of human lives and delays in preventive interventions. Therefore, we formulate this problem within a one-class classification framework, in which the data for healthy patients is abundantly available, whereas no training data is present for the class of interest (COVID-19 in our case). To solve this problem, we present COVIDomaly, a convolutional autoencoder framework to detect unseen COVID-19 cases from the chest radiographs. We tested two settings on a publicly available dataset (COVIDx) by training the model on chest X-rays from (i) only healthy adults, and (ii) healthy and other non-COVID-19 pneumonia, and detected COVID-19 as an anomaly. After performing 3-fold cross validation, we obtain a pooled ROC-AUC of 0.7652 and 0.6902 in the two settings respectively. These results are very encouraging and pave the way towards research for ensuring emergency preparedness in future pandemics, especially the ones that could be detected from chest X-rays.

Faraz Khoshbakhtian, Ahmed Bilal Ashraf, Shehroz S. Khan

2020-10-06

General General

COVID-19 and Media Datasets: Period- and location-specific textual data mining.

In Data in brief

The vocabulary used in news on a disease such as COVID-19 changes according the period [4]. This aspect is discussed on the basis of MEDISYS-sourced media datasets via two studies. The first focuses on terminology extraction and the second on period prediction according to the textual content using machine learning approaches.

Roche Mathieu

2020-Sep-30

COVID-19, Classification, Corpus, NLP, Terminology Extraction, Text-Mining

General General

Reference ontology and database annotation of the COVID-19 Open Research Dataset (CORD-19)

bioRxiv Preprint

The COVID-19 Open Research Dataset (CORD-19) was released in March 2020 to allow the machine learning and wider research community to develop techniques to answer scientific questions on COVID-19. The data set consists of a large collection of scientific literature, including over 100,000 full text papers. Annotating training data to normalise variability in biological entities can improve the performance of downstream analysis and interpretation. To facilitate and enhance the use of the CORD-19 data in these applications, in late March 2020 we performed a comprehensive annotation process using named entity recognition tool, TERMite, along with a number of large reference ontologies and vocabularies including domains of genes, proteins, drugs and virus strains. The additional annotation has identified and tagged over 45 million entities within the corpus made up of 62,746 unique biomedical entities. The latest updated version of the annotated data, as well as older versions, is made openly available under GPL-2.0 License for the community to use at: https://github.com/SciBiteLabs/CORD19 .

Giles, O.; Huntley, R.; Karlsson, A.; Lomax, J.; Malone, J.

2020-10-05

Radiology Radiology

Effectiveness of COVID-19 diagnosis and management tools: A review.

In Radiography (London, England : 1995)

OBJECTIVE : To review the available literature concerning the effectiveness of the COVID-19 diagnostic tools.

BACKGROUND : With the absence of specific treatment/vaccines for the coronavirus COVID-19, the most appropriate approach to control this infection is to quarantine people and isolate symptomatic people and suspected or infected cases. Although real-time reverse transcription-polymerase chain reaction (RT-PCR) assay is considered the first tool to make a definitive diagnosis of COVID-19 disease, the high false negative rate, low sensitivity, limited supplies and strict requirements for laboratory settings might delay accurate diagnosis. Computed tomography (CT) has been reported as an important tool to identify and investigate suspected patients with COVID-19 disease at early stage.

KEY FINDINGS : RT-PCR shows low sensitivity (60-71%) in diagnosing patients with COVID-19 infection compared to the CT chest. Several studies reported that chest CT scans show typical imaging features in all patients with COVID-19. This high sensitivity and initial presentation in CT chest can be helpful in rectifying false negative results obtained from RT-PCR. As COVID-19 has similar manifestations to other pneumonia diseases, artificial intelligence (AI) might help radiologists to differentiate COVID-19 from other pneumonia diseases.

CONCLUSION : Although CT scan is a powerful tool in COVID-19 diagnosis, it is not sufficient to detect COVID-19 alone due to the low specificity (25%), and challenges that radiologists might face in differentiating COVID-19 from other viral pneumonia on chest CT scans. AI might help radiologists to differentiate COVID-19 from other pneumonia diseases.

IMPLICATION FOR PRACTICE : Both RT-PCR and CT tests together would increase sensitivity and improve quarantine efficacy, an impact neither could achieve alone.

Alsharif W, Qurashi A

2020-Sep-21

Artificial intelligence, CT scan, Consolidation, Crazy-paving, Ground-glass opacification, RT-PCR

General General

Practicing precision medicine with intelligently integrative clinical and multi-omics data analysis.

In Human genomics

Precision medicine aims to empower clinicians to predict the most appropriate course of action for patients with complex diseases like cancer, diabetes, cardiomyopathy, and COVID-19. With a progressive interpretation of the clinical, molecular, and genomic factors at play in diseases, more effective and personalized medical treatments are anticipated for many disorders. Understanding patient's metabolomics and genetic make-up in conjunction with clinical data will significantly lead to determining predisposition, diagnostic, prognostic, and predictive biomarkers and paths ultimately providing optimal and personalized care for diverse, and targeted chronic and acute diseases. In clinical settings, we need to timely model clinical and multi-omics data to find statistical patterns across millions of features to identify underlying biologic pathways, modifiable risk factors, and actionable information that support early detection and prevention of complex disorders, and development of new therapies for better patient care. It is important to calculate quantitative phenotype measurements, evaluate variants in unique genes and interpret using ACMG guidelines, find frequency of pathogenic and likely pathogenic variants without disease indicators, and observe autosomal recessive carriers with a phenotype manifestation in metabolome. Next, ensuring security to reconcile noise, we need to build and train machine-learning prognostic models to meaningfully process multisource heterogeneous data to identify high-risk rare variants and make medically relevant predictions. The goal, today, is to facilitate implementation of mainstream precision medicine to improve the traditional symptom-driven practice of medicine, and allow earlier interventions using predictive diagnostics and tailoring better-personalized treatments. We strongly recommend automated implementation of cutting-edge technologies, utilizing machine learning (ML) and artificial intelligence (AI) approaches for the multimodal data aggregation, multifactor examination, development of knowledgebase of clinical predictors for decision support, and best strategies for dealing with relevant ethical issues.

Ahmed Zeeshan

2020-Oct-02

Artificial intelligence, Clinics, Genomics, Integrative analysis, Machine learning, Metabolomics, Precision medicine

General General

Blockchain in Healthcare: Insights on COVID-19.

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

The SARS-CoV2 pandemic has impacted risk management globally. Blockchain has been increasingly applied to healthcare management, as a strategic tool to strengthen operative protocols and to create the proper basis for an efficient and effective evidence-based decisional process. We aim to validate blockchain in healthcare, and to suggest a trace-route for a COVID19-safe clinical practice. The use of blockchain in combination with artificial intelligence systems allows the creation of a generalizable predictive system that could contribute to the containment of pandemic risk on national territory. A SWOT analysis of the adoption of a blockchain-based prediction model in healthcare and SARS-CoV-2 infection has been carried out to underline opportunities and limits to its adoption. Blockchain could play a strategic role in future digital healthcare: specifically, it may work to improve COVID19-safe clinical practice. The main concepts, and particularly those related to clinical workflow, obtainable from different blockchain-based models have been reported here and critically discussed.

Fusco Antonio, Dicuonzo Grazia, Dell’Atti Vittorio, Tatullo Marco

2020-Sep-30

COVID-19, artificial intelligence, blockchain, global health, healthcare management

Pathology Pathology

In-silico drug repurposing study predicts the combination of pirfenidone and melatonin as a promising candidate therapy to reduce SARS-CoV-2 infection progression and respiratory distress caused by cytokine storm.

In PloS one ; h5-index 176.0

From January 2020, COVID-19 is spreading around the world producing serious respiratory symptoms in infected patients that in some cases can be complicated by the severe acute respiratory syndrome, sepsis and septic shock, multiorgan failure, including acute kidney injury and cardiac injury. Cost and time efficient approaches to reduce the burthen of the disease are needed. To find potential COVID-19 treatments among the whole arsenal of existing drugs, we combined system biology and artificial intelligence-based approaches. The drug combination of pirfenidone and melatonin has been identified as a candidate treatment that may contribute to reduce the virus infection. Starting from different drug targets the effect of the drugs converges on human proteins with a known role in SARS-CoV-2 infection cycle. Simultaneously, GUILDify v2.0 web server has been used as an alternative method to corroborate the effect of pirfenidone and melatonin against the infection of SARS-CoV-2. We have also predicted a potential therapeutic effect of the drug combination over the respiratory associated pathology, thus tackling at the same time two important issues in COVID-19. These evidences, together with the fact that from a medical point of view both drugs are considered safe and can be combined with the current standard of care treatments for COVID-19 makes this combination very attractive for treating patients at stage II, non-severe symptomatic patients with the presence of virus and those patients who are at risk of developing severe pulmonary complications.

Artigas Laura, Coma Mireia, Matos-Filipe Pedro, Aguirre-Plans Joaquim, Farrés Judith, Valls Raquel, Fernandez-Fuentes Narcis, de la Haba-Rodriguez Juan, Olvera Alex, Barbera Jose, Morales Rafael, Oliva Baldo, Mas Jose Manuel

2020

General General

A Novel Strategy for the Development of Vaccines for SARS-CoV-2 (COVID-19) and Other Viruses Using AI and Viral Shell Disorder.

In Journal of proteome research

A model that predicts levels of coronavirus (CoV) respiratory and fecal-oral transmission potentials based on the shell disorder has been built using neural network (artificial intelligence, AI) analysis of the percentage of disorder (PID) in the nucleocapsid, N, and membrane, M, proteins of the inner and outer viral shells, respectively. Using primarily the PID of N, SARS-CoV-2 is grouped as having intermediate levels of both respiratory and fecal-oral transmission potentials. Related studies, using similar methodologies, have found strong positive correlations between virulence and inner shell disorder among numerous viruses, including Nipah, Ebola, and Dengue viruses. There is some evidence that this is also true for SARS-CoV-2 and SARS-CoV, which have N PIDs of 48% and 50%, and case-fatality rates of 0.5-5% and 10.9%, respectively. The underlying relationship between virulence and respiratory potentials has to do with the viral loads of vital organs and body fluids, respectively. Viruses can spread by respiratory means only if the viral loads in saliva and mucus exceed certain minima. Similarly, a patient is likelier to die when the viral load overwhelms vital organs. Greater disorder in inner shell proteins has been known to play important roles in the rapid replication of viruses by enhancing the efficiency pertaining to protein-protein/DNA/RNA/lipid bindings. This paper suggests a novel strategy in attenuating viruses involving comparison of disorder patterns of inner shells (N) of related viruses to identify residues and regions that could be ideal for mutation. The M protein of SARS-CoV-2 has one of the lowest M PID values (6%) in its family, and therefore, this virus has one of the hardest outer shells, which makes it resistant to antimicrobial enzymes in body fluid. While this is likely responsible for its greater contagiousness, the risks of creating an attenuated virus with a more disordered M are discussed.

Goh Gerard Kian-Meng, Dunker A Keith, Foster James A, Uversky Vladimir N

2020-Oct-02

Nipah, antibody, attenuate, coronavirus, covid, disorder, ebola, function, shell, immune, intrinsic, matrix, nucleocapsid, nucleoprotein, protein, shell, structure, vaccine, viral, virulence

General General

Artificial intelligence in pulmonary medicine: computer vision, predictive model and COVID-19.

In European respiratory review : an official journal of the European Respiratory Society

Artificial intelligence (AI) is transforming healthcare delivery. The digital revolution in medicine and healthcare information is prompting a staggering growth of data intertwined with elements from many digital sources such as genomics, medical imaging and electronic health records. Such massive growth has sparked the development of an increasing number of AI-based applications that can be deployed in clinical practice. Pulmonary specialists who are familiar with the principles of AI and its applications will be empowered and prepared to seize future practice and research opportunities. The goal of this review is to provide pulmonary specialists and other readers with information pertinent to the use of AI in pulmonary medicine. First, we describe the concept of AI and some of the requisites of machine learning and deep learning. Next, we review some of the literature relevant to the use of computer vision in medical imaging, predictive modelling with machine learning, and the use of AI for battling the novel severe acute respiratory syndrome-coronavirus-2 pandemic. We close our review with a discussion of limitations and challenges pertaining to the further incorporation of AI into clinical pulmonary practice.

Khemasuwan Danai, Sorensen Jeffrey S, Colt Henri G

2020-Sep-30

General General

Evaluation of Country Dietary Habits Using Machine Learning Techniques in Relation to Deaths from COVID-19.

In Healthcare (Basel, Switzerland)

COVID-19 disease has affected almost every country in the world. The large number of infected people and the different mortality rates between countries has given rise to many hypotheses about the key points that make the virus so lethal in some places. In this study, the eating habits of 170 countries were evaluated in order to find correlations between these habits and mortality rates caused by COVID-19 using machine learning techniques that group the countries together according to the different distribution of fat, energy, and protein across 23 different types of food, as well as the amount ingested in kilograms. Results shown how obesity and the high consumption of fats appear in countries with the highest death rates, whereas countries with a lower rate have a higher level of cereal consumption accompanied by a lower total average intake of kilocalories.

García-Ordás María Teresa, Arias Natalia, Benavides Carmen, García-Olalla Oscar, Benítez-Andrades José Alberto

2020-Sep-29

COVID-19, K-Means, KCal, countries, deaths, fat, machine learning, protein

Cardiology Cardiology

Application of Artificial Intelligence Trilogy Accelerates Survey Efficacy for Severe Acute Respiratory Syndrome Coronavirus 2 Infection within Smart Quarantine Stations.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : As the coronavirus disease (COVID-19) epidemic worsens, the burden of quarantine stations (Q stations) outside of emergency rooms (ERs) at every hospital increases daily. To prepare for the screening workload inside Q stations, all staff with medical licenses are required to support the working shift. Therefore, the need to simplify the workflow and decision-making process for physicians and surgeons from all subspecialist fields is necessary.

OBJECTIVE : To demonstrate how the NCKUH AI trilogy of smart Q station diversion, AI-assisted image interpretation, and a built-in clinical decision-making algorithm improves medical care and reduces quarantine processing time.

METHODS : This observational study on the emerging COVID-19 pandemic included constitutively 643 patients. The artificial intelligence (AI) trilogy, i.e., 1) smart Q station diversion, 2) AI-assisted image interpretation, and 3) a built-in clinical decision-making algorithm on a tablet computer, was applied to shorten the quarantine survey and reduce processing time during the COVID-19 pandemic.

RESULTS : The use of the AI trilogy facilitated the processing of suspected cases, with or without symptoms, travel, occupation, and contact or clustering histories, which were performed with a tablet computer device. A separate AI-mode function that could quickly recognize pulmonary infiltrates on chest X-rays was merged into the smart clinical assisting system (SCAS), and this model was subsequently trained with COVID-19 pneumonia cases from the GitHub open source dataset. The detection rates were 93.2% and 45.5% in posteroanterior and anteroposterior chest X-rays, respectively. The SCAS algorithm was continuously adjusted based on the frequently updated Taiwan Center for Disease Control public safety guidelines for faster clinical decision making. Our ex vivo study demonstrated the efficiency of 75% alcohol disinfection on the tablet computer surface for a 20-μL positive SARS-CoV-2 virus solution. The positive rate of a real-time polymerase chain reaction was 100% and became 75% and 0% after one and two disinfection procedures (n=4), respectively. To further analyze the effect of the AI application in the Q station, we subdivided the Q station into with or without AI groups. Compared with the conventional ER track (n=281), the survey time at the clinical Q station (n=1520) was significantly shortened [median survey time (95% confidence interval; CI) at the ER: 153 (108.5-205) min vs. at the clinical Q station: 35 (24-56) min; p<0.0001]. Furthermore, the use of the AI application in the Q station reduced the survey time in the Q station [median survey time (95% CI) without AI: 100.5 (40.3-152.5) min vs. with AI in the Q station: 34 (24-53) min; p<0.0001].

CONCLUSIONS : The AI trilogy improves medical care workflow safely by shortening the quarantine survey and reducing processing time, especially during an emerging epidemic infectious disease.

CLINICALTRIAL :

Liu Ping-Yen, Tsai Yi-Shan, Chen Po-Lin, Tsai Huey-Pin, Hsu Ling-Wei, Wang Chi-Shiang, Lee Nan-Yao, Huang Mu-Shiang, Wu Yun-Chiao, Ko Wen-Chien, Yang Yi-Ching, Chiang Jung-Hsien, Shen Meng-Ru

2020-Sep-16

Internal Medicine Internal Medicine

Correction: COVID-19 risk and outcomes in patients with substance use disorders: analyses from electronic health records in the United States.

In Molecular psychiatry ; h5-index 103.0

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Wang Quan Qiu, Kaelber David C, Xu Rong, Volkow Nora D

2020-Sep-30

Surgery Surgery

Frontiers of Robotic Gastroscopy: A Comprehensive Review of Robotic Gastroscopes and Technologies.

In Cancers

Upper gastrointestinal (UGI) tract pathology is common worldwide. With recent advancements in robotics, innovative diagnostic and treatment devices have been developed and several translational attempts made. This review paper aims to provide a highly pictorial critical review of robotic gastroscopes, so that clinicians and researchers can obtain a swift and comprehensive overview of key technologies and challenges. Therefore, the paper presents robotic gastroscopes, either commercial or at a progressed technology readiness level. Among them, we show tethered and wireless gastroscopes, as well as devices aimed for UGI surgery. The technological features of these instruments, as well as their clinical adoption and performance, are described and compared. Although the existing endoscopic devices have thus far provided substantial improvements in the effectiveness of diagnosis and treatment, there are certain aspects that represent unwavering predicaments of the current gastroenterology practice. A detailed list includes difficulties and risks, such as transmission of communicable diseases (e.g., COVID-19) due to the doctor-patient proximity, unchanged learning curves, variable detection rates, procedure-related adverse events, endoscopists' and nurses' burnouts, limited human and/or material resources, and patients' preferences to choose non-invasive options that further interfere with the successful implementation and adoption of routine screening. The combination of robotics and artificial intelligence, as well as remote telehealth endoscopy services, are also discussed, as viable solutions to improve existing platforms for diagnosis and treatment are emerging.

Marlicz Wojciech, Ren Xuyang, Robertson Alexander, Skonieczna-Żydecka Karolina, Łoniewski Igor, Dario Paolo, Wang Shuxin, Plevris John N, Koulaouzidis Anastasios, Ciuti Gastone

2020-Sep-28

artificial intelligence, gastric cancer, gastroscopy, machine learning, robotic gastroscopy

General General

A multimodal deep learning-based drug repurposing approach for treatment of COVID-19.

In Molecular diversity

Recently, various computational methods have been proposed to find new therapeutic applications of the existing drugs. The Multimodal Restricted Boltzmann Machine approach (MM-RBM), which has the capability to connect the information about the multiple modalities, can be applied to the problem of drug repurposing. The present study utilized MM-RBM to combine two types of data, including the chemical structures data of small molecules and differentially expressed genes as well as small molecules perturbations. In the proposed method, two separate RBMs were applied to find out the features and the specific probability distribution of each datum (modality). Besides, RBM was used to integrate the discovered features, resulting in the identification of the probability distribution of the combined data. The results demonstrated the significance of the clusters acquired by our model. These clusters were used to discover the medicines which were remarkably similar to the proposed medications to treat COVID-19. Moreover, the chemical structures of some small molecules as well as dysregulated genes' effect led us to suggest using these molecules to treat COVID-19. The results also showed that the proposed method might prove useful in detecting the highly promising remedies for COVID-19 with minimum side effects. All the source codes are accessible using https://github.com/LBBSoft/Multimodal-Drug-Repurposing.git.

Hooshmand Seyed Aghil, Zarei Ghobadi Mohadeseh, Hooshmand Seyyed Emad, Azimzadeh Jamalkandi Sadegh, Alavi Seyed Mehdi, Masoudi-Nejad Ali

2020-Sep-30

COVID-19, Deep learning, Drug repurposing, Multimodal data fusion, Restricted Boltzmann machine

General General

Exploring the Potential of Artificial Intelligence and Machine Learning to Combat COVID-19 and Existing Opportunities for LMIC: A Scoping Review.

In Journal of primary care & community health

BACKGROUND : In the face of the current time-sensitive COVID-19 pandemic, the limited capacity of healthcare systems resulted in an emerging need to develop newer methods to control the spread of the pandemic. Artificial Intelligence (AI), and Machine Learning (ML) have a vast potential to exponentially optimize health care research. The use of AI-driven tools in LMIC can help in eradicating health inequalities and decrease the burden on health systems.

METHODS : The literature search for this Scoping review was conducted through the PubMed database using keywords: COVID-19, Artificial Intelligence (AI), Machine Learning (ML), and Low Middle-Income Countries (LMIC). Forty-three articles were identified and screened for eligibility and 13 were included in the final review. All the items of this Scoping review are reported using guidelines for PRISMA extension for scoping reviews (PRISMA-ScR).

RESULTS : Results were synthesized and reported under 4 themes. (a) The need of AI during this pandemic: AI can assist to increase the speed and accuracy of identification of cases and through data mining to deal with the health crisis efficiently, (b) Utility of AI in COVID-19 screening, contact tracing, and diagnosis: Efficacy for virus detection can a be increased by deploying the smart city data network using terminal tracking system along-with prediction of future outbreaks, (c) Use of AI in COVID-19 patient monitoring and drug development: A Deep learning system provides valuable information regarding protein structures associated with COVID-19 which could be utilized for vaccine formulation, and (d) AI beyond COVID-19 and opportunities for Low-Middle Income Countries (LMIC): There is a lack of financial, material, and human resources in LMIC, AI can minimize the workload on human labor and help in analyzing vast medical data, potentiating predictive and preventive healthcare.

CONCLUSION : AI-based tools can be a game-changer for diagnosis, treatment, and management of COVID-19 patients with the potential to reshape the future of healthcare in LMIC.

Naseem Maleeha, Akhund Ramsha, Arshad Hajra, Ibrahim Muhammad Talal

COVID-19, artificial intelligence, low middle-income countries, machine learning, pandemic

General General

Outcomes associated with SARS-CoV-2 viral clades in COVID-19.

In medRxiv : the preprint server for health sciences

Background The COVID-19 epidemic of 2019-20 is due to the novel coronavirus SARS-CoV-2. Following first case description in December, 2019 this virus has infected over 10 million individuals and resulted in at least 500,000 deaths world-wide. The virus is undergoing rapid mutation, with two major clades of sequence variants emerging. This study sought to determine whether SARS-CoV-2 sequence variants are associated with differing outcomes among COVID-19 patients in a single medical system. Methods Whole genome SARS-CoV-2 RNA sequence was obtained from isolates collected from patients registered in the University of Washington Medicine health system between March 1 and April 15, 2020. Demographic and baseline medical data along with outcomes of hospitalization and death were collected. Statistical and machine learning models were applied to determine if viral genetic variants were associated with specific outcomes of hospitalization or death. Findings Full length SARS-CoV-2 sequence was obtained 190 subjects with clinical outcome data. 35 (18.4%) were hospitalized and 14 (7.4%) died from complications of infection. A total of 289 single nucleotide variants were identified. Clustering methods demonstrated two major viral clades, which could be readily distinguished by 12 polymorphisms in 5 genes. A trend toward higher rates of hospitalization of patients with Clade 2 was observed (p=0.06). Machine learning models utilizing patient demographics and co-morbidities achieved area-under-the-curve (AUC) values of 0.93 for predicting hospitalization. Addition of viral clade or sequence information did not significantly improve models for outcome prediction. Conclusion SARS-CoV-2 shows substantial sequence diversity in a community-based sample. Two dominant clades of virus are in circulation. Among patients sufficiently ill to warrant testing for virus, no significant difference in outcomes of hospitalization or death could be discerned between clades in this sample. Major risk factors for hospitalization and death for either major clade of virus include patient age and comorbid conditions.

Nakamichi Kenji, Shen Jolie Zhu, Lee Cecilia S, Lee Aaron Y, Roberts Emma Adaline, Simonson Paul D, Roychoudhury Pavitra, Andriesen Jessica G, Randhawa April K, Mathias Patrick C, Greninger Alex, Jerome Keith R, Van Gelder Russell N

2020-Sep-25

Pathology Pathology

AI for radiographic COVID-19 detection selects shortcuts over signal.

In medRxiv : the preprint server for health sciences

Artificial intelligence (AI) researchers and radiologists have recently reported AI systems that accurately detect COVID-19 in chest radiographs. However, the robustness of these systems remains unclear. Using state-of-the-art techniques in explainable AI, we demonstrate that recent deep learning systems to detect COVID-19 from chest radiographs rely on confounding factors rather than medical pathology, creating an alarming situation in which the systems appear accurate, but fail when tested in new hospitals.

DeGrave Alex J, Janizek Joseph D, Lee Su-In

2020-Sep-14

General General

Improvement and Multi-Population Generalizability of a Deep Learning-Based Chest Radiograph Severity Score for COVID-19.

In medRxiv : the preprint server for health sciences

PURPOSE : To improve and test the generalizability of a deep learning-based model for assessment of COVID-19 lung disease severity on chest radiographs (CXRs) from different patient populations.

MATERIALS AND METHODS : A published convolutional Siamese neural network-based model previously trained on hospitalized patients with COVID-19 was tuned using 250 outpatient CXRs. This model produces a quantitative measure of COVID-19 lung disease severity (pulmonary x-ray severity (PXS) score). The model was evaluated on CXRs from four test sets, including 3 from the United States (patients hospitalized at an academic medical center (N=154), patients hospitalized at a community hospital (N=113), and outpatients (N=108)) and 1 from Brazil (patients at an academic medical center emergency department (N=303)). Radiologists from both countries independently assigned reference standard CXR severity scores, which were correlated with the PXS scores as a measure of model performance (Pearson r). The Uniform Manifold Approximation and Projection (UMAP) technique was used to visualize the neural network results.

RESULTS : Tuning the deep learning model with outpatient data improved model performance in two United States hospitalized patient datasets (r=0.88 and r=0.90, compared to baseline r=0.86). Model performance was similar, though slightly lower, when tested on the United States outpatient and Brazil emergency department datasets (r=0.86 and r=0.85, respectively). UMAP showed that the model learned disease severity information that generalized across test sets.

CONCLUSIONS : Performance of a deep learning-based model that extracts a COVID-19 severity score on CXRs improved using training data from a different patient cohort (outpatient versus hospitalized) and generalized across multiple populations.

Li Matthew D, Arun Nishanth T, Aggarwal Mehak, Gupta Sharut, Singh Praveer, Little Brent P, Mendoza Dexter P, Corradi Gustavo C A, Takahashi Marcelo S, Ferraciolli Suely F, Succi Marc D, Lang Min, Bizzo Bernardo C, Dayan Ittai, Kitamura Felipe C, Kalpathy-Cramer Jayashree

2020-Sep-18

General General

Tailoring Time Series Models For Forecasting Coronavirus Spread: Case Studies of 187 Countries.

In Computational and structural biotechnology journal

When will the coronavirus end? Are the current precautionary measures effective? To answer these questions it is important to forecast regularly and accurately the spread of COVID-19 infections. Different time series forecasting models have been applied in the literature to tackle the pandemic situation. The current research efforts developed few of these models and validates its accuracy for selected countries. It becomes difficult to draw an objective comparison between the performance of these models at a global scale. This is because, the time series trend for the infection differs between the countries depending on the strategies adopted by the healthcare organizations to decrease the spread. Consequently, it is important to develop a tailored model for a country that allows healthcare organizations to better judge the effect of the undertaken precautionary measures, and provision more efficiently the needed resources to face this disease. This paper addresses this void. We develop and compare the performance of the time series models in the literature in terms of root mean squared error and mean absolute percentage error.

Ismail Leila, Materwala Huned, Znati Taieb, Turaev Sherzod, Khan Moien A B

2020-Sep-24

COVID-19, Coronavirus, Epidemic transmission, Forecasting models, Machine learning models, Pandemic, Time series models

General General

The Emerging Role of Artificial Intelligence in the Fight Against COVID-19.

In European urology ; h5-index 128.0

The coronavirus disease 2019 (COVID-19) pandemic has generated large volumes of clinical data that can be an invaluable resource towards answering a number of important questions for this and future pandemics. Artificial intelligence can have an important role in analysing such data to identify populations at higher risk of COVID-19-related urological pathologies and to suggest treatments that block viral entry into cells by interrupting the angiotensin-converting enzyme 2-transmembrane serine protease 2 (ACE2-TMPRSS2) pathway.

Ghose Aruni, Roy Sabyasachi, Vasdev Nikhil, Olsburgh Jonathon, Dasgupta Prokar

2020-Sep-17

Radiology Radiology

Using machine learning of clinical data to diagnose COVID-19: a systematic review and meta-analysis.

In BMC medical informatics and decision making ; h5-index 38.0

BACKGROUND : The recent Coronavirus Disease 2019 (COVID-19) pandemic has placed severe stress on healthcare systems worldwide, which is amplified by the critical shortage of COVID-19 tests.

METHODS : In this study, we propose to generate a more accurate diagnosis model of COVID-19 based on patient symptoms and routine test results by applying machine learning to reanalyzing COVID-19 data from 151 published studies. We aim to investigate correlations between clinical variables, cluster COVID-19 patients into subtypes, and generate a computational classification model for discriminating between COVID-19 patients and influenza patients based on clinical variables alone.

RESULTS : We discovered several novel associations between clinical variables, including correlations between being male and having higher levels of serum lymphocytes and neutrophils. We found that COVID-19 patients could be clustered into subtypes based on serum levels of immune cells, gender, and reported symptoms. Finally, we trained an XGBoost model to achieve a sensitivity of 92.5% and a specificity of 97.9% in discriminating COVID-19 patients from influenza patients.

CONCLUSIONS : We demonstrated that computational methods trained on large clinical datasets could yield ever more accurate COVID-19 diagnostic models to mitigate the impact of lack of testing. We also presented previously unknown COVID-19 clinical variable correlations and clinical subgroups.

Li Wei Tse, Ma Jiayan, Shende Neil, Castaneda Grant, Chakladar Jaideep, Tsai Joseph C, Apostol Lauren, Honda Christine O, Xu Jingyue, Wong Lindsay M, Zhang Tianyi, Lee Abby, Gnanasekar Aditi, Honda Thomas K, Kuo Selena Z, Yu Michael Andrew, Chang Eric Y, Rajasekaran Mahadevan Raj, Ongkeko Weg M

2020-Sep-29

COVID-19, Diagnostic model, Machine learning

Public Health Public Health

Exploring U.S. Shifts in Anti-Asian Sentiment with the Emergence of COVID-19.

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

Background: Anecdotal reports suggest a rise in anti-Asian racial attitudes and discrimination in response to COVID-19. Racism can have significant social, economic, and health impacts, but there has been little systematic investigation of increases in anti-Asian prejudice. Methods: We utilized Twitter's Streaming Application Programming Interface (API) to collect 3,377,295 U.S. race-related tweets from November 2019-June 2020. Sentiment analysis was performed using support vector machine (SVM), a supervised machine learning model. Accuracy for identifying negative sentiments, comparing the machine learning model to manually labeled tweets was 91%. We investigated changes in racial sentiment before and following the emergence of COVID-19. Results: The proportion of negative tweets referencing Asians increased by 68.4% (from 9.79% in November to 16.49% in March). In contrast, the proportion of negative tweets referencing other racial/ethnic minorities (Blacks and Latinx) remained relatively stable during this time period, declining less than 1% for tweets referencing Blacks and increasing by 2% for tweets referencing Latinx. Common themes that emerged during the content analysis of a random subsample of 3300 tweets included: racism and blame (20%), anti-racism (20%), and daily life impact (27%). Conclusion: Social media data can be used to provide timely information to investigate shifts in area-level racial sentiment.

Nguyen Thu T, Criss Shaniece, Dwivedi Pallavi, Huang Dina, Keralis Jessica, Hsu Erica, Phan Lynn, Nguyen Leah H, Yardi Isha, Glymour M Maria, Allen Amani M, Chae David H, Gee Gilbert C, Nguyen Quynh C

2020-Sep-25

big data, content analysis, minority groups, racial bias, social media

Public Health Public Health

Early prediction of mortality risk among patients with severe COVID-19, using machine learning.

In International journal of epidemiology ; h5-index 76.0

BACKGROUND : Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 infection, has been spreading globally. We aimed to develop a clinical model to predict the outcome of patients with severe COVID-19 infection early.

METHODS : Demographic, clinical and first laboratory findings after admission of 183 patients with severe COVID-19 infection (115 survivors and 68 non-survivors from the Sino-French New City Branch of Tongji Hospital, Wuhan) were used to develop the predictive models. Machine learning approaches were used to select the features and predict the patients' outcomes. The area under the receiver operating characteristic curve (AUROC) was applied to compare the models' performance. A total of 64 with severe COVID-19 infection from the Optical Valley Branch of Tongji Hospital, Wuhan, were used to externally validate the final predictive model.

RESULTS : The baseline characteristics and laboratory tests were significantly different between the survivors and non-survivors. Four variables (age, high-sensitivity C-reactive protein level, lymphocyte count and d-dimer level) were selected by all five models. Given the similar performance among the models, the logistic regression model was selected as the final predictive model because of its simplicity and interpretability. The AUROCs of the external validation sets were 0.881. The sensitivity and specificity were 0.839 and 0.794 for the validation set, when using a probability of death of 50% as the cutoff. Risk score based on the selected variables can be used to assess the mortality risk. The predictive model is available at [https://phenomics.fudan.edu.cn/risk_scores/].

CONCLUSIONS : Age, high-sensitivity C-reactive protein level, lymphocyte count and d-dimer level of COVID-19 patients at admission are informative for the patients' outcomes.

Hu Chuanyu, Liu Zhenqiu, Jiang Yanfeng, Shi Oumin, Zhang Xin, Xu Kelin, Suo Chen, Wang Qin, Song Yujing, Yu Kangkang, Mao Xianhua, Wu Xuefu, Wu Mingshan, Shi Tingting, Jiang Wei, Mu Lina, Tully Damien C, Xu Lei, Jin Li, Li Shusheng, Tao Xuejin, Zhang Tiejun, Chen Xingdong

2020-Sep-23

COVID-19, death, fatality rate, machine learning, predictive model

General General

Detection Methods of COVID-19.

In SLAS technology

Since being first detected in China, coronavirus disease 2019 (COVID-19) has spread rapidly across the world, triggering a global pandemic with no viable cure in sight. As a result, national responses have focused on the effective minimization of the spread. Border control measures and travel restrictions have been implemented in a number of countries to limit the import and export of the virus. The detection of COVID-19 is a key task for physicians. The erroneous results of early laboratory tests and their delays led researchers to focus on different options. Information obtained from computed tomography (CT) and radiological images is important for clinical diagnosis. Therefore, it is worth developing a rapid method of detection of viral diseases through the analysis of radiographic images. We propose a novel method of detection of COVID-19. The purpose is to provide clinical decision support to healthcare workers and researchers. The article is to support researchers working on early detection of COVID-19 as well as similar viral diseases.

Echtioui Amira, Zouch Wassim, Ghorbel Mohamed, Mhiri Chokri, Hamam Habib

2020-Sep-30

CNN, COVID-19, convolutional neural network, deep learning, diagnosis

General General

Risk Factors for Mortality in Critically Ill Patients with COVID-19 in Huanggang, China: A Single-Centre Multivariate Pattern Analysis.

In Journal of medical virology

To date, the coronavirus disease 2019 (COVID-19) has a worldwide distribution. Risk factors for mortality in critically ill patients, especially detailed self-evaluation indicators and laboratory-examination indicators, have not been well described. In this paper, a total of 192 critically ill patients (142 were discharged and 50 died in the hospital) with COVID-19 were included. Self-evaluation indicators including demographics, baseline characteristics and symptoms and detailed lab-examination indicators were extracted. Data were first compared between survivors and non-survivors. Multivariate pattern analysis (MVPA) was performed to identify possible risk factors for mortality of COVID-19 patients. MVPA achieved a relatively high classification accuracy of 93% when using both self-evaluation indicators and laboratory-examination indicators. Several self-evaluation factors related to COVID-19 were highly associated with mortality, including age, duration (time from illness onset to admission), and the Barthel index score. When the duration, age and Barthel index increased by one day, one year and one point, the mortality increased by 3.6%, 2.4% and 0.9% respectively. Laboratory-examination indicators including C-reactive protein (CRP), white blood cell (WBC) count, platelet count, fibrin degradation products (FDP), oxygenation index (OI), lymphocyte count and D-dimer were also risk factors. Among them, duration was the strongest predictor of all-cause mortality. Several self-evaluation indicators that can simply be obtained by questionnaires and without clinical examination were the risk factors of all-cause mortality in critically ill COVID-19 patients. The prediction model can be used by individuals to improve health awareness, and by clinicians to identify high-risk individuals. This article is protected by copyright. All rights reserved.

Chen Yinyin, Linli Zeqiang, Lei Yuting, Yang Yiya, Liu Zhipeng, Xia Youchun, Liang Yumei, Zhu Huabo, Guo Shuixia

2020-Sep-30

COVID-19, Clinical indicators, Machine learning, Risk factor, Self-evaluation

General General

Improving the performance of CNN to predict the likelihood of COVID-19 using chest X-ray images with preprocessing algorithms.

In International journal of medical informatics ; h5-index 49.0

OBJECTIVE : This study aims to develop and test a new computer-aided diagnosis (CAD) scheme of chest X-ray images to detect coronavirus (COVID-19) infected pneumonia.

METHOD : CAD scheme first applies two image preprocessing steps to remove the majority of diaphragm regions, process the original image using a histogram equalization algorithm, and a bilateral low-pass filter. Then, the original image and two filtered images are used to form a pseudo color image. This image is fed into three input channels of a transfer learning-based convolutional neural network (CNN) model to classify chest X-ray images into 3 classes of COVID-19 infected pneumonia, other community-acquired no-COVID-19 infected pneumonia, and normal (non-pneumonia) cases. To build and test the CNN model, a publicly available dataset involving 8474 chest X-ray images is used, which includes 415, 5179 and 2,880 cases in three classes, respectively. Dataset is randomly divided into 3 subsets namely, training, validation, and testing with respect to the same frequency of cases in each class to train and test the CNN model.

RESULTS : The CNN-based CAD scheme yields an overall accuracy of 94.5 % (2404/2544) with a 95 % confidence interval of [0.93,0.96] in classifying 3 classes. CAD also yields 98.4 % sensitivity (124/126) and 98.0 % specificity (2371/2418) in classifying cases with and without COVID-19 infection. However, without using two preprocessing steps, CAD yields a lower classification accuracy of 88.0 % (2239/2544).

CONCLUSION : This study demonstrates that adding two image preprocessing steps and generating a pseudo color image plays an important role in developing a deep learning CAD scheme of chest X-ray images to improve accuracy in detecting COVID-19 infected pneumonia.

Heidari Morteza, Mirniaharikandehei Seyedehnafiseh, Khuzani Abolfazl Zargari, Danala Gopichandh, Qiu Yuchen, Zheng Bin

2020-Sep-23

COVID-19 diagnosis, Computer-aided diagnosis, Convolution neural network (CNN), Coronavirus, Disease classification, VGG16 network

General General

Leveraging Computational Modeling to Understand Infectious Diseases.

In Current pathobiology reports

Purpose of Review : Computational and mathematical modeling have become a critical part of understanding in-host infectious disease dynamics and predicting effective treatments. In this review, we discuss recent findings pertaining to the biological mechanisms underlying infectious diseases, including etiology, pathogenesis, and the cellular interactions with infectious agents. We present advances in modeling techniques that have led to fundamental disease discoveries and impacted clinical translation.

Recent Findings : Combining mechanistic models and machine learning algorithms has led to improvements in the treatment of Shigella and tuberculosis through the development of novel compounds. Modeling of the epidemic dynamics of malaria at the within-host and between-host level has afforded the development of more effective vaccination and antimalarial therapies. Similarly, in-host and host-host models have supported the development of new HIV treatment modalities and an improved understanding of the immune involvement in influenza. In addition, large-scale transmission models of SARS-CoV-2 have furthered the understanding of coronavirus disease and allowed for rapid policy implementations on travel restrictions and contract tracing apps.

Summary : Computational modeling is now more than ever at the forefront of infectious disease research due to the COVID-19 pandemic. This review highlights how infectious diseases can be better understood by connecting scientists from medicine and molecular biology with those in computer science and applied mathematics.

Jenner Adrianne L, Aogo Rosemary A, Davis Courtney L, Smith Amber M, Craig Morgan

2020-Sep-24

Bacteria, Computational modeling, Infectious diseases, Mathematics, Parasites, Viruses

General General

Learning distinctive filters for COVID-19 detection from chest X-ray using shuffled residual CNN.

In Applied soft computing

COVID-19 is a deadly viral infection that has brought a significant threat to human lives. Automatic diagnosis of COVID-19 from medical imaging enables precise medication, helps to control community outbreak, and reinforces coronavirus testing methods in place. While there exist several challenges in manually inferring traces of this viral infection from X-ray, Convolutional Neural Network (CNN) can mine data patterns that capture subtle distinctions between infected and normal X-rays. To enable automated learning of such latent features, a custom CNN architecture has been proposed in this research. It learns unique convolutional filter patterns for each kind of pneumonia. This is achieved by restricting certain filters in a convolutional layer to maximally respond only to a particular class of pneumonia/COVID-19. The CNN architecture integrates different convolution types to aid better context for learning robust features and strengthen gradient flow between layers. The proposed work also visualizes regions of saliency on the X-ray that have had the most influence on CNN's prediction outcome. To the best of our knowledge, this is the first attempt in deep learning to learn custom filters within a single convolutional layer for identifying specific pneumonia classes. Experimental results demonstrate that the proposed work has significant potential in augmenting current testing methods for COVID-19. It achieves an F1-score of 97.20% and an accuracy of 99.80% on the COVID-19 X-ray set.

Karthik R, Menaka R, M Hariharan

2020-Sep-23

CNN, COVID-19, Chest X-ray, Deep learning, Pneumonia

General General

Clinical features of COVID-19 mortality: development and validation of a clinical prediction model.

In The Lancet. Digital health

Background : The COVID-19 pandemic has affected millions of individuals and caused hundreds of thousands of deaths worldwide. Predicting mortality among patients with COVID-19 who present with a spectrum of complications is very difficult, hindering the prognostication and management of the disease. We aimed to develop an accurate prediction model of COVID-19 mortality using unbiased computational methods, and identify the clinical features most predictive of this outcome.

Methods : In this prediction model development and validation study, we applied machine learning techniques to clinical data from a large cohort of patients with COVID-19 treated at the Mount Sinai Health System in New York City, NY, USA, to predict mortality. We analysed patient-level data captured in the Mount Sinai Data Warehouse database for individuals with a confirmed diagnosis of COVID-19 who had a health system encounter between March 9 and April 6, 2020. For initial analyses, we used patient data from March 9 to April 5, and randomly assigned (80:20) the patients to the development dataset or test dataset 1 (retrospective). Patient data for those with encounters on April 6, 2020, were used in test dataset 2 (prospective). We designed prediction models based on clinical features and patient characteristics during health system encounters to predict mortality using the development dataset. We assessed the resultant models in terms of the area under the receiver operating characteristic curve (AUC) score in the test datasets.

Findings : Using the development dataset (n=3841) and a systematic machine learning framework, we developed a COVID-19 mortality prediction model that showed high accuracy (AUC=0·91) when applied to test datasets of retrospective (n=961) and prospective (n=249) patients. This model was based on three clinical features: patient's age, minimum oxygen saturation over the course of their medical encounter, and type of patient encounter (inpatient vs outpatient and telehealth visits).

Interpretation : An accurate and parsimonious COVID-19 mortality prediction model based on three features might have utility in clinical settings to guide the management and prognostication of patients affected by this disease. External validation of this prediction model in other populations is needed.

Funding : National Institutes of Health.

Yadaw Arjun S, Li Yan-Chak, Bose Sonali, Iyengar Ravi, Bunyavanich Supinda, Pandey Gaurav

2020-Oct

Radiology Radiology

Deep learning-based triage and analysis of lesion burden for COVID-19: a retrospective study with external validation.

In The Lancet. Digital health

Background : Prompt identification of patients suspected to have COVID-19 is crucial for disease control. We aimed to develop a deep learning algorithm on the basis of chest CT for rapid triaging in fever clinics.

Methods : We trained a U-Net-based model on unenhanced chest CT scans obtained from 2447 patients admitted to Tongji Hospital (Wuhan, China) between Feb 1, 2020, and March 3, 2020 (1647 patients with RT-PCR-confirmed COVID-19 and 800 patients without COVID-19) to segment lung opacities and alert cases with COVID-19 imaging manifestations. The ability of artificial intelligence (AI) to triage patients suspected to have COVID-19 was assessed in a large external validation set, which included 2120 retrospectively collected consecutive cases from three fever clinics inside and outside the epidemic centre of Wuhan (Tianyou Hospital [Wuhan, China; area of high COVID-19 prevalence], Xianning Central Hospital [Xianning, China; area of medium COVID-19 prevalence], and The Second Xiangya Hospital [Changsha, China; area of low COVID-19 prevalence]) between Jan 22, 2020, and Feb 14, 2020. To validate the sensitivity of the algorithm in a larger sample of patients with COVID-19, we also included 761 chest CT scans from 722 patients with RT-PCR-confirmed COVID-19 treated in a makeshift hospital (Guanggu Fangcang Hospital, Wuhan, China) between Feb 21, 2020, and March 6, 2020. Additionally, the accuracy of AI was compared with a radiologist panel for the identification of lesion burden increase on pairs of CT scans obtained from 100 patients with COVID-19.

Findings : In the external validation set, using radiological reports as the reference standard, AI-aided triage achieved an area under the curve of 0·953 (95% CI 0·949-0·959), with a sensitivity of 0·923 (95% CI 0·914-0·932), specificity of 0·851 (0·842-0·860), a positive predictive value of 0·790 (0·777-0·803), and a negative predictive value of 0·948 (0·941-0·954). AI took a median of 0·55 min (IQR: 0·43-0·63) to flag a positive case, whereas radiologists took a median of 16·21 min (11·67-25·71) to draft a report and 23·06 min (15·67-39·20) to release a report. With regard to the identification of increases in lesion burden, AI achieved a sensitivity of 0·962 (95% CI 0·947-1·000) and a specificity of 0·875 (95 %CI 0·833-0·923). The agreement between AI and the radiologist panel was high (Cohen's kappa coefficient 0·839, 95% CI 0·718-0·940).

Interpretation : A deep learning algorithm for triaging patients with suspected COVID-19 at fever clinics was developed and externally validated. Given its high accuracy across populations with varied COVID-19 prevalence, integration of this system into the standard clinical workflow could expedite identification of chest CT scans with imaging indications of COVID-19.

Funding : Special Project for Emergency of the Science and Technology Department of Hubei Province, China.

Wang Minghuan, Xia Chen, Huang Lu, Xu Shabei, Qin Chuan, Liu Jun, Cao Ying, Yu Pengxin, Zhu Tingting, Zhu Hui, Wu Chaonan, Zhang Rongguo, Chen Xiangyu, Wang Jianming, Du Guang, Zhang Chen, Wang Shaokang, Chen Kuan, Liu Zheng, Xia Liming, Wang Wei

2020-Oct

General General

Artificial intelligence in COVID-19 drug repurposing.

In The Lancet. Digital health

Drug repurposing or repositioning is a technique whereby existing drugs are used to treat emerging and challenging diseases, including COVID-19. Drug repurposing has become a promising approach because of the opportunity for reduced development timelines and overall costs. In the big data era, artificial intelligence (AI) and network medicine offer cutting-edge application of information science to defining disease, medicine, therapeutics, and identifying targets with the least error. In this Review, we introduce guidelines on how to use AI for accelerating drug repurposing or repositioning, for which AI approaches are not just formidable but are also necessary. We discuss how to use AI models in precision medicine, and as an example, how AI models can accelerate COVID-19 drug repurposing. Rapidly developing, powerful, and innovative AI and network medicine technologies can expedite therapeutic development. This Review provides a strong rationale for using AI-based assistive tools for drug repurposing medications for human disease, including during the COVID-19 pandemic.

Zhou Yadi, Wang Fei, Tang Jian, Nussinov Ruth, Cheng Feixiong

2020-Sep-18

General General

Challenges and Opportunities of Preclinical Medical Education: COVID-19 Crisis and Beyond.

In SN comprehensive clinical medicine

COVID-19 pandemic has disrupted face-to-face teaching in medical schools globally. The use of remote learning as an emergency measure has affected students, faculty, support staff, and administrators. The aim of this narrative review paper is to examine the challenges and opportunities faced by medical schools in implementing remote learning for basic science teaching in response to the COVID-19 crisis. We searched relevant literature in PubMed, Scopus, and Google Scholar using specific keywords, e.g., "COVID-19 pandemic," "preclinical medical education," "online learning," "remote learning," "challenges," and "opportunities." The pandemic has posed several challenges to premedical education (e.g., suspension of face-to-face teaching, lack of cadaveric dissections, and practical/laboratory sessions) but has provided many opportunities as well, such as the incorporation of online learning in the curriculum and upskilling and reskilling in new technologies. To date, many medical schools have successfully transitioned their educational environment to emergency remote teaching and assessments. During COVID-19 crisis, the preclinical phase of medical curricula has successfully introduced the novel culture of "online home learning" using technology-oriented innovations, which may extend to post-COVID era to maintain teaching and learning in medical education. However, the lack of hands-on training in the preclinical years may have serious implications on the training of the current cohort of students, and they may struggle later in the clinical years. The use of emergent technology (e.g., artificial intelligence for adaptive learning, virtual simulation, and telehealth) for education is most likely to be indispensable components of the transformative change and post-COVID medical education.

Gaur Uma, Majumder Md Anwarul Azim, Sa Bidyadhar, Sarkar Sankalan, Williams Arlene, Singh Keerti

2020-Sep-22

COVID-19 pandemic, Challenges, Online learning, Opportunities, Preclinical medical education, Remote learning

General General

Machine Learning and Image Analysis Applications in the Fight against COVID-19 Pandemic: Datasets, Research Directions, Challenges and Opportunities.

In Materials today. Proceedings

COVID-19 pandemic has become the most devastating disease of the current century and spread over 216 countries around the world. The disease is spreading through outbreaks despite the availability of modern sophisticated medical treatment. Machine Learning and Image Analysis research has been making great progress in many directions in the healthcare field for providing support to subsequent medical diagnosis. In this paper, we have propose three research directions with methodologies in the fight against the pandemic namely: Chest X-Ray (CXR) images classification using deep convolution neural networks with transfer learning to assist diagnosis; Patient Risk prediction of pandemic based on risk factors such as patient characteristics, comorbidities, initial symptoms, vital signs for prognosis of disease; and forecasting of disease spread & case fatality rate using deep neural networks. Further, some of the challenges, open datasets and opportunities are discussed for researchers.

Somasekar J, Pavan Kumar Visulaization P, Sharma Avinash, Ramesh G

2020-Sep-22

COVID-19, Chest X-Ray Images, Classification, Diagnosis, Machine Learning, medical image analysis

General General

Mitigating the Impact of the Novel Coronavirus Pandemic on Neuroscience and Music Research Protocols in Clinical Populations.

In Frontiers in psychology ; h5-index 92.0

The COVID-19 disease and the systemic responses to it has impacted lives, routines and procedures at an unprecedented level. While medical care and emergency response present immediate needs, the implications of this pandemic will likely be far-reaching. Most practices that the clinical research within neuroscience and music field rely on, take place in hospitals or closely connected clinical settings which have been hit hard by the contamination. So too have its preventive and treatment measures. This means that clinical research protocols may have been altered, postponed or put in complete jeopardy. In this context, we would like to present and discuss the problems arising under the current crisis. We do so by critically approaching an online discussion facilitated by an expert panel in the field of music and neuroscience. This effort is hoped to provide an efficient basis to orient ourselves as we begin to map the needs and elements in this field of research as we further propose ideas and solutions on how to overcome, or at least ease the problems and questions we encounter or will encounter, with foresight. Among others, we hope to answer questions on technical or social problems that can be expected, possible solutions and preparatory steps to take in order to improve or ease research implementation, ethical implications and funding considerations. Finally, we further hope to facilitate the process of creating new protocols in order to minimize the impact of this crisis on essential research which may have the potential to relieve health systems.

Papatzikis Efthymios, Zeba Fathima, Särkämö Teppo, Ramirez Rafael, Grau-Sánchez Jennifer, Tervaniemi Mari, Loewy Joanne

2020

COVID-19, music and neuroscience, music and neuroscience research protocols, music therapy, research crisis response

General General

An optimized deep learning architecture for the diagnosis of COVID-19 disease based on gravitational search optimization.

In Applied soft computing

In this paper, a novel approach called GSA-DenseNet121-COVID-19 based on a hybrid convolutional neural network (CNN) architecture is proposed using an optimization algorithm. The CNN architecture that was used is called DenseNet121, and the optimization algorithm that was used is called the gravitational search algorithm (GSA). The GSA is used to determine the best values for the hyperparameters of the DenseNet121 architecture. To help this architecture to achieve a high level of accuracy in diagnosing COVID-19 through chest x-ray images. The obtained results showed that the proposed approach could classify 98.38% of the test set correctly. To test the efficacy of the GSA in setting the optimum values for the hyperparameters of DenseNet121. The GSA was compared to another approach called SSD-DenseNet121, which depends on the DenseNet121 and the optimization algorithm called social ski driver (SSD). The comparison results demonstrated the efficacy of the proposed GSA-DenseNet121-COVID-19. As it was able to diagnose COVID-19 better than SSD-DenseNet121 as the second was able to diagnose only 94% of the test set. The proposed approach was also compared to another method based on a CNN architecture called Inception-v3 and manual search to quantify hyperparameter values. The comparison results showed that the GSA-DenseNet121-COVID-19 was able to beat the comparison method, as the second was able to classify only 95% of the test set samples. The proposed GSA-DenseNet121-COVID-19 was also compared with some related work. The comparison results showed that GSA-DenseNet121-COVID-19 is very competitive.

Ezzat Dalia, Hassanien Aboul Ella, Ella Hassan Aboul

2020-Sep-22

Convolutional neural networks, Deep learning, Gravitational search algorithm, Hyperparameters optimization, SARS-CoV-2, Transfer learning

General General

Predicting Psychological State Among Chinese Undergraduate Students in the COVID-19 Epidemic: A Longitudinal Study Using a Machine Learning.

In Neuropsychiatric disease and treatment

Background : The outbreak of the 2019 novel coronavirus disease (COVID-19) not only caused physical abnormalities, but also caused psychological distress, especially for undergraduate students who are facing the pressure of academic study and work. We aimed to explore the prevalence rate of probable anxiety and probable insomnia and to find the risk factors among a longitudinal study of undergraduate students using the approach of machine learning.

Methods : The baseline data (T1) were collected from freshmen who underwent psychological evaluation at two months after entering the university. At T2 stage (February 10th to 13th, 2020), we used a convenience cluster sampling to assess psychological state (probable anxiety was assessed by general anxiety disorder-7 and probable insomnia was assessed by insomnia severity index-7) based on a web survey. We integrated information attained at T1 stage to predict probable anxiety and probable insomnia at T2 stage using a machine learning algorithm (XGBoost).

Results : Finally, we included 2009 students (response rate: 80.36%). The prevalence rate of probable anxiety and probable insomnia was 12.49% and 16.87%, respectively. The XGBoost algorithm predicted 1954 out of 2009 students (translated into 97.3% accuracy) and 1932 out of 2009 students (translated into 96.2% accuracy) who suffered anxiety and insomnia symptoms, respectively. The most relevant variables in predicting probable anxiety included romantic relationship, suicidal ideation, sleep symptoms, and a history of anxiety symptoms. The most relevant variables in predicting probable insomnia included aggression, psychotic experiences, suicidal ideation, and romantic relationship.

Conclusion : Risks for probable anxiety and probable insomnia among undergraduate students can be identified at an individual level by baseline data. Thus, timely psychological intervention for anxiety and insomnia symptoms among undergraduate students is needed considering the above factors.

Ge Fenfen, Zhang Di, Wu Lianhai, Mu Hongwei

2020

COVID-19, anxiety, cohort, insomnia, machine learning

General General

Morphological Cell Profiling of SARS-CoV-2 Infection Identifies Drug Repurposing Candidates for COVID-19

bioRxiv Preprint

The global spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and the associated disease COVID-19, requires therapeutic interventions that can be rapidly translated to clinical care. Unfortunately, traditional drug discovery methods have a >90% failure rate and can take 10-15 years from target identification to clinical use. In contrast, drug repurposing can significantly accelerate translation. We developed a quantitative high-throughput screen to identify efficacious single agents and combination therapies against SARS-CoV-2. Quantitative high-content morphological profiling was coupled with an AI-based machine learning strategy to classify features of cells for infection and stress. This assay detected multiple antiviral mechanisms of action (MOA), including inhibition of viral entry, propagation, and modulation of host cellular responses. From a library of 1,425 FDA-approved compounds and clinical candidates, we identified 16 dose-responsive compounds with antiviral effects. In particular, we discovered that lactoferrin is an effective inhibitor of SARS-CoV-2 infection with an IC50 of 308 nM and that it potentiates the efficacy of both remdesivir and hydroxychloroquine. Lactoferrin also stimulates an antiviral host cell response and retains inhibitory activity in iPSC-derived alveolar epithelial cells, a model for the primary site of infection. Given its safety profile in humans, these data suggest that lactoferrin is a readily translatable therapeutic adjunct for COVID-19. Additionally, several commonly prescribed drugs were found to exacerbate viral infection and warrant clinical investigation. We conclude that morphological profiling for drug repurposing is an effective strategy for the selection and optimization of drugs and drug combinations as viable therapeutic options for COVID-19 pandemic and other emerging infectious diseases.

Mirabelli, C.; Wotring, J. W.; Zhang, C. J.; McCarty, S. M.; Fursmidt, R.; Frum, T.; Kadambi, N. S.; Amin, A. T.; O’Meara, T. R.; Pretto-Kernahan, C. D.; Spence, J. R.; Huang, J.; Alysandratos, K. D.; Kotton, D. N.; Handelman, S. K.; Wobus, C. E.; Weatherwax, K. J.; Mashour, G. A.; O’Meara, M. J.; Sexton, J. Z.

2020-09-28

General General

Public discourse and sentiment during the COVID 19 pandemic: Using Latent Dirichlet Allocation for topic modeling on Twitter.

In PloS one ; h5-index 176.0

The study aims to understand Twitter users' discourse and psychological reactions to COVID-19. We use machine learning techniques to analyze about 1.9 million Tweets (written in English) related to coronavirus collected from January 23 to March 7, 2020. A total of salient 11 topics are identified and then categorized into ten themes, including "updates about confirmed cases," "COVID-19 related death," "cases outside China (worldwide)," "COVID-19 outbreak in South Korea," "early signs of the outbreak in New York," "Diamond Princess cruise," "economic impact," "Preventive measures," "authorities," and "supply chain." Results do not reveal treatments and symptoms related messages as prevalent topics on Twitter. Sentiment analysis shows that fear for the unknown nature of the coronavirus is dominant in all topics. Implications and limitations of the study are also discussed.

Xue Jia, Chen Junxiang, Chen Chen, Zheng Chengda, Li Sijia, Zhu Tingshao

2020

General General

predCOVID-19: A Systematic Study of Clinical Predictive Models for Coronavirus Disease 2019.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Coronavirus Disease 2019 (COVID-19) is a rapidly emerging respiratory disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Due to the rapid human-to-human transmission of SARS-CoV-2, many healthcare systems are at risk of exceeding their healthcare capacities, in particular in terms of SARS-CoV-2 tests, hospital and intensive care unit (ICU) beds and mechanical ventilators. Predictive algorithms could potentially ease the strain on healthcare systems by identifying those who are most likely to receive a positive SARS-CoV-2 test, be hospitalised or admitted to the ICU.

OBJECTIVE : To develop, study and evaluate clinical predictive models that estimate, using machine learning and based on routinely collected clinical data, which patients are likely to receive a positive SARS-CoV-2 test, require hospitalisation or intensive care.

METHODS : Using a systematic approach to model development and optimisation, we train and compare various types of machine learning models, including logistic regression, neural networks, support vector machines, random forests, and gradient boosting. To evaluate the developed models, we perform a retrospective evaluation on demographic, clinical and blood analysis data from a cohort of 5644 patients. In addition, we determine which clinical features are predictive to what degree for each of the aforementioned clinical tasks using causal explanations.

RESULTS : Our experimental results indicate that our predictive models identify (i) patients that test positive for SARS-CoV-2 a priori at a sensitivity of 75% (95% confidence interval [CI]: 67%, 81%) and a specificity of 49% (95% CI: 46%, 51%), (ii) SARS-CoV-2 positive patients that require hospitalisation with 0.92 area under the receiver operator characteristic curve [AUC] (95% CI: 0.81, 0.98), and (iii) SARS-CoV-2 positive patients that require critical care with 0.98 AUC (95% CI: 0.95, 1.00).

CONCLUSIONS : Our results indicate that predictive models trained on routinely collected clinical data could be used to predict clinical pathways for COVID-19, and therefore help inform care and prioritise resources.

CLINICALTRIAL :

Schwab Patrick, Schütte DuMont August, Dietz Benedikt, Bauer Stefan

2020-Sep-14

Surgery Surgery

Lung Mechanics of Mechanically Ventilated Patients With COVID-19: Analytics With High-Granularity Ventilator Waveform Data.

In Frontiers in medicine

Background: Lung mechanics during invasive mechanical ventilation (IMV) for both prognostic and therapeutic implications; however, the full trajectory lung mechanics has never been described for novel coronavirus disease 2019 (COVID-19) patients requiring IMV. The study aimed to describe the full trajectory of lung mechanics of mechanically ventilated COVID-19 patients. The clinical and ventilator setting that can influence patient-ventilator asynchrony (PVA) and compliance were explored. Post-extubation spirometry test was performed to assess the pulmonary function after COVID-19 induced ARDS. Methods: This was a retrospective study conducted in a tertiary care hospital. All patients with IMV due to COVID-19 induced ARDS were included. High-granularity ventilator waveforms were analyzed with deep learning algorithm to obtain PVAs. Asynchrony index (AI) was calculated as the number of asynchronous events divided by the number of ventilator cycles and wasted efforts. Mortality was recorded as the vital status on hospital discharge. Results: A total of 3,923,450 respiratory cycles in 2,778 h were analyzed (average: 24 cycles/min) for seven patients. Higher plateau pressure (Coefficient: -0.90; 95% CI: -1.02 to -0.78) and neuromuscular blockades (Coefficient: -6.54; 95% CI: -9.92 to -3.16) were associated with lower AI. Survivors showed increasing compliance over time, whereas non-survivors showed persistently low compliance. Recruitment maneuver was not able to improve lung compliance. Patients were on supine position in 1,422 h (51%), followed by prone positioning (499 h, 18%), left positioning (453 h, 16%), and right positioning (404 h, 15%). As compared with supine positioning, prone positioning was associated with 2.31 ml/cmH2O (95% CI: 1.75 to 2.86; p < 0.001) increase in lung compliance. Spirometry tests showed that pulmonary functions were reduced to one third of the predicted values after extubation. Conclusions: The study for the first time described full trajectory of lung mechanics of patients with COVID-19. The result showed that prone positioning was associated with improved compliance; higher plateau pressure and use of neuromuscular blockades were associated with lower risk of AI.

Ge Huiqing, Pan Qing, Zhou Yong, Xu Peifeng, Zhang Lingwei, Zhang Junli, Yi Jun, Yang Changming, Zhou Yuhan, Liu Limin, Zhang Zhongheng

2020

COVID-19, asynchonized, asynchrony, lung mechanics, mechanical ventilation, prone positioning

oncology Oncology

Unveiling COVID-19 from CHEST X-Ray with Deep Learning: A Hurdles Race with Small Data.

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

The possibility to use widespread and simple chest X-ray (CXR) imaging for early screening of COVID-19 patients is attracting much interest from both the clinical and the AI community. In this study we provide insights and also raise warnings on what is reasonable to expect by applying deep learning to COVID classification of CXR images. We provide a methodological guide and critical reading of an extensive set of statistical results that can be obtained using currently available datasets. In particular, we take the challenge posed by current small size COVID data and show how significant can be the bias introduced by transfer-learning using larger public non-COVID CXR datasets. We also contribute by providing results on a medium size COVID CXR dataset, just collected by one of the major emergency hospitals in Northern Italy during the peak of the COVID pandemic. These novel data allow us to contribute to validate the generalization capacity of preliminary results circulating in the scientific community. Our conclusions shed some light into the possibility to effectively discriminate COVID using CXR.

Tartaglione Enzo, Barbano Carlo Alberto, Berzovini Claudio, Calandri Marco, Grangetto Marco

2020-Sep-22

COVID-19, chest X-ray, classification, deep learning

Radiology Radiology

Imaging Diagnostics and Pathology in SARS-CoV-2-Related Diseases.

In International journal of molecular sciences ; h5-index 102.0

In December 2019, physicians reported numerous patients showing pneumonia of unknown origin in the Chinese region of Wuhan. Following the spreading of the infection over the world, The World Health Organization (WHO) on 11 March 2020 declared the novel severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) outbreak a global pandemic. The scientific community is exerting an extraordinary effort to elucidate all aspects related to SARS-CoV-2, such as the structure, ultrastructure, invasion mechanisms, replication mechanisms, or drugs for treatment, mainly through in vitro studies. Thus, the clinical in vivo data can provide a test bench for new discoveries in the field of SARS-CoV-2, finding new solutions to fight the current pandemic. During this dramatic situation, the normal scientific protocols for the development of new diagnostic procedures or drugs are frequently not completely applied in order to speed up these processes. In this context, interdisciplinarity is fundamental. Specifically, a great contribution can be provided by the association and interpretation of data derived from medical disciplines based on the study of images, such as radiology, nuclear medicine, and pathology. Therefore, here, we highlighted the most recent histopathological and imaging data concerning the SARS-CoV-2 infection in lung and other human organs such as the kidney, heart, and vascular system. In addition, we evaluated the possible matches among data of radiology, nuclear medicine, and pathology departments in order to support the intense scientific work to address the SARS-CoV-2 pandemic. In this regard, the development of artificial intelligence algorithms that are capable of correlating these clinical data with the new scientific discoveries concerning SARS-CoV-2 might be the keystone to get out of the pandemic.

Scimeca Manuel, Urbano Nicoletta, Bonfiglio Rita, Montanaro Manuela, Bonanno Elena, Schillaci Orazio, Mauriello Alessandro

2020-Sep-22

SARS-CoV-2, artificial intelligence, imaging diagnostic, pandemic, pathology

Radiology Radiology

Detection of COVID-19 Using Deep Learning Algorithms on Chest Radiographs.

In Journal of thoracic imaging

PURPOSE : To evaluate the performance of a deep learning (DL) algorithm for the detection of COVID-19 on chest radiographs (CXR).

MATERIALS AND METHODS : In this retrospective study, a DL model was trained on 112,120 CXR images with 14 labeled classifiers (ChestX-ray14) and fine-tuned using initial CXR on hospital admission of 509 patients, who had undergone COVID-19 reverse transcriptase-polymerase chain reaction (RT-PCR). The test set consisted of a CXR on presentation of 248 individuals suspected of COVID-19 pneumonia between February 16 and March 3, 2020 from 4 centers (72 RT-PCR positives and 176 RT-PCR negatives). The CXR were independently reviewed by 3 radiologists and using the DL algorithm. Diagnostic performance was compared with radiologists' performance and was assessed by area under the receiver operating characteristics (AUC).

RESULTS : The median age of the subjects in the test set was 61 (interquartile range: 39 to 79) years (51% male). The DL algorithm achieved an AUC of 0.81, sensitivity of 0.85, and specificity of 0.72 in detecting COVID-19 using RT-PCR as the reference standard. On subgroup analyses, the model achieved an AUC of 0.79, sensitivity of 0.80, and specificity of 0.74 in detecting COVID-19 in patients presented with fever or respiratory systems and an AUC of 0.87, sensitivity of 0.85, and specificity of 0.81 in distinguishing COVID-19 from other forms of pneumonia. The algorithm significantly outperforms human readers (P<0.001 using DeLong test) with higher sensitivity (P=0.01 using McNemar test).

CONCLUSIONS : A DL algorithm (COV19NET) for the detection of COVID-19 on chest radiographs can potentially be an effective tool in triaging patients, particularly in resource-stretched health-care systems.

Chiu Wan Hang Keith, Vardhanabhuti Varut, Poplavskiy Dmytro, Yu Philip Leung Ho, Du Richard, Yap Alistair Yun Hee, Zhang Sailong, Fong Ambrose Ho-Tung, Chin Thomas Wing-Yan, Lee Jonan Chun Yin, Leung Siu Ting, Lo Christine Shing Yen, Lui Macy Mei-Sze, Fang Benjamin Xin Hao, Ng Ming-Yen, Kuo Michael D

2020-Sep-22

Radiology Radiology

Advancing COVID-19 differentiation with a robust preprocessing and integration of multi-institutional open-repository computer tomography datasets for deep learning analysis.

In Experimental and therapeutic medicine

The coronavirus pandemic and its unprecedented consequences globally has spurred the interest of the artificial intelligence research community. A plethora of published studies have investigated the role of imaging such as chest X-rays and computer tomography in coronavirus disease 2019 (COVID-19) automated diagnosis. Οpen repositories of medical imaging data can play a significant role by promoting cooperation among institutes in a world-wide scale. However, they may induce limitations related to variable data quality and intrinsic differences due to the wide variety of scanner vendors and imaging parameters. In this study, a state-of-the-art custom U-Net model is presented with a dice similarity coefficient performance of 99.6% along with a transfer learning VGG-19 based model for COVID-19 versus pneumonia differentiation exhibiting an area under curve of 96.1%. The above was significantly improved over the baseline model trained with no segmentation in selected tomographic slices of the same dataset. The presented study highlights the importance of a robust preprocessing protocol for image analysis within a heterogeneous imaging dataset and assesses the potential diagnostic value of the presented COVID-19 model by comparing its performance to the state of the art.

Trivizakis Eleftherios, Tsiknakis Nikos, Vassalou Evangelia E, Papadakis Georgios Z, Spandidos Demetrios A, Sarigiannis Dimosthenis, Tsatsakis Aristidis, Papanikolaou Nikolaos, Karantanas Apostolos H, Marias Kostas

2020-Nov

COVID-19, artificial intelligence, deep learning analysis, multi-institutional data

General General

Pandemic number five - Latest insights into the COVID-19 crisis.

In Biomedical journal

About nine months after the emergence of SARS-CoV-2, this special issue of the Biomedical Journal takes stock of its evolution into a pandemic. We acquire an elaborate overview of the history and virology of SARS-CoV-2, the epidemiology of COVID-19, and the development of therapies and vaccines, based on useful tools such as a pseudovirus system, artificial intelligence, and repurposing of existing drugs. Moreover, we learn about a potential link between COVID-19 and oral health, and some of the strategies that allowed Taiwan to handle the outbreak exceptionally well, including a COVID-19 biobank establishment, online tools for contact tracing, and the efficient management of emergency departments.

Häfner Sophia Julia

2020-Aug-27

COVID-19, Contact tracing, Pseudovirus system, Repurposing drugs, SARS-CoV-2

General General