Doctor Penguin

General

Analyzing hCov Genome Sequences: Predicting Virulence and Mutation

bioRxiv Preprint
Covid-19 pandemic, caused by the SARS-CoV-2 genome sequence of coronavirus, has affected millions of people all over the world and taken thousands of lives. It is of utmost importance that the character of this deadly virus be studied and its nature be analyzed. We present here an analysis pipeline comprising a classification exercise to identify the virulence of the genome sequences and extraction of important features from its genetic material that are used subsequently to predict mutation at those interesting sites using deep learning techniques. We have classified the SARS-CoV-2 genome sequences with high accuracy and predicted the mutations in the sites of Interest. In a nutshell, we have prepared an analysis pipeline for hCov genome sequences leveraging the power of machine intelligence and uncovered what remained apparently shrouded by raw data.
Sawmya, S.; Saha, A.; Tasnim, S.; Toufikuzzaman, M.; Anjum, N.; Rafid, A. H. M.; Rahman, M. S.; Rahman, M. S.

2021-04-20

General

Functional binding dynamics relevant to the evolution of zoonotic spillovers in endemic and emergent Betacoronavirus strains

bioRxiv Preprint
Comparative functional analysis of the dynamic interactions between various Betacoronavirus mutant strains and broadly utilized target proteins such as ACE2 and CD26, is crucial for a more complete understanding of zoonotic spillovers of viruses that cause diseases such as COVID-19. Here, we employ machine learning to replicated sets of nanosecond scale GPU accelerated molecular dynamics simulations to statistically compare and classify atom motions of these target proteins in both the presence and absence of different endemic and emergent strains of the viral receptor binding domain (RBD) of the S spike glycoprotein. Machine learning was used to identify functional binding dynamics that are evolutionarily conserved from bat CoV-HKU4 to human endemic/emergent strains. Conserved dynamics regions of ACE2 involve both the N-terminal helices, as well as a region of more transient dynamics encompassing K353, Q325 and a novel motif AAQPFLL 386-92 that appears to coordinate their dynamic interactions with the viral RBD at N501. We also demonstrate that the functional evolution of Betacoronavirus zoonotic spillovers involving ACE2 interaction dynamics are likely pre-adapted from two precise and stable binding sites involving the viral bat progenitor strain interaction with CD26 at SAMLI 291-5 and SS 333-334. Our analyses further indicate that the human endemic strains hCoV-HKU1 and hCoV-OC43 have evolved more stable N-terminal helix interactions through enhancement of an interfacing loop region on the viral RBD, whereas the highly transmissible SARS-CoV-2 variants (B.1.1.7, B.1.351 and P.1) have evolved more stable viral binding via more focused interactions between the viral N501 and ACE2 K353 alone.
Rynkiewicz, P.; Babbitt, G. A.; Cui, F.; Hudson, A. O.; Lynch, M. L.

2021-04-20

General

Masked Face Recognition using ResNet-50

ArXiv Preprint
Over the last twenty years, there have seen several outbreaks of different coronavirus diseases across the world. These outbreaks often led to respiratory tract diseases and have proved to be fatal sometimes. Currently, we are facing an elusive health crisis with the emergence of COVID-19 disease of the coronavirus family. One of the modes of transmission of COVID- 19 is airborne transmission. This transmission occurs as humans breathe in the droplets released by an infected person through breathing, speaking, singing, coughing, or sneezing. Hence, public health officials have mandated the use of face masks which can reduce disease transmission by 65%. For face recognition programs, commonly used for security verification purposes, the use of face mask presents an arduous challenge since these programs were typically trained with human faces devoid of masks but now due to the onset of Covid-19 pandemic, they are forced to identify faces with masks. Hence, this paper investigates the same problem by developing a deep learning based model capable of accurately identifying people with face-masks. In this paper, the authors train a ResNet-50 based architecture that performs well at recognizing masked faces. The outcome of this study could be seamlessly integrated into existing face recognition programs that are designed to detect faces for security verification purposes.
Bishwas Mandal, Adaeze Okeukwu, Yihong Theis

2021-04-19

Public Health

BS-Net: Learning COVID-19 pneumonia severity on a large chest X-ray dataset.

In Medical image analysis
In this work we design an end-to-end deep learning architecture for predicting, on Chest X-rays images (CXR), a multi-regional score conveying the degree of lung compromise in COVID-19 patients. Such semi-quantitative scoring system, namely Brixia score, is applied in serial monitoring of such patients, showing significant prognostic value, in one of the hospitals that experienced one of the highest pandemic peaks in Italy. To solve such a challenging visual task, we adopt a weakly supervised learning strategy structured to handle different tasks (segmentation, spatial alignment, and score estimation) trained with a "from-the-part-to-the-whole" procedure involving different datasets. In particular, we exploit a clinical dataset of almost 5,000 CXR annotated images collected in the same hospital. Our BS-Net demonstrates self-attentive behavior and a high degree of accuracy in all processing stages. Through inter-rater agreement tests and a gold standard comparison, we show that our solution outperforms single human annotators in rating accuracy and consistency, thus supporting the possibility of using this tool in contexts of computer-assisted monitoring. Highly resolved (super-pixel level) explainability maps are also generated, with an original technique, to visually help the understanding of the network activity on the lung areas. We also consider other scores proposed in literature and provide a comparison with a recently proposed non-specific approach. We eventually test the performance robustness of our model on an assorted public COVID-19 dataset, for which we also provide Brixia score annotations, observing good direct generalization and fine-tuning capabilities that highlight the portability of BS-Net in other clinical settings. The CXR dataset along with the source code and the trained model are publicly released for research purposes.
Signoroni Alberto, Savardi Mattia, Benini Sergio, Adami Nicola, Leonardi Riccardo, Gibellini Paolo, Vaccher Filippo, Ravanelli Marco, Borghesi Andrea, Maroldi Roberto, Farina Davide

2021-Mar-31

COVID-19 severity assessment, Chest X-rays, Convolutional neural networks, End-to-end learning, Semi-quantitative rating

Pathology

Novel application of automated machine learning with MALDI-TOF-MS for rapid high-throughput screening of COVID-19: a proof of concept.

In Scientific reports ; h5-index 158.0
The 2019 novel coronavirus infectious disease (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has created an unsustainable need for molecular diagnostic testing. Molecular approaches such as reverse transcription (RT) polymerase chain reaction (PCR) offers highly sensitive and specific means to detect SARS-CoV-2 RNA, however, despite it being the accepted "gold standard", molecular platforms often require a tradeoff between speed versus throughput. Matrix assisted laser desorption ionization (MALDI)-time of flight (TOF)-mass spectrometry (MS) has been proposed as a potential solution for COVID-19 testing and finding a balance between analytical performance, speed, and throughput, without relying on impacted supply chains. Combined with machine learning (ML), this MALDI-TOF-MS approach could overcome logistical barriers encountered by current testing paradigms. We evaluated the analytical performance of an ML-enhanced MALDI-TOF-MS method for screening COVID-19. Residual nasal swab samples from adult volunteers were used for testing and compared against RT-PCR. Two optimized ML models were identified, exhibiting accuracy of 98.3%, positive percent agreement (PPA) of 100%, negative percent agreement (NPA) of 96%, and accuracy of 96.6%, PPA of 98.5%, and NPA of 94% respectively. Machine learning enhanced MALDI-TOF-MS for COVID-19 testing exhibited performance comparable to existing commercial SARS-CoV-2 tests.
Tran Nam K, Howard Taylor, Walsh Ryan, Pepper John, Loegering Julia, Phinney Brett, Salemi Michelle R, Rashidi Hooman H

2021-Apr-15

General

Patients at high risk of suicide before and during a COVID-19 lockdown: ecological momentary assessment study.

In BJPsych open
The coronavirus disease 2019 (COVID-19) outbreak may have affected the mental health of patients at high risk of suicide. In this study we explored the wish to die and other suicide risk factors using smartphone-based ecological momentary assessment (EMA) in patients with a history of suicidal thoughts and behaviour. Contrary to our expectations we found a decrease in the wish to die during lockdown. This is consistent with previous studies showing that suicide rates decrease during periods of social emergency. Smartphone-based EMA can allow us to remotely assess patients and overcome the physical barriers imposed by lockdown.
Cobo Aurora, Porras-Segovia Alejandro, Pérez-Rodríguez María Mercedes, Artés-Rodríguez Antonio, Barrigón Maria Luisa, Courtet Philippe, Baca-García Enrique

2021-Apr-16

COVID-19, Suicide, ecological momentary assessment, machine learning, suicide attempt

General

Identifying Water Stress in Chickpea Plant by Analyzing Progressive Changes in Shoot Images using Deep Learning

ArXiv Preprint
To meet the needs of a growing world population, we need to increase the global agricultural yields by employing modern, precision, and automated farming methods. In the recent decade, high-throughput plant phenotyping techniques, which combine non-invasive image analysis and machine learning, have been successfully applied to identify and quantify plant health and diseases. However, these image-based machine learning usually do not consider plant stress's progressive or temporal nature. This time-invariant approach also requires images showing severe signs of stress to ensure high confidence detections, thereby reducing this approach's feasibility for early detection and recovery of plants under stress. In order to overcome the problem mentioned above, we propose a temporal analysis of the visual changes induced in the plant due to stress and apply it for the specific case of water stress identification in Chickpea plant shoot images. For this, we have considered an image dataset of two chickpea varieties JG-62 and Pusa-372, under three water stress conditions; control, young seedling, and before flowering, captured over five months. We then develop an LSTM-CNN architecture to learn visual-temporal patterns from this dataset and predict the water stress category with high confidence. To establish a baseline context, we also conduct a comparative analysis of the CNN architecture used in the proposed model with the other CNN techniques used for the time-invariant classification of water stress. The results reveal that our proposed LSTM-CNN model has resulted in the ceiling level classification performance of \textbf{98.52\%} on JG-62 and \textbf{97.78\%} on Pusa-372 and the chickpea plant data. Lastly, we perform an ablation study to determine the LSTM-CNN model's performance on decreasing the amount of temporal session data used for training.
Shiva Azimi, Rohan Wadhawan, Tapan K. Gandhi

2021-04-16

Public Health

Modeling the impact of public response on the COVID-19 pandemic in Ontario.

In PloS one ; h5-index 176.0
The outbreak of SARS-CoV-2 is thought to have originated in Wuhan, China in late 2019 and has since spread quickly around the world. To date, the virus has infected tens of millions of people worldwide, compelling governments to implement strict policies to counteract community spread. Federal, provincial, and municipal governments have employed various public health policies, including social distancing, mandatory mask wearing, and the closure of schools and businesses. However, the implementation of these policies can be difficult and costly, making it imperative that both policy makers and the citizenry understand their potential benefits and the risks of non-compliance. In this work, a mathematical model is developed to study the impact of social behaviour on the course of the pandemic in the province of Ontario. The approach is based upon a standard SEIRD model with a variable transmission rate that depends on the behaviour of the population. The model parameters, which characterize the disease dynamics, are estimated from Ontario COVID-19 epidemiological data using machine learning techniques. A key result of the model, following from the variable transmission rate, is the prediction of the occurrence of a second wave using the most current infection data and disease-specific traits. The qualitative behaviour of different future transmission-reduction strategies is examined, and the time-varying reproduction number is analyzed using existing epidemiological data and future projections. Importantly, the effective reproduction number, and thus the course of the pandemic, is found to be sensitive to the adherence to public health policies, illustrating the need for vigilance as the economy continues to reopen.
Eastman Brydon, Meaney Cameron, Przedborski Michelle, Kohandel Mohammad

2021

General

Signatures of COVID-19 severity and immune response in the respiratory tract microbiome.

In medRxiv : the preprint server for health sciences

Rationale : Viral infection of the respiratory tract can be associated with propagating effects on the airway microbiome, and microbiome dysbiosis may influence viral disease.

Objective : To define the respiratory tract microbiome in COVID-19 and relationship disease severity, systemic immunologic features, and outcomes.

Methods and Measurements : We examined 507 oropharyngeal, nasopharyngeal and endotracheal samples from 83 hospitalized COVID-19 patients, along with non-COVID patients and healthy controls. Bacterial communities were interrogated using 16S rRNA gene sequencing, commensal DNA viruses Anelloviridae and Redondoviridae were quantified by qPCR, and immune features were characterized by lymphocyte/neutrophil (L/N) ratios and deep immune profiling of peripheral blood mononuclear cells (PBMC).

Main Results : COVID-19 patients had upper respiratory microbiome dysbiosis, and greater change over time than critically ill patients without COVID-19. Diversity at the first time point correlated inversely with disease severity during hospitalization, and microbiome composition was associated with L/N ratios and PBMC profiles in blood. Intubated patients showed patient-specific and dynamic lung microbiome communities, with prominence of Staphylococcus . Anelloviridae and Redondoviridae showed more frequent colonization and higher titers in severe disease. Machine learning analysis demonstrated that integrated features of the microbiome at early sampling points had high power to discriminate ultimate level of COVID-19 severity.

Conclusions : The respiratory tract microbiome and commensal virome are disturbed in COVID-19, correlate with systemic immune parameters, and early microbiome features discriminate disease severity. Future studies should address clinical consequences of airway dysbiosis in COVID-19, possible use as biomarkers, and role of bacterial and viral taxa identified here in COVID-19 pathogenesis.

Merenstein Carter, Liang Guanxiang, Whiteside Samantha A, Cobián-Güemes Ana G, Merlino Madeline S, Taylor Louis J, Glascock Abigail, Bittinger Kyle, Tanes Ceylan, Graham-Wooten Jevon, Khatib Layla A, Fitzgerald Ayannah S, Reddy Shantan, Baxter Amy E, Giles Josephine R, Oldridge Derek A, Meyer Nuala J, Wherry E John, McGinniss John E, Bushman Frederic D, Collman Ronald G

2021-Apr-05

General

Early stage risk communication and community engagement (RCCE) strategies and measures against the coronavirus disease 2019 (COVID-19) pandemic crisis.

In Global health journal (Amsterdam, Netherlands)
Coronavirus disease 2019 (COVID-19) pandemic has proven to be tenacious and shows that the global community is still poorly prepared to handling such emerging pandemics. Enhancing global solidarity in emergency preparedness and response, and the mobilization of conscience and cooperation, can serve as an excellent source of ideas and measures in a timely manner. The article provides an overview of the key components of risk communication and community engagement (RCCE) strategies at the early stages in vulnerable nations and populations, and highlight contextual recommendations for strengthening coordinated and sustainable RCCE preventive and emergency response strategies against COVID-19 pandemic. Global solidarity calls for firming governance, abundant community participation and enough trust to boost early pandemic preparedness and response. Promoting public RCCE response interventions needs crucially improving government health systems and security proactiveness, community to individual confinement, trust and resilience solutions. To better understand population risk and vulnerability, as well as COVID-19 transmission dynamics, it is important to build intelligent systems for monitoring isolation/quarantine and tracking by use of artificial intelligence and machine learning systems algorithms. Experiences and lessons learned from the international community is crucial for emerging pandemics prevention and control programs, especially in promoting evidence-based decision-making, integrating data and models to inform effective and sustainable RCCE strategies, such as local and global safe and effective COVID-19 vaccines and mass immunization programs.
Zhang Yanjie, Tambo Ernest, Djuikoue Ingrid C, Tazemda Gildas K, Fotsing Michael F, Zhou Xiao-Nong

2021-Mar

Coronavirus disease 2019 (COVID-19), Governance, Pandemic, Response, Risk communication and community engagement (RCCE), Trust, Vaccination

General

Leveraging Genomic Associations in Precision Digital Care for Weight Loss: Cohort Study.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : In this age of global COVID-19 pandemic, the urgency of addressing an epidemic of obesity and associated inflammatory illnesses has come to the fore. Studies have demonstrated that interactions between single nucleotide polymorphisms (SNPs) and lifestyle interventions like food and exercise may vary metabolic outcomes, contributing to obesity and therapeutic response. However, there is a paucity of research relating outcomes from digital therapeutics to inclusion of genetic data in care interventions.

OBJECTIVE : This study aims to describe and model weight loss of subjects enrolled in a precision digital weight loss program informed by machine learning analysis of subject data, including genomic. It was hypothesized that weight loss models would exhibit better fit when incorporating genomic data than utilizing demographic and engagement variables alone.

METHODS : A cohort of 393 participants enrolled in Digbi's personalized digital care program for 120 days was analyzed retrospectively. Care protocol included the use of subject data informing precision coaching by mobile app and personal coach. Two linear regression models of weight loss in this cohort (pounds lost, percentage lost) as a function of demographic and behavioral engagement variables were fit. Genomic-enhanced models were built by adding 197 SNPs from subject genomic data as predictors, then refitting, employing Lasso regression on SNPs for variable selection. Success/failure logistic regression models were also fit, with and without genomic data.

RESULTS : 72% of subjects in this cohort lost weight, while 17% maintained stable weight. 142 subjects lost 5% within 120 days. Models describe the impact of demographic and clinical factors, behavioral engagement, and genomic risk on weight loss. The addition of genomic predictors improved the mean squared error of weight loss models (pounds lost and percent) from 70 to 60 and 16 to 13 respectively. The logistic model improved pseudo R2 from 0.193 to 0.285. Gender, engagement and specific SNPs were significantly associated with weight loss. SNPs within genes involved in metabolic pathways that process food and regulate storage of fat were associated with weight loss in this cohort. This included rs17300539_G (insulin resistance, monounsaturated fat metabolism), rs2016520_C (BMI, waist circumference, cholesterol metabolism), and rs4074995_A (calcium-potassium transport, serum calcium levels). Models described greater average weight loss for subjects having more of these risk alleles. Notably, coaching for dietary modification was personalized to these genetic risks.

CONCLUSIONS : Adding genomic information in modeling outcomes of a digital precision weight loss program greatly enhanced model accuracy. Interpretable weight loss models pointed to efficacy of coaching informed by subjects' genomic risk, accompanied by active engagement of subjects in their own success. While large-scale validation is needed, our study preliminarily supports precision dietary interventions for weight loss utilizing genetic risk, with digitally delivered recommendations alongside health-coaching to improve intervention efficacy.

CLINICALTRIAL :

Sinha Ranjan, Kachru Dashyanng, Ricchetti Roshni Ray, Singh-Rambiritch Simitha, Muthukumar Karthik Marimuthu, Singaravel Vidhya, Irudayanathan Carmel, Reddy-Sinha Chandana, Junaid Imran, Sharma Garima, Airey Catherine, Francis-Lyon Patricia Alice

2021-Apr-11

General

BERT based Transformers lead the way in Extraction of Health Information from Social Media

ArXiv Preprint
This paper describes our submissions for the Social Media Mining for Health (SMM4H)2021 shared tasks. We participated in 2 tasks:(1) Classification, extraction and normalization of adverse drug effect (ADE) mentions in English tweets (Task-1) and (2) Classification of COVID-19 tweets containing symptoms(Task-6). Our approach for the first task uses the language representation model RoBERTa with a binary classification head. For the second task, we use BERTweet, based on RoBERTa. Fine-tuning is performed on the pre-trained models for both tasks. The models are placed on top of a custom domain-specific processing pipeline. Our system ranked first among all the submissions for subtask-1(a) with an F1-score of 61%. For subtask-1(b), our system obtained an F1-score of 50% with improvements up to +8% F1 over the score averaged across all submissions. The BERTweet model achieved an F1 score of 94% on SMM4H 2021 Task-6.
Sidharth R, Abhiraj Tiwari, Parthivi Choubey, Saisha Kashyap, Sahil Khose, Kumud Lakara, Nishesh Singh, Ujjwal Verma

2021-04-15

Radiology

Deep CNN-Based CAD System for COVID-19 Detection Using Multiple Lung CT Scans.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Due to the COVID-19 pandemic and the imminent collapse of healthcare systems following the excessive consumption of financial, hospital, and medicinal resources, the WHO changed the alert level on the COVID-19 pandemic from high to very high. Meanwhile, the world began to favor less expensive and more precise COVID-19 detection methods.

OBJECTIVE : Machine vision-based COVID-19 detection methods especially Deep learning as a diagnostic technique in the early stages of the disease have found great importance during the pandemic. This study aimed to design a highly efficient CAD system for COVID-19 by using a NASNet-based algorithm.

METHODS : A state-of-the-art pre-trained CNN network for image feature extraction, called NASNet, was adopted to identify patients with COVID-19 in the first stages of the disease. A local dataset, comprising 10153 CT-scan images of 190 patients with COVID-19 and 59 with Non Covid-19, was used.

RESULTS : After fitting on the training dataset, hyper-parameter tuning and finally topological alterations of the classifier block, the proposed NASNet-based model was evaluated on the test dataset and yielded remarkable results. The proposed model's performance achieved a detection sensitivity, specificity, and accuracy of 0.999, 0.986, and 0.996, respectively.

CONCLUSIONS : The proposed model achieved acceptable results in the categorization of two data classes. Therefore, a CAD system was designed based on this model for COVID-19 detection using multiple lung CT scans. The system managed to differentiate all the COVID-19 cases from non-COVID-19 ones without any error in the application phase. Overall, the proposed deep learning-based CAD system can greatly aid radiologists in the detection of COVID-19 in its early stages. During the COVID-19 pandemic, the use of CAD system as a screening tool accelerates the process of disease detection and prevents the loss of healthcare resources.

CLINICALTRIAL :

Ghaderzadeh Mustafa, Asadi Farkhondeh, Jafari Ramezan, Bashash Davood, Abolghasemi Hassan, Aria Mehrad

2021-Apr-03

Public Health

Impact of Big Data Analytics on People's Health: Overview of Systematic Reviews and Recommendations for Future Studies.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Although the potential of big data analytics for health care is well recognized, evidence is lacking on its effects on public health.

OBJECTIVE : The aim of this study was to assess the impact of the use of big data analytics on people's health based on the health indicators and core priorities in the World Health Organization (WHO) General Programme of Work 2019/2023 and the European Programme of Work (EPW), approved and adopted by its Member States, in addition to SARS-CoV-2-related studies. Furthermore, we sought to identify the most relevant challenges and opportunities of these tools with respect to people's health.

METHODS : Six databases (MEDLINE, Embase, Cochrane Database of Systematic Reviews via Cochrane Library, Web of Science, Scopus, and Epistemonikos) were searched from the inception date to September 21, 2020. Systematic reviews assessing the effects of big data analytics on health indicators were included. Two authors independently performed screening, selection, data extraction, and quality assessment using the AMSTAR-2 (A Measurement Tool to Assess Systematic Reviews 2) checklist.

RESULTS : The literature search initially yielded 185 records, 35 of which met the inclusion criteria, involving more than 5,000,000 patients. Most of the included studies used patient data collected from electronic health records, hospital information systems, private patient databases, and imaging datasets, and involved the use of big data analytics for noncommunicable diseases. "Probability of dying from any of cardiovascular, cancer, diabetes or chronic renal disease" and "suicide mortality rate" were the most commonly assessed health indicators and core priorities within the WHO General Programme of Work 2019/2023 and the EPW 2020/2025. Big data analytics have shown moderate to high accuracy for the diagnosis and prediction of complications of diabetes mellitus as well as for the diagnosis and classification of mental disorders; prediction of suicide attempts and behaviors; and the diagnosis, treatment, and prediction of important clinical outcomes of several chronic diseases. Confidence in the results was rated as "critically low" for 25 reviews, as "low" for 7 reviews, and as "moderate" for 3 reviews. The most frequently identified challenges were establishment of a well-designed and structured data source, and a secure, transparent, and standardized database for patient data.

CONCLUSIONS : Although the overall quality of included studies was limited, big data analytics has shown moderate to high accuracy for the diagnosis of certain diseases, improvement in managing chronic diseases, and support for prompt and real-time analyses of large sets of varied input data to diagnose and predict disease outcomes.

TRIAL REGISTRATION : International Prospective Register of Systematic Reviews (PROSPERO) CRD42020214048; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=214048.

Borges do Nascimento Israel Júnior, Marcolino Milena Soriano, Abdulazeem Hebatullah Mohamed, Weerasekara Ishanka, Azzopardi-Muscat Natasha, Gonçalves Marcos André, Novillo-Ortiz David

2021-Apr-13

World Health Organization, big data, big data analytics, evidence-based medicine, health status, machine learning, overview, public health, secondary data analysis, systematic review

Radiology

Quantitative analysis based on chest CT classifies common and severe patients with coronavirus disease 2019 pneumonia in Wuhan, China.

In Chinese journal of academic radiology

Objective : This study aimed to compare quantifiable radiologic findings and their dynamic change throughout the clinical course of common and severe coronavirus disease 2019 (COVID-19), and to provide valuable evidence for radiologic classification of the two types of this disease.

Methods : 112 patients with laboratory-confirmed COVID-19 were retrospectively analyzed. Volumetric percentage of infection and density of the lung were measured by a computer-aided software. Clinical parameters were recorded to reflect disease progression. Baseline data and dynamic change were compared between two groups and a decision-tree algorithm was developed to determine the cut-off value for classification.

Results : 93 patients were finally included and were divided into common group (n = 76) and severe group (n = 17) based on current criteria. Compared with common patients, severe patients experienced shorter advanced stage, peak time and plateau, but longer absorption stage. The dynamic change of volume and density coincided with the clinical course. The interquartile range of volumetric percentage of the two groups were 1.0-7.2% and 11.4-31.2%, respectively. Baseline volumetric percentage of infection was significantly higher in severe group, and the cut-off value of it was 10.10%.

Conclusions : Volumetric percentage between severe and common patients was significantly different. Because serial CT scans are systemically performed in patients with COVID-19 pneumonia, this quantitative analysis can simultaneously provide valuable information for physicians to evaluate their clinical course and classify common and severe patients accurately.

Yang Chongtu, Cao Guijuan, Liu Fen, Liu Jiacheng, Huang Songjiang, Xiong Bin

2021-Apr-08

Artificial intelligence, Computer-assisted, Coronavirus disease 2019, Decision trees, Multidetector computed tomography, Numerical analysis

General

Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images.

In Biomedical signal processing and control
The coronavirus (COVID-19) is currently the most common contagious disease which is prevalent all over the world. The main challenge of this disease is the primary diagnosis to prevent secondary infections and its spread from one person to another. Therefore, it is essential to use an automatic diagnosis system along with clinical procedures for the rapid diagnosis of COVID-19 to prevent its spread. Artificial intelligence techniques using computed tomography (CT) images of the lungs and chest radiography have the potential to obtain high diagnostic performance for Covid-19 diagnosis. In this study, a fusion of convolutional neural network (CNN), support vector machine (SVM), and Sobel filter is proposed to detect COVID-19 using X-ray images. A new X-ray image dataset was collected and subjected to high pass filter using a Sobel filter to obtain the edges of the images. Then these images are fed to CNN deep learning model followed by SVM classifier with ten-fold cross validation strategy. This method is designed so that it can learn with not many data. Our results show that the proposed CNN-SVM with Sobel filter (CNN-SVM + Sobel) achieved the highest classification accuracy, sensitivity and specificity of 99.02%, 100% and 95.23%, respectively in automated detection of COVID-19. It showed that using Sobel filter can improve the performance of CNN. Unlike most of the other researches, this method does not use a pre-trained network. We have also validated our developed model using six public databases and obtained the highest performance. Hence, our developed model is ready for clinical application.
Sharifrazi Danial, Alizadehsani Roohallah, Roshanzamir Mohamad, Joloudari Javad Hassannataj, Shoeibi Afshin, Jafari Mahboobeh, Hussain Sadiq, Sani Zahra Alizadeh, Hasanzadeh Fereshteh, Khozeimeh Fahime, Khosravi Abbas, Nahavandi Saeid, Panahiazar Maryam, Zare Assef, Islam Sheikh Mohammed Shariful, Acharya U Rajendra

2021-Apr-08

CNN., Covid-19, Data Mining, Deep Learning, Feature Extraction, Image Processing, Machine Learning, SVM, Sobel operator

General

A novel framework of collaborative early warning for COVID-19 based on blockchain and smart contracts.

In Information sciences
Early warning is a vital component of emergency repsonse systems for infectious diseases. However, most early warning systems are centralized and isolated, thus there are potential risks of single evidence bias and decision-making errors. In this paper, we tackle this issue via proposing a novel framework of collaborative early warning for COVID-19 based on blockchain and smart contracts, aiming to crowdsource early warning tasks to distributed channels including medical institutions, social organinzations, and even individuals. Our framework supports two surveillance modes, namely, medical federation surveillance based on federated learning and social collaboration surveillance based on the learning markets approach, and fuses their monitoring results on emerging cases to alert. By using our framework, medical institutions are expected to obtain better federated surveillance models with privacy protection, and social participants without mutual trusts can also share verified surveillance resources such as data and models, and fuse their surveillance solutions. We implemented our proposed framework based on the Ethereum and IPFS platforms. Experimental results show that our framework has advantages of decentralized decision-making, fairness, auditability, and universality, and also has potential guidance and reference value for the early warning and prevention of unknown infectious diseases.
Ouyang Liwei, Yuan Yong, Cao Yumeng, Wang Fei-Yue

2021-Apr-08

blockchain, collaborative early warning, federated learning, learning markets, smart contracts

General

COVID-19 information retrieval with deep-learning based semantic search, question answering, and abstractive summarization.

In NPJ digital medicine
The COVID-19 global pandemic has resulted in international efforts to understand, track, and mitigate the disease, yielding a significant corpus of COVID-19 and SARS-CoV-2-related publications across scientific disciplines. Throughout 2020, over 400,000 coronavirus-related publications have been collected through the COVID-19 Open Research Dataset. Here, we present CO-Search, a semantic, multi-stage, search engine designed to handle complex queries over the COVID-19 literature, potentially aiding overburdened health workers in finding scientific answers and avoiding misinformation during a time of crisis. CO-Search is built from two sequential parts: a hybrid semantic-keyword retriever, which takes an input query and returns a sorted list of the 1000 most relevant documents, and a re-ranker, which further orders them by relevance. The retriever is composed of a deep learning model (Siamese-BERT) that encodes query-level meaning, along with two keyword-based models (BM25, TF-IDF) that emphasize the most important words of a query. The re-ranker assigns a relevance score to each document, computed from the outputs of (1) a question-answering module which gauges how much each document answers the query, and (2) an abstractive summarization module which determines how well a query matches a generated summary of the document. To account for the relatively limited dataset, we develop a text augmentation technique which splits the documents into pairs of paragraphs and the citations contained in them, creating millions of (citation title, paragraph) tuples for training the retriever. We evaluate our system ( http://einstein.ai/covid ) on the data of the TREC-COVID information retrieval challenge, obtaining strong performance across multiple key information retrieval metrics.
Esteva Andre, Kale Anuprit, Paulus Romain, Hashimoto Kazuma, Yin Wenpeng, Radev Dragomir, Socher Richard

2021-Apr-12

Radiology

A multi-center study of COVID-19 patient prognosis using deep learning-based CT image analysis and electronic health records.

In European journal of radiology ; h5-index 47.0

PURPOSE : As of August 30th, there were in total 25.1 million confirmed cases and 845 thousand deaths caused by coronavirus disease of 2019 (COVID-19) worldwide. With overwhelming demands on medical resources, patient stratification based on their risks is essential. In this multi-center study, we built prognosis models to predict severity outcomes, combining patients' electronic health records (EHR), which included vital signs and laboratory data, with deep learning- and CT-based severity prediction.

METHOD : We first developed a CT segmentation network using datasets from multiple institutions worldwide. Two biomarkers were extracted from the CT images: total opacity ratio (TOR) and consolidation ratio (CR). After obtaining TOR and CR, further prognosis analysis was conducted on datasets from INSTITUTE-1, INSTITUTE-2 and INSTITUTE-3. For each data cohort, generalized linear model (GLM) was applied for prognosis prediction.

RESULTS : For the deep learning model, the correlation coefficient of the network prediction and manual segmentation was 0.755, 0.919, and 0.824 for the three cohorts, respectively. The AUC (95 % CI) of the final prognosis models was 0.85(0.77,0.92), 0.93(0.87,0.98), and 0.86(0.75,0.94) for INSTITUTE-1, INSTITUTE-2 and INSTITUTE-3 cohorts, respectively. Either TOR or CR exist in all three final prognosis models. Age, white blood cell (WBC), and platelet (PLT) were chosen predictors in two cohorts. Oxygen saturation (SpO2) was a chosen predictor in one cohort.

CONCLUSION : The developed deep learning method can segment lung infection regions. Prognosis results indicated that age, SpO2, CT biomarkers, PLT, and WBC were the most important prognostic predictors of COVID-19 in our prognosis model.

Gong Kuang, Wu Dufan, Arru Chiara Daniela, Homayounieh Fatemeh, Neumark Nir, Guan Jiahui, Buch Varun, Kim Kyungsang, Bizzo Bernardo Canedo, Ren Hui, Tak Won Young, Park Soo Young, Lee Yu Rim, Kang Min Kyu, Park Jung Gil, Carriero Alessandro, Saba Luca, Masjedi Mahsa, Talari Hamidreza, Babaei Rosa, Mobin Hadi Karimi, Ebrahimian Shadi, Guo Ning, Digumarthy Subba R, Dayan Ittai, Kalra Mannudeep K, Li Quanzheng

2021-Feb-05

COVID-19, Computed tomography, Deep learning, Electronic health records, Prognosis

Surgery

Abdominal Organ Transplantation: Noteworthy Literature in 2020.

In Seminars in cardiothoracic and vascular anesthesia ; h5-index 16.0
In 2020, we identified and screened over 490 peer-reviewed publications on pancreatic transplantation, over 500 on intestinal transplantation, and over 5000 on kidney transplantation. The liver transplantation section specially focused on clinical trials and systematic reviews published in 2020 and featured selected articles. This review highlights noteworthy literature pertinent to anesthesiologists and critical care physicians caring for patients undergoing abdominal organ transplantation. We explore a wide range of topics, including COVID-19 and organ transplantation, risk factors and outcomes, pain management, artificial intelligence, robotic donor surgery, and machine perfusion.
Wang Ryan F, Fagelman Erica J, Smith Natalie K, Sakai Tetsuro

2021-Apr-13

COVID-19, anesthesiology, intestine, kidney, liver, pancreas, transplantation

General

How does "A Bit of Everything American" state feel about COVID-19? A quantitative Twitter analysis of the pandemic in Ohio.

In Journal of computational social science
COVID-19 has proven itself to be one of the most important events of the last two centuries. This defining moment in our lives has created wide-ranging discussions in many segments of our societies, both politically and socially. Over time, the pandemic has been associated with many social and political topics, as well as sentiments and emotions. Twitter offers a platform to understand these effects. The primary objective of this study is to capture the awareness and sentiment about COVID-19-related issues and to find how they relate to the number of cases and deaths in a representative region of the United States. The study uses a unique dataset consisting of over 46 million tweets from over 91,000 users in 88 counties of the state of Ohio, a state-of-the-art deep learning model to measure and detect awareness and emotions. The data collected is analyzed using OLS regression and System-GMM dynamic panel. Findings indicate that the pandemic has drastically changed the perception of the Republican party in the society. Individual motivations are strongly influenced by ideological choices and this ultimately affects individual pandemic-related outcomes. The paper contributes to the literature by expanding the knowledge on COVID-19 (i), offering a representative result for the United States by focusing on an "average" state like Ohio (ii), and incorporating the sentiment and emotions into the calculation of awareness (iii).
Caliskan Cantay

2021-Apr-05

Awareness, COVID-19, Emotion classification, Twitter

General

COVID-19 prediction using LSTM Algorithm: GCC Case Study.

In Informatics in medicine unlocked
Coronavirus-19 (COVID-19) is the black swan of 2020. Still, the human response to restrain the virus is also creating massive ripples through different systems, such as health, economy, education, and tourism. This paper focuses on research and applying Artificial Intelligence (AI) algorithms to predict COVID-19 propagation using the available time-series data and study the effect of the quality of life, the number of tests performed, and the awareness of citizens on the virus in the Gulf Cooperation Council (GCC) countries at the Gulf area. So we focused on cases in the Kingdom of Saudi Arabia (KSA), United Arab of Emirates (UAE), Kuwait, Bahrain, Oman, and Qatar. For this aim, we accessed the time-series real-datasets collected from Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE). The timeline of our data is from 22 January 2020 to 25 January 2021. We have implemented the proposed model based on Long Short-Term Memory (LSTM) with ten hidden units (neurons) to predict COVID-19 confirmed and death cases. From the experimental results, we confirmed that KSA and Qatar would take the most extended period to recover from the COVID-19 virus, and the situation will be controllable in the second half of March 2021 in UAE, Kuwait, Oman, and Bahrain. Also, we calculated the root mean square error (RMSE) between the actual and predicted values of each country for confirmed and death cases, and we found that the best values for both confirmed and death cases are 320.79 and 1.84, respectively, and both are related to Bahrain. While the worst values are 1768.35 and 21.78, respectively, and both are related to KSA. On the other hand, we also calculated the mean absolute relative errors (MARE) between the actual and predicted values of each country for confirmed and death cases, and we found that the best values for both confirmed and deaths cases are 37.76 and 0.30, and these are related to Kuwait and Qatar respectively. While the worst values are 71.45 and 1.33, respectively, and both are related to KSA.
Ghany Kareem Kamal A, Zawbaa Hossam M, Sabri Heba M

2021-Apr-06

Artificial Intelligence, COVID-19, Deep Learning, LSTM, Prediction

General

Machine Learning Approaches in COVID-19 Diagnosis, Mortality, and Severity Risk Prediction: A Review.

In Informatics in medicine unlocked
The existence of widespread COVID-19 infections has prompted worldwide efforts to control and manage the virus, and hopefully curb it completely. One important line of research is the use of machine learning (ML) to understand and fight COVID-19. This is currently an active research field. Although there are already many surveys in the literature, there is a need to keep up with the rapidly growing number of publications on COVID-19-related applications of ML. This paper presents a review of recent reports on ML algorithms used in relation to COVID-19. We focus on the potential of ML for two main applications: diagnosis of COVID-19 and prediction of mortality risk and severity, using readily available clinical and laboratory data. Aspects related to algorithm types, training data sets, and feature selection are discussed. As we cover work published between January 2020 and January 2021, a few key points have come to light. The bulk of the machine learning algorithms used in these two applications are supervised learning algorithms. The established models are yet to be used in real-world implementations, and much of the associated research is experimental. The diagnostic and prognostic features discovered by ML models are consistent with results presented in the medical literature. A limitation of the existing applications is the use of imbalanced data sets that are prone to selection bias.
Alballa Norah, Al-Turaiki Isra

2021-Apr-03

COVID-19, Machine learning, artificial intelligence, diagnosis, feature selection, prognosis

General

Covid, AI, and Robotics-A Neurologist's Perspective.

In Frontiers in robotics and AI
Two of the major revolutions of this century are the Artificial Intelligence and Robotics. These technologies are penetrating through all disciplines and faculties at a very rapid pace. The application of these technologies in medicine, specifically in the context of Covid 19 is paramount. This article briefly reviews the commonly applied protocols in the Health Care System and provides a perspective in improving the efficiency and effectiveness of the current system. This article is not meant to provide a literature review of the current technology but rather provides a personal perspective of the author regarding what could happen in the ideal situation.
Ahmed Syed Nizamuddin

2021

AI, COVID-19, artificial intelligence, neurologist, neurology, robotics, telemedicine

General

Evaluating Simulations as Preparation for Health Crises like CoVID-19: Insights on Incorporating Simulation Exercises for Effective Response.

In International journal of disaster risk reduction : IJDRR
Today's health emergencies are increasingly complex due to factors such as globalization, urbanization and increased connectivity where people, goods and potential vectors of disease are constantly on the move. These factors amplify the threats to our health from infectious hazards, natural disasters, armed conflicts and other emergencies wherever they may occur. The current CoVID-19 pandemic has provided a clear demonstration of the fact that our ability to detect and predict the initial emergence of a novel human pathogen (for example, the spill-over of a virus from its animal reservoir to a human host), and our capacity to forecast the spread and transmission the pathogen in human society remains limited. Improving ways in which we prepare will enable a more rapid and effective response and enable proactive preparations (including exercising) to respond to any novel emerging infectious disease outbreaks. This study aims to explore the current state of pandemic preparedness exercising and provides an assessment of a number of case study exercises for health hazards against the key components of the WHO's Exercises for Pandemic Prepared Plans (EPPP) framework in order to gauge their usefulness in preparation for pandemics. The paper also examines past crises involving large-scale epidemics and pandemics and whether simulations took place to test health security capacities either in advance of the crisis based on risk assessments, strategy and plans or after the crisis in order to be better prepared should a similar scenario arise in the future. Exercises for animal and human diseases have been included to provide a "one health" perspective [1,2]. This article then goes on to examine approaches to simulation exercises relevant to prepare for health crisis involving a novel emergent pathogen like CoVID-19. This article demonstrates that while simulations are useful as part of a preparedness strategy, the key is to ensure that lessons from these simulations are learned and the associated changes made as soon as possible following any simulation in order to ensure that simulations are effective in bringing about changes in practice that will improve pandemic preparedness. Furthermore, Artificial Intelligence (AI) technologies could also be applied in preparing communities for outbreak detection, surveillance and containment, and be a useful tool for providing immersive environments for simulation exercises for pandemic preparedness and associated interventions which may be particularly useful at the strategic level. This article contributes to the limited literature in pandemic preparedness simulation exercising to deal with novel health crises, like CoVID-19. The analysis has also identified potential areas for further research or work on pandemic preparedness exercising.
Reddin Karen, Bang Henry, Miles Lee

2021-Apr-05

Emergency Exercise, Epidemic, Lessons learnt, Pandemic, Simulation

General

K-SEIR-Sim: A simple customized software for simulating the spread of infectious diseases.

In Computational and structural biotechnology journal
Infectious disease is a great enemy of humankind. The ravages of COVID-19 are leading to profound crises across the world. There is an urgent requirement for analyzing the current pandemic situation, predicting trends over time, and assessing the effectiveness of containment measures. Thus, numerous statistical models, primarily based on the susceptible-exposed-infected-recovered or removed (SEIR) model, have been established. However, these models are highly technical, which are difficult for the public and governing bodies to understand and use. To address this issue, we developed a simple operating software based on our improved K-SEIR model termed as the kernelkernel SEIR simulator (K-SEIR-Sim). This software includes natural propagation parameters, containment measure parameters, and certain characteristic parameters that can deduce the effects of natural propagation and containment measures. Further, the applicability of the proposed software was demonstrated using the example of the COVID-19 outbreak in the United States and the city of Wuhan, China. Operating results verified the potency of the proposed software in evaluating the epidemic situation and human intervention during COVID-19. Importantly, the software can perform real-time, backward-looking, and forward-looking analysis by functioning in data-driven and model-driven ways. All of them have considerable practical values in their applications according to the actual needs of personal use. Conclusively, K-SEIR-Sim is the first simple customized operating software that is highly valuable for the global fight against COVID-19 and other infectious diseases.
Wang Hongzhi, Miao Zhiying, Zhang Chaobao, Wei Xiaona, Li Xiangqi

2021-Apr-07

2019-nCoV, COVID-19, SEIR model, artificial intelligence, python, simulation analysis, software

General

A statistical and deep learning-based daily infected count prediction system for the coronavirus pandemic.

In Evolutionary intelligence

: We present new data analytics-based predictions results that can help governments to plan their future actions and also help medical services to be better prepared for the future. Our system can predict new corona cases with 99.82% accuracy using susceptible infected recovered (SIR) model. We have predicted the results of new COVID cases per day for dense and highly populated country i.e. India. We found that traditional statistical methods will not work efficiently as they do not consider the limited population in a particular country. Using the data analytics-based curve we predicted four most likely possibilities for the number of new cases in India. Hence, we expect that the results mentioned in the manuscript help people to better understand the progress of this disease.

Supplementary Information** : The online version contains supplementary material available at 10.1007/s12065-021-00600-2.

Shah Vruddhi, Shelke Ankita, Parab Mamata, Shah Jainam, Mehendale Ninad

2021-Apr-03

Coronavirus, Covid-19 simulations, Daily count

General

Retrospective and prospective application of robots and artificial intelligence in global pandemic and epidemic diseases.

In Vacunas

: About 4.25% of people have lost their lives due to COVID-19 disease, among SARS-CoV-2 infected patients. In an unforeseen situation, approximately 25,000 frontline healthcare workers have also been infected by this disease while providing treatment to the infected patients. In this devastating scenario, without any drug or vaccine available for the treatment, frontline healthcare workers are highly prone to viral infection. However, some countries are drastically facing a shortage of healthcare workers in hospitals.

Methods : The literature search was conducted in ScienceDirect and ResearchGate, using words "Medical Robots", and "AI in Covid-19" as descriptors. To identify and evaluate the articles that create the impact of robots and artificial intelligence in pandemic diseases. Eligible articles were included publications and laboratory studies before and after covid-19 and also the prospective and retrospective of application of Robots and AI.

Conclusion** : In this pandemic situation, robots were employed in some countries during the COVID-19 outbreak, which are medical robots, UV-disinfectant robots, social robots, drones, and COBOTS. Implementation of these robots was found effective in successful disease management, treatment, most importantly ensures the safety of healthcare workers. Mainly, the Disposal of deceased bodies and the location and transportation of infected patients to hospitals and hospitals were tough tasks and risk of infection. These tasks will be performed by employing mobile robots and automated guided robots respectively. Therefore, in the future, advanced automated robots would be a promising choice in hospitals and healthcare centers to minimize the risk of frontline healthcare workers.

Yoganandhan A, Rajesh Kanna G, Subhash S D, Hebinson Jothi J

2021-Apr-05

Artificial intelligence, COVID-19, Disease management, Healthcare safety, Medical robots, Pandemic disease

General

Forecasting of the COVID-19 pandemic situation of Korea.

In Genomics & informatics
For the novel coronavirus disease 2019 (COVID-19), predictive modeling, in the literature, uses broadly susceptible exposed infected recoverd (SEIR)/SIR, agent-based, curve-fitting models. Governments and legislative bodies rely on insights from prediction models to suggest new policies and to assess the effectiveness of enforced policies. Therefore, access to accurate outbreak prediction models is essential to obtain insights into the likely spread and consequences of infectious diseases. The objective of this study is to predict the future COVID-19 situation of Korea. Here, we employed 5 models for this analysis; SEIR, local linear regression (LLR), negative binomial (NB) regression, segment Poisson, deep-learning based long short-term memory models (LSTM) and tree based gradient boosting machine (GBM). After prediction, model performance comparison was evelauated using relative mean squared errors (RMSE) for two sets of train (January 20, 2020‒December 31, 2020 and January 20, 2020‒January 31, 2021) and testing data (January 1, 2021‒February 28, 2021 and February 1, 2021‒February 28, 2021) . Except for segmented Poisson model, the other models predicted a decline in the daily confirmed cases in the country for the coming future. RMSE values' comparison showed that LLR, GBM, SEIR, NB, and LSTM respectively, performed well in the forecasting of the pandemic situation of the country. A good understanding of the epidemic dynamics would greatly enhance the control and prevention of COVID-19 and other infectious diseases. Therefore, with increasing daily confirmed cases since this year, these results could help in the pandemic response by informing decisions about planning, resource allocation, and decision concerning social distancing policies.
Goo Taewan, Apio Catherine, Heo Gyujin, Lee Doeun, Lee Jong Hyeok, Lim Jisun, Han Kyulhee, Park Taesung

2021-Mar

COVID-19, deep learning, disease transmission, mathematical model, pandemics, statistical model

General

Identification of novel compounds against three targets of SARS CoV-2 coronavirus by combined virtual screening and supervised machine learning.

In Computers in biology and medicine
Coronavirus disease 2019 (COVID-19) is a major threat worldwide due to its fast spreading. As yet, there are no established drugs available. Speeding up drug discovery is urgently required. We applied a workflow of combined in silico methods (virtual drug screening, molecular docking and supervised machine learning algorithms) to identify novel drug candidates against COVID-19. We constructed chemical libraries consisting of FDA-approved drugs for drug repositioning and of natural compound datasets from literature mining and the ZINC database to select compounds interacting with SARS-CoV-2 target proteins (spike protein, nucleocapsid protein, and 2'-o-ribose methyltransferase). Supported by the supercomputer MOGON, candidate compounds were predicted as presumable SARS-CoV-2 inhibitors. Interestingly, several approved drugs against hepatitis C virus (HCV), another enveloped (-) ssRNA virus (paritaprevir, simeprevir and velpatasvir) as well as drugs against transmissible diseases, against cancer, or other diseases were identified as candidates against SARS-CoV-2. This result is supported by reports that anti-HCV compounds are also active against Middle East Respiratory Virus Syndrome (MERS) coronavirus. The candidate compounds identified by us may help to speed up the drug development against SARS-CoV-2.
Kadioglu Onat, Saeed Mohamed, Greten Henry Johannes, Efferth Thomas

2021-Mar-30

Artificial intelligence, COVID-19, Chemotherapy, Infectious diseases, Natural products

Public Health

Comparison of Multiple Machine Learning-based Predictions of Growth in COVID-19 Confirmed Infection Cases in Countries using Non-Pharmaceutical Interventions and Cultural Dimensions Data: Development and Validation.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : National governments have implemented non-pharmaceutical interventions to control and mitigate against the COVID-19 pandemic.

OBJECTIVE : We investigate the prediction of future daily national Confirmed Infection Growths - the percentage change in total cumulative cases across 14 days for 114 countries using non-pharmaceutical intervention metrics and cultural dimension metrics, which are metrics indicative of specific national sociocultural norms.

METHODS : We combine the OxCGRT dataset, Hofstede's cultural dimensions, and COVID-19 daily reported infection case numbers to train and evaluate five non-time series machine learning models in predicting Confirmed Infection Growth. We use three validation methods - in-distribution, out-of-distribution, and country-based cross-validation - for evaluation, each applicable to a different use case of the models.

RESULTS : Our results demonstrate high R2 values between the labels and predictions for the in-distribution method (0.959), and moderate R2 values for the out-of-distribution and country-based cross-validation methods (0.513 and 0.574, respectively) using random forest and AdaBoost regression. While these models may be used to predict the Confirmed Infection Growth, the differing accuracies obtained from the three tasks suggest a strong influence of the use case.

CONCLUSIONS : This work provides new considerations in using machine learning techniques with non-pharmaceutical interventions and cultural dimensions data for predicting the national growth of confirmed infections of COVID-19.

CLINICALTRIAL :

Yeung Arnold Ys, Roewer-Despres Francois, Rosella Laura, Rudzicz Frank

2021-Mar-23

Cardiology

Medical Education and Training Within Congenital Cardiology: Current Global Status and Future Directions in A Post COVID-19 World.

In Cardiology in the young
Despite enormous strides in our field with respect to patient care, there has been surprisingly limited dialogue on how to train and educate the next generation of congenital cardiologists. This paper reviews the current status of training and evolving developments in medical education pertinent to congenital cardiology. The adoption of competency-based medical education has been lauded as a robust framework for contemporary medical education over the last two decades. However, inconsistencies in frameworks across different jurisdictions remain, and bridging gaps between competency frameworks and clinical practice has proved challenging. Entrustable professional activities have been proposed as a solution but integration of such activities into busy clinical cardiology practices will present its own challenges. Consequently, this pivot toward a more structured approach to medical education necessitates the widespread availability of appropriately trained medical educationalists; a development that will better inform curriculum development, instructional design, and assessment. Differentiation between superficial and deep learning, the vital role of rich formative feedback and coaching, should guide our trainees to become self-regulated learners, capable of critical reasoning yet retaining an awareness of uncertainty and ambiguity. Furthermore, disruptive innovations such as 'technology enhanced learning' may be leveraged to improve education, especially for trainees from low- and middle-income countries. Each of these initiatives will require resources, widespread advocacy and raised awareness, and publication of supporting data, and so it is especially gratifying that Cardiology in The Young has fostered a progressive approach, agreeing to publish one or two articles in each journal issue in this domain.
McMahon Colin J, Tretter Justin T, Redington Andrew N, Bu’Lock Frances, Zühlke Liesl, Heying Ruth, Mattos Sandra, Kumar R Krishna, Jacobs Jeffrey P, Windram Jonathan D

2021-Apr-12

Adult Congenital Heart Disease, Congenital Cardiology, Congenital Heart Disease, Education, Paediatric Cardiology, Training

General

Knowledge graphs and their applications in drug discovery.

In Expert opinion on drug discovery ; h5-index 34.0

INTRODUCTION : Knowledge graphs have proven to be promising systems of information storage and retrieval. Due to the recent explosion of heterogeneous multimodal data sources generated in the biomedical domain, and an industry shift toward a systems biology approach, knowledge graphs have emerged as attractive methods of data storage and hypothesis generation.

AREAS COVERED : In this review, the author summarizes the applications of knowledge graphs in drug discovery. They evaluate their utility; differentiating between academic exercises in graph theory, and useful tools to derive novel insights, highlighting target identification and drug repurposing as two areas showing particular promise. They provide a case study on COVID-19, summarizing the research that used knowledge graphs to identify repurposable drug candidates. They describe the dangers of degree and literature bias, and discuss mitigation strategies.

EXPERT OPINION : Whilst knowledge graphs and graph-based machine learning have certainly shown promise, they remain relatively immature technologies. Many popular link prediction algorithms fail to address strong biases in biomedical data, and only highlight biological associations, failing to model causal relationships in complex dynamic biological systems. These problems need to be addressed before knowledge graphs reach their true potential in drug discovery.

MacLean Finlay

2021-Apr-12

Biomedical knowledge graphs, drug repositioning, drug repurposing, graph machine learning, heterogeneous information networks, knowledge graph embedding, network embeddings, network medicine, network pharmacology

General

Revealing the threat of emerging SARS-CoV-2 mutations to antibody therapies

bioRxiv Preprint
The ongoing massive vaccination and the development of effective intervention offer the long-awaited hope to end the global rage of the COVID-19 pandemic. However, the rapidly growing SARS-CoV-2 variants might compromise existing vaccines and monoclonal antibody (mAb) therapies. Although there are valuable experimental studies about the potential threats from emerging variants, the results are limited to a handful of mutations and Eli Lilly and Regeneron mAbs. The potential threats from frequently occurring mutations on the SARS-CoV-2 spike (S) protein receptor-binding domain (RBD) to many mAbs in clinical trials are largely unknown. We fill the gap by developing a topology-based deep learning strategy that is validated with tens of thousands of experimental data points. We analyze 261,348 genome isolates from patients to identify 514 non-degenerate RBD mutations and investigate their impacts on 16 mAbs in clinical trials. Our findings, which are highly consistent with existing experimental results about variants from the UK, South Africa, Brazil, US-California, and Mexico shed light on potential threats of 95 high-frequency mutations to mAbs not only from Eli Lilly and Regeneron but also from Celltrion and Rockefeller University that are in clinical trials. We unveil, for the first time, that high-frequency mutations R346K/S, N439K, G446V, L455F, V483F/A, E484Q/V/A/G/D, F486L, F490L/V/S, Q493L, and S494P/L might compromise some of mAbs in clinical trials. Our study gives rise to a general perspective about how mutations will affect current vaccines.
Chen, J.; Gao, K.; Wang, R.; Wei, G.-W.

2021-04-12

Public Health

Prediction of COVID-19 cases using the weather integrated deep learning approach for India.

In Transboundary and emerging diseases ; h5-index 40.0
Advanced and accurate forecasting of COVID-19 cases plays a crucial role in planning and supplying resources effectively. Artificial Intelligence (AI) techniques have proved their capability in time series forecasting non-linear problems. In the present study, the relationship between weather factor and COVID-19 cases was assessed, and also developed a forecasting model using long short-term memory (LSTM), a deep learning model. The study found that the specific humidity has a strong positive correlation, whereas there is a negative correlation with maximum temperature, and a positive correlation with minimum temperature was observed in various geographic locations of India. The weather data and COVID-19 confirmed case data (1^st April to 30^th June 2020) were used to optimize univariate and multivariate LSTM time series forecast models. The optimized models were utilized to forecast the daily COVID-19 cases for the period 1^st July 2020 to 31^st July 2020 with 1 to 14 days of lead time. The results showed that the univariate LSTM model was reasonably good for the short-term (1 day lead) forecast of COVID-19 cases (relative error < 20%). Moreover, the multivariate LSTM model improved the medium-range forecast skill (1-7 days lead) after including the weather factors. The study observed that the specific humidity played a crucial role in improving the forecast skill majorly in the West and northwest region of India. Similarly, the temperature played a significant role in model enhancement in the Southern and Eastern regions of India.
Bhimala Kantha Rao, Patra Gopal Krishna, Mopuri Rajashekar, Mutheneni Srinivasa Rao

2021-Apr-10

COVID-19, India, LSTM, Prediction, SARS-CoV-2, Specific Humidity, Temperature

Radiology

Get Protected! Recommendations for Staff in IR.

In Cardiovascular and interventional radiology

PURPOSE : Evaluation and registration of patient and staff doses are mandatory under the current European legislation, and the occupational dose limits recommended by the ICRP have been adopted by most of the countries in the world.

METHODS : Relevant documents and guidelines published by international organisations and interventional radiology societies are referred. Any potential reduction of patient and staff doses should be compatible with the clinical outcomes of the procedures.

RESULTS : The review summarises the most common protective measures and the needed quality control for them, the criteria to select the appropriate protection devices, and how to avoid unnecessary occupational radiation exposures. Moreover, the current and future advancements in personnel radiation protection using medical simulation with virtual and augmented reality, robotics, and artificial intelligence (AI) are commented. A section on the personnel radiation protection in the era of COVID-19 is introduced, showing the expanding role of the interventional radiology during the pandemic.

CONCLUSION : The review is completed with a summary of the main factors to be considered in the selection of the appropriate radiation protection tools and practical advices to improve the protection of the staff.

Bartal Gabriel, Vano Eliseo, Paulo Graciano

2021-Apr-09

Lead aprons, Musculoskeletal symptoms in interventional radiologist, Occupational radiation protection, Protective goggles, Shielding

General

COVID-19 in Portugal: predictability of hospitalization, ICU and respiratory-assistance needs.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : In face of the current SARS-COV-2 pandemic, the timely prediction of upcoming medical needs for infected individuals enables a better and quicker care provision when necessary and management decisions within health care systems.

OBJECTIVE : This work aims to predict medical needs (hospitalizations, ICU admission, respiratory assistance) and survivability of individuals testing SARS-CoV-2 positive using a retrospective cohort with 38.545 infected individuals in Portugal during 2020.

METHODS : Predictions of medical needs are performed using state-of-the-art machine learning approaches at various stages of a patient's cycle, namely: testing time (pre-hospitalization), post-hospitalization, and post-intensive care. A thorough optimization of state-of-the-art predictors is undertaken to assess the ability to anticipate medical needs and infection outcomes using demographic and comorbidity variables, as well as onset date of symptoms, test and hospitalization.

RESULTS : For the target cohort, 75% of hospitalization needs can be identified at the SARS-CoV-2 testing time and over 60% respiratory needs at hospitalization time, both with >50% precision.

CONCLUSIONS : The conducted study pinpoints the relevance of the proposed predictive models as good candidates to support medical decisions for the Portuguese population, including both monitoring and in-hospital care decisions. A clinical decision support system (CDSS) is further provided to this end.

CLINICALTRIAL :

Patrício André, Costa Rafael S, Henriques Rui

2021-Mar-18

General

Multilevel Deep-Aggregated Boosted Network to Recognize COVID-19 Infection from Large-Scale Heterogeneous Radiographic Data.

In IEEE journal of biomedical and health informatics
In the present epidemic of the coronavirus disease 2019 (COVID-19), radiological imaging modalities, such as X-ray and computed tomography (CT), have been identified as effective diagnostic tools. However, the subjective assessment of radiographic examination is a time-consuming task and demands expert radiologists. Recent advancements in artificial intelligence have enhanced the diagnostic power of computer-aided diagnosis (CAD) tools and assisted medical specialists in making efficient diagnostic decisions. In this work, we propose an optimal multilevel deep-aggregated boosted network to recognize COVID-19 infection from heterogeneous radiographic data, including X-ray and CT images. Our method leverages multilevel deep-aggregated features and multistage training via a mutually beneficial approach to maximize the overall CAD performance. To improve the interpretation of CAD predictions, these multilevel deep features are visualized as additional outputs that can assist radiologists in validating the CAD results. A total of six publicly available datasets were fused to build a single large-scale heterogeneous radiographic collection that was used to analyze the performance of the proposed technique and other baseline methods. To preserve generality of our method, we selected different patient data for training, validation, and testing, and consequently, the data of same patient were not included in training, validation, and testing subsets. In addition, fivefold cross-validation was performed in all the experiments for a fair evaluation. Our method exhibits promising performance values of 95.38%, 95.57%, 92.53%, 98.14%, 93.16%, and 98.55% in terms of average accuracy, F-measure, specificity, sensitivity, precision, and area under the curve, respectively and outperforms various state-of-the-art methods.
Owais Muhammad, Lee Young Won, Mahmood Tahir, Haider Adnan, Sultan Haseeb, Park Kang Ryoung

2021-Apr-09

Internal Medicine

Classification and analysis of outcome predictors in non-critically ill COVID-19 patients.

In Internal medicine journal

BACKGROUND : Early detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected patients who could develop a severe form of COVID-19 must be considered of great importance to carry out adequate care and optimise the use of limited resources.

AIMS : To use several machine learning classification models to analyse a series of non-critically ill COVID-19 patients admitted to a general medicine ward to verify if any clinical variables recorded could predict the clinical outcome.

METHODS : We retrospectively analysed non-critically ill patients with COVID-19 admitted to the general ward of the hospital in Pordenone from 1 March 2020 to 30 April 2020. Patients' characteristics were compared based on clinical outcomes. Through several machine learning classification models, some predictors for clinical outcome were detected.

RESULTS : In the considered period, we analysed 176 consecutive patients admitted: 119 (67.6%) were discharged, 35 (19.9%) dead and 22 (12.5%) were transferred to intensive care unit. The most accurate models were a random forest model (M2) and a conditional inference tree model (M5) (accuracy = 0.79; 95% confidence interval 0.64-0.90, for both). For M2, glomerular filtration rate and creatinine were the most accurate predictors for the outcome, followed by age and fraction-inspired oxygen. For M5, serum sodium, body temperature and arterial pressure of oxygen and inspiratory fraction of oxygen ratio were the most reliable predictors.

CONCLUSIONS : In non-critically ill COVID-19 patients admitted to a medical ward, glomerular filtration rate, creatinine and serum sodium were promising predictors for the clinical outcome. Some factors not determined by COVID-19, such as age or dementia, influence clinical outcomes.

Venturini Sergio, Orso Daniele, Cugini Francesco, Crapis Massimo, Fossati Sara, Callegari Astrid, Pellis Tommaso, Tonizzo Maurizio, Grembiale Alessandro, Rosso Alessia, Tamburrini Mario, D’Andrea Natascia, Vetrugno Luigi, Bove Tiziana

2021-Apr-09

COVID-19, machine learning, non-critically ill, prediction

General

A deep learning algorithm to detect coronavirus (COVID-19) disease using CT images.

In PeerJ. Computer science

Background : COVID-19 pandemic imposed a lockdown situation to the world these past months. Researchers and scientists around the globe faced serious efforts from its detection to its treatment.

Methods : Pathogenic laboratory testing is the gold standard but it is time-consuming. Lung CT-scans and X-rays are other common methods applied by researchers to detect COVID-19 positive cases. In this paper, we propose a deep learning neural network-based model as an alternative fast screening method that can be used for detecting the COVID-19 cases by analyzing CT-scans.

Results : Applying the proposed method on a publicly available dataset collected of positive and negative cases showed its ability on distinguishing them by analyzing each individual CT image. The effect of different parameters on the performance of the proposed model was studied and tabulated. By selecting random train and test images, the overall accuracy and ROC-AUC of the proposed model can easily exceed 95% and 90%, respectively, without any image pre-selecting or preprocessing.

Mohammadpoor Mojtaba, Sheikhi Karizaki Mehran, Sheikhi Karizaki Mina

2021

COVID-19 detection, CT-scan, Convolutional neural networks (CNN), Deep learning

Oncology

ActiveDriverDB: Interpreting Genetic Variation in Human and Cancer Genomes Using Post-translational Modification Sites and Signaling Networks (2021 Update).

In Frontiers in cell and developmental biology
Deciphering the functional impact of genetic variation is required to understand phenotypic diversity and the molecular mechanisms of inherited disease and cancer. While millions of genetic variants are now mapped in genome sequencing projects, distinguishing functional variants remains a major challenge. Protein-coding variation can be interpreted using post-translational modification (PTM) sites that are core components of cellular signaling networks controlling molecular processes and pathways. ActiveDriverDB is an interactive proteo-genomics database that uses more than 260,000 experimentally detected PTM sites to predict the functional impact of genetic variation in disease, cancer and the human population. Using machine learning tools, we prioritize proteins and pathways with enriched PTM-specific amino acid substitutions that potentially rewire signaling networks via induced or disrupted short linear motifs of kinase binding. We then map these effects to site-specific protein interaction networks and drug targets. In the 2021 update, we increased the PTM datasets by nearly 50%, included glycosylation, sumoylation and succinylation as new types of PTMs, and updated the workflows to interpret inherited disease mutations. We added a recent phosphoproteomics dataset reflecting the cellular response to SARS-CoV-2 to predict the impact of human genetic variation on COVID-19 infection and disease course. Overall, we estimate that 16-21% of known amino acid substitutions affect PTM sites among pathogenic disease mutations, somatic mutations in cancer genomes and germline variants in the human population. These data underline the potential of interpreting genetic variation through the lens of PTMs and signaling networks. The open-source database is freely available at www.ActiveDriverDB.org.
Krassowski Michal, Pellegrina Diogo, Mee Miles W, Fradet-Turcotte Amelie, Bhat Mamatha, Reimand Jüri

2021

cancer drivers, cell signaling, databases, disease genes, genome variation, post-translational modifications (PTM), protein interaction networks

General

Preparing Workplaces for Digital Transformation: An Integrative Review and Framework of Multi-Level Factors.

In Frontiers in psychology ; h5-index 92.0
The rapid advancement of new digital technologies, such as smart technology, artificial intelligence (AI) and automation, robotics, cloud computing, and the Internet of Things (IoT), is fundamentally changing the nature of work and increasing concerns about the future of jobs and organizations. To keep pace with rapid disruption, companies need to update and transform business models to remain competitive. Meanwhile, the growth of advanced technologies is changing the types of skills and competencies needed in the workplace and demanded a shift in mindset among individuals, teams and organizations. The recent COVID-19 pandemic has accelerated digitalization trends, while heightening the importance of employee resilience and well-being in adapting to widespread job and technological disruption. Although digital transformation is a new and urgent imperative, there is a long trajectory of rigorous research that can readily be applied to grasp these emerging trends. Recent studies and reviews of digital transformation have primarily focused on the business and strategic levels, with only modest integration of employee-related factors. Our review article seeks to fill these critical gaps by identifying and consolidating key factors important for an organization's overarching digital transformation. We reviewed studies across multiple disciplines and integrated the findings into a multi-level framework. At the individual level, we propose five overarching factors related to effective digital transformation among employees: technology adoption; perceptions and attitudes toward technological change; skills and training; workplace resilience and adaptability, and work-related wellbeing. At the group-level, we identified three factors necessary for digital transformation: team communication and collaboration; workplace relationships and team identification, and team adaptability and resilience. Finally, at the organizational-level, we proposed three factors for digital transformation: leadership; human resources, and organizational culture/climate. Our review of the literature confirms that multi-level factors are important when planning for and embarking on digital transformation, thereby providing a framework for future research and practice.
Trenerry Brigid, Chng Samuel, Wang Yang, Suhaila Zainal Shah, Lim Sun Sun, Lu Han Yu, Oh Peng Ho

2021

digital disruption, digital technology, digital transformation, employee, literature review, multi-level framework, organization, workplace

General

Gemelli decision tree Algorithm to Predict the need for home monitoring or hospitalization of confirmed and unconfirmed COVID-19 patients (GAP-Covid19): preliminary results from a retrospective cohort study.

In European review for medical and pharmacological sciences

OBJECTIVE : To develop a deep learning-based decision tree for the primary care setting, to stratify adult patients with confirmed and unconfirmed coronavirus disease 2019 (COVID-19), and to predict the need for hospitalization or home monitoring.

PATIENTS AND METHODS : We performed a retrospective cohort study on data from patients admitted to a COVID hospital in Rome, Italy, between 5 March 2020 and 5 June 2020. A confirmed case was defined as a patient with a positive nasopharyngeal RT-PCR test result, while an unconfirmed case had negative results on repeated swabs. Patients' medical history and clinical, laboratory and radiological findings were collected, and the dataset was used to train a predictive model for COVID-19 severity.

RESULTS : Data of 198 patients were included in the study. Twenty-eight (14.14%) had mild disease, 62 (31.31%) had moderate disease, 64 (32.32%) had severe disease, and 44 (22.22%) had critical disease. The G2 value assessed the contribution of each collected value to decision tree building. On this basis, SpO2 (%) with a cut point at 92 was chosen for the optimal first split. Therefore, the decision tree was built using values maximizing G2 and LogWorth. After the tree was built, the correspondence between inputs and outcomes was validated.

CONCLUSIONS : We developed a machine learning-based tool that is easy to understand and apply. It provides good discrimination in stratifying confirmed and unconfirmed COVID-19 patients with different prognoses in every context. Our tool might allow general practitioners visiting patients at home to decide whether the patient needs to be hospitalized.

Vetrugno G, Laurenti P, Franceschi F, Foti F, D’Ambrosio F, Cicconi M, LA Milia D I, Di Pumpo M, Carini E, Pascucci D, Boccia S, Pastorino R, Damiani G, De-Giorgio F, Oliva A, Nicolotti N, Cambieri A, Ghisellini R, Murri R, Sabatelli G, Musolino M, Gasbarrini A

2021-Mar

Oncology

Robotic Ultrasound Scanning With Real-Time Image-Based Force Adjustment: Quick Response for Enabling Physical Distancing During the COVID-19 Pandemic.

In Frontiers in robotics and AI
During an ultrasound (US) scan, the sonographer is in close contact with the patient, which puts them at risk of COVID-19 transmission. In this paper, we propose a robot-assisted system that automatically scans tissue, increasing sonographer/patient distance and decreasing contact duration between them. This method is developed as a quick response to the COVID-19 pandemic. It considers the preferences of the sonographers in terms of how US scanning is done and can be trained quickly for different applications. Our proposed system automatically scans the tissue using a dexterous robot arm that holds US probe. The system assesses the quality of the acquired US images in real-time. This US image feedback will be used to automatically adjust the US probe contact force based on the quality of the image frame. The quality assessment algorithm is based on three US image features: correlation, compression and noise characteristics. These US image features are input to the SVM classifier, and the robot arm will adjust the US scanning force based on the SVM output. The proposed system enables the sonographer to maintain a distance from the patient because the sonographer does not have to be holding the probe and pressing against the patient's body for any prolonged time. The SVM was trained using bovine and porcine biological tissue, the system was then tested experimentally on plastisol phantom tissue. The result of the experiments shows us that our proposed quality assessment algorithm successfully maintains US image quality and is fast enough for use in a robotic control loop.
Akbari Mojtaba, Carriere Jay, Meyer Tyler, Sloboda Ron, Husain Siraj, Usmani Nawaid, Tavakoli Mahdi

2021

artificial intelligence, medical image quality assessment, medical robotic, robotics for COVID-19, ultrasound scanning

General

Diagnosing Covid-19 chest x-rays with a lightweight truncated DenseNet with partial layer freezing and feature fusion.

In Biomedical signal processing and control
Due to the unforeseen turn of events, our world has undergone another global pandemic from a highly contagious novel coronavirus named COVID-19. The novel virus inflames the lungs similarly to Pneumonia, making it challenging to diagnose. Currently, the common standard to diagnose the virus's presence from an individual is using a molecular real-time Reverse-Transcription Polymerase Chain Reaction (rRT-PCR) test from fluids acquired through nasal swabs. Such a test is difficult to acquire in most underdeveloped countries with a few experts that can perform the test. As a substitute, the widely available Chest X-Ray (CXR) became an alternative to rule out the virus. However, such a method does not come easy as the virus still possesses unknown characteristics that even experienced radiologists and other medical experts find difficult to diagnose through CXRs. Several studies have recently used computer-aided methods to automate and improve such diagnosis of CXRs through Artificial Intelligence (AI) based on computer vision and Deep Convolutional Neural Networks (DCNN), which some require heavy processing costs and other tedious methods to produce. Therefore, this work proposed the Fused-DenseNet-Tiny, a lightweight DCNN model based on a densely connected neural network (DenseNet) truncated and concatenated. The model trained to learn CXR features based on transfer learning, partial layer freezing, and feature fusion. Upon evaluation, the proposed model achieved a remarkable 97.99 % accuracy, with only 1.2 million parameters and a shorter end-to-end structure. It has also shown better performance than some existing studies and other massive state-of-the-art models that diagnosed COVID-19 from CXRs.
Montalbo Francis Jesmar P

2021-Jul

AP, Average Pooling, AUC, Area Under the Curve, BN, Batch Normalization, BS, Batch Size, CAD, Computer-Aided Diagnosis, CCE, Categorical Cross-Entropy, CNN, Convolutional Neural Networks, CT, Computer Tomography, CV, Computer Vision, CXR, Chest X-Rays, Chest x-rays, Computer-aided diagnosis, Covid-19, DCNN, Deep Convolutional Neural Networks, DL, Deep Learning, DR, Dropout Rate, Deep learning, Densely connected neural networks, GAP, Global Average Pooling, GRAD-CAM, Gradient-Weighted Class Activation Maps, JPG, Joint Photographic Group, LR, Learning Rate, MP, Max-Pooling, P-R, Precision-Recall, PEPX, Projection-Expansion-Projection-Extension, ROC, Receiver Operating Characteristic, ReLU, Rectified Linear Unit, SGD, Stochastic Gradient Descent, WHO, World Health Organization, rRT-PCR, real-time Reverse-Transcription Polymerase Chain Reaction

Dermatology

Does Your Dermatology Classifier Know What It Doesn't Know? Detecting the Long-Tail of Unseen Conditions

ArXiv Preprint
We develop and rigorously evaluate a deep learning based system that can accurately classify skin conditions while detecting rare conditions for which there is not enough data available for training a confident classifier. We frame this task as an out-of-distribution (OOD) detection problem. Our novel approach, hierarchical outlier detection (HOD) assigns multiple abstention classes for each training outlier class and jointly performs a coarse classification of inliers vs. outliers, along with fine-grained classification of the individual classes. We demonstrate the effectiveness of the HOD loss in conjunction with modern representation learning approaches (BiT, SimCLR, MICLe) and explore different ensembling strategies for further improving the results. We perform an extensive subgroup analysis over conditions of varying risk levels and different skin types to investigate how the OOD detection performance changes over each subgroup and demonstrate the gains of our framework in comparison to baselines. Finally, we introduce a cost metric to approximate downstream clinical impact. We use this cost metric to compare the proposed method against a baseline system, thereby making a stronger case for the overall system effectiveness in a real-world deployment scenario.
Abhijit Guha Roy, Jie Ren, Shekoofeh Azizi, Aaron Loh, Vivek Natarajan, Basil Mustafa, Nick Pawlowski, Jan Freyberg, Yuan Liu, Zach Beaver, Nam Vo, Peggy Bui, Samantha Winter, Patricia MacWilliams, Greg S. Corrado, Umesh Telang, Yun Liu, Taylan Cemgil, Alan Karthikesalingam, Balaji Lakshminarayanan, Jim Winkens

2021-04-08

Radiology

A Fully Automated Deep Learning-based Network For Detecting COVID-19 from a New And Large Lung CT Scan Dataset.

In Biomedical signal processing and control
This paper aims to propose a high-speed and accurate fully-automated method to detect COVID-19 from the patient's chest CT scan images. We introduce a new dataset that contains 48260 CT scan images from 282 normal persons and 15589 images from 95 patients with COVID-19 infections. At the first stage, this system runs our proposed image processing algorithm that analyzes the view of the lung to discard those CT images that inside the lung is not properly visible in them. This action helps to reduce the processing time and false detections. At the next stage, we introduce a novel architecture for improving the classification accuracy of convolutional networks on images containing small important objects. Our architecture applies a new feature pyramid network designed for classification problems to the ResNet50V2 model so the model becomes able to investigate different resolutions of the image and do not lose the data of small objects. As the infections of COVID-19 exist in various scales, especially many of them are tiny, using our method helps to increase the classification performance remarkably. After running these two phases, the system determines the condition of the patient using a selected threshold. We are the first to evaluate our system in two different ways on Xception, ResNet50V2, and our model. In the single image classification stage, our model achieved 98.49% accuracy on more than 7996 test images. At the patient condition identification phase, the system correctly identified almost 234 of 245 patients with high speed.
Rahimzadeh Mohammad, Attar Abolfazl, Sakhaei Seyed Mohammad

2021-Mar-31

Automatic medical diagnosis, COVID-19, CT scan, Convolutional Neural networks, Coronavirus, Deep learning, Medical image analysis, Radiology, lung CT scan dataset

General

An automated and fast system to identify COVID-19 from X-ray radiograph of the chest using image processing and machine learning.

In International journal of imaging systems and technology
A type of coronavirus disease called COVID-19 is spreading all over the globe. Researchers and scientists are endeavoring to find new and effective methods to diagnose and treat this disease. This article presents an automated and fast system that identifies COVID-19 from X-ray radiographs of the chest using image processing and machine learning algorithms. Initially, the system extracts the feature descriptors from the radiographs of both healthy and COVID-19 affected patients using the speeded up robust features algorithm. Then, visual vocabulary is built by reducing the number of feature descriptors via quantization of feature space using the K-means clustering algorithm. The visual vocabulary train the support vector machine (SVM) classifier. During testing, an X-ray radiograph's visual vocabulary is sent to the trained SVM classifier to detect the absence or presence of COVID-19. The study used the dataset of 340 X-ray radiographs, 170 images of each Healthy and Positive COVID-19 class. During simulations, the dataset split into training and testing parts at various ratios. After training, the system does not require any human intervention and can process thousands of images with high precision in a few minutes. The performance of the system is measured using standard parameters of accuracy and confusion matrix. We compared the performance of the proposed SVM-based classier with the deep-learning-based convolutional neural networks (CNN). The SVM yields better results than CNN and achieves a maximum accuracy of up to 94.12%.
Khan Murtaza Ali

2021-Mar-01

COVID‐19, artificial intelligence, chest X‐ray radiograph, feature descriptors, medical image processing

General

COVID-19 vs influenza viruses: A cockroach optimized deep neural network classification approach.

In International journal of imaging systems and technology
Among Coronavirus, as with many other viruses, receptor interactions are an essential determinant of species specificity, virulence, and pathogenesis. The pathogenesis of the COVID-19 depends on the virus's ability to attach to and enter into a suitable human host cell. This paper presents a cockroach optimized deep neural network to detect COVID-19 and differentiate between COVID-19 and influenza types A, B, and C. The deep network architecture is inspired using a cockroach optimization algorithm to optimize the deep neural network hyper-parameters. COVID-19 sequences are obtained from repository 2019 Novel Coronavirus Resource, and influenza A, B, and C sub-dataset are obtained from other repositories. Five hundred ninety-four unique genomes sequences are used in the training and testing process with 99% overall accuracy for the classification model.
El-Dosuky Mohamed A, Soliman Mona, Hassanien Aboul Ella

2021-Feb-24

COVID‐19, SARS‐CoV‐2, cockroach swarm optimization, convolutional neural networks, coronavirus, deep learning, influenza

General

Convolutional capsule network for COVID-19 detection using radiography images.

In International journal of imaging systems and technology
Novel corona virus COVID-19 has spread rapidly all over the world. Due to increasing COVID-19 cases, there is a dearth of testing kits. Therefore, there is a severe need for an automatic recognition system as a solution to reduce the spreading of the COVID-19 virus. This work offers a decision support system based on the X-ray image to diagnose the presence of the COVID-19 virus. A deep learning-based computer-aided decision support system will be capable to differentiate between COVID-19 and pneumonia. Recently, convolutional neural network (CNN) is designed for the diagnosis of COVID-19 patients through chest radiography (or chest X-ray, CXR) images. However, due to the usage of CNN, there are some limitations with these decision support systems. These systems suffer with the problem of view-invariance and loss of information due to down-sampling. In this paper, the capsule network (CapsNet)-based system named visual geometry group capsule network (VGG-CapsNet) for the diagnosis of COVID-19 is proposed. Due to the usage of capsule network (CapsNet), the authors have succeeded in removing the drawbacks found in the CNN-based decision support system for the detection of COVID-19. Through simulation results, it is found that VGG-CapsNet has performed better than the CNN-CapsNet model for the diagnosis of COVID-19. The proposed VGG-CapsNet-based system has shown 97% accuracy for COVID-19 versus non-COVID-19 classification, and 92% accuracy for COVID-19 versus normal versus viral pneumonia classification. Proposed VGG-CapsNet-based system available at https://github.com/shamiktiwari/COVID19_Xray can be used to detect the existence of COVID-19 virus in the human body through chest radiographic images.
Tiwari Shamik, Jain Anurag

2021-Mar-02

COVID‐19, X‐ray, capsule network, convolutional neural network, decision support system, deep learning, visual geometry group

Radiology

A deep learning model for mass screening of COVID-19.

In International journal of imaging systems and technology
The objective of this research is to develop a convolutional neural network model 'COVID-Screen-Net' for multi-class classification of chest X-ray images into three classes viz. COVID-19, bacterial pneumonia, and normal. The model performs the automatic feature extraction from X-ray images and accurately identifies the features responsible for distinguishing the X-ray images of different classes. It plots these features on the GradCam. The authors optimized the number of convolution and activation layers according to the size of the dataset. They also fine-tuned the hyperparameters to minimize the computation time and to enhance the efficiency of the model. The performance of the model has been evaluated on the anonymous chest X-ray images collected from hospitals and the dataset available on the web. The model attains an average accuracy of 97.71% and a maximum recall of 100%. The comparative analysis shows that the 'COVID-Screen-Net' outperforms the existing systems for screening of COVID-19. The effectiveness of the model is validated by the radiology experts on the real-time dataset. Therefore, it may prove a useful tool for quick and low-cost mass screening of patients of COVID-19. This tool may reduce the burden on health experts in the present situation of the Global Pandemic. The copyright of this tool is registered in the names of authors under the laws of Intellectual Property Rights in India with the registration number 'SW-13625/2020'.
Dhaka Vijaypal Singh, Rani Geeta, Oza Meet Ganpatlal, Sharma Tarushi, Misra Ankit

2021-Feb-03

CNN model, COVID‐19, Corona, X‐ray, deep learning, global pandemic

Cardiology

Future IoT tools for COVID-19 contact tracing and prediction: A review of the state-of-the-science.

In International journal of imaging systems and technology
In 2020 the world is facing unprecedented challenges due to COVID-19. To address these challenges, many digital tools are being explored and developed to contain the spread of the disease. With the lack of availability of vaccines, there is an urgent need to avert resurgence of infections by putting some measures, such as contact tracing, in place. While digital tools, such as phone applications are advantageous, they also pose challenges and have limitations (eg, wireless coverage could be an issue in some cases). On the other hand, wearable devices, when coupled with the Internet of Things (IoT), are expected to influence lifestyle and healthcare directly, and they may be useful for health monitoring during the global pandemic and beyond. In this work, we conduct a literature review of contact tracing methods and applications. Based on the literature review, we found limitations in gathering health data, such as insufficient network coverage. To address these shortcomings, we propose a novel intelligent tool that will be useful for contact tracing and prediction of COVID-19 clusters. The solution comprises a phone application combined with a wearable device, infused with unique intelligent IoT features (complex data analysis and intelligent data visualization) embedded within the system to aid in COVID-19 analysis. Contact tracing applications must establish data collection and data interpretation. Intelligent data interpretation can assist epidemiological scientists in anticipating clusters, and can enable them to take necessary action in improving public health management. Our proposed tool could also be used to curb disease incidence in future global health crises.
Jahmunah Vicnesh, Sudarshan Vidya K, Oh Shu Lih, Gururajan Raj, Gururajan Rashmi, Zhou Xujuan, Tao Xiaohui, Faust Oliver, Ciaccio Edward J, Ng Kwan Hoong, Acharya U Rajendra

2021-Feb-09

COVID‐19, contact tracing, coronavirus disease, deep learning, digital tools, intelligent internet of things, wearable devices

Radiology

Automatic detection and localization of COVID-19 pneumonia using axial computed tomography images and deep convolutional neural networks.

In International journal of imaging systems and technology
COVID-19 was first reported as an unknown group of pneumonia in Wuhan City, Hubei province of China in late December of 2019. The rapid increase in the number of cases diagnosed with COVID-19 and the lack of experienced radiologists can cause diagnostic errors in the interpretation of the images along with the exceptional workload occurring in this process. Therefore, the urgent development of automated diagnostic systems that can scan radiological images quickly and accurately is important in combating the pandemic. With this motivation, a deep convolutional neural network (CNN)-based model that can automatically detect patterns related to lesions caused by COVID-19 from chest computed tomography (CT) images is proposed in this study. In this context, the image ground-truth regarding the COVID-19 lesions scanned by the radiologist was evaluated as the main criteria of the segmentation process. A total of 16 040 CT image segments were obtained by applying segmentation to the raw 102 CT images. Then, 10 420 CT image segments related to healthy lung regions were labeled as COVID-negative, and 5620 CT image segments, in which the findings related to the lesions were detected in various forms, were labeled as COVID-positive. With the proposed CNN architecture, 93.26% diagnostic accuracy performance was achieved. The sensitivity and specificity performance metrics for the proposed automatic diagnosis model were 93.27% and 93.24%, respectively. Additionally, it has been shown that by scanning the small regions of the lungs, COVID-19 pneumonia can be localized automatically with high resolution and the lesion densities can be successfully evaluated quantitatively.
Polat Hasan, Özerdem Mehmet Siraç, Ekici Faysal, Akpolat Veysi

2021-Feb-16

COVID‐19, classification, computer‐aided diagnosis, convolutional neural networks, coronavirus, deep learning, radiology

Oncology

An efficient primary screening of COVID-19 by serum Raman spectroscopy.

In Journal of Raman spectroscopy : JRS
The outbreak of COVID-19 coronavirus disease around the end of 2019 has become a pandemic. The preferred method for COVID-19 detection is the real-time polymerase chain reaction (RT-PCR)-based technique; however, it also has certain limitations, such as sample-dependent procedures with a relatively high false negative ratio. We propose a safe and efficient method for screening COVID-19 based on Raman spectroscopy. A total of 177 serum samples are collected from 63 confirmed COVID-19 patients, 59 suspected cases, and 55 healthy individuals as a control group. Raman spectroscopy is adopted to analyze these samples, and a machine learning support-vector machine (SVM) method is applied to the spectrum dataset to build a diagnostic algorithm. Furthermore, 20 independent individuals, including 5 asymptomatic COVID-19 patients and 5 symptomatic COVID-19 patients, 5 suspected patients, and 5 healthy patients, were sampled for external validation. In these three groups-confirmed COVID-19, suspected, and healthy individuals-the distribution of statistically significant points of difference showed highly consistency for intergroups after repeated sampling processes. The classification accuracy between the COVID-19 cases and the suspected cases is 0.87 (95% confidence interval [CI]: 0.85-0.88), and the accuracy between the COVID-19 and the healthy controls is 0.90 (95% CI: 0.89-0.91), while the accuracy between the suspected cases and the healthy control group is 0.68 (95% CI: 0.67-0.73). For the independent test dataset, we apply the obtained SVM model to the classification of the independent test dataset to have all the results correctly classified. Our model showed that the serum-level classification results were all correct for independent test dataset. Our results suggest that Raman spectroscopy could be a safe and efficient technique for COVID-19 screening.
Yin Gang, Li Lintao, Lu Shun, Yin Yu, Su Yuanzhang, Zeng Yilan, Luo Mei, Ma Maohua, Zhou Hongyan, Orlandini Lucia, Yao Dezhong, Liu Gang, Lang Jinyi

2021-Feb-19

COVID‐19, Raman spectroscopy, machine learning, screening, support vector machine

General

Classification of the social distance during the COVID-19 pandemic from electricity consumption using artificial intelligence.

In International journal of energy research
Accurately quantifying the social distancing (SD) practice of a population is essential for governments and health agencies to better plan and adapt restrictions during a pandemic crisis. In such a scenario, the reduction of social mobility also has a significant impact on electricity consumption, since people are encouraged to stay at home and many commercial and industrial activities are reduced or even halted. This paper proposes a methodology to qualify the SD of a medium-sized city, located in the northwest of the state of Rio Grande do Sul (RS), Brazil, using data of electricity consumption measured by the municipality's energy utility. The methodology consists of combining a data set, and an average consumption profile of Sundays is obtained using data from 4-months, it is then defined as a high SD profile due to the typical lower social activities on Sundays. An supervised and an unsupervised artificial neural network (ANN) are trained with this profile and used to analyze electricity consumption of this city during the COVID-19 pandemic. Low, moderate, and high SD ranges are also created, and the daily population behavior is evaluated by the ANNs. The results are strongly correlated and discussed with government restrictions imposed during the analyzed period and indicate that the ANNs can correctly classify the intensity of SD practiced by people. The unsupervised ANN is used more easily and in different scenarios, so it can be indicated for use by public administration for purposes of assess the effectiveness of SD policies based on the guidelines established during the COVID-19 pandemic.
Sausen Airam T Z R, de Campos Maurício, Sausen Paulo S, Binelo Manuel O, Binelo Marcia F B, da Silva João M L V, Dos Santos Moises

2021-Jan-26

COVID‐19, artificial neural network, energy demand, social distancing

General

Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data

ArXiv Preprint
Electronic Health Record (EHR) data has been of tremendous utility in Artificial Intelligence (AI) for healthcare such as predicting future clinical events. These tasks, however, often come with many challenges when using classical machine learning models due to a myriad of factors including class imbalance and data heterogeneity (i.e., the complex intra-class variances). To address some of these research gaps, this paper leverages the exciting contrastive learning framework and proposes a novel contrastive regularized clinical classification model. The contrastive loss is found to substantially augment EHR-based prediction: it effectively characterizes the similar/dissimilar patterns (by its "push-and-pull" form), meanwhile mitigating the highly skewed class distribution by learning more balanced feature spaces (as also echoed by recent findings). In particular, when naively exporting the contrastive learning to the EHR data, one hurdle is in generating positive samples, since EHR data is not as amendable to data augmentation as image data. To this end, we have introduced two unique positive sampling strategies specifically tailored for EHR data: a feature-based positive sampling that exploits the feature space neighborhood structure to reinforce the feature learning; and an attribute-based positive sampling that incorporates pre-generated patient similarity metrics to define the sample proximity. Both sampling approaches are designed with an awareness of unique high intra-class variance in EHR data. Our overall framework yields highly competitive experimental results in predicting the mortality risk on real-world COVID-19 EHR data with a total of 5,712 patients admitted to a large, urban health system. Specifically, our method reaches a high AUROC prediction score of 0.959, which outperforms other baselines and alternatives: cross-entropy(0.873) and focal loss(0.931).
Tingyi Wanyan, Jing Zhang, Ying Ding, Ariful Azad, Zhangyang Wang, Benjamin S Glicksberg

2021-04-07

General

Diagnostic accuracy estimates for COVID-19 RT-PCR and Lateral flow immunoassay tests with Bayesian latent class models.

In American journal of epidemiology ; h5-index 65.0
The objective was to estimate the diagnostic accuracy of real time polymerase chain reaction (RT-PCR) and lateral flow immunoassay (LFIA) tests for COVID-19, depending on the time post symptom onset. Based on the cross-classified results of RT-PCR and LFIA, we used Bayesian latent class models (BLCMs), which do not require a gold standard for the evaluation of diagnostics. Data were extracted from studies that evaluated LFIA (IgG and/or IgM) assays using RT-PCR as the reference method. ${Se}_{RT- PCR}$ was 0.68 (95% probability intervals: 0.63; 0.73). ${Se}_{IgG/M}$ was 0.32 (0.23; 0.41) for the first week and increased steadily. It was 0.75 (0.67; 0.83) and 0.93 (0.88; 0.97) for the second and third week post symptom onset, respectively. Both tests had a high to absolute Sp, with higher point median estimates for ${Sp}_{RT- PCR}$ and narrower probability intervals: ${Sp}_{RT- PCR}$ was 0.99 (0.98; 1.00) and ${Sp}_{IgG/M}$ was 0.97 (0.92; 1.00), 0.98 (0.95; 1.00) and 0.98 (0.94; 1.00) for the first, second and third week post symptom onset. The diagnostic accuracy of LFIA varies with time post symptom onset. BLCMs provide a valid and efficient alternative for evaluating the rapidly evolving diagnostics for COVID-19, under various clinical settings and different risk profiles.
Kostoulas Polychronis, Eusebi Paolo, Hartnack Sonja

2021-Mar-31

Bayesian latent class models, COVID-19, LFIA, RT-PCR, Sensitivity, Specificity

Internal Medicine

Prediction models for clinical severity of COVID-19 patients using multi-center clinical data in Korea.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : There is limited information describing present characteristics and dynamic clinical changes that occur in patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection during the early phase of illness.

OBJECTIVE : The objective is to develop and validate machine-learning model based on clinical features for severity risk assessment and triage for COVID-19 patients at hospital admission.

METHODS : This is a retrospective cohort of multicenter COVID-19 patients released from quarantine until April 30th, 2020 in Korea. A total of 5,628 patients were used to train and validate the models that predict the clinical severity and duration of hospitalization, where clinical severity score was defined in 4 levels: mild, moderate, severe, and critical.

RESULTS : The proportion of patients in the mild, moderate, severe, and critical levels were 79.5% (4455/5601), 5.9% (330/5601), 9.1% (512/5601), and 5.4% (301/5601), respectively. As risk factors for predicting critical patients, older age, shortness of breath, higher white blood cell, lower hemoglobin, lower lymphocyte, and lower platelet count were selected. Three prediction models were built to classify clinical severity levels. For example, the prediction model with 6 variables showed the predictive power of 0.93 or higher for the area under the receiver operating characteristic curve (AUC). Based on these models, a web-based nomogram was developed (http://statgen.snu.ac.kr/covid19/nomogram/maxcss/).

CONCLUSIONS : Our prediction models, along with the web-based nomogram are expected to be useful to access the onset of severe and critical illness among COVID-19 patients and triage at hospital admission.

CLINICALTRIAL :

Oh Bumjo, Hwangbo Suhyun, Jung Taeyeong, Min Kyungha, Lee Chanhee, Apio Catherine, Lee Hyejin, Lee Seungyeoun, Moon Min Kyong, Kim Shin-Woo, Park Taesung

2021-Mar-18

General

Lung organoid simulations for modelling and predicting the effect of mutations on SARS-CoV-2 infectivity.

In Computational and structural biotechnology journal
The global pandemic caused by the SARS-CoV-2 virus continues to spread. Infection with SARS- CoV-2 causes COVID-19, a disease of variable severity. Mutation has already altered the SARS-CoV-2 genome from its original reported sequence and continued mutation is highly probable. These mutations can: (i) have no significant impact (they are silent), (ii) result in a complete loss or reduction of infectivity, or (iii) induce increase in infectivity. Physical generation, for research purposes, of viral mutations that could enhance infectivity are controversial and highly regulated. The primary purpose of this project was to evaluate the ability of the DeepNEU machine learning stem-cell simulation platform to enable rapid and efficient assessment of the potential impact of viral loss-of-function (LOF) and gain-of-function (GOF) mutations on SARS-CoV-2 infectivity. Our data suggest that SARS-CoV-2 infection can be simulated in human alveolar type lung cells. Simulation of infection in these lung cells can be used to model and assess the impact of LOF and GOF mutations in the SARS-CoV2 genome. We have also created a four- factor infectivity measure: the DeepNEU Case Fatality Rate (dnCFR). dnCFR can be used to assess infectivity based on the presence or absence of the key viral proteins (NSP3, Spike-RDB, N protein, and M protein). dnCFR was used in this study, not to only assess the impact of different mutations on SARS-CoV2 infectivity, but also to categorize the effects of mutations as loss of infectivity or gain of infectivity events.
Esmail Sally, Danter Wayne R

2021

Infectivity, Lung organoid, Machine learning, SARS-CoV2 evolution, Simulations of COVID-19, Viral mutations

General

Predicting the pandemic: sentiment evaluation and predictive analysis from large-scale tweets on Covid-19 by deep convolutional neural network.

In Evolutionary intelligence
Engaging deep neural networks for textual sentiment analysis is an extensively practiced domain of research. Textual sentiment classification harnesses the full computational potential of deep learning models. Typically, these research works are carried either with a popular open-source data corpus, or self-extracted short phrase texts from Twitter, Reddit, or web-scrapped text data from other resources. Rarely do we see a large amount of data on a current ongoing event is being collected and cultured further. Also, an even more complex task would be to model the data from a currently ongoing event, not only for scaling the sentiment accuracy but also for making a predictive analysis for the same. In this paper, we propose a novel approach for achieving sentiment evaluation accuracy by using a deep neural network on live-streamed tweets on Coronavirus and future case growth prediction. We develop a large tweet corpus exclusively based on the Coronavirus tweets. We split the data into train and test sets, alongside we perform polarity classification and trend analysis. The refined outcome from the trend analysis helps to train the data to provide an incremental learning curvature for our neural network, and we obtain an accuracy of 90.67%. Finally, we provide a statistical-based future prediction for Coronavirus cases growth. Not only our model outperforms several previous state-of-art experiments in overall sentiment accuracy comparison for similar tasks, but it also maintains a throughout performance stability among all the test cases when tested with several popular open-source text corpora.
Das Sourav, Kolya Anup Kumar

2021-Mar-30

Coronavirus, Covid-19, Deep convolutional network, Predictive analysis, Sentiment analysis, Twitter

Radiology

Helping Roles of Artificial Intelligence (AI) in the Screening and Evaluation of COVID-19 Based on the CT Images.

In Journal of inflammation research

Objective : The aim of this study was to explore the role of the AI system which was designed and developed based on the characteristics of COVID-19 CT images in the screening and evaluation of COVID-19.

Methods : The research team adopted an improved U-shaped neural network to segment lungs and pneumonia lesions in CT images through multilayer convolution iterations. Then the appropriate 159 cases were selected to establish and train the model, and Dice loss function and Adam optimizer were used for network training with the initial learning rate of 0.001. Finally, 39 cases (29 positive and 10 negative) were selected for the comparative test. Experimental group: an attending physician a and an associate chief physician a read the CT images to diagnose COVID-19 with the help of the AI system. Control group: an attending physician b and an associate chief physician b did the diagnosis only by their experience, without the help of the AI system. The time spent by each doctor in the diagnosis and their diagnostic results were recorded. Paired t-test, univariate ANOVA, chi-squared test, receiver operating characteristic curves, and logistic regression analysis were used for the statistical analysis.

Results : There was statistical significance in the time spent in the diagnosis of different groups (P<0.05). For the group with the optimal diagnostic results, univariate and multivariate analyses both suggested no significant correlation for all variables, and thus it might be the assistance of the AI system, the epidemiological history and other factors that played an important role.

Conclusion : The AI system developed by us, which was created due to COVID-19, had certain clinical practicability and was worth popularizing.

Xie Hui, Li Qing, Hu Ping-Feng, Zhu Sen-Hua, Zhang Jian-Fang, Zhou Hong-Da, Zhou Hai-Bo

2021

AI, COVID-19, CT, helping role, intelligent analysis

General

Radiographic findings in COVID-19: Comparison between AI and radiologist.

In The Indian journal of radiology & imaging

Context : As the burden of COVID-19 enhances, the need of a fast and reliable screening method is imperative. Chest radiographs plays a pivotal role in rapidly triaging the patients. Unfortunately, in low-resource settings, there is a scarcity of trained radiologists.

Aim : This study evaluates and compares the performance of an artificial intelligence (AI) system with a radiologist in detecting chest radiograph findings due to COVID-19.

Subjects and Methods : The test set consisted of 457 CXR images of patients with suspected COVID-19 pneumonia over a period of three months. The radiographs were evaluated by a radiologist with experience of more than 13 years and by the AI system (NeuraCovid, a web application that pairs with the AI model COVID-NET). Performance of AI system and the radiologist were compared by calculating the sensitivity, specificity and generating a receiver operating characteristic curve. RT-PCR test results were used as the gold standard.

Results : The radiologist obtained a sensitivity and specificity of 44.1% and 92.5%, respectively, whereas the AI had a sensitivity and specificity of 41.6% and 60%, respectively. The area under curve for correctly classifying CXR images as COVID-19 pneumonia was 0.48 for the AI system and 0.68 for the radiologist. The radiologist's prediction was found to be superior to that of the AI with a P VALUE of 0.005.

Conclusion : The specificity and sensitivity of detecting lung involvement in COVID-19, by the radiologist, was found to be superior to that by the AI system.

Sukhija Arsh, Mahajan Mangal, Joshi Priscilla C, Dsouza John, Seth Nagesh D N, Patil Karamchand H

2021-Jan

Artificial intelligence, COVID pneumonia, chest radiographs, rapid triaging

General

The value of AI based CT severity scoring system in triage of patients with Covid-19 pneumonia as regards oxygen requirement and place of admission.

In The Indian journal of radiology & imaging

Context : CT scan is a quick and effective method to triage patients in the Covid-19 pandemic to prevent the heathcare facilities from getting overwhelmed.

Aims : To find whether an initial HRCT chest can help triage patient by determining their oxygen requirement, place of treatment, laboratory parameters and risk of mortality and to compare 3 CT scoring systems (0-20, 0-25 and percentage of involved lung models) to find if one is a better predictor of prognosis than the other.

Settings and Design : This was a prospective observational study conducted at a Tertiary care hospital in Mumbai, Patients undergoing CT scan were included by complete enumeration method.

Methods and Material : Data collected included demographics, days from swab positivity to CT scan, comorbidities, place of treatment, laboratory parameters, oxygen requirement and mortality. We divided the patients into mild, moderate and severe based on 3 criteria - 20 point CT score (OS1), 25 point CT score (OS2) and opacity percentage (OP). CT scans were analysed using CT pneumonia analysis prototype software (Siemens Healthcare version 2.5.2, Erlangen, Germany).

Statistical Analysis : ROC curve and Youden's index were used to determine cut off points. Multinomial logistic regression used to study the relations with oxygen requirement and place of admission. Hosmer-Lemeshow test was done to test the goodness of fit of our models.

Results : A total of 740 patients were included in our study. All the 3 scoring systems showed a significant positive correlation with oxygen requirement, place of admission and death. Based on ROC analysis a score of 4 for OS1, 9 for OS2 and 12.7% for OP was determined as the cut off for oxygen requirement.

Conclusions : CT severity scoring using an automated deep learning software programme is a boon for determining oxygen requirement and triage. As the score increases, the chances of requirement of higher oxygen and intubation increase. All the three scoring systems are predictive of oxygen requirement.

Kohli Anirudh, Jha Tanya, Pazhayattil Amal Babu

2021-Jan

Covid-19, HRCT chest, oxygen requirement

Internal Medicine

Comparing a deep learning model's diagnostic performance to that of radiologists to detect Covid -19 features on chest radiographs.

In The Indian journal of radiology & imaging

Background : Whether the sensitivity of Deep Learning (DL) models to screen chest radiographs (CXR) for CoVID-19 can approximate that of radiologists, so that they can be adopted and used if real-time review of CXRs by radiologists is not possible, has not been explored before.

Objective : To evaluate the diagnostic performance of a doctor-trained DL model (Svita_DL8) to screen for COVID-19 on CXR, and to compare the performance of the DL model with that of expert radiologists.

Materials and Methods : We used a pre-trained convolutional neural network to develop a publicly available online DL model to evaluate CXR examinations saved in .jpeg or .png format. The initial model was subsequently curated and trained by an internist and a radiologist using 1062 chest radiographs to classify a submitted CXR as either normal, COVID-19, or a non-COVID-19 abnormal. For validation, we collected a separate set of 430 CXR examinations from numerous publicly available datasets from 10 different countries, case presentations, and two hospital repositories. These examinations were assessed for COVID-19 by the DL model and by two independent radiologists. Diagnostic performance was compared between the model and the radiologists and the correlation coefficient calculated.

Results : For detecting COVID-19 on CXR, our DL model demonstrated sensitivity of 91.5%, specificity of 55.3%, PPV 60.9%, NPV 77.9%, accuracy 70.1%, and AUC 0.73 (95% CI: 0.86, 0.95). There was a significant correlation (r = 0.617, P = 0.000) between the results of the DL model and the radiologists' interpretations. The sensitivity of the radiologists is 96% and their overall diagnostic accuracy is 90% in this study.

Conclusions : The DL model demonstrated high sensitivity for detecting COVID-19 on CXR.

Clinical Impact : The doctor trained DL tool Svita_DL8 can be used in resource-constrained settings to quickly triage patients with suspected COVID-19 for further in-depth review and testing.

Krishnamoorthy Sabitha, Ramakrishnan Sudhakar, Colaco Lanson Brijesh, Dias Akshay, Gopi Indu K, Gowda Gautham A G, Aishwarya K C, Ramanan Veena, Chandran Manju

2021-Jan

Artificial intelligence, COVID 19, CXR, deep learning

Radiology

Financial impact of COVID-19 on radiology practice in India.

In The Indian journal of radiology & imaging
The COVID-19 pandemic will have serious financial effects on the healthcare sector business. There will be significant short-term and long-term effects of this on Radiology services throughout the country. Various social distancing measures undertaken by the government will bring larger economic hurdles with them. An attempt to achieve COVID-19 preparedness by hospitals has led to a significant decline in patient footfall and in turn imaging volumes. Despite relief measures provided by the government like providing a moratorium on EMIs of all outstanding loans for a specified period and allocating funds toward reinforcing healthcare infrastructure, the effects of this pandemic will leave the radiology business in a crippled state, in the foreseeable future. Radiology practices have seen a significant impact on business to the extent of almost 60%-70% reduction in imaging volumes and this will be the case for the next few months to come. Administrators and radiologists should proactively take measures to device strategies and plans to tide over this crisis. Eventually, this pandemic will end, and life will have a "New Normal." Medical aid that is being deferred today will be sought out later. Alternate means of reporting like teleradiology and artificial intelligence should be strongly pursued and providing education regarding these to their staff and the younger generation of radiologists should be of prime concern.
Ahuja Gauri, Verma Mitusha, Patkar Deepak

2021-Jan

COVID-19 impact, Economic impact radiology, financial impact of COVID, radiology in India, reduced revenue radiology

Radiology

Artificial intelligence and radiology: Combating the COVID-19 conundrum.

In The Indian journal of radiology & imaging
The COVID-19 pandemic has necessitated rapid testing and diagnosis to manage its spread. While reverse transcriptase polymerase chain reaction (RT-PCR) is being used as the gold standard method to diagnose COVID-19, many scientists and doctors have pointed out some challenges related to the variability, accuracy, and affordability of this technique. At the same time, radiological methods, which were being used to diagnose COVID-19 in the early phase of the pandemic in China, were sidelined by many primarily due to their low specificity and the difficulty in conducting a differential diagnosis. However, the utility of radiological methods cannot be neglected. Indeed, over the past few months, healthcare consultants and radiologists in India have been using or advising the use of high-resolution computed tomography (HRCT) of the chest for early diagnosis and tracking of COVID-19, particularly in preoperative and asymptomatic patients. At the same time, scientists have been trying to improve upon the radiological method of COVID-19 diagnosis and monitoring by using artificial intelligence (AI)-based interpretation models. This review is an effort to compile and compare such efforts. To this end, the latest scientific literature on the use of radiology and AI-assisted radiology for the diagnosis and monitoring of COVID-19 has been reviewed and presented, highlighting the strengths and limitations of such techniques.
Pankhania Mayur

2021-Jan

Artificial intelligence, COVID-19, HRCT, coronavirus, radiology

Public Health

Emotions of COVID-19: A Study of Self-Reported Information and Emotions during the COVID-19 Pandemic using Artificial Intelligence.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : The COVID-19 pandemic has disrupted human societies across the world. Starting with a public health emergency, followed by a significant loss of human life, and the ensuing social restrictions leading to loss of employment, lack of interactions and burgeoning psychological distress. As physical distancing regulations were introduced to manage outbreaks, individuals, groups and communities engaged extensively on social media to express their thoughts and emotions. This internet-mediated communication of self-reported information encapsulates the emotional health and mental wellbeing of all individuals impacted by the pandemic.

OBJECTIVE : This research aims to investigate the human emotions of the COVID-19 pandemic expressed on social media over time, using an Artificial Intelligence framework.

METHODS : Our study explores emotion classifications, intensities, transitions, profiles and alignment to key themes and topics, across the four stages of the pandemic; declaration of a global health crisis, first lockdown, easing of restrictions, and the second lockdown. This study employs an artificial intelligence framework comprising of natural language processing, word embeddings, Markov models and Growing Self-Organizing Maps that are collectively used to investigate the social media conversations. The investigation was carried out using 73,000 public Twitter conversations from users in Australia from January to September 2020.

RESULTS : The outcomes of this study enabled us to analyse and visualise different emotions and related concerns expressed, reflected on social media during the COVID-19 pandemic, that can be used to gain insights on citizens' mental health. First, the topic analysis showed the diverse as well as common concerns people have expressed during the four stages of the pandemic. It was noted that starting from personal level concerns, the concerns expressed over social media has escalated to broader concerns over time. Second, the emotion intensity and emotion state transitions showed that 'fear' and 'sad' emotions were more prominently expressed at first, however, they transition into 'anger' and 'disgust' over time. Negative emotions except 'sad' were significantly higher (P < .05) in the second lockdown showing increased frustration. The temporal emotion analysis was conducted by modelling the emotion state changes across the four stages which demonstrated how different emotions emerge and shift over time. Third, the concerns expressed by social media users were categorized into profiles, where differences could be seen between the first and second lockdown profiles.

CONCLUSIONS : This study showed diverse emotions and concerns expressed and recorded on social media during the COVID-19 pandemic reflected the mental health of the general public. While this study establishes the use of social media to discover informed insights during a time where physical communication is impossible, the outcomes also contribute towards post-pandemic recovery, understanding psychological impact via emotion changes and potentially informing healthcare decision-making. The study exploits AI and social media to enhance our understanding of human behaviours in global emergencies, leading to improved planning and policymaking for future crises.

CLINICALTRIAL :

Adikari Achini, Nawaratne Rashmika, De Silva Daswin, Ranasinghe Sajani, Alahakoon Oshadi, Alahakoon Damminda

2021-Apr-01

Internal Medicine

Tele-management of home isolated COVID-19 patients via oxygen therapy with non-invasive positive pressure ventilation and physical therapy techniques: A randomized clinical trial.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : With the enlarging stress on hospitals caused by the novel coronavirus disease 2019 (Covid-19) pandemic, the need for home based solutions has become a necessity to support these overwhelmed hospitals.

OBJECTIVE : To compare two non-pharmacological respiratory treatment methods for home isolated Covid-19 patients using a new developed tele-management healthcare system.

METHODS : In this randomized, single-blinded, clinical trial, sixty patients with stage one pneumonia caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection were treated. Group (A) receiving oxygen therapy with Bi-level positive airway pressure ventilation (BiPAP), and group (B) receiving osteopathic manipulative respiratory and physical therapy techniques. Arterial blood gases of partial pressure of oxygen (PaO2) and partial pressure of carbon dioxide (PaCo2), potential of hydrogen (pH), vital signs (temperature, respiratory rate, oxygen saturation, heart rate and blood pressure), and chest CT scan, were utilized for follow up and for assessment of the course and duration of recovery.

RESULTS : Analysis of the results showed a significant difference between the two groups (p<0.05) with group (A) showing shorter recovery period than group (B) (14.9±1.7 days and 23.9±2.3 days respectively). Significant differences were also observed between base line and final readings in all of the outcome measures in both groups (p<0.05). The post-treatment patient satisfaction with our proposed tele-management healthcare system showed positive response for most of the patients in both groups.

CONCLUSIONS : It was found that home oxygen therapy with BiPAP can be a more effective prophylactic treatment approach than osteopathic manipulative respiratory and physical therapy techniques as it can impede exacerbation of the early stage COVID-19 pneumonia. Tele-management healthcare systems are promising methods to help pandemic-related shortage of hospital beds as they showed reasonable effectiveness and reliability in monitoring and management of the early stage COVID-19 pneumonia patients.

CLINICALTRIAL : ClinicalTrials.gov, identifier: NCT04368923.

Adly Aya Sedky, Adly Mahmoud Sedky, Adly Afnan Sedky

2021-Apr-01

General

The Rapid Development and Early Success of Covid 19 Vaccines Have Raised Hopes for Accelerating the Cancer Treatment Mechanism.

In Archives of Razi Institute
The Covid-19 pandemic has brought about rapid change in medical science. The production of new generation vaccines for this disease has surprised even their most optimistic supporters. Not only have these vaccines proven to be effective, but the importance of this disease and pandemic situation also significantly shortened the long-standing process of validating such products. Vaccination is a type of immunotherapy. Researchers have long been looking at vaccines as a possible treatment for cancer (Geynisman et al., 2014). In the same way that vaccines work against infectious diseases, attempts are being made to develop vaccines to identify specific proteins on cancer cells. This helps the immune system recognize and attack cancer cells. Cancer vaccines may help: I) Prevent the growth of cancer cells (Bialkowski et al., 2016), II) Prevent recurrence of cancer (Stanton and Disis, 2015), III) Destroy cancer cells left over from other treatments. The following types of cancer vaccines are being studied: Antigen Vaccines. These vaccines are made from specific proteins or antigens of cancerous cells. Their purpose is to stimulate the immune system to attack cancer cells (Tagliamonte et al., 2014). Whole-Cell Vaccines. A whole-cell vaccine uses the entire cancer cell, not just a specific molecule (antigen), to generate the vaccine. (Keenan and Jaffee, 2012).Dendritic Cell Vaccines. Dendritic cells help the immune system identify abnormal cells, such as cancerous cells. Dendritic cells are grown with cancer cells in the laboratory to produce the vaccine. The vaccine then stimulates the immune system to attack cancer. (Wang et al., 2014; Mastelic-Gavillet et al., 2019). DNA Vaccines. These vaccines are made from DNA fragments of cancer cells. They can be injected into the body to facilitate immune system cells can better respond and kill cancer cells (Gatti-Mays et al., 2017).Other Types of Cancer Vaccines. such as Anti idiotype vaccines. This vaccine stimulates the body to generate antibodies against cancerous cells. An example of an anti-idiotype antibody is Racotumomab or Vaxira (Cancer, 2016). However, conditions and considerations after Corona does not seem to be the same as before. The current pandemic situation has also led to major changes in the pharmaceutical and Vaccine production process and international protocols. Some of the most critical issues that can accelerate the introduction of cancer vaccines are: 1. Typical drug and vaccine development timeline. A typical vaccine needs 5 to 10 years and sometimes longer to design secure funding, and get approval (Figure 1). Less than 10 percent of new drugs, which are entered in the different phases of clinical trials, are advanced to approval by the Food and Drug Administration (FDA)(Cancer, 2020a). However, now the situation is not normal. Dozens of Covid 19 vaccines are starting clinical trials. Some of them use RNA and DNA technology, which delivers the body with missions to produce its antibodies against the virus. There are already at least 254 therapies and 95 vaccines related to Covid-19 being explored. However, it seems that the experiences gained in this pandemic, and advances in technology, may be effective in shortening the production path of other vaccines and drugs and the process of its approval at the national and international levels in the future. In Figure 2, the time course of production of conventional vaccines in comparison with Covid 19 vaccines (Cancer, 2020b) is shown.2. The introduction of messenger RNA (mRNA) technology into the field of prevention and treatment. Over the past decades, this technology has been considered an excellent alternative to conventional vaccination methods. Proper potency and low side effects, the possibility of fast production and relatively low production cost are its advantages. However, until recently, the instability of this molecule has been a major problem in its application. This research was started many years ago by two companies that played a significant role in developing the first Covid vaccines, so BioNTech and Moderna were able to quickly transfer their experience in the field of Covid vaccine development (Pardi et al., 2018; Moderna, 2020). Figure 3 shows how mRNA vaccines work. Bout Pfizer &ndash; BioNTech and Moderna mRNA vaccines were more than 90 % effective in preclinical stages. Millions of doses of these two vaccines are currently being injected into eligible individuals worldwide. 3. Considering the use of artificial intelligence in assessing the effectiveness of vaccines. There are always doubts about the effectiveness of the new drug in treating the disease. Once the vaccine is widely available, we will know more about its effectiveness versus it works under carefully controlled scientific testing conditions. Vaccines will continue to be monitored after use. The data collected helps professionals understand how they work in different groups of people (depending on factors such as age, ethnicity, and people with different health conditions) and also the length of protection provided by the vaccine. Artificial intelligence (AI) is an emerging field, which reaches everywhere and not only as a beneficial industrial tool but also as a practical tool in medical science and plays a crucial role in developing the computation vision, risk assessment, diagnostic, prognostic, etc. models in the field of medicine (Amisha et al., 2019). According to the wide range of AI applications in the analysis of different types of data, it can be used in vaccine production, safety assessments, clinical and preclinical studies and Covid 19 vaccines adverse reactions (CDC, 2019). Indeed, most cancer vaccines are therapeutic, rather than prophylactic, and seek to stimulate cell-mediated responses, such as those from CTLs, capable of clearing or reducing tumor burden. There are currently FDA-approved products for helping cancer treatment such as BREYANZI, TECARTUS and YESCARTA for lymphoma, IMLYGIC for melanoma, KYMRIAH for acute lymphoblastic leukemia, and PROVENGE for prostate cancer. Over the past decade, most of BioNTech&#39;s activities have been in the field of cancer vaccine design and production for melanoma (two clinical trials), breast cancer (one clinical trial), and the rest concerning viral and veterinary vaccines (two clinical trials). Also Maderno company has been working on Individualized cancer vaccines (one clinical trials), and vaccines for viral infections such as Zika and Influenza and veterinary vaccines (several clinical trials) (Pardi et al., 2018). Therefore, it can be said, mRNA technology that has been the subject of much research into the treatment of cancer has been shifted and rapidly used to produce and use the Covid 19 vaccine. The current pandemic situation has necessitated the acceleration of Covid 19 vaccines and drugs and national and international protocols for their approval. If the currently produced vaccines can continue to be as successful as the preclinical and early phase studies, these changes and evolution have raised hopes for accelerating the use of these technologies and mechanisms in the field of cancer and other diseases vaccines, including HIV and influenza.
Amanpour S

2021-Mar

Vaccine, cancer, covid 19

General

Using machine learning to investigate the public's emotional responses to work from home during the COVID-19 pandemic.

In The Journal of applied psychology
According to event system theory (EST; Morgeson et al., Academy of Management Review, 40, 2015, 515-537), the coronavirus disease 2019 (COVID-19) pandemic and resultant stay-at-home orders are novel, critical, and disruptive events at the environmental level that substantially changed people's work, for example, where they work and how they interact with colleagues. Although many studies have examined events' impact on features or behaviors, few studies have examined how events impact aggregate emotions and how these effects may unfold over time. Applying a state-of-the-art deep learning technique (i.e., the fine-tuned Bidirectional Encoder Representations from Transformers [BERT] algorithm), the current study extracted the public's daily emotion associated with working from home (WFH) at the U.S. state level over four months (March 01, 2020-July 01, 2020) from 1.56 million tweets. We then applied discontinuous growth modeling (DGM) to investigate how COVID-19 and resultant stay-at-home orders changed the trajectories of the public's emotions associated with WFH. Our results indicated that stay-at-home orders demonstrated both immediate (i.e., intercept change) and longitudinal (i.e., slope change) effects on the public's emotion trajectories. Daily new COVID-19 case counts did not significantly change the emotion trajectories. We discuss theoretical implications for testing EST with the global pandemic and practical implications. We also make Python and R codes for fine-tuning BERT models and DGM analyses open source so that future researchers can adapt and apply the codes in their own studies. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Min Hanyi, Peng Yisheng, Shoss Mindy, Yang Baojiang

2021-Feb

General

Classification of COVID-19 individuals using adaptive neuro-fuzzy inference system.

In Multimedia systems
Coronavirus is a fatal disease that affects mammals and birds. Usually, this virus spreads in humans through aerial precipitation of any fluid secreted from the infected entity's body part. This type of virus is fatal than other unpremeditated viruses. Meanwhile, another class of coronavirus was developed in December 2019, named Novel Coronavirus (2019-nCoV), first seen in Wuhan, China. From January 23, 2020, the number of affected individuals from this virus rapidly increased in Wuhan and other countries. This research proposes a system for classifying and analyzing the predictions obtained from symptoms of this virus. The proposed system aims to determine those attributes that help in the early detection of Coronavirus Disease (COVID-19) using the Adaptive Neuro-Fuzzy Inference System (ANFIS). This work computes the accuracy of different machine learning classifiers and selects the best classifier for COVID-19 detection based on comparative analysis. ANFIS is used to model and control ill-defined and uncertain systems to predict this globally spread disease's risk factor. COVID-19 dataset is classified using Support Vector Machine (SVM) because it achieved the highest accuracy of 100% among all classifiers. Furthermore, the ANFIS model is implemented on this classified dataset, which results in an 80% risk prediction for COVID-19.
Iwendi Celestine, Mahboob Kainaat, Khalid Zarnab, Javed Abdul Rehman, Rizwan Muhammad, Ghosh Uttam

2021-Mar-28

ANFIS, COVID-19, Detection, Machine learning, Risk prediction, SVM

Surgery

An observational study to develop a scoring system and model to detect risk of hospital admission due to COVID-19.

In Journal of the American College of Emergency Physicians open

Background : COVID-19 has caused an unprecedented global health emergency. The strains of such a pandemic can overwhelm hospital capacity. Efficient clinical decision-making is crucial for proper healthcare resource utilization in this crisis. Using observational study data, we set out to create a predictive model that could anticipate which COVID-19 patients would likely be admitted and developed a scoring tool that could be used in the clinical setting and for population risk stratification.

Methods : We retrospectively evaluated data from COVID-19 patients across a network of 6 hospitals in northeastern Pennsylvania. Analysis was limited to age, gender, and historical variables. After creating a variable importance plot, we chose a selection of the best predictors to train a logistic regression model. Variable selection was done using a lasso regularization technique. Using the coefficients in our logistic regression model, we then created a scoring tool and validated the score on a test set data.

Results : A total of 6485 COVID-19 patients were included in our analysis, of which 707 were hospitalized. The biggest predictors of patient hospitalization included age, a history of hypertension, diabetes, chronic heart disease, gender, tobacco use, and chronic kidney disease. The logistic regression model demonstrated an AUC of 0.81. The coefficients for our logistic regression model were used to develop a scoring tool. Low-, intermediate-, and high-risk patients were deemed to have a 3.5%, 26%, and 38% chance of hospitalization, respectively. The best predictors of hospitalization included age (odds ratio [OR] = 1.03, confidence interval [CI] = 1.02-1.03), diabetes (OR = 2.08, CI = 1.69-2.57), hypertension (OR = 2.36, CI = 1.90-2.94), chronic heart disease (OR = 1.53, CI = 1.22-1.91), and male gender (OR = 1.32, CI = 1.11-1.58).

Conclusions : Using retrospective observational data from a 6-hospital network, we determined risk factors for admission and developed a predictive model and scoring tool for use in the clinical and population setting that could anticipate admission for COVID-19 patients.

Chen Zhe, Russo Nicholas W, Miller Matthew M, Murphy Robert X, Burmeister David B

2021-Apr

COVID, machine learning, predictive model, risk of admission

General

Network-based Virus-Host Interaction Prediction with Application to SARS-CoV-2.

In Patterns (New York, N.Y.)
COVID-19, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has quickly become a global health crisis since the first report of infection in December of 2019. However, the infection spectrum of SARS-CoV-2 and its comprehensive protein-level interactions with hosts remain unclear. There is a massive amount of under-utilized data and knowledge about RNA viruses highly relevant to SARS-CoV-2 and proteins of their hosts. More in-depth and more comprehensive analyses of that knowledge and data can shed new insight into the molecular mechanisms underlying the COVID-19 pandemic and reveal potential risks. In this work, we constructed a multi-layer virus-host interaction network to incorporate these data and knowledge. We developed a machine learning-based method to predict virus-host interactions at both protein and organism levels. Our approach revealed five potential infection targets of SARS-CoV-2 and 19 highly possible interactions between SARS-CoV-2 proteins and human proteins in the innate immune pathway.
Du Hangyu, Chen Feng, Liu Hongfu, Hong Pengyu

2021-Mar-29

COVID-19, Interaction Prediction, Keywords: Coronavirus, Machine Learning, Protein-Protein Interaction, SARS-CoV-2, virus-host interaction network

General

A novel perceptual two layer image fusion using deep learning for imbalanced COVID-19 dataset.

In PeerJ. Computer science

Background and Purpose : COVID-19 is a new strain of viruses that causes life stoppage worldwide. At this time, the new coronavirus COVID-19 is spreading rapidly across the world and poses a threat to people's health. Experimental medical tests and analysis have shown that the infection of lungs occurs in almost all COVID-19 patients. Although Computed Tomography of the chest is a useful imaging method for diagnosing diseases related to the lung, chest X-ray (CXR) is more widely available, mainly due to its lower price and results. Deep learning (DL), one of the significant popular artificial intelligence techniques, is an effective way to help doctors analyze how a large number of CXR images is crucial to performance.

Materials and Methods : In this article, we propose a novel perceptual two-layer image fusion using DL to obtain more informative CXR images for a COVID-19 dataset. To assess the proposed algorithm performance, the dataset used for this work includes 87 CXR images acquired from 25 cases, all of which were confirmed with COVID-19. The dataset preprocessing is needed to facilitate the role of convolutional neural networks (CNN). Thus, hybrid decomposition and fusion of Nonsubsampled Contourlet Transform (NSCT) and CNN_VGG19 as feature extractor was used.

Results : Our experimental results show that imbalanced COVID-19 datasets can be reliably generated by the algorithm established here. Compared to the COVID-19 dataset used, the fuzed images have more features and characteristics. In evaluation performance measures, six metrics are applied, such as Q^AB/F, Q^MI, PSNR, SSIM, SF, and STD, to determine the evaluation of various medical image fusion (MIF). In the Q^MI, PSNR, SSIM, the proposed algorithm NSCT + CNN_VGG19 achieves the greatest and the features characteristics found in the fuzed image is the largest. We can deduce that the proposed fusion algorithm is efficient enough to generate CXR COVID-19 images that are more useful for the examiner to explore patient status.

Conclusions : A novel image fusion algorithm using DL for an imbalanced COVID-19 dataset is the crucial contribution of this work. Extensive results of the experiment display that the proposed algorithm NSCT + CNN_VGG19 outperforms competitive image fusion algorithms.

Elzeki Omar M, Abd Elfattah Mohamed, Salem Hanaa, Hassanien Aboul Ella, Shams Mahmoud

2021

CNN, COVID19, Coronavirus, Deep learning, Feature analysis, Feature extraction, Image fusion, Machine learning, NSCT, VGG19

General

COVID-19: a new deep learning computer-aided model for classification.

In PeerJ. Computer science
Chest X-ray (CXR) imaging is one of the most feasible diagnosis modalities for early detection of the infection of COVID-19 viruses, which is classified as a pandemic according to the World Health Organization (WHO) report in December 2019. COVID-19 is a rapid natural mutual virus that belongs to the coronavirus family. CXR scans are one of the vital tools to early detect COVID-19 to monitor further and control its virus spread. Classification of COVID-19 aims to detect whether a subject is infected or not. In this article, a model is proposed for analyzing and evaluating grayscale CXR images called Chest X-Ray COVID Network (CXRVN) based on three different COVID-19 X-Ray datasets. The proposed CXRVN model is a lightweight architecture that depends on a single fully connected layer representing the essential features and thus reducing the total memory usage and processing time verse pre-trained models and others. The CXRVN adopts two optimizers: mini-batch gradient descent and Adam optimizer, and the model has almost the same performance. Besides, CXRVN accepts CXR images in grayscale that are a perfect image representation for CXR and consume less memory storage and processing time. Hence, CXRVN can analyze the CXR image with high accuracy in a few milliseconds. The consequences of the learning process focus on decision making using a scoring function called SoftMax that leads to high rate true-positive classification. The CXRVN model is trained using three different datasets and compared to the pre-trained models: GoogleNet, ResNet and AlexNet, using the fine-tuning and transfer learning technologies for the evaluation process. To verify the effectiveness of the CXRVN model, it was evaluated in terms of the well-known performance measures such as precision, sensitivity, F1-score and accuracy. The evaluation results based on sensitivity, precision, recall, accuracy, and F1 score demonstrated that, after GAN augmentation, the accuracy reached 96.7% in experiment 2 (Dataset-2) for two classes and 93.07% in experiment-3 (Dataset-3) for three classes, while the average accuracy of the proposed CXRVN model is 94.5%.
Elzeki Omar M, Shams Mahmoud, Sarhan Shahenda, Abd Elfattah Mohamed, Hassanien Aboul Ella

2021

COVID-19, Classification, Deep convolutional neural network, X-ray images

General

Early survey with bibliometric analysis on machine learning approaches in controlling COVID-19 outbreaks.

In PeerJ. Computer science

Background and Objective : The COVID-19 pandemic has caused severe mortality across the globe, with the USA as the current epicenter of the COVID-19 epidemic even though the initial outbreak was in Wuhan, China. Many studies successfully applied machine learning to fight COVID-19 pandemic from a different perspective. To the best of the authors' knowledge, no comprehensive survey with bibliometric analysis has been conducted yet on the adoption of machine learning to fight COVID-19. Therefore, the main goal of this study is to bridge this gap by carrying out an in-depth survey with bibliometric analysis on the adoption of machine learning-based technologies to fight COVID-19 pandemic from a different perspective, including an extensive systematic literature review and bibliometric analysis.

Methods : We applied a literature survey methodology to retrieved data from academic databases and subsequently employed a bibliometric technique to analyze the accessed records. Besides, the concise summary, sources of COVID-19 datasets, taxonomy, synthesis and analysis are presented in this study. It was found that the Convolutional Neural Network (CNN) is mainly utilized in developing COVID-19 diagnosis and prognosis tools, mostly from chest X-ray and chest CT scan images. Similarly, in this study, we performed a bibliometric analysis of machine learning-based COVID-19 related publications in the Scopus and Web of Science citation indexes. Finally, we propose a new perspective for solving the challenges identified as direction for future research. We believe the survey with bibliometric analysis can help researchers easily detect areas that require further development and identify potential collaborators.

Results : The findings of the analysis presented in this article reveal that machine learning-based COVID-19 diagnose tools received the most considerable attention from researchers. Specifically, the analyses of results show that energy and resources are more dispenses towards COVID-19 automated diagnose tools while COVID-19 drugs and vaccine development remains grossly underexploited. Besides, the machine learning-based algorithm that is predominantly utilized by researchers in developing the diagnostic tool is CNN mainly from X-rays and CT scan images.

Conclusions : The challenges hindering practical work on the application of machine learning-based technologies to fight COVID-19 and new perspective to solve the identified problems are presented in this article. Furthermore, we believed that the presented survey with bibliometric analysis could make it easier for researchers to identify areas that need further development and possibly identify potential collaborators at author, country and institutional level, with the overall aim of furthering research in the focused area of machine learning application to disease control.

Chiroma Haruna, Ezugwu Absalom E, Jauro Fatsuma, Al-Garadi Mohammed A, Abdullahi Idris N, Shuib Liyana

2020

Bibliometric analysis, COVID-19 diagnosis tool, COVID-19 pandemic, Convolutional neural network, Machine learning

General

FUSI-CAD: Coronavirus (COVID-19) diagnosis based on the fusion of CNNs and handcrafted features.

In PeerJ. Computer science
The precise and rapid diagnosis of coronavirus (COVID-19) at the very primary stage helps doctors to manage patients in high workload conditions. In addition, it prevents the spread of this pandemic virus. Computer-aided diagnosis (CAD) based on artificial intelligence (AI) techniques can be used to distinguish between COVID-19 and non-COVID-19 from the computed tomography (CT) imaging. Furthermore, the CAD systems are capable of delivering an accurate faster COVID-19 diagnosis, which consequently saves time for the disease control and provides an efficient diagnosis compared to laboratory tests. In this study, a novel CAD system called FUSI-CAD based on AI techniques is proposed. Almost all the methods in the literature are based on individual convolutional neural networks (CNN). Consequently, the FUSI-CAD system is based on the fusion of multiple different CNN architectures with three handcrafted features including statistical features and textural analysis features such as discrete wavelet transform (DWT), and the grey level co-occurrence matrix (GLCM) which were not previously utilized in coronavirus diagnosis. The SARS-CoV-2 CT-scan dataset is used to test the performance of the proposed FUSI-CAD. The results show that the proposed system could accurately differentiate between COVID-19 and non-COVID-19 images, as the accuracy achieved is 99%. Additionally, the system proved to be reliable as well. This is because the sensitivity, specificity, and precision attained to 99%. In addition, the diagnostics odds ratio (DOR) is ≥ 100. Furthermore, the results are compared with recent related studies based on the same dataset. The comparison verifies the competence of the proposed FUSI-CAD over the other related CAD systems. Thus, the novel FUSI-CAD system can be employed in real diagnostic scenarios for achieving accurate testing for COVID-19 and avoiding human misdiagnosis that might exist due to human fatigue. It can also reduce the time and exertion made by the radiologists during the examination process.
Ragab Dina A, Attallah Omneya

2020

Computer-aided diagnosis (CAD), Convolution neural networks (CNN), Coronavirus (COVID-19), Discrete wavelet transform (DWT), Grey level co-occurrence matrix (GLCM)

General

A multi-task pipeline with specialized streams for classification and segmentation of infection manifestations in COVID-19 scans.

In PeerJ. Computer science
We are concerned with the challenge of coronavirus disease (COVID-19) detection in chest X-ray and Computed Tomography (CT) scans, and the classification and segmentation of related infection manifestations. Even though it is arguably not an established diagnostic tool, using machine learning-based analysis of COVID-19 medical scans has shown the potential to provide a preliminary digital second opinion. This can help in managing the current pandemic, and thus has been attracting significant research attention. In this research, we propose a multi-task pipeline that takes advantage of the growing advances in deep neural network models. In the first stage, we fine-tuned an Inception-v3 deep model for COVID-19 recognition using multi-modal learning, that is, using X-ray and CT scans. In addition to outperforming other deep models on the same task in the recent literature, with an attained accuracy of 99.4%, we also present comparative analysis for multi-modal learning against learning from X-ray scans alone. The second and the third stages of the proposed pipeline complement one another in dealing with different types of infection manifestations. The former features a convolutional neural network architecture for recognizing three types of manifestations, while the latter transfers learning from another knowledge domain, namely, pulmonary nodule segmentation in CT scans, to produce binary masks for segmenting the regions corresponding to these manifestations. Our proposed pipeline also features specialized streams in which multiple deep models are trained separately to segment specific types of infection manifestations, and we show the significant impact that this framework has on various performance metrics. We evaluate the proposed models on widely adopted datasets, and we demonstrate an increase of approximately 2.5% and 4.5% for dice coefficient and mean intersection-over-union (mIoU), respectively, while achieving 60% reduction in computational time, compared to the recent literature.
El-Bana Shimaa, Al-Kabbany Ahmad, Sharkas Maha

2020

COVID-19, Deeplab, Medical imaging, Multimodal learning, Pneumonia, Transfer learning

Radiology

Segmentation of COVID-19 pneumonia lesions: A deep learning approach.

In Medical journal of the Islamic Republic of Iran
Background: Lung CT scan has a pivotal role in diagnosis and monitoring of COVID-19 patients, and with growing number of affected individuals, the need for artificial intelligence (AI)-based systems for interpretation of CT images is emerging. In current investigation we introduce a new deep learning-based automatic segmentation model for localization of COVID-19 pulmonary lesions. Methods: A total of 2469 CT scan slices, containing 1402 manually segmented abnormal and 1067 normal slices form 55 COVID-19 patients and 41 healthy individuals, were used to train a deep convolutional neural network (CNN) model based on Detectron2, an open-source modular object detection library. A dataset, including 1224 CT slices of 18 COVID-19 patients and 9 healthy individuals, was used to test the model. Results: The accuracy, sensitivity, and specificity of the trained model in marking a single image slice with COVID-19 lesion were 0.954, 0.928, and 0.961, respectively. Considering a threshold of 0.4% for percentage of lung involvement, the model was capable of diagnosing the patients with COVID-19 pneumonia, with a sensitivity of 0.982% and a specificity of 88.5%. Furthermore, the mean Intersection over Union (IoU) index for the test dataset was 0.865. Conclusion: The deep learning-based automatic segmentation method provides an acceptable accuracy in delineation and localization of COVID-19 lesions, assisting the clinicians and researchers for quantification of abnormal findings in chest CT scans. Moreover, instance segmentation is capable of monitoring longitudinal changes of the lesions, which could be beneficial to patients' follow-up.
Ghomi Zahra, Mirshahi Reza, Khameneh Bagheri Arash, Fattahpour Ali, Mohammadiun Saeed, Alavi Gharahbagh Abdorreza, Djavadifar Abtin, Arabalibeik Hossein, Sadiq Rehan, Hewage Kasun

2020

Artificial intelligence, COVID-19, Deep learning, Pneumonia, Tomography, X-ray

General

Derivation of a Contextually-Appropriate COVID-19 Mortality Scale for Low-Resource Settings.

In Annals of global health

Background : In many low- and middle-income countries, where vaccinations will be delayed and healthcare systems are underdeveloped, the COVID-19 pandemic will continue for the foreseeable future. Mortality scales can aid frontline providers in low-resource settings (LRS) in identifying those at greatest risk of death so that limited resources can be directed towards those in greatest need and unnecessary loss of life is prevented. While many prognostication tools have been developed for, or applied to, COVID-19 patients, no tools to date have been purpose-designed for, and validated in, LRS.

Objectives : This study aimed to develop a pragmatic tool to assist LRS frontline providers in evaluating in-hospital mortality risk using only easy-to-obtain demographic and clinical inputs.

Methods : Machine learning was used on data from a retrospective cohort of Sudanese COVID-19 patients at two government referral hospitals to derive contextually appropriate mortality indices for COVID-19, which were then assessed by C-indices.

Findings : Data from 467 patients were used to derive two versions of the AFEM COVID-19 Mortality Scale (AFEM-CMS), which evaluates in-hospital mortality risk using demographic and clinical inputs that are readily obtainable in hospital receiving areas. Both versions of the tool include age, sex, number of comorbidities, Glasgow Coma Scale, respiratory rate, and systolic blood pressure; in settings with pulse oximetry, oxygen saturation is included and in settings without access, heart rate is included. The AFEM-CMS showed good discrimination: the model including pulse oximetry had a C-statistic of 0.775 (95% CI: 0.737-0.813) and the model excluding it had a C-statistic of 0.719 (95% CI: 0.678-0.760).

Conclusions : In the face of an enduring pandemic in many LRS, the AFEM-CMS serves as a practical solution to aid frontline providers in effectively allocating healthcare resources. The tool's generalisability is likely narrow outside of similar extremely LRS settings, and further validation studies are essential prior to broader use.

Pigoga J L, Omer Y O, Wallis L A

2021-Mar-26

General

Analysis, modeling and optimal control of COVID-19 outbreak with three forms of infection in Democratic Republic of the Congo.

In Results in physics
This paper deals with modeling and simulation of the novel coronavirus in which the infectious individuals are divided into three subgroups representing three forms of infection. The rigorous analysis of the mathematical model is provided. We provide also a rigorous derivation of the basic reproduction number R 0 . For R 0 < 1 , we prove that the Disease Free Equilibium (DFE) is Globally Asymptotically Stable (GAS), thus COVID-19 extincts; whereas for R 0 > 1 , we found the co-existing phenomena under some assumptions and parametric values. Elasticity indices for R 0 with respect to different parameters are calculated with baseline parameter values estimated. We also prove that a transcritical bifurcation occurs at R 0 = 1 . Taking into account the control strategies like screening, treatment and isolation (social distancing measures), we present the optimal control problem of minimizing the cost due to the application of these measures. By reducing the values of some parameters, such as death rates (representing a management effort for all categories of people) and recovered rates (representing the action of reduction in transmission, improved screening, treatment for individuals diagnosed positive to COVID-19 and the implementation of barrier measures limiting contamination for undiagnosed individuals), it appears that after 140 - 170 days, the peak of the pandemic is reached and shows that by continuing with this strategy, COVID-19 could be eliminated in the population.
Ndondo A M, Kasereka S K, Bisuta S F, Kyamakya K, Doungmo E F G, Ngoie R-B M

2021-Mar-27

COVID-19, DRC, Differential equation, Mathematical model, Optimal control, Simulation

General

GUIdEStaR (G-quadruplex, uORF, IRES, Epigenetics, Small RNA, Repeats), the integrated matadatabase: transcript-indexed binary information creation for chaining with neural network analysis

bioRxiv Preprint
GUIdEStaR integrates existing databases of important compositional and structural elements of sequences- various types of G-quadruplex, upstream open reading frame (uORF), Internal Ribosome Entry Site (IRES), epigenetic modification (histone protein and RNA), and repeats. It contains binary information (presence/absence of the elements) that are organized into 5 regions (5'UTR, 3'UTR, exon, intron, and biological region) per transcript and per gene. These elements are highly interdependent in controlling functional interaction of a gene. The database contains information of approx. 40,000 genes and 320,000 transcripts, where each transcript has 845 presence/absence information. Recently, artificial intelligence (AI) based analysis of sequencing data has been gaining popularity in the area of bioinformatics. To create a dataset that can be used as an input to AI methods, GUIdEStaR comes with example Java codes. Here, we demonstrates the database usage with three neural network classification examples- 1) small RNA example for identifying the attributes that are unique to transcription factor (TF) genes mediated by small RNAs originated from SARS-CoV-2 vs. from human, 2) cell membrane receptor study for classifying virus interacting vs. non-interacting receptors, and 3) receptors targeted by nonsense mediated mRNA decay (NMD) vs. of non-target. GUIdEStaR is freely available at www.guidestar.kr and https://sourceforge.net/projects/guidestar.
Kang, J. E.

2021-04-04

General

Detection of COVID-19 Disease using Deep Neural Networks with Ultrasound Imaging

ArXiv Preprint
The new coronavirus 2019 (COVID-2019) has rapidly become a pandemic and has had a devastating effect on both everyday life, public health and the global economy. It is critical to detect positive cases as early as possible to prevent the further spread of this epidemic and to treat affected patients quickly. The need for auxiliary diagnostic tools has increased as accurate automated tool kits are not available. This paper presents a work in progress that proposes the analysis of images of lung ultrasound scans using a convolutional neural network. The trained model will be used on a Raspberry Pi to predict on new images.
Carlos Rojas-Azabache, Karen Vilca-Janampa, Renzo Guerrero-Huayta, Dennis Núñez-Fernández

2021-04-04

General

Digital Is Political: Why We Need a Feminist Conceptual Lens on Determinants of Digital Health.

In Omics : a journal of integrative biology
Digital health is a rapidly emerging field that offers several promising potentials: health care delivery remotely, in urban and rural areas, in any time zone, and in times of pandemics and ecological crises. Digital health encompasses electronic health, computing science, big data, artificial intelligence, and the Internet of Things, to name but a few technical components. Digital health is part of a vision for systems medicine. The advances in digital health have been, however, uneven and highly variable across communities, countries, medical specialties, and societal contexts. This article critically examines the determinants of digital health (DDH). DDH describes and critically responds to inequities and differences in digital health theory and practice across people, places, spaces, and time. DDH is not limited to studying variability in design and access to digital technologies. DDH is situated within a larger context of the political determinants of health. Hence, this article presents an analysis of DDH, as seen through political science, and the feminist studies of technology and society. A feminist lens would strengthen systems-driven, historically and critically informed governance for DDH. This would be a timely antidote against unchecked destructive/extractive governance narratives (e.g., technocracy and patriarchy) that produce and reproduce the health inequities. Moreover, feminist framing of DDH can help cultivate epistemic competence to detect and reject false equivalences in how we understand the emerging digital world(s). False equivalence, very common in the current pandemic and post-truth era, is a type of flawed reasoning in decision-making where equal weight is given to arguments with concrete material evidence, and those that are conjecture, untrue, or unjust. A feminist conceptual lens on DDH would help remedy what I refer to in this article as "the normative deficits" in science and technology policy that became endemic with the rise of neoliberal governance since the 1980s in particular. In this context, it is helpful to recall the feminist writer Ursula K. Le Guin. Le Guin posed "what if?" questions, to break free from oppressive narratives such as patriarchy and re-imagine technology futures. It is time to envision an emancipated, equitable, and more democratic world by asking "what if we lived in a feminist world?" That would be truly awesome, for everyone, women and men, children, youth, and future generations, to steer digital technologies and the new field of DDH toward broadly relevant, ethical, experiential, democratic, and socially responsive health outcomes.
Özdemir Vural

2021-Apr-01

COVID-19, Ursula K. Le Guin, critical governance, determinants of digital health, digital transformation, feminist studies of digital health, postgrowth

Surgery

Machine learning methods to predict mechanical ventilation and mortality in patients with COVID-19.

In PloS one ; h5-index 176.0

BACKGROUND : The Coronavirus disease 2019 (COVID-19) pandemic has affected millions of people across the globe. It is associated with a high mortality rate and has created a global crisis by straining medical resources worldwide.

OBJECTIVES : To develop and validate machine-learning models for prediction of mechanical ventilation (MV) for patients presenting to emergency room and for prediction of in-hospital mortality once a patient is admitted.

METHODS : Two cohorts were used for the two different aims. 1980 COVID-19 patients were enrolled for the aim of prediction ofMV. 1036 patients' data, including demographics, past smoking and drinking history, past medical history and vital signs at emergency room (ER), laboratory values, and treatments were collected for training and 674 patients were enrolled for validation using XGBoost algorithm. For the second aim to predict in-hospital mortality, 3491 hospitalized patients via ER were enrolled. CatBoost, a new gradient-boosting algorithm was applied for training and validation of the cohort.

RESULTS : Older age, higher temperature, increased respiratory rate (RR) and a lower oxygen saturation (SpO2) from the first set of vital signs were associated with an increased risk of MV amongst the 1980 patients in the ER. The model had a high accuracy of 86.2% and a negative predictive value (NPV) of 87.8%. While, patients who required MV, had a higher RR, Body mass index (BMI) and longer length of stay in the hospital were the major features associated with in-hospital mortality. The second model had a high accuracy of 80% with NPV of 81.6%.

CONCLUSION : Machine learning models using XGBoost and catBoost algorithms can predict need for mechanical ventilation and mortality with a very high accuracy in COVID-19 patients.

Yu Limin, Halalau Alexandra, Dalal Bhavinkumar, Abbas Amr E, Ivascu Felicia, Amin Mitual, Nair Girish B

2021

General

Machine Learning Applied to Spanish Clinical Laboratory Data for COVID-19 Outcome Prediction: Model Development and Validation.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : The pandemic caused by the SARS-Cov2 virus will probably stand as the greatest health catastrophe of the modern era. The Spanish healthcare system has been exposed to uncontrollable numbers of patients in a short period of time, causing system collapse. Given that diagnosis is not immediate and there is no effective treatment, other tools have had to be developed to identify patients at risk of severe disease complications, and thus optimize material and human resources in health care. There are no tools to establish which patients have a worse prognosis than others.

OBJECTIVE : In this study, we aimed to process a sample of electronic health records of COVID-19 patients in order to develop a machine learning model to predict the severity of infection and mortality through clinical laboratory parameters. Early patient classification can help optimize material and human resources, and analysis of the most important features of the model could provide insights into the disease.

METHODS : After an initial performance evaluation based on a comparison with several other well-known methods, the extreme gradient boosting (XGBoost) algorithm was chosen as the predictive method for this study. In addition, SHAP (SHapley Additive exPlanations) was used to analyze the importance of the features of the resulting model.

RESULTS : After data preprocessing, 1823 confirmed COVID-19 patients and 32 predictor features were selected. On bootstrap validation, the XGBoost classifier yielded a value of 0.97 (95% CI 0.96-0.98) for the area under the receiver operator characteristic curve, 0.86 (95% CI 0.80-0.91) for the area under the precision-recall curve, 0.94 (95% CI 0.92-0.95) for accuracy, 0.77 (95% CI 0.72-0.83) for F-score, 0.93 (95% CI 0.89-0.98) for sensitivity, and 0.91 (95% CI 0.86-0.96) for specificity. The four most relevant features for model prediction were LDH, C-reactive protein, neutrophils, and urea.

CONCLUSIONS : The predictive model obtained in this work achieved excellent results in the discrimination of COVID-19 dead patients, by mainly employing laboratory parameter values. The analysis of the resulting model identified a set of features with the most significant impact on the prediction, and so relating them to a higher risk of mortality.

Domínguez-Olmedo Juan L, Gragera-Martínez Álvaro, Mata Jacinto, Pachón Victoria

2021-Mar-08

General

Perspectives on Virtual (Remote) Clinical Trials as the 'New Normal' to Accelerate Drug Development.

In Clinical pharmacology and therapeutics
While the digital revolution has transformed many areas of human endeavor, pharmaceutical drug development has been relatively slow to embrace the emerging technologies to enhance efficiency and optimize value in clinical trials. The topic has garnered even greater attention in the face of the COVID-19 outbreak, which has caused unprecedented disruption in the conduct of clinical trials and presented considerable challenges and opportunities for clinical trialists and data analysts. In this paper, we highlight the potential opportunity with virtual or digital clinical trials as viable options to enhance efficiency in drug development and, more importantly, in offering diverse patients easier and attractive means to participate in clinical trials. Special reference is made to the implication of artificial intelligence and machine learning tools in trial execution and data acquisition, processing, and analysis in a virtual trial setting. Issues of patient safety, measurement validity and data integrity are reviewed, and considerations are put forth with reference to the mitigation of underlying regulatory and operational barriers.
Alemayehu Demissie, Hemmings Robert, Natarajan Kannan, Roychoudhury Satrajit

2021-Apr-01

COVID-19, Virtual clinical trial, analytics, digital clinical trial, enhanced drug development, machine learning, pandemic, remote trial, site-less trial

General

Femtomolar SARS-CoV-2 Antigen Detection Using the Microbubbling Digital Assay with Smartphone Readout Enables Antigen Burden Quantitation and Dynamics Tracking.

In medRxiv : the preprint server for health sciences

Background : Little is known about the dynamics of SARS-CoV-2 antigen burden in respiratory samples in different patient populations at different stages of infection. Current rapid antigen tests cannot quantitate and track antigen dynamics with high sensitivity and specificity in respiratory samples.

Methods : We developed and validated an ultra-sensitive SARS-CoV-2 antigen assay with smartphone readout using the Microbubbling Digital Assay previously developed by our group, which is a platform that enables highly sensitive detection and quantitation of protein biomarkers. A computer vision-based algorithm was developed for microbubble smartphone image recognition and quantitation. A machine learning-based classifier was developed to classify the smartphone images based on detected microbubbles. Using this assay, we tracked antigen dynamics in serial swab samples from COVID patients hospitalized in ICU and immunocompromised COVID patients.

Results : The limit of detection (LOD) of the Microbubbling SARS-CoV-2 Antigen Assay was 0.5 pg/mL (10.6 fM) recombinant nucleocapsid (N) antigen or 4000 copies/mL inactivated SARS-CoV-2 virus in nasopharyngeal (NP) swabs, comparable to many rRT-PCR methods. The assay had high analytical specificity towards SARS-CoV-2. Compared to EUA-approved rRT-PCR methods, the Microbubbling Antigen Assay demonstrated a positive percent agreement (PPA) of 97% (95% confidence interval (CI), 92-99%) in symptomatic individuals within 7 days of symptom onset and positive SARS-CoV-2 nucleic acid results, and a negative percent agreement (NPA) of 97% (95% CI, 94-100%) in symptomatic and asymptomatic individuals with negative nucleic acid results. Antigen positivity rate in NP swabs gradually decreased as days-after-symptom-onset increased, despite persistent nucleic acid positivity of the same samples. The computer vision and machine learning-based automatic microbubble image classifier could accurately identify positives and negatives, based on microbubble counts and sizes. Total microbubble volume, a potential marker of antigen burden, correlated inversely with Ct values and days-after-symptom-onset. Antigen was detected for longer periods of time in immunocompromised patients with hematologic malignancies, compared to immunocompetent individuals. Simultaneous detectable antigens and nucleic acids may indicate the presence of replicating viruses in patients with persistent infections.

Conclusions : The Microbubbling SARS-CoV-2 Antigen Assay enables sensitive and specific detection of acute infections, and quantitation and tracking of antigen dynamics in different patient populations at various stages of infection. With smartphone compatibility and automated image processing, the assay is well-positioned to be adapted for point-of-care diagnosis and to explore the clinical implications of antigen dynamics in future studies.

Chen Hui, Li Zhao, Feng Sheng, Wang Anni, Richard-Greenblatt Melissa, Hutson Emily, Andrianus Stefen, Glaser Laurel J, Rodino Kyle G, Qian Jianing, Jayaraman Dinesh, Collman Ronald G, Glascock Abigail, Bushman Frederic D, Lee Jae Seung, Cherry Sara, Fausto Alejandra, Weiss Susan R, Koo Hyun, Corby Patricia M, O’Doherty Una, Garfall Alfred L, Vogl Dan T, Stadtmauer Edward A, Wang Ping

2021-Mar-26

General

Different Appearance of Chest CT Images of T2DM and NDM Patients with COVID-19 Pneumonia Based on an Artificial Intelligent Quantitative Method.

In International journal of endocrinology ; h5-index 44.0
COVID-19 is a kind of pneumonia with new coronavirus infection, and the risk of death in COVID-19 patients with diabetes is four times higher than that in healthy people. It is unclear whether there is a difference in chest CT images between type 2 diabetes mellitus (T2DM) and non-diabetes mellitus (NDM) COVID-19 patients. The aim of this study was to investigate the differences in chest CT images between T2DM and NDM patients with COVID-19 based on a quantitative method of artificial intelligence. A total of 62 patients with COVID-19 pneumonia were retrospectively enrolled and divided into group A (T2DM COVID-19 pneumonia group, n = 15) and group B (NDM COVID-19 pneumonia group, n = 47). The clinical and laboratory examination information of the two groups was collected. Quantitative features (volume of consolidation shadows and ground glass shadows, proportion of consolidation shadow (or ground glass shadow) to lobe volume, total volume, total proportion, and number) of chest spiral CT images were extracted using Dr. Wise @Pneumonia software. The results showed that among the 26 CT image features, the total volume and proportion of bilateral pulmonary consolidation shadow in group A were larger than those in group B (P=0.031 and 0.019, respectively); there was no significant difference in the total volume and proportion of bilateral pulmonary ground glass density shadow between the two groups (P > 0.05). In group A, the blood glucose level was correlated with the volume of consolidation shadow and the proportion of consolidation shadow to right middle lobe volume, and higher than those patients in group B. In conclusion, the inflammatory exudation in the lung of COVID-19 patients with diabetes is more serious than that of patients without diabetes based on the quantitative method of artificial intelligence. Moreover, the blood glucose level is positively correlated with pulmonary inflammatory exudation in COVID-19 patients.
Lu Shan, Xing Zhiheng, Zhao Shiyu, Meng Xianglu, Yang Juhong, Ding Wenlong, Wang Jigang, Huang Chencui, Xu Jingxu, Chang Baocheng, Shen Jun

2021

Public Health

Trends in reasons for emergency calls during the COVID-19 crisis in the department of Gironde, France using artificial neural network for natural language classification.

In Scandinavian journal of trauma, resuscitation and emergency medicine ; h5-index 32.0

OBJECTIVES : During periods such as the COVID-19 crisis, there is a need for responsive public health surveillance indicators in order to monitor both the epidemic growth and potential public health consequences of preventative measures such as lockdown. We assessed whether the automatic classification of the content of calls to emergency medical communication centers could provide relevant and responsive indicators.

METHODS : We retrieved all 796,209 free-text call reports from the emergency medical communication center of the Gironde department, France, between 2018 and 2020. We trained a natural language processing neural network model with a mixed unsupervised/supervised method to classify all reasons for calls in 2020. Validation and parameter adjustment were performed using a sample of 39,907 manually-coded free-text reports.

RESULTS : The number of daily calls for flu-like symptoms began to increase from February 21, 2020 and reached an unprecedented level by February 28, 2020 and peaked on March 14, 2020, 3 days before lockdown. It was strongly correlated with daily emergency room admissions, with a delay of 14 days. Calls for chest pain and stress and anxiety, peaked 12 days later. Calls for malaises with loss of consciousness, non-voluntary injuries and alcohol intoxications sharply decreased, starting one month before lockdown. No noticeable trends in relation to lockdown was found for other groups of reasons including gastroenteritis and abdominal pain, stroke, suicide and self-harm, pregnancy and delivery problems.

DISCUSSION : The first wave of the COVID-19 crisis came along with increased levels of stress and anxiety but no increase in alcohol intoxication and violence. As expected, call related to road traffic crashes sharply decreased. The sharp decrease in the number of calls for malaise was more surprising.

CONCLUSION : The content of calls to emergency medical communication centers is an efficient epidemiological surveillance data source that provides insights into the societal upheavals induced by a health crisis. The use of an automatic classification system using artificial intelligence makes it possible to free itself from the context that could influence a human coder, especially in a crisis situation. The COVID-19 crisis and/or lockdown induced deep modifications in the population health profile.

Gil-Jardiné Cédric, Chenais Gabrielle, Pradeau Catherine, Tentillier Eric, Revel Philippe, Combes Xavier, Galinski Michel, Tellier Eric, Lagarde Emmanuel

2021-Mar-31

COVID-19, Emergency medical communication centers, Lockdown, Public health

General

High Throughput Virtual Screening and Validation of a SARS-CoV-2 Main Protease Non-Covalent Inhibitor

bioRxiv Preprint
Despite the recent availability of vaccines against the acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the search for inhibitory therapeutic agents has assumed importance especially in the context of emerging new viral variants. In this paper, we describe the discovery of a novel non-covalent small-molecule inhibitor, MCULE-5948770040, that binds to and inhibits the SARS-Cov-2 main protease (Mpro) by employing a scalable high throughput virtual screening (HTVS) framework and a targeted compound library of over 6.5 million molecules that could be readily ordered and purchased. Our HTVS framework leverages the U.S. supercomputing infrastructure achieving nearly 91% resource utilization and nearly 126 million docking calculations per hour. Downstream biochemical assays validate this Mpro inhibitor with an inhibition constant (Ki) of 2.9 uM [95% CI 2.2, 4.0]. Further, using room-temperature X-ray crystallography, we show that MCULE-5948770040 binds to a cleft in the primary binding site of Mpro forming stable hydrogen bond and hydrophobic interactions. We then used multiple s-timescale molecular dynamics (MD) simulations, and machine learning (ML) techniques to elucidate how the bound ligand alters the conformational states accessed by Mpro, involving motions both proximal and distal to the binding site. Together, our results demonstrate how MCULE-5948770040 inhibits Mpro and offers a springboard for further therapeutic design.
Clyde, A.; Galanie, S.; Kneller, D. W.; Ma, H.; Babuji, Y.; Blaiszik, B.; Brace, A.; Brettin, T.; Chard, K.; Chard, R.; Coates, L.; Foster, I.; Hauner, D.; Kertesz, V.; Kumar, N.; Lee, H.; Li, Z.; Merzky, A.; Schmidt, J. G.; Tan, L.; Titov, M.; Trifan, A.; Turilli, M.; Van Dam, H.; Chennubhotla, S. C.; Jha, S.; Kovalevsky, A.; Ramanathan, A.; Head, M.; Stevens, R.

2021-04-02

General

Literature Review and Knowledge Distribution During an Outbreak: A Methodology for Managing Infodemics.

In Academic medicine : journal of the Association of American Medical Colleges

PROBLEM : The COVID-19 pandemic has challenged health care systems in an unprecedented way by imposing new demands on health care resources and scientific knowledge. There has also been an exceedingly fast accumulation of new information on this novel virus. As the traditional peer-review process takes time, there is currently a significant gap between the ability to generate new data and the ability to critically evaluate it. This problem of an excess of mixed-quality data, or infodemic, is echoing throughout the scientific community.

APPROACH : The authors aimed to help their colleagues at the Rambam Medical Center, Haifa, Israel, manage the COVID-19 infodemic with a methodologic solution: establishing an in-house mechanism for continuous literature review and knowledge distribution (March-April 2020). Their methodology included the following building blocks: a dedicated literature review team, artificial intelligence-based research algorithms, brief written updates in a graphical format, large-scale webinars and online meetings, and a feedback loop.

OUTCOMES : During the first month (April 2020), the project produced 21 graphical updates. After consideration of feedback from colleagues and final editing, 13 graphical updates were uploaded to the center's website; of these, 31% addressed the clinical presentation of the disease and 38% referred to specific treatments. This methodology as well as the graphical updates it generated were adopted by the Israeli Ministry of Health and distributed in a hospital preparation kit.

NEXT STEPS : The authors believe they have established a novel methodology that can assist in the battle against COVID-19 by making high-quality scientific data more accessible to clinicians. In the future, they expect this methodology to create a favorable uniform standard for evidence-guided health care during infodemics. Further evolution of the methodology may include evaluation of its long-term sustainability and impact on the day-to-day clinical practice and self-confidence of clinicians who treat COVID-19 patients.

Gruber Amit, Ghiringhelli Matteo, Edri Oded, Abboud Yousef, Shiti Assad, Shaheen Naim, Ballan Nimer, Neuberger Ami, Caspi Oren

2021-Mar-30

Pathology

Automated Detection of COVID-19 Cases on Radiographs using Shape-Dependent Fibonacci-p Patterns.

In IEEE journal of biomedical and health informatics
The coronavirus (COVID-19) pandemic has been adversely affecting people's health globally. To diminish the effect of this widespread pandemic, it is essential to detect COVID-19 cases as quickly as possible. Chest radiographs are less expensive and are a widely available imaging modality for detecting chest pathology compared with CT images. They play a vital role in early prediction and developing treatment plans for suspected or confirmed COVID-19 chest infection patients. In this paper, a novel shape-dependent Fibonacci-p patterns-based feature descriptor using a machine learning approach is proposed. Computer simulations show that the presented system (1) increases the effectiveness of differentiating COVID-19, viral pneumonia, and normal conditions, (2) is effective on small datasets, and (3) has faster inference time compared to deep learning methods with comparable performance. Computer simulations are performed on two publicly available datasets; (a) the Kaggle dataset, and (b) the COVIDGR dataset. To assess the performance of the presented system, various evaluation parameters, such as accuracy, recall, specificity, precision, and f1-score are used. Nearly 100% differentiation between normal and COVID-19 radiographs is observed for the three-class classification scheme using the cropped Kaggle radiographs. While Recall of 72.656.83 and specificity of 77.728.06 are observed for the COVIDGR dataset.
Panetta Karen, Sanghavi Foram, Agaian Sos, Madan Neel

2021-Mar-31

General

Drugmonizome and Drugmonizome-ML: integration and abstraction of small molecule attributes for drug enrichment analysis and machine learning.

In Database : the journal of biological databases and curation
Understanding the underlying molecular and structural similarities between seemingly heterogeneous sets of drugs can aid in identifying drug repurposing opportunities and assist in the discovery of novel properties of preclinical small molecules. A wealth of information about drug and small molecule structure, targets, indications and side effects; induced gene expression signatures; and other attributes are publicly available through web-based tools, databases and repositories. By processing, abstracting and aggregating information from these resources into drug set libraries, knowledge about novel properties of drugs and small molecules can be systematically imputed with machine learning. In addition, drug set libraries can be used as the underlying database for drug set enrichment analysis. Here, we present Drugmonizome, a database with a search engine for querying annotated sets of drugs and small molecules for performing drug set enrichment analysis. Utilizing the data within Drugmonizome, we also developed Drugmonizome-ML. Drugmonizome-ML enables users to construct customized machine learning pipelines using the drug set libraries from Drugmonizome. To demonstrate the utility of Drugmonizome, drug sets from 12 independent SARS-CoV-2 in vitro screens were subjected to consensus enrichment analysis. Despite the low overlap among these 12 independent in vitro screens, we identified common biological processes critical for blocking viral replication. To demonstrate Drugmonizome-ML, we constructed a machine learning pipeline to predict whether approved and preclinical drugs may induce peripheral neuropathy as a potential side effect. Overall, the Drugmonizome and Drugmonizome-ML resources provide rich and diverse knowledge about drugs and small molecules for direct systems pharmacology applications. Database URL: https://maayanlab.cloud/drugmonizome/.
Kropiwnicki Eryk, Evangelista John E, Stein Daniel J, Clarke Daniel J B, Lachmann Alexander, Kuleshov Maxim V, Jeon Minji, Jagodnik Kathleen M, Ma’ayan Avi

2021-Mar-31

General

Assessing the COVID-19 Impact on Air Quality: A Machine Learning Approach.

In Geophysical research letters
The worldwide research initiatives on Corona Virus disease 2019 lockdown effect on air quality agree on pollution reduction, but the most reliable method to pollution reduction quantification is still in debate. In this paper, machine learning models based on a Gradient Boosting Machine algorithm are built to assess the outbreak impact on air quality in Quito, Ecuador. First, the precision of the prediction was evaluated by cross-validation on the four years prelockdown, showing a high accuracy to estimate the real pollution levels. Then, the changes in pollution are quantified. During the full lockdown, air pollution decreased by -53 ± 2%, -45 ± 11%, -30 ± 13%, and -15 ± 9% for NO₂, SO₂, CO, and PM_2.5, respectively. The traffic-busy districts were the most impacted areas of the city. After the transition to the partial relaxation, the concentrations have nearly returned to the levels as before the pandemic. The quantification of pollution drop is supported by an assessment of the prediction confidence.
Rybarczyk Yves, Zalakeviciute Rasa

2021-Feb-28

COVID‐19, air pollution, quarantine measures, urban air quality

General

Machine learning is the key to diagnose COVID-19: a proof-of-concept study.

In Scientific reports ; h5-index 158.0
The reverse transcription-polymerase chain reaction (RT-PCR) assay is the accepted standard for coronavirus disease 2019 (COVID-19) diagnosis. As any test, RT-PCR provides false negative results that can be rectified by clinicians by confronting clinical, biological and imaging data. The combination of RT-PCR and chest-CT could improve diagnosis performance, but this would requires considerable resources for its rapid use in all patients with suspected COVID-19. The potential contribution of machine learning in this situation has not been fully evaluated. The objective of this study was to develop and evaluate machine learning models using routine clinical and laboratory data to improve the performance of RT-PCR and chest-CT for COVID-19 diagnosis among post-emergency hospitalized patients. All adults admitted to the ED for suspected COVID-19, and then hospitalized at Rennes academic hospital, France, between March 20, 2020 and May 5, 2020 were included in the study. Three model types were created: logistic regression, random forest, and neural network. Each model was trained to diagnose COVID-19 using different sets of variables. Area under the receiving operator characteristics curve (AUC) was the primary outcome to evaluate model's performances. 536 patients were included in the study: 106 in the COVID group, 430 in the NOT-COVID group. The AUC values of chest-CT and RT-PCR increased from 0.778 to 0.892 and from 0.852 to 0.930, respectively, with the contribution of machine learning. After generalization, machine learning models will allow increasing chest-CT and RT-PCR performances for COVID-19 diagnosis.
Gangloff Cedric, Rafi Sonia, Bouzillé Guillaume, Soulat Louis, Cuggia Marc

2021-Mar-30

General

Structural dynamics of the β-coronavirus Mpro protease ligand binding sites

bioRxiv Preprint
{beta}-coronaviruses alone have been responsible for three major global outbreaks in the 21st century. The current crisis has led to an urgent requirement to develop therapeutics. Even though a number of vaccines are available, alternative strategies targeting essential viral components are required as a back-up against the emergence of lethal viral variants. One such target is the main protease (Mpro) that plays an indispensible role in viral replication. The availability of over 270 Mpro X-ray structures in complex with inhibitors provides unique insights into ligand-protein interactions. Herein, we provide a comprehensive comparison of all non-redundant ligand-binding sites available for SARS-CoV2, SARS-CoV and MERS-CoV Mpro. Extensive adaptive sampling has been used to explore conformational dynamics employing convolutional variational auto encoder-based deep learning, and investigates structural conservation of the ligand binding sites using Markov state models across {beta}-coronavirus homologs. Our results indicate that not all ligand-binding sites are dynamically conserved despite high sequence and structural conservation across {beta}-coronavirus homologs. This highlights the complexity in targeting all three Mpro enzymes with a single pan inhibitor.
Cho, E.; Rosa, M.; Anjum, R.; Mehmood, S.; Soban, M.; Mujtaba, M.; Bux, K.; Dantu, S. C.; Pandini, A.; Yin, J.; Ma, H.; Ramanathan, A.; Islam, B.; Mey, A.; BHOWMIK, D.; Haider, S.

2021-04-01

General

A Wearable Tele-Health System towards Monitoring COVID-19 and Chronic Diseases.

In IEEE reviews in biomedical engineering
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a pandemic since early 2020. The coronavirus disease 2019 (COVID-19) has already caused more than two million deaths worldwide and affected people's physical and mental health. COVID-19 patients with mild symptoms are generally required to self-isolate and monitor for symptoms at least for 14 days in the case the disease turns towards severe complications. Here, we overviewed the impact of COVID-19 on the patients' general health with a focus on their cardiovascular, respiratory and mental health, and investigated several existing patient monitoring systems. We addressed the limitations of these systems and proposed a wearable telehealth solution for monitoring a set of physiological parameters that are critical for COVID-19 patients such as body temperature, heart rate, heart rate variability, blood oxygen saturation, respiratory rate, blood pressure, and cough. This physiological information can be further combined to potentially estimate the lung function using artificial intelligence (AI) and sensor fusion techniques. The prototype, which includes the hardware and a smartphone app, showed promising results with performance comparable to or better than similar commercial devices, thus potentially making the proposed system an ideal wearable solution for long-term monitoring of COVID-19 patients and other chronic diseases.
Jiang Wei, Majumder Sumit, Subramaniam Sophini, Li Xiaohe, Khedri Ridha, Monday Tapas, Abolghasemian Mansour, Satia Imran, Deen M Jamal

2021-Mar-30

General

Comprehensive Comparative Genomic and Microsatellite Analysis of SARS, MERS, BAT-SARS and COVID-19 Coronaviruses.

In Journal of medical virology
The COVID-19 pandemic spread around the globe very rapidly. Previously, the evolution pattern and similarity among the COVID-19 causative organism SARS-CoV-2 and causative organisms of other similar infections have been determined using a single type of genetic marker in different studies. Herein, the SARS-CoV-2 and related beta coronaviruses MERS-CoV, SARS-CoV, BAT-CoV were comprehensively analyzed using a custom-built pipeline that employed the phylogenetic approaches based on multiple types of genetic markers including the whole genome sequences, mutations in nucleotide sequences, mutations in protein sequences, and microsatellites. The whole-genome sequence-based phylogeny revealed that the strains of SARS-CoV-2 are more similar to the BAT-CoV strains. The mutational analysis showed that on average MERS-CoV and BAT-CoV genomes differed at 134.21 and 136.72 sites respectively, whereas, SARS-CoV genome differed at 26.64 sites from the reference genome of SARS-CoV-2. Furthermore, the microsatellite analysis highlighted a relatively higher number of average microsatellites for MERS-CoV, and SARS-CoV-2 (106.8, 107 respectively), and a lower number for SARS-CoV, and BAT-CoV (95.8, and 98.5 respectively). Collectively, the analysis of multiple genetic markers of selected beta viral genomes revealed that the newly born SARS-COV-2 is closely related to BAT-CoV, whereas, MERS-CoV is more distinct from the SARS-CoV-2 than BAT-CoV and SARS-CoV. This article is protected by copyright. All rights reserved.
Rehman Hafiz Abdul, Ramzan Farheen, Basharat Zarrin, Shakeel Muhammad, Khan Muhammad Usman Ghani, Khan Ishtiaq Ahmad

2021-Mar-30

COVID-19, MERS, Pandemic, Phylogenetic, SARS, SARS-CoV-2

General

Can COVID-19 and environmental research in developing countries support these countries to meet the environmental challenges induced by the pandemic?

In Environmental science and pollution research international
Meeting the huge impact of COVID-19 on the environment requires better research on pandemic and pollution. What is the research capacity of the COVID-19 and environment in developing countries? Can this research capacity support developing countries to deal with the environmental challenges induced by the pandemic? This work is addressed to comprehensively assess the research capacity of the COVID-19 and environment in developing countries using bibliometric analysis techniques and content analysis approach to mining the Web of Science database. The results of data mining were unexpected: the global leader of the COVID-19 and environmental research was not these developed countries, but these developing countries so far, the end of 2020. Developing countries have published more papers on the pandemic and environment than developed countries, and developing countries also dominate pandemic and environmental research in terms of research institutions and authors. The results showed that (i) the impact of COVID-19 and the environment was bidirectional; (ii) energy consumption has posed great impact on environment; (iii) application of big data and artificial intelligence played an important role in improving environmental quality during the COVID-19 pandemic. Finally, policy recommendations such as formulating relevant policies and environmental standards, strengthening international exchanges and cooperation, and adjusting and improving energy consumption structure that were put forward for developing countries to meet the environmental challenges induced by the pandemic were offered. Graphical abstract.
Wang Qiang, Zhang Chen

2021-Mar-29

Bibliometric analysis, COVID-19, Content analysis, Developing countries, Environment

Radiology

Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study.

In NPJ digital medicine
Data privacy mechanisms are essential for rapidly scaling medical training databases to capture the heterogeneity of patient data distributions toward robust and generalizable machine learning systems. In the current COVID-19 pandemic, a major focus of artificial intelligence (AI) is interpreting chest CT, which can be readily used in the assessment and management of the disease. This paper demonstrates the feasibility of a federated learning method for detecting COVID-19 related CT abnormalities with external validation on patients from a multinational study. We recruited 132 patients from seven multinational different centers, with three internal hospitals from Hong Kong for training and testing, and four external, independent datasets from Mainland China and Germany, for validating model generalizability. We also conducted case studies on longitudinal scans for automated estimation of lesion burden for hospitalized COVID-19 patients. We explore the federated learning algorithms to develop a privacy-preserving AI model for COVID-19 medical image diagnosis with good generalization capability on unseen multinational datasets. Federated learning could provide an effective mechanism during pandemics to rapidly develop clinically useful AI across institutions and countries overcoming the burden of central aggregation of large amounts of sensitive data.
Dou Qi, So Tiffany Y, Jiang Meirui, Liu Quande, Vardhanabhuti Varut, Kaissis Georgios, Li Zeju, Si Weixin, Lee Heather H C, Yu Kevin, Feng Zuxin, Dong Li, Burian Egon, Jungmann Friederike, Braren Rickmer, Makowski Marcus, Kainz Bernhard, Rueckert Daniel, Glocker Ben, Yu Simon C H, Heng Pheng Ann

2021-Mar-29

General

Decrease in hospital admissions for respiratory diseases during the COVID-19 pandemic: a nationwide claims study.

In Thorax ; h5-index 75.0
Non-pharmaceutical interventions (NPIs) have been widely implemented to mitigate the spread of COVID-19. We assessed the effect of NPIs on hospitalisations for pneumonia, influenza, COPD and asthma. This retrospective, ecological study compared the weekly incidence of hospitalisation for four respiratory conditions before (January 2016-January 2020) and during (February-July 2020) the implementation of NPI against COVID-19. Hospitalisations for all four respiratory conditions decreased substantially during the intervention period. The cumulative incidence of admissions for COPD and asthma was 58% and 48% of the mean incidence during the 4 preceding years, respectively.
Huh Kyungmin, Kim Young-Eun, Ji Wonjun, Kim Dong Wook, Lee Eun-Joo, Kim Jong-Hun, Kang Ji-Man, Jung Jaehun

2021-Mar-29

COVID-19, respiratory infection

General

Extracting COVID-19 Diagnoses and Symptoms From Clinical Text: A New Annotated Corpus and Neural Event Extraction Framework.

In Journal of biomedical informatics ; h5-index 55.0
Coronavirus disease 2019 (COVID-19) is a global pandemic. Although much has been learned about the novel coronavirus since its emergence, there are many open questions related to tracking its spread, describing symptomology, predicting the severity of infection, and forecasting healthcare utilization. Free-text clinical notes contain critical information for resolving these questions. Data-driven, automatic information extraction models are needed to use this text-encoded information in large-scale studies. This work presents a new clinical corpus, referred to as the COVID-19 Annotated Clinical Text (CACT) Corpus, which comprises 1,472 notes with detailed annotations characterizing COVID-19 diagnoses, testing, and clinical presentation. We introduce a span-based event extraction model that jointly extracts all annotated phenomena, achieving high performance in identifying COVID-19 and symptom events with associated assertion values (0.83-0.97 F1 for events and 0.73-0.79 F1 for assertions). Our span-based event extraction model outperforms an extractor built on MetaMapLite for the identification of symptoms with assertion values. In a secondary use application, we predicted COVID-19 test results using structured patient data (e.g. vital signs and laboratory results) and automatically extracted symptom information, to explore the clinical presentation of COVID-19. Automatically extracted symptoms improve COVID-19 prediction performance, beyond structured data alone.
Lybarger Kevin, Ostendorf Mari, Thompson Matthew, Yetisgen Meliha

2021-Mar-26

COVID-19, coronavirus, information extraction, machine learning, natural language processing

General

Protein-ligand Docking Simulations with AutoDock4 Focused on the Main Protease of SARS-CoV-2.

In Current medicinal chemistry ; h5-index 49.0

BACKGROUND : The main protease of SARS-CoV-2 (Mpro) is one of the targets identified in SARS-CoV-2, the causative agent of COVID-19. The application of X-ray diffraction crystallography made available the three-dimensional structure of this protein target in complex with ligands, which paved the way for docking studies.

OBJECTIVE : Our goal here is to review recent efforts in the application of docking simulations to identify inhibitors of the Mpro using the program AutoDock4.

METHOD : We searched PubMed to identify studies that applied AutoDock4 for docking against this protein target. We used the structures available for Mpro to analyze intermolecular interactions and reviewed the methods used to search for inhibitors.

RESULTS : The application of docking against the structures available for the Mpro found ligands with an estimated inhibition in the nanomolar range. Such computational approaches focused on the crystal structures revealed potential inhibitors of Mpro that might exhibit pharmacological activity against SARS-CoV-2. Nevertheless, most of these studies lack the proper validation of the docking protocol. Also, they all ignored the potential use of machine learning to predict affinity.

CONCLUSION : The combination of structural data with computational approaches opened the possibility to accelerate the search for drugs to treat COVID-19. Several studies used AutoDock4 to search for inhibitors of Mpro. Most of them did not employ a validated docking protocol, which lends support to critics of their computational methodology. Furthermore, one of these studies reported the binding of chloroquine and hydroxychloroquine to Mpro. This study ignores the scientific evidence against the use of these antimalarial drugs to treat COVID-19.

de Azevedo Junior Walter Filgueira, Bitencourt-Ferreira Gabriela, Godoy Joana Retzke, Adriano Hilda Mayela Aran, Dos Santos Bezerra Wallyson André, Dos Santos Soares Alexandra Martins

2021-Mar-28

AutoDock4, COVID-19, SARS-CoV-2, docking, machine learning, main protease. , protein-ligand interaction

General

Mobility Functional Areas and COVID-19 Spread

ArXiv Preprint
This work introduces a new concept of functional areas called Mobility Functional Areas (MFAs), i.e., the geographic zones highly interconnected according to the analysis of mobile positioning data. The MFAs do not coincide necessarily with administrative borders as they are built observing natural human mobility and, therefore, they can be used to inform, in a bottom-up approach, local transportation, health and economic policies. After presenting the methodology behind the MFAs, this study focuses on the link between the COVID-19 pandemic and the MFAs in Austria. It emerges that the MFAs registered an average number of infections statistically larger than the areas in the rest of the country, suggesting the usefulness of the MFAs in the context of targeted re-escalation policy responses to this health crisis.
Stefano Maria Iacus, Carlos Santamaria, Francesco Sermi, Spyridon Spyratos, Dario Tarchi, Michele Vespe

2021-03-31

Radiology

Quantitative Burden of COVID-19 Pneumonia on Chest CT Predicts Adverse Outcomes: A Post-Hoc Analysis of a Prospective International Registry.

In Radiology. Cardiothoracic imaging

Purpose : To examine the independent and incremental value of CT-derived quantitative burden and attenuation of COVID-19 pneumonia for the prediction of clinical deterioration or death.

Methods : This was a retrospective analysis of a prospective international registry of consecutive patients with laboratory-confirmed COVID-19 and chest CT imaging, admitted to four centers between January 10 and May 6, 2020. Total burden (expressed as a percentage) and mean attenuation of ground glass opacities (GGO) and consolidation were quantified from CT using semi-automated research software. The primary outcome was clinical deterioration (intensive care unit admission, invasive mechanical ventilation, or vasopressor therapy) or in-hospital death. Logistic regression was performed to assess the predictive value of clinical and CT parameters for the primary outcome.

Results : The final population comprised 120 patients (mean age 64 ± 16 years, 78 men), of whom 39 (32.5%) experienced clinical deterioration or death. In multivariable regression of clinical and CT parameters, consolidation burden (odds ratio [OR], 3.4; 95% confidence interval [CI]: 1.7, 6.9 per doubling; P = .001) and increasing GGO attenuation (OR, 3.2; 95% CI: 1.3, 8.3 per standard deviation, P = .02) were independent predictors of deterioration or death; as was C-reactive protein (OR, 2.1; 95% CI: 1.3, 3.4 per doubling; P = .004), history of heart failure (OR 1.3; 95% CI: 1.1, 1.6, P = .01), and chronic lung disease (OR, 1.3; 95% CI: 1.0, 1.6; P = .02). Quantitative CT measures added incremental predictive value beyond a model with only clinical parameters (area under the curve, 0.93 vs 0.82, P = .006). The optimal prognostic cutoffs for burden of COVID-19 pneumonia as determined by Youden's index were consolidation of greater than or equal to 1.8% and GGO of greater than or equal to 13.5%.

Conclusions : Quantitative burden of consolidation or GGO on chest CT independently predict clinical deterioration or death in patients with COVID-19 pneumonia. CT-derived measures have incremental prognostic value over and above clinical parameters, and may be useful for risk stratifying patients with COVID-19.

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

2020-Oct

Radiology

Serial Quantitative Chest CT Assessment of COVID-19: A Deep Learning Approach.

In Radiology. Cardiothoracic imaging

Purpose : To quantitatively evaluate lung burden changes in patients with coronavirus disease 2019 (COVID-19) by using serial CT scan by an automated deep learning method.

Materials and Methods : Patients with COVID-19, who underwent chest CT between January 1 and February 3, 2020, were retrospectively evaluated. The patients were divided into mild, moderate, severe, and critical types, according to their baseline clinical, laboratory, and CT findings. CT lung opacification percentages of the whole lung and five lobes were automatically quantified by a commercial deep learning software and compared with those at follow-up CT scans. Longitudinal changes of the CT quantitative parameter were also compared among the four clinical types.

Results : A total of 126 patients with COVID-19 (mean age, 52 years ± 15 [standard deviation]; 53.2% males) were evaluated, including six mild, 94 moderate, 20 severe, and six critical cases. CT-derived opacification percentage was significantly different among clinical groups at baseline, gradually progressing from mild to critical type (all P < .01). Overall, the whole-lung opacification percentage significantly increased from baseline CT to first follow-up CT (median [interquartile range]: 3.6% [0.5%, 12.1%] vs 8.7% [2.7%, 21.2%]; P < .01). No significant progression of the opacification percentages was noted from the first follow-up to second follow-up CT (8.7% [2.7%, 21.2%] vs 6.0% [1.9%, 24.3%]; P = .655).

Conclusion : The quantification of lung opacification in COVID-19 measured at chest CT by using a commercially available deep learning-based tool was significantly different among groups with different clinical severity. This approach could potentially eliminate the subjectivity in the initial assessment and follow-up of pulmonary findings in COVID-19.Supplemental material is available for this article.© RSNA, 2020.

Huang Lu, Han Rui, Ai Tao, Yu Pengxin, Kang Han, Tao Qian, Xia Liming

2020-Apr

Radiology

Imaging Profile of the COVID-19 Infection: Radiologic Findings and Literature Review.

In Radiology. Cardiothoracic imaging

Purpose : To present the findings of 21 coronavirus disease 2019 (COVID-19) cases from two Chinese centers with CT and chest radiographic findings, as well as follow-up imaging in five cases.

Materials and Methods : This was a retrospective study in Shenzhen and Hong Kong. Patients with COVID-19 infection were included. A systematic review of the published literature on radiologic features of COVID-19 infection was conducted.

Results : The predominant imaging pattern was of ground-glass opacification with occasional consolidation in the peripheries. Pleural effusions and lymphadenopathy were absent in all cases. Patients demonstrated evolution of the ground-glass opacities into consolidation and subsequent resolution of the airspace changes. Ground-glass and consolidative opacities visible on CT are sometimes undetectable on chest radiography, suggesting that CT is a more sensitive imaging modality for investigation. The systematic review identified four other studies confirming the findings of bilateral and peripheral ground glass with or without consolidation as the predominant finding at CT chest examinations.

Conclusion : Pulmonary manifestation of COVID-19 infection is predominantly characterized by ground-glass opacification with occasional consolidation on CT. Radiographic findings in patients presenting in Shenzhen and Hong Kong are in keeping with four previous publications from other sites.© RSNA, 2020See editorial by Kay and Abbara in this issue.

Ng Ming-Yen, Lee Elaine Y P, Yang Jin, Yang Fangfang, Li Xia, Wang Hongxia, Lui Macy Mei-Sze, Lo Christine Shing-Yen, Leung Barry, Khong Pek-Lan, Hui Christopher Kim-Ming, Yuen Kwok-Yung, Kuo Michael D

2020-Feb

General

Exercise and Use of Enhancement Drugs at the Time of the COVID-19 Pandemic: A Multicultural Study on Coping Strategies During Self-Isolation and Related Risks.

In Frontiers in psychiatry
Introduction: Little is known about the impact of restrictive measures during the COVID-19 pandemic on self-image and engagement in exercise and other coping strategies alongside the use of image and performance-enhancing drugs (IPEDs) to boost performance and appearance. Objectives: To assess the role of anxiety about appearance and self-compassion on the practice of physical exercise and use of IPEDs during lockdown. Methods: An international online questionnaire was carried out using the Exercise Addiction Inventory (EAI), the Appearance Anxiety Inventory (AAI), and the Self-Compassion Scale (SCS) in addition to questions on the use of IPEDs. Results: The sample consisted of 3,161 (65% female) adults from Italy (41.1%), Spain (15.7%), the United Kingdom (UK) (12.0%), Lithuania (11.6%), Portugal (10.5%), Japan (5.5%), and Hungary (3.5%). The mean age was 35.05 years (SD = 12.10). Overall, 4.3% of the participants were found to engage in excessive or problematic exercise with peaks registered in the UK (11.0%) and Spain (5.4%). The sample reported the use of a wide range of drugs and medicines to boost image and performance (28%) and maintained use during the lockdown, mostly in Hungary (56.6%), Japan (46.8%), and the UK (33.8%), with 6.4% who started to use a new drug. Significant appearance anxiety levels were found across the sample, with 18.1% in Italy, 16.9% in Japan, and 16.7% in Portugal. Logistic regression models revealed a strong association between physical exercise and IPED use. Anxiety about appearance also significantly increased the probability of using IPEDs. However, self-compassion did not significantly predict such behavior. Anxiety about appearance and self-compassion were non-significant predictors associated with engaging in physical exercise. Discussion and Conclusion: This study identified risks of problematic exercising and appearance anxiety among the general population during the COVID-19 lockdown period across all the participating countries with significant gender differences. Such behaviors were positively associated with the unsupervised use of IPEDs, although no interaction between physical exercise and appearance anxiety was observed. Further considerations are needed to explore the impact of socially restrictive measures among vulnerable groups, and the implementation of more targeted responses.
Dores Artemisa R, Carvalho Irene P, Burkauskas Julius, Simonato Pierluigi, De Luca Ilaria, Mooney Roisin, Ioannidis Konstantinos, Gómez-Martínez M Ángeles, Demetrovics Zsolt, Ábel Krisztina Edina, Szabo Attila, Fujiwara Hironobu, Shibata Mami, Ventola Alejandra Rebeca Melero, Arroyo-Anlló Eva Maria, Santos-Labrador Ricardo M, Griskova-Bulanova Inga, Pranckeviciene Aiste, Kobayashi Kei, Martinotti Giovanni, Fineberg Naomi A, Barbosa Fernando, Corazza Ornella

2021

body dysmorphic disorders, body image, compulsive exercise, obsessive-compulsive disorder, performance-enhancing substances

General

Correction to: Diagnosis and combating COVID-19 using wearable Oura smart ring with deep learning methods.

In Personal and ubiquitous computing
[This corrects the article DOI: 10.1007/s00779-021-01541-4.].
Poongodi M, Hamdi Mounir, Malviya Mohit, Sharma Ashutosh, Dhiman Gaurav, Vimal S

2021-Mar-23

General

Federated learning for COVID-19 screening from Chest X-ray images.

In Applied soft computing
Today, the whole world is facing a great medical disaster that affects the health and lives of the people: the COVID-19 disease, colloquially known as the Corona virus. Deep learning is an effective means to assist radiologists to analyze the vast amount of chest X-ray images, which can potentially have a substantial role in streamlining and accelerating the diagnosis of COVID-19. Such techniques involve large datasets for training and all such data must be centralized in order to be processed. Due to medical data privacy regulations, it is often not possible to collect and share patient data in a centralized data server. In this work, we present a collaborative federated learning framework allowing multiple medical institutions screening COVID-19 from Chest X-ray images using deep learning without sharing patient data. We investigate several key properties and specificities of federated learning setting including the not independent and identically distributed (non-IID) and unbalanced data distributions that naturally arise. We experimentally demonstrate that the proposed federated learning framework provides competitive results to that of models trained by sharing data, considering two different model architectures. These findings would encourage medical institutions to adopt collaborative process and reap benefits of the rich private data in order to rapidly build a powerful model for COVID-19 screening.
Feki Ines, Ammar Sourour, Kessentini Yousri, Muhammad Khan

2021-Jul

CNN, COVID-19 screening, Decentralized training, Deep learning, Federated learning, X-ray images

General

Combat COVID-19 infodemic using explainable natural language processing models.

In Information processing & management
Misinformation of COVID-19 is prevalent on social media as the pandemic unfolds, and the associated risks are extremely high. Thus, it is critical to detect and combat such misinformation. Recently, deep learning models using natural language processing techniques, such as BERT (Bidirectional Encoder Representations from Transformers), have achieved great successes in detecting misinformation. In this paper, we proposed an explainable natural language processing model based on DistilBERT and SHAP (Shapley Additive exPlanations) to combat misinformation about COVID-19 due to their efficiency and effectiveness. First, we collected a dataset of 984 claims about COVID-19 with fact-checking. By augmenting the data using back-translation, we doubled the sample size of the dataset and the DistilBERT model was able to obtain good performance (accuracy: 0.972; areas under the curve: 0.993) in detecting misinformation about COVID-19. Our model was also tested on a larger dataset for AAAI2021 - COVID-19 Fake News Detection Shared Task and obtained good performance (accuracy: 0.938; areas under the curve: 0.985). The performance on both datasets was better than traditional machine learning models. Second, in order to boost public trust in model prediction, we employed SHAP to improve model explainability, which was further evaluated using a between-subjects experiment with three conditions, i.e., text (T), text+SHAP explanation (TSE), and text+SHAP explanation+source and evidence (TSESE). The participants were significantly more likely to trust and share information related to COVID-19 in the TSE and TSESE conditions than in the T condition. Our results provided good implications for detecting misinformation about COVID-19 and improving public trust.
Ayoub Jackie, Yang X Jessie, Zhou Feng

2021-Jul

BERT, COVID-19, DistilBERT, Misinformation detection, SHAP, Trust

General

Topic evolution, disruption and resilience in early COVID-19 research.

In Scientometrics
The COVID-19 pandemic presented a challenge to the global research community as scientists rushed to find solutions to the devastating crisis. Drawing expectations from resilience theory, this paper explores how the trajectory of and research community around the coronavirus research was affected by the COVID-19 pandemic. Characterizing epistemic clusters and pathways of knowledge through extracting terms featured in articles in early COVID-19 research, combined with evolutionary pathways and statistical analysis, the results reveal that the pandemic disrupted existing lines of coronavirus research to a large degree. While some communities of coronavirus research are similar pre- and during COVID-19, topics themselves change significantly and there is less cohesion amongst early COVID-19 research compared to that before the pandemic. We find that some lines of research revert to basic research pursued almost a decade earlier, whilst others pursue brand new trajectories. The epidemiology topic is the most resilient among the many subjects related to COVID-19 research. Chinese researchers in particular appear to be driving more novel research approaches in the early months of the pandemic. The findings raise questions about whether shifts are advantageous for global scientific progress, and whether the research community will return to the original equilibrium or reorganize into a different knowledge configuration.
Zhang Yi, Cai Xiaojing, Fry Caroline V, Wu Mengjia, Wagner Caroline S

2021-Mar-20

COVID-19, International collaboration., Research and development, Science, Topic analysis

General

Environmental Survival of SARS-CoV-2 - A solid waste perspective.

In Environmental research ; h5-index 67.0
The advent of COVID-19 has kept the whole world on their toes. Countries are maximizing their efforts to combat the virus and to minimize the infection. Since infectious microorganisms may be transmitted by variety of routes, respiratory and facial protection is required for those that are usually transmitted via droplets/aerosols. Therefore this pandemic has caused a sudden increase in the demand for personal protective equipment (PPE) such as gloves, masks, and many other important items since, the evidence of individual-to-individual transmission (through respiratory droplets/coughing) and secondary infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). But the disposal of these personal protective measures remains a huge question mark towards the environmental impact. Huge waste generation demands proper segregation according to waste types, collection, and recycling to minimize the risk of infection spread through aerosols and attempts to implement measures to monitor infections. Hence, this review focuses on the impact of environment due to improper disposal of these personal protective measures and to investigate the safe disposal methods for these protective measures by using the safe, secure and innovative biological methods such as the use of Artificial Intelligence (AI) and Ultraviolet (UV) lights for killing such deadly viruses.
Iyer Mahalaxmi, Tiwari Sushmita, Renu Kaviyarasi, Pasha Md Younus, Pandit Shraddha, Singh Bhupender, Raj Neethu, Saikrishna Krothapalli, Kwak Hee Jeong, Balasubramanian Venkatesh, Jang Soo Bin, Dileep Kumar G, Anand Uttpal, Narayanasamy Arul, Kinoshita Masako, Subramaniam Mohana Devi, Kumar Nachimuthu Senthil, Roy Ayan, Gopalakrishnan Abilash Valsala, Parthasarathi Ramakrishnan, Cho Ssang-Goo, Vellingiri Balachandar

2021-Mar-25

Artificial intelligence, Biomedical waste, Biomedical waste management, COVID-19, Environmental damage, Personnel protective equipment (PPE)

General

Deep Learning and Machine Vision for Food Processing: A Survey

ArXiv Preprint
The quality and safety of food is an important issue to the whole society, since it is at the basis of human health, social development and stability. Ensuring food quality and safety is a complex process, and all stages of food processing must be considered, from cultivating, harvesting and storage to preparation and consumption. However, these processes are often labour-intensive. Nowadays, the development of machine vision can greatly assist researchers and industries in improving the efficiency of food processing. As a result, machine vision has been widely used in all aspects of food processing. At the same time, image processing is an important component of machine vision. Image processing can take advantage of machine learning and deep learning models to effectively identify the type and quality of food. Subsequently, follow-up design in the machine vision system can address tasks such as food grading, detecting locations of defective spots or foreign objects, and removing impurities. In this paper, we provide an overview on the traditional machine learning and deep learning methods, as well as the machine vision techniques that can be applied to the field of food processing. We present the current approaches and challenges, and the future trends.
Lili Zhu, Petros Spachos, Erica Pensini, Konstantinos Plataniotis

2021-03-30

General

Deep learning for diagnosis of COVID-19 using 3D CT scans.

In Computers in biology and medicine
A new pneumonia-type coronavirus, COVID-19, recently emerged in Wuhan, China. COVID-19 has subsequently infected many people and caused many deaths worldwide. Isolating infected people is one of the methods of preventing the spread of this virus. CT scans provide detailed imaging of the lungs and assist radiologists in diagnosing COVID-19 in hospitals. However, a person's CT scan contains hundreds of slides, and the diagnosis of COVID-19 using such scans can lead to delays in hospitals. Artificial intelligence techniques could assist radiologists with rapidly and accurately detecting COVID-19 infection from these scans. This paper proposes an artificial intelligence (AI) approach to classify COVID-19 and normal CT volumes. The proposed AI method uses the ResNet-50 deep learning model to predict COVID-19 on each CT image of a 3D CT scan. Then, this AI method fuses image-level predictions to diagnose COVID-19 on a 3D CT volume. We show that the proposed deep learning model provides 96% AUC value for detecting COVID-19 on CT scans.
Serte Sertan, Demirel Hasan

2021-Mar-10

COVID-19, CT image, CT scan, Convolutional neural networks, Deep learning, Fusion

Public Health

Pandemic velocity: Forecasting COVID-19 in the US with a machine learning & Bayesian time series compartmental model.

In PLoS computational biology
Predictions of COVID-19 case growth and mortality are critical to the decisions of political leaders, businesses, and individuals grappling with the pandemic. This predictive task is challenging due to the novelty of the virus, limited data, and dynamic political and societal responses. We embed a Bayesian time series model and a random forest algorithm within an epidemiological compartmental model for empirically grounded COVID-19 predictions. The Bayesian case model fits a location-specific curve to the velocity (first derivative) of the log transformed cumulative case count, borrowing strength across geographic locations and incorporating prior information to obtain a posterior distribution for case trajectories. The compartmental model uses this distribution and predicts deaths using a random forest algorithm trained on COVID-19 data and population-level characteristics, yielding daily projections and interval estimates for cases and deaths in U.S. states. We evaluated the model by training it on progressively longer periods of the pandemic and computing its predictive accuracy over 21-day forecasts. The substantial variation in predicted trajectories and associated uncertainty between states is illustrated by comparing three unique locations: New York, Colorado, and West Virginia. The sophistication and accuracy of this COVID-19 model offer reliable predictions and uncertainty estimates for the current trajectory of the pandemic in the U.S. and provide a platform for future predictions as shifting political and societal responses alter its course.
Watson Gregory L, Xiong Di, Zhang Lu, Zoller Joseph A, Shamshoian John, Sundin Phillip, Bufford Teresa, Rimoin Anne W, Suchard Marc A, Ramirez Christina M

2021-Mar-29

Public Health

On realized serial and generation intervals given control measures: The COVID-19 pandemic case.

In PLoS computational biology
The SARS-CoV-2 pathogen is currently spreading worldwide and its propensity for presymptomatic and asymptomatic transmission makes it difficult to control. The control measures adopted in several countries aim at isolating individuals once diagnosed, limiting their social interactions and consequently their transmission probability. These interventions, which have a strong impact on the disease dynamics, can affect the inference of the epidemiological quantities. We first present a theoretical explanation of the effect caused by non-pharmaceutical intervention measures on the mean serial and generation intervals. Then, in a simulation study, we vary the assumed efficacy of control measures and quantify the effect on the mean and variance of realized generation and serial intervals. The simulation results show that the realized serial and generation intervals both depend on control measures and their values contract according to the efficacy of the intervention strategies. Interestingly, the mean serial interval differs from the mean generation time. The deviation between these two values depends on two factors. First, the number of undiagnosed infectious individuals. Second, the relationship between infectiousness, symptom onset and timing of isolation. Similarly, the standard deviations of realized serial and generation intervals do not coincide, with the former shorter than the latter on average. The findings of this study are directly relevant to estimates performed for the current COVID-19 pandemic. In particular, the effective reproduction number is often inferred using both daily incidence data and the generation interval. Failing to account for either contraction or mis-specification by using the serial interval could lead to biased estimates of the effective reproduction number. Consequently, this might affect the choices made by decision makers when deciding which control measures to apply based on the value of the quantity thereof.
Torneri Andrea, Libin Peter, Scalia Tomba Gianpaolo, Faes Christel, Wood James G, Hens Niel

2021-Mar-29

General

Digital Mental Health Challenges and the Horizon Ahead for Solutions.

In JMIR mental health
The demand outstripping supply of mental health resources during the COVID-19 pandemic presents opportunities for digital technology tools to fill this new gap and, in the process, demonstrate capabilities to increase their effectiveness and efficiency. However, technology-enabled services have faced challenges in being sustainably implemented despite showing promising outcomes in efficacy trials since the early 2000s. The ongoing failure of these implementations has been addressed in reconceptualized models and frameworks, along with various efforts to branch out among disparate developers and clinical researchers to provide them with a key for furthering evaluative research. However, the limitations of traditional research methods in dealing with the complexities of mental health care warrant a diversified approach. The crux of the challenges of digital mental health implementation is the efficacy and evaluation of existing studies. Web-based interventions are increasingly used during the pandemic, allowing for affordable access to psychological therapies. However, a lagging infrastructure and skill base has limited the application of digital solutions in mental health care. Methodologies need to be converged owing to the rapid development of digital technologies that have outpaced the evaluation of rigorous digital mental health interventions and strategies to prevent mental illness. The functions and implications of human-computer interaction require a better understanding to overcome engagement barriers, especially with predictive technologies. Explainable artificial intelligence is being incorporated into digital mental health implementation to obtain positive and responsible outcomes. Investment in digital platforms and associated apps for real-time screening, tracking, and treatment offer the promise of cost-effectiveness in vulnerable populations. Although machine learning has been limited by study conduct and reporting methods, the increasing use of unstructured data has strengthened its potential. Early evidence suggests that the advantages outweigh the disadvantages of incrementing such technology. The limitations of an evidence-based approach require better integration of decision support tools to guide policymakers with digital mental health implementation. There is a complex range of issues with effectiveness, equity, access, and ethics (eg, privacy, confidentiality, fairness, transparency, reproducibility, and accountability), which warrant resolution. Evidence-informed policies, development of eminent digital products and services, and skills to use and maintain these solutions are required. Studies need to focus on developing digital platforms with explainable artificial intelligence-based apps to enhance resilience and guide the treatment decisions of mental health practitioners. Investments in digital mental health should ensure their safety and workability. End users should encourage the use of innovative methods to encourage developers to effectively evaluate their products and services and to render them a worthwhile investment. Technology-enabled services in a hybrid model of care are most likely to be effective (eg, specialists using these services among vulnerable, at-risk populations but not severe cases of mental ill health).
Balcombe Luke, De Leo Diego

2021-Mar-29

COVID-19, challenges, digital mental health implementation, explainable artificial intelligence, human-computer interaction, hybrid model of care, resilience, technology

Public Health

Predicting Patient COVID-19 Disease Severity by means of Statistical and Machine Learning Analysis of Clinical Blood Testing Data.

In JMIR medical informatics ; h5-index 23.0

BACKGROUND : An accurate prediction of COVID-19 patient disease severity would greatly improve care delivery and resource allocation, and thereby reduce mortality risks, especially in less developed countries. There are many patient-related factors, such as pre-existing comorbidities that affect disease severity that could be used to aid prediction.

OBJECTIVE : Since rapid automated profiling of peripheral blood samples is widely available, we investigated how such data from the peripheral blood of COVID-19 patients might be used to predict clinical outcomes.

METHODS : We thus investigated such clinical datasets from COVID-19 patients with known outcomes by combining statistical comparison and correlation methods with machine learning algorithms; the latter included decision tree, random forest, variants of gradient boosting machine, support vector machine, K-nearest neighbour and deep learning methods.

RESULTS : Our work revealed several clinical parameters measurable in blood samples as factors that can discriminate between healthy people and COVID-19 positive patients, and showed their value in predicting later severity of COVID-19 symptoms. We thus developed a number of analytical methods that showed accuracy and precision scores for disease severity predictions as above 90%.

CONCLUSIONS : We developed methodologies to analyse patient routine clinical data which enables more accurate prediction of COVID-19 patient outcomes. This type of approach could, by employing standard hospital laboratory analyses of patient blood, be utilised to identify COVID-19 patients at high risk of mortality and so enable optimised hospital facility for COVID-19 treatment.

CLINICALTRIAL :

Aktar Sakifa, Ahamad Md Martuza, Rashed-Al-Mahfuz Md, Azad Akm, Uddin Shahadat, Kamal A H M, Alyami Salem A, Lin Ping-I, Islam Sheikh Mohammed Shariful, Quinn Julian M W, Eapen Valsamma, Moni Mohammad Ali

2021-Mar-21

General

Why human factors science is demonstrably necessary: Historical and evolutionary foundations.

In Ergonomics
We review the theoretical foundation for the need for human factors science. Over the past 2.8 million years, humans and tools have co-evolved. However, in the last century, technology is introduced at a rate that exceeds human evolution. The proliferation of computers and, more recently, robots, introduces new cognitive demands, as the human is required to be a monitor rather than a direct controller. The usage of robots and artificial intelligence is only expected to increase, and the present COVID-19 pandemic may prove to be catalytic in this regard. One way to improve overall system performance is to 'adapt the human to the machine' via task procedures, operator training, operator selection, a Procrustean mandate. Using classic research examples, we demonstrate that Procrustean methods can improve performance only to a limited extent. For a viable future, therefore, technology must adapt to the human, which underwrites the necessity of human factors science.Practioner summary: Various research articles have reported that the science of Human Factors is of vital importance in improving human-machine systems. However, what is lacking is a fundamental historical outline of why Human Factors is important. This article provides such a foundation, using arguments ranging from pre-history to post-COVID.
de Winter J C F, Hancock P A

2021-Mar-29

Public Health

Dynamics of SARS-CoV-2 neutralising antibody responses and duration of immunity: a longitudinal study.

In The Lancet. Microbe

Background : Studies have found different waning rates of neutralising antibodies compared with binding antibodies against SARS-CoV-2. The impact of neutralising antibody waning rate at the individual patient level on the longevity of immunity remains unknown. We aimed to investigate the peak levels and dynamics of neutralising antibody waning and IgG avidity maturation over time, and correlate this with clinical parameters, cytokines, and T-cell responses.

Methods : We did a longitudinal study of patients who had recovered from COVID-19 up to day 180 post-symptom onset by monitoring changes in neutralising antibody levels using a previously validated surrogate virus neutralisation test. Changes in antibody avidities and other immune markers at different convalescent stages were determined and correlated with clinical features. Using a machine learning algorithm, temporal change in neutralising antibody levels was classified into five groups and used to predict the longevity of neutralising antibody-mediated immunity.

Findings : We approached 517 patients for participation in the study, of whom 288 consented for outpatient follow-up and collection of serial blood samples. 164 patients were followed up and had adequate blood samples collected for analysis, with a total of 546 serum samples collected, including 128 blood samples taken up to 180 days post-symptom onset. We identified five distinctive patterns of neutralising antibody dynamics as follows: negative, individuals who did not, at our intervals of sampling, develop neutralising antibodies at the 30% inhibition level (19 [12%] of 164 patients); rapid waning, individuals who had varying levels of neutralising antibodies from around 20 days after symptom onset, but seroreverted in less than 180 days (44 [27%] of 164 patients); slow waning, individuals who remained neutralising antibody-positive at 180 days post-symptom onset (52 [29%] of 164 patients); persistent, although with varying peak neutralising antibody levels, these individuals had minimal neutralising antibody decay (52 [32%] of 164 patients); and delayed response, a small group that showed an unexpected increase of neutralising antibodies during late convalescence (at 90 or 180 days after symptom onset; three [2%] of 164 patients). Persistence of neutralising antibodies was associated with disease severity and sustained level of pro-inflammatory cytokines, chemokines, and growth factors. By contrast, T-cell responses were similar among the different neutralising antibody dynamics groups. On the basis of the different decay dynamics, we established a prediction algorithm that revealed a wide range of neutralising antibody longevity, varying from around 40 days to many decades.

Interpretation : Neutralising antibody response dynamics in patients who have recovered from COVID-19 vary greatly, and prediction of immune longevity can only be accurately determined at the individual level. Our findings emphasise the importance of public health and social measures in the ongoing pandemic outbreak response, and might have implications for longevity of immunity after vaccination.

Funding : National Medical Research Council, Biomedical Research Council, and ASTAR, Singapore.

Chia Wan Ni, Zhu Feng, Ong Sean Wei Xiang, Young Barnaby Edward, Fong Siew-Wai, Le Bert Nina, Tan Chee Wah, Tiu Charles, Zhang Jinyan, Tan Seow Yen, Pada Surinder, Chan Yi-Hao, Tham Christine Y L, Kunasegaran Kamini, Chen Mark I-C, Low Jenny G H, Leo Yee-Sin, Renia Laurent, Bertoletti Antonio, Ng Lisa F P, Lye David Chien, Wang Lin-Fa

2021-Mar-23*

Radiology

Natural Language Processing and Machine Learning for Detection of Respiratory Illness by Chest CT Imaging and Tracking of COVID-19 Pandemic in the US.

In Radiology. Cardiothoracic imaging

Background : Coronavirus disease 2019 (COVID-19) has spread quickly throughout the United States (US) causing significant disruption in healthcare and society. Tools to identify hot spots are important for public health planning. The goal of our study was to determine if natural language processing (NLP) algorithm assessment of thoracic computed tomography (CT) imaging reports correlated with the incidence of official COVID-19 cases in the US.

Methods : Using de-identified HIPAA compliant patient data from our common imaging platform interconnected with over 2,100 facilities covering all 50 states, we developed three NLP algorithms to track positive CT imaging features of respiratory illness typical in SARS-CoV-2 viral infection. We compared our findings against the number of official COVID-19 daily, weekly and state-wide.

Results : The NLP algorithms were applied to 450,114 patient chest CT comprehensive reports gathered from January 1^st to October 3^rd, 2020. The best performing NLP model exhibited strong correlation with daily official COVID-19 cases (r²=0.82, p<0.005). The NLP models demonstrated an early rise in cases followed by the increase of official cases, suggesting the possibility of an early predictive marker, with strong correlation to official cases on a weekly basis (r²=0.91, p<0.005). There was also substantial correlation between the NLP and official COVID-19 incidence by state (r²=0.92, p<0.005).

Conclusion : Using big data, we developed a novel machine-learning based NLP algorithm that can track imaging findings of respiratory illness detected on chest CT imaging reports with strong correlation with the progression of the COVID-19 pandemic in the US.

Cury Ricardo C, Megyeri Istvan, Lindsey Tony, Macedo Robson, Batlle Juan, Kim Shwan, Baker Brian, Harris Robert, Clark Reese H

2021-Feb

big data, chest CT, computed tomography, machine learning, natural language processing, public health, viral outbreak

General

A deep learning framework for real-time detection of novel pathogens during sequencing

bioRxiv Preprint
Motivation: Novel pathogens evolve quickly and may emerge rapidly, causing dangerous outbreaks or even global pandemics. Next-generation sequencing is the state-of-the art in open-view pathogen detection, and one of the few methods available at the earliest stages of an epidemic, even when the biological threat is unknown. Analyzing the samples as the sequencer is running can greatly reduce the turnaround time, but existing tools rely on close matches to lists of known pathogens and perform poorly on novel species. Machine learning approaches can predict if single reads originate from more distant, unknown pathogens, but require relatively long input sequences and processed data from a finished sequencing run. Results: We present DeePaC-Live, a Python package for real-time pathogenic potential prediction directly from incomplete sequencing reads. We train deep neural networks to classify Illumina and Nanopore reads and integrate our models with HiLive2, a real-time Illumina mapper. DeePaC-Live outperforms alternatives based on machine learning and sequence alignment on simulated and real data, including SARS-CoV-2 sequencing runs. After just 50 Illumina cycles, we increase the true positive rate 80-fold compared to the live-mapping approach. The first 250bp of Nanopore reads, corresponding to 0.5s of sequencing time, are enough to yield predictions more accurate than mapping the finished long reads. Our approach could also be used for screening synthetic sequences against biosecurity threats. Availability: The code is available at: https://gitlab.com/dacs-hpi/deepac-live and https://gitlab.com/dacs-hpi/deepac. The package can be installed with Bioconda, Docker or pip.
Bartoszewicz, J. M.; Genske, U.; Renard, B. Y.

2021-03-29

General

Machine Learning Research Towards Combating COVID-19: Virus Detection, Spread Prevention, and Medical Assistance.

In Journal of biomedical informatics ; h5-index 55.0
COVID-19 was first discovered in December 2019 and has continued to rapidly spread across countries worldwide infecting thousands and millions of people. The virus is deadly, and people who are suffering from prior illnesses or are older than the age of 60 are at a higher risk of mortality. Medicine and Healthcare industries have surged towards finding a cure, and different policies have been amended to mitigate the spread of the virus. While Machine Learning (ML) methods have been widely used in other domains, there is now a high demand for ML-aided diagnosis systems for screening, tracking, predicting the spread of COVID-19 and finding a cure against it. In this paper, we present a journey of what role ML has played so far in combating the virus, mainly looking at it from a screening, forecasting, and vaccine perspective. We present a comprehensive survey of the ML algorithms and models that can be used on this expedition and aid with battling the virus.
Shahid Osama, Nasajpour Mohammad, Pouriyeh Seyedamin, Parizi Reza M, Han Meng, Valero Maria, Li Fangyu, Aledhari Mohammed, Sheng Quan Z

2021-Mar-23

Artificial Intelligence, COVID-19, Drug Development, Healthcare, Machine Learning, Predictive Analysis

General

COVID-19 in CXR: from Detection and Severity Scoring to Patient Disease Monitoring.

In IEEE journal of biomedical and health informatics
This work estimates the severity of pneumonia in COVID-19 patients and reports the findings of a longitudinal study of disease progression. It presents a deep learning model for simultaneous detection and localization of pneumonia in chest Xray (CXR) images, which is shown to generalize to COVID-19 pneumonia. The localization maps are utilized to calculate a "Pneumonia Ratio" which indicates disease severity. The assessment of disease severity serves to build a temporal disease extent profile for hospitalized patients. To validate the model's applicability to the patient monitoring task, we developed a validation strategy which involves a synthesis of Digital Reconstructed Radiographs (DRRs - synthetic Xray) from serial CT scans; we then compared the disease progression profiles that were generated from the DRRs to those that were generated from CT volumes.
Fridadar Maayan, Amer Rula, Gozes Ophir, Nassar Jannette, Greenspan Hayit

2021-Mar-26

General

A Survey on Mathematical, Machine Learning and Deep Learning Models for COVID-19 Transmission and Diagnosis.

In IEEE reviews in biomedical engineering
COVID-19 is a life threatening disease which has a enormous global impact. As the cause of the disease is a novel coronavirus whose gene information is unknown, drugs and vaccines are yet to be found. For the present situation, disease spread analysis and prediction with the help of mathematical and data driven model will be of great help to initiate prevention and control action, namely lockdown and qurantine. There are various mathematical and machine-learning models proposed for analyzing the spread and prediction. Each model has its own limitations and advantages for a particluar scenario. This article reviews the state-of-the art mathematical models for COVID-19, including compartment models, statistical models and machine learning models to provide more insight, so that an appropriate model can be well adopted for the disease spread analysis. Furthermore, accurate diagnose of COVID-19 is another essential process to identify the infected person and control further spreading. As the spreading is fast, there is a need for quick auotomated diagnosis mechanism to handle large population. Deep-learning and machine-learning based diagnostic mechanism will be more appropriate for this purpose. In this aspect, a comprehensive review on the deep learning models for the diagnosis of the disease is also provided in this article.
John Christopher Clement, Ponnusamy Vijayakumar, Krishnan Chandrasekaran Sriharipriya, R Nandakumar

2021-Mar-26

General

Relational Learning Improves Prediction of Mortality in COVID-19 in the Intensive Care Unit.

In IEEE transactions on big data
Traditional Machine Learning (ML) models have had limited success in predicting Coronoavirus-19 (COVID-19) outcomes using Electronic Health Record (EHR) data partially due to not effectively capturing the inter-connectivity patterns between various data modalities. In this work, we propose a novel framework that utilizes relational learning based on a heterogeneous graph model (HGM) for predicting mortality at different time windows in COVID-19 patients within the intensive care unit (ICU). We utilize the EHRs of one of the largest and most diverse patient populations across five hospitals in major health system in New York City. In our model, we use an LSTM for processing time varying patient data and apply our proposed relational learning strategy in the final output layer along with other static features. Here, we replace the traditional softmax layer with a Skip-Gram relational learning strategy to compare the similarity between a patient and outcome embedding representation. We demonstrate that the construction of a HGM can robustly learn the patterns classifying patient representations of outcomes through leveraging patterns within the embeddings of similar patients. Our experimental results show that our relational learning-based HGM model achieves higher area under the receiver operating characteristic curve (auROC) than both comparator models in all prediction time windows, with dramatic improvements to recall.
Wanyan Tingyi, Vaid Akhil, De Freitas Jessica K, Somani Sulaiman, Miotto Riccardo, Nadkarni Girish N, Azad Ariful, Ding Ying, Glicksberg Benjamin S

2021-Mar

COVID-19, Electronic health records, ICU, LSTM, deep learning, embeddings, heterogeneous graph model, machine learning, mortality, relational learning

Radiology

Generalized chest CT and lab curves throughout the course of COVID-19.

In Scientific reports ; h5-index 158.0
A better understanding of temporal relationships between chest CT and labs may provide a reference for disease severity over the disease course. Generalized curves of lung opacity volume and density over time can be used as standardized references from well before symptoms develop to over a month after recovery, when residual lung opacities remain. 739 patients with COVID-19 underwent CT and RT-PCR in an outbreak setting between January 21st and April 12th, 2020. 29 of 739 patients had serial exams (121 CTs and 279 laboratory measurements) over 50 ± 16 days, with an average of 4.2 sequential CTs each. Sequential volumes of total lung, overall opacity and opacity subtypes (ground glass opacity [GGO] and consolidation) were extracted using deep learning and manual segmentation. Generalized temporal curves of CT and laboratory measurements were correlated. Lung opacities appeared 3.4 ± 2.2 days prior to symptom onset. Opacity peaked 1 day after symptom onset. GGO onset was earlier and resolved later than consolidation. Lactate dehydrogenase, and C-reactive protein peaked earlier than procalcitonin and leukopenia. The temporal relationships of quantitative CT features and clinical labs have distinctive patterns and peaks in relation to symptom onset, which may inform early clinical course in patients with mild COVID-19 pneumonia, or may shed light upon chronic lung effects or mechanisms of medical countermeasures in clinical trials.
Kassin Michael T, Varble Nicole, Blain Maxime, Xu Sheng, Turkbey Evrim B, Harmon Stephanie, Yang Dong, Xu Ziyue, Roth Holger, Xu Daguang, Flores Mona, Amalou Amel, Sun Kaiyun, Kadri Sameer, Patella Francesca, Cariati Maurizio, Scarabelli Alice, Stellato Elvira, Ierardi Anna Maria, Carrafiello Gianpaolo, An Peng, Turkbey Baris, Wood Bradford J

2021-Mar-25

General

High Throughput Virtual Screening and Validation of a SARS-CoV-2 Main Protease Non-Covalent Inhibitor

bioRxiv Preprint
Despite the recent availability of vaccines against the acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the search for inhibitory therapeutic agents has assumed importance especially in the context of emerging new viral variants. In this paper, we describe the discovery of a novel non-covalent small-molecule inhibitor, MCULE-5948770040, that binds to and inhibits the SARS-Cov-2 main protease (Mpro) by employing a scalable high throughput virtual screening (HTVS) framework and a targeted compound library of over 6.5 million molecules that could be readily ordered and purchased. Our HTVS framework leverages the U.S. supercomputing infrastructure achieving nearly 91% resource utilization and nearly 126 million docking calculations per hour. Downstream biochemical assays validate this Mpro inhibitor with an inhibition constant (Ki) of 2.9 uM [95% CI 2.2, 4.0]. Further, using room-temperature X-ray crystallography, we show that MCULE-5948770040 binds to a cleft in the primary binding site of Mpro forming stable hydrogen bond and hydrophobic interactions. We then used multiple s-timescale molecular dynamics (MD) simulations, and machine learning (ML) techniques to elucidate how the bound ligand alters the conformational states accessed by Mpro, involving motions both proximal and distal to the binding site. Together, our results demonstrate how MCULE-5948770040 inhibits Mpro and offers a springboard for further therapeutic design.
Clyde, A.; Galanie, S.; Kneller, D. W.; Ma, H.; Babuji, Y.; Blaiszik, B.; Brace, A.; Brettin, T.; Chard, K.; Chard, R.; Coates, L.; Foster, I.; Hauner, D.; Kertesz, V.; Kumar, N.; Lee, H.; Li, Z.; Merzky, A.; Schmidt, J. G.; Tan, L.; Titov, M.; Trifan, A.; Turilli, M.; Van Dam, H.; Chennubhotla, S. C.; Jha, S.; Kovalevsky, A.; Ramanathan, A.; Head, M.; Stevens, R.

2021-03-27

General

App-based symptom tracking to optimize SARS-CoV-2 testing strategy using machine learning.

In PloS one ; h5-index 176.0

BACKGROUND : Tests are scarce resources, especially in low and middle-income countries, and the optimization of testing programs during a pandemic is critical for the effectiveness of the disease control. Hence, we aim to use the combination of symptoms to build a predictive model as a screening tool to identify people and areas with a higher risk of SARS-CoV-2 infection to be prioritized for testing.

MATERIALS AND METHODS : We performed a retrospective analysis of individuals registered in "Dados do Bem," a Brazilian app-based symptom tracker. We applied machine learning techniques and provided a SARS-CoV-2 infection risk map of Rio de Janeiro city.

RESULTS : From April 28 to July 16, 2020, 337,435 individuals registered their symptoms through the app. Of these, 49,721 participants were tested for SARS-CoV-2 infection, being 5,888 (11.8%) positive. Among self-reported symptoms, loss of smell (OR[95%CI]: 4.6 [4.4-4.9]), fever (2.6 [2.5-2.8]), and shortness of breath (2.1 [1.6-2.7]) were independently associated with SARS-CoV-2 infection. Our final model obtained a competitive performance, with only 7% of false-negative users predicted as negatives (NPV = 0.93). The model was incorporated by the "Dados do Bem" app aiming to prioritize users for testing. We developed an external validation in the city of Rio de Janeiro. We found that the proportion of positive results increased significantly from 14.9% (before using our model) to 18.1% (after the model).

CONCLUSIONS : Our results showed that the combination of symptoms might predict SARS-Cov-2 infection and, therefore, can be used as a tool by decision-makers to refine testing and disease control strategies.

Dantas Leila F, Peres Igor T, Bastos Leonardo S L, Marchesi Janaina F, de Souza Guilherme F G, Gelli João Gabriel M, Baião Fernanda A, Maçaira Paula, Hamacher Silvio, Bozza Fernando A

2021

Dermatology

Application of Artificial Intelligence for Screening COVID-19 Patients Using Digital Images: A Meta-Analysis.

In JMIR medical informatics ; h5-index 23.0

BACKGROUND : The Coronavirus Disease 2019 (COVID-2019) outbreak has spread rapidly and hospitals are overwhelmed with COVID-19 patients. While using swabs from patients is the main way for detecting coronavirus, analyzing chest images could offer an alternative to hospitals where healthcare personnel and testing kits are scarce. Deep learning, in particular, has shown impressive performances for analyzing medical images including COVID-19 pneumonia.

OBJECTIVE : To perform a systematic review with a meta-analysis of relevant studies to quantify the performance of the DL algorithms for automatic stratification of COVID-19 using chest images.

METHODS : A search strategy for use of PubMed, Scopus, Google Scholar, and Web of Science was developed (between January 1, 2020, and April 25) using the key terms COVID-19, coronavirus, SARS-CoV-2, novel corona, 2019-ncov and deep learning. Two authors independently extracted data on study characteristics, methods, risk of bias, and outcomes. Any disagreement between them was resolved by consensus.

RESULTS : Sixteen studies were included in the meta-analysis, including 5,896 chest images of COVID-19. The pooled sensitivity and specificity of DL for detecting COVID-19 was 0.95 (95%CI: 0.94-0.95), and 0.96 (95%CI: 0.96-0.97), respectively, with an AUROC of 0.98. The positive likelihood, negative likelihood, and diagnostic odds ratio were 19.02 (12.83-28.19), 0.06(95%CI:0.04-0.10), and 368.07 (95%CI: 162.30-834.75), respectively. The pooled sensitivity and specificity for detecting Pneumonia was 0.93 (95%CI:0.92-0.94), and 0.95(95%CI: 0.94-0.95). The performance of radiologists for detecting COVID-19 was lower than DL; however, the performance of junior radiologists was improved when they used DL-based prediction tools.

CONCLUSIONS : Our study findings show that deep learning models have immense potential accurately stratified COVID-19, and correctly differentiate from other pneumonia and normal patients. Implementation of deep learning-based tools can assist radiologists to correctly and quickly detect COVID-19 and to combat the COVID-19 pandemic.

CLINICALTRIAL : N/a.

Poly Tahmina Nasrin, Islam Md Mohaimenul, Alsinglawi Belal, Hsu Min-Huei, Jian Wen Shan, Yang Hsuan-Chia, Li Yu-Chuan Jack

2021-Mar-21

Radiology

Prediction and feature importance analysis for severity of COVID-19 using artificial intelligence: A nationwide analysis in South Korea.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : The number of deaths from COVID-19 continues to surge worldwide. In particular, if the patient's condition is sufficiently severe to require invasive ventilation, it is more likely to lead to death than to recovery.

OBJECTIVE : To analyze the factors of severe COVID-19 patients and develop an artificial intelligence (AI) model to predict the severity of COVID-19 at an early stage.

METHODS : We developed an AI model that predicts severity based on data from 5,601 COVID-19 patients from all national and regional hospitals across South Korea as of April, 2020. The clinical severity has two categories: low and high severity. The conditions of patients in the low-severity group correspond to no limit of activity, oxygen support with nasal prong or facial mask, and non-invasive ventilation. The conditions of patients in the high-severity group correspond to invasive ventilation, multi-organ failure with extracorporeal membrane oxygenation required, and death. For the AI model input, we used 37 medical records including basic patient information, physical index, initial examination findings, clinical findings, omorbidity disease and general blood test results at an early stage. Feature importance analysis was performed with AdaBoost, random forest and XGBoost; AI model for predicting severe COVID-19 patients was developed with 5-layer deep neural network with 20 most important features. The ranked feature importance values of the 37 medical records; sensitivity, specificity, accuracy, balanced accuracy, and area under receiver operating characteristic (AUROC) metrics of the AI model.

RESULTS : We found that age is the most important factor for predicting the disease severity, followed by lymphocyte level, platelet count, and shortness of breath/dyspnea. Our proposed 5-layer deep neural network with 20 most important features provided high sensitivity (90.2%), specificity (90.4%), accuracy (90.4%), balanced accuracy (90.3%), and area under the curve (0.96).

CONCLUSIONS : Our proposed AI model with the selected features was able to predict the severity of COVID-19 accurately. We also made a web application (http://kcovidnet.site/) for anyone to access the model. We believe that opening the AI model to the public is helpful to validate and improve its performance.

CLINICALTRIAL :

Chung Heewon, Ko Hoon, Kang Wu Seong, Kim Kyung Won, Lee Hooseok, Park Chul, Song Hyun-Ok, Choi Tae-Young, Seo Jae Ho, Lee Jinseok

2021-Mar-24

General

Metaheuristic-based Deep COVID-19 Screening Model from Chest X-Ray Images.

In Journal of healthcare engineering
COVID-19 has affected the whole world drastically. A huge number of people have lost their lives due to this pandemic. Early detection of COVID-19 infection is helpful for treatment and quarantine. Therefore, many researchers have designed a deep learning model for the early diagnosis of COVID-19-infected patients. However, deep learning models suffer from overfitting and hyperparameter-tuning issues. To overcome these issues, in this paper, a metaheuristic-based deep COVID-19 screening model is proposed for X-ray images. The modified AlexNet architecture is used for feature extraction and classification of the input images. Strength Pareto evolutionary algorithm-II (SPEA-II) is used to tune the hyperparameters of modified AlexNet. The proposed model is tested on a four-class (i.e., COVID-19, tuberculosis, pneumonia, or healthy) dataset. Finally, the comparisons are drawn among the existing and the proposed models.
Kaur Manjit, Kumar Vijay, Yadav Vaishali, Singh Dilbag, Kumar Naresh, Das Nripendra Narayan

2021

General

Predicting Lyme Disease From Patients' Peripheral Blood Mononuclear Cells Profiled With RNA-Sequencing.

In Frontiers in immunology ; h5-index 100.0
Although widely prevalent, Lyme disease is still under-diagnosed and misunderstood. Here we followed 73 acute Lyme disease patients and uninfected controls over a period of a year. At each visit, RNA-sequencing was applied to profile patients' peripheral blood mononuclear cells in addition to extensive clinical phenotyping. Based on the projection of the RNA-seq data into lower dimensions, we observe that the cases are separated from controls, and almost all cases never return to cluster with the controls over time. Enrichment analysis of the differentially expressed genes between clusters identifies up-regulation of immune response genes. This observation is also supported by deconvolution analysis to identify the changes in cell type composition due to Lyme disease infection. Importantly, we developed several machine learning classifiers that attempt to perform various Lyme disease classifications. We show that Lyme patients can be distinguished from the controls as well as from COVID-19 patients, but classification was not successful in distinguishing those patients with early Lyme disease cases that would advance to develop post-treatment persistent symptoms.
Clarke Daniel J B, Rebman Alison W, Bailey Allison, Wojciechowicz Megan L, Jenkins Sherry L, Evangelista John E, Danieletto Matteo, Fan Jinshui, Eshoo Mark W, Mosel Michael R, Robinson William, Ramadoss Nitya, Bobe Jason, Soloski Mark J, Aucott John N, Ma’ayan Avi

2021

Lyme disease, PBMCs, PTLDS, RNA-seq, data mining, machine learning

Internal Medicine

Deep learning diagnostic and risk-stratification pattern detection for COVID-19 in digital lung auscultations: clinical protocol for a case-control and prospective cohort study.

In BMC pulmonary medicine ; h5-index 38.0

BACKGROUND : Lung auscultation is fundamental to the clinical diagnosis of respiratory disease. However, auscultation is a subjective practice and interpretations vary widely between users. The digitization of auscultation acquisition and interpretation is a particularly promising strategy for diagnosing and monitoring infectious diseases such as Coronavirus-19 disease (COVID-19) where automated analyses could help decentralise care and better inform decision-making in telemedicine. This protocol describes the standardised collection of lung auscultations in COVID-19 triage sites and a deep learning approach to diagnostic and prognostic modelling for future incorporation into an intelligent autonomous stethoscope benchmarked against human expert interpretation.

METHODS : A total of 1000 consecutive, patients aged ≥ 16 years and meeting COVID-19 testing criteria will be recruited at screening sites and amongst inpatients of the internal medicine department at the Geneva University Hospitals, starting from October 2020. COVID-19 is diagnosed by RT-PCR on a nasopharyngeal swab and COVID-positive patients are followed up until outcome (i.e., discharge, hospitalisation, intubation and/or death). At inclusion, demographic and clinical data are collected, such as age, sex, medical history, and signs and symptoms of the current episode. Additionally, lung auscultation will be recorded with a digital stethoscope at 6 thoracic sites in each patient. A deep learning algorithm (DeepBreath) using a Convolutional Neural Network (CNN) and Support Vector Machine classifier will be trained on these audio recordings to derive an automated prediction of diagnostic (COVID positive vs negative) and risk stratification categories (mild to severe). The performance of this model will be compared to a human prediction baseline on a random subset of lung sounds, where blinded physicians are asked to classify the audios into the same categories.

DISCUSSION : This approach has broad potential to standardise the evaluation of lung auscultation in COVID-19 at various levels of healthcare, especially in the context of decentralised triage and monitoring.

TRIAL REGISTRATION : PB_2016-00500, SwissEthics. Registered on 6 April 2020.

Glangetas Alban, Hartley Mary-Anne, Cantais Aymeric, Courvoisier Delphine S, Rivollet David, Shama Deeksha M, Perez Alexandre, Spechbach Hervé, Trombert Véronique, Bourquin Stéphane, Jaggi Martin, Barazzone-Argiroffo Constance, Gervaix Alain, Siebert Johan N

2021-Mar-24

Artificial intelligence, Auscultation, COVID-19, Deep learning, Pneumonia, Respiratory sounds, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)

Radiology

The clinical classification of patients with COVID-19 pneumonia was predicted by Radiomics using chest CT.

In Medicine
In 2020, the new type of coronal pneumonitis became a pandemic in the world, and has firstly been reported in Wuhan, China. Chest CT is a vital component in the diagnostic algorithm for patients with suspected or confirmed COVID-19 infection. Therefore, it is necessary to conduct automatic and accurate detection of COVID-19 by chest CT.The clinical classification of patients with COVID-19 pneumonia was predicted by Radiomics using chest CT.From the COVID-19 cases in our institution, 136 moderate patients and 83 severe patients were screened, and their clinical and laboratory data on admission were collected for statistical analysis. Initial CT Radiomics were modeled by automatic machine learning, and diagnostic performance was evaluated according to AUC, TPR, TNR, PPV and NPV of the subjects. At the same time, the initial CT main features of the two groups were analyzed semi-quantitatively, and the results were statistically analyzed.There was a statistical difference in age between the moderate group and the severe group. The model cohort showed TPR 96.9%, TNR 99.1%, PPV98.4%, NPV98.2%, and AUC 0.98. The test cohort showed TPR 94.4%, TNR100%, PPV100%, NPV96.2%, and AUC 0.97. There was statistical difference between the two groups with grade 1 score (P = .001), the AUC of grade 1 score, grade 2 score, grade 3 score and CT score were 0.619, 0.519, 0.478 and 0.548, respectively.Radiomics' Auto ML model was built by CT image of initial COVID -19 pneumonia, and it proved to be effectively used to predict the clinical classification of COVID-19 pneumonia. CT features have limited ability to predict the clinical typing of Covid-19 pneumonia.
Xiong Fei, Wang Ye, You Tao, Li Han Han, Fu Ting Ting, Tan Huibin, Huang Weicai, Jiang Yuanliang

2021-Mar-26

General

DrugComb update: a more comprehensive drug sensitivity data repository and analysis portal

bioRxiv Preprint
Combinatorial therapies that target multiple pathways have shown great promises for treating complex diseases. DrugComb (https://drugcomb.org/) is a web-based portal for the deposition and analysis of drug combination screening datasets. Since its first release, DrugComb has received continuous updates on the coverage of data resources, as well as on the functionality of the web server to improve the analysis, visualization and interpretation of drug combination screens. Here we report significant updates of DrugComb, including: 1) manual curation and harmonization of more comprehensive drug combination and monotherapy screening data, not only for cancers but also for other diseases such as malaria and COVID-19; 2) enhanced algorithms for assessing the sensitivity and synergy of drug combinations; 3) network modelling tools to visualize the mechanisms of action of drugs or drug combinations for a given cancer sample; and 4) state-of-the-art machine learning models to predict drug combination sensitivity and synergy. These improvements have been provided with more user-friendly graphical interface and faster database infrastructure, which make DrugComb the most comprehensive web-based resources for the study of drug sensitivities for multiple diseases.
Zheng, S.; Aldahdooh, J.; Shadbahr, T.; Wang, Y.; Aldahdooh, D.; Bao, J.; Wang, W.; Tang, J.

2021-03-26

Public Health

Automated Travel History Extraction From Clinical Notes for Informing the Detection of Emergent Infectious Disease Events: Algorithm Development and Validation.

In JMIR public health and surveillance

BACKGROUND : Patient travel history can be crucial in evaluating evolving infectious disease events. Such information can be challenging to acquire in electronic health records, as it is often available only in unstructured text.

OBJECTIVE : This study aims to assess the feasibility of annotating and automatically extracting travel history mentions from unstructured clinical documents in the Department of Veterans Affairs across disparate health care facilities and among millions of patients. Information about travel exposure augments existing surveillance applications for increased preparedness in responding quickly to public health threats.

METHODS : Clinical documents related to arboviral disease were annotated following selection using a semiautomated bootstrapping process. Using annotated instances as training data, models were developed to extract from unstructured clinical text any mention of affirmed travel locations outside of the continental United States. Automated text processing models were evaluated, involving machine learning and neural language models for extraction accuracy.

RESULTS : Among 4584 annotated instances, 2659 (58%) contained an affirmed mention of travel history, while 347 (7.6%) were negated. Interannotator agreement resulted in a document-level Cohen kappa of 0.776. Automated text processing accuracy (F1 85.6, 95% CI 82.5-87.9) and computational burden were acceptable such that the system can provide a rapid screen for public health events.

CONCLUSIONS : Automated extraction of patient travel history from clinical documents is feasible for enhanced passive surveillance public health systems. Without such a system, it would usually be necessary to manually review charts to identify recent travel or lack of travel, use an electronic health record that enforces travel history documentation, or ignore this potential source of information altogether. The development of this tool was initially motivated by emergent arboviral diseases. More recently, this system was used in the early phases of response to COVID-19 in the United States, although its utility was limited to a relatively brief window due to the rapid domestic spread of the virus. Such systems may aid future efforts to prevent and contain the spread of infectious diseases.

Peterson Kelly S, Lewis Julia, Patterson Olga V, Chapman Alec B, Denhalter Daniel W, Lye Patricia A, Stevens Vanessa W, Gamage Shantini D, Roselle Gary A, Wallace Katherine S, Jones Makoto

2021-Mar-24

COVID-19, Zika, biosurveillance, electronic health record, infectious disease surveillance, machine learning, natural language processing, surveillance applications, travel history

General

All Models Are Useful: Bayesian Ensembling for Robust High Resolution COVID-19 Forecasting.

In medRxiv : the preprint server for health sciences

: Timely, high-resolution forecasts of infectious disease incidence are useful for policy makers in deciding intervention measures and estimating healthcare resource burden. In this paper, we consider the task of forecasting COVID-19 confirmed cases at the county level for the United States. Although multiple methods have been explored for this task, their performance has varied across space and time due to noisy data and the inherent dynamic nature of the pandemic. We present a forecasting pipeline which incorporates probabilistic forecasts from multiple statistical, machine learning and mechanistic methods through a Bayesian ensembling scheme, and has been operational for nearly 6 months serving local, state and federal policymakers in the United States. While showing that the Bayesian ensemble is at least as good as the individual methods, we also show that each individual method contributes significantly for different spatial regions and time points. We compare our model's performance with other similar models being integrated into CDC-initiated COVID-19 Forecast Hub, and show better performance at longer forecast horizons. Finally, we also describe how such forecasts are used to increase lead time for training mechanistic scenario projections. Our work demonstrates that such a real-time high resolution forecasting pipeline can be developed by integrating multiple methods within a performance-based ensemble to support pandemic response.

ACM Reference Format** : Aniruddha Adiga, Lijing Wang, Benjamin Hurt, Akhil Peddireddy, Przemys-law Porebski,, Srinivasan Venkatramanan, Bryan Lewis, Madhav Marathe. 2021. All Models Are Useful: Bayesian Ensembling for Robust High Resolution COVID-19 Forecasting. In Proceedings of ACM Conference (Conference'17) . ACM, New York, NY, USA, 9 pages. https://doi.org/10.1145/nnnnnnn.nnnnnnn.

Adiga Aniruddha, Wang Lijing, Hurt Benjamin, Peddireddy Akhil, Porebski Przemyslaw, Venkatramanan Srinivasan, Lewis Bryan, Marathe Madhav

2021-Mar-13

General

A novel classifier architecture based on deep neural network for COVID-19 detection using laboratory findings.

In Applied soft computing
Unfortunately, Coronavirus disease 2019 (COVID-19) is spreading rapidly all over the world. Along with causing many deaths, it has substantially affected the social life, economics, and infrastructure worldwide in a negative manner. Therefore, it is very important to be able to diagnose the COVID-19 quickly and correctly. In this study, a new feature group based on laboratory findings was obtained considering ethnical and genetic differences for interpretation of blood data. Then, using this feature group, a new hybrid classifier architecture based on deep learning was designed and COVID-19 detection was made. Classification performance indicators were obtained as accuracy of 94.95%, F1-score of 94.98%, precision of 94.98%, recall of 94.98% and AUC of 100%. Achieved results were compared with those of the deep learning classifiers suggested in literature. According to these results, proposed method shows superior performance and can provide more convenience and precision to experts for diagnosis of COVID-19 disease.
Göreke Volkan, Sarı Vekil, Kockanat Serdar

2021-Jul

ABC algorithm, Blood findings, COVID-19 disease, Deep neural network

General

A deep learning-based medication behavior monitoring system.

In Mathematical biosciences and engineering : MBE
The internet of things (IoT) and deep learning are emerging technologies in diverse research fields, including the provision of IT services in medical domains. In the COVID-19 era, intelligent medication behavior monitoring systems for stable patient monitoring are further required, because many patients cannot easily visit hospitals. Several previous studies made use of wearable devices to detect medication behaviors of patients. However, the wearable devices cause inconvenience while equipping the devices. In addition, they suffer from inconsistency problems due to errors of measured values. We devise a medication behavior monitoring system that uses the IoT and deep learning to avoid sensing errors and improve user experiences by effectively detecting various activities of patients. Based on the real-time operation of our proposed IoT device, the proposed solution processes captured images of patents via OpenPose to check medication situations. The proposed system identifies medication status on time by using a human activity recognition scheme and provides various notifications to patients' mobile devices. To support reliable communication between our system and doctors, we employ MQTT protocol with periodic data transmissions. Thus, the measured information of patient's medication status is transmitted to the doctors so that they can periodically perform remote treatments. Experimental results show that all medication behaviors are accurately detected and notified to the doctor efficiently, improving the accuracy of monitoring the patient's medication behavior.
Roh Hyeji, Shin Seulgi, Han Jinseo, Lim Sangsoon

2021-Jan-28

IoT , deep learning , healthcare , medication, monitoring

General

Mini-COVIDNet: Efficient Light Weight Deep Neural Network for Ultrasound based Point-of-Care Detection of COVID-19.

In IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Lung ultrasound imaging has the potential to be an effective point-of-care test for detection of COVID-19, due to its ease of operation with minimal personal protection equipment along with easy disinfection. The current state-of-the-art deep learning models for detection of COVID-19 are heavy models that may not be easy to deploy in commonly utilized mobile platforms in point-of-care testing. In this work, we develop a light weight mobile friendly efficient deep learning model for detection of COVID-19 using lung ultrasound images. Three different classes including COVID-19, pneumonia, and healthy were included in this task. The developed network, named as Mini-COVIDNet, was bench-marked with other light weight neural network models along with state-of-the-art heavy model. It was shown that the proposed network can achieve the highest accuracy of 83.2% and requires training time of only 24 minutes. The proposed Mini-COVIDNet has 4.39 times less number of parameters in the network compared to its next best performing network and requires a memory of only 51.29 MB, making the point-of-care detection of COVID-19 using lung ultrasound imaging plausible on a mobile platform. Deployment of these light weight networks on embedded platforms shows that the proposed Mini-COVIDNet is highly versatile and provides optimal performance in terms of being accurate as well as having latency in the same order as other light weight networks. The developed light weight models are available at https://github.com/navchetan-awasthi/Mini-COVIDNet.
Awasthi Navchetan, Dayal Aveen, Cenkeramaddi Linga Reddy, Yalavarthy Phaneendra K

2021-Mar-23

Public Health

The mediating effect of media usage on the relationship between anxiety/fear and physician-patient trust during the COVID-19 pandemic.

In Psychology & health

OBJECTIVE : Our study explored whether and how media usage can mediate the path from anxiety and fear to physician-patient trust.

DESIGN : Study 1 was a population-based, longitudinal study using nationally representative data from 29 provinces in mainland China. The baseline sample (N = 3233) was obtained from February 1 to 9, 2020. Follow-up (N = 1380) took place during March 17 to 24, 2020. Study 2 was a machine learning-based sentiment analysis in which data were captured from Sina Weibo, a Chinese microblogging website, among the most popular official, unofficial, and health-related media accounts. The screened blogs from November to December 2019 and February to March 2020 were scored by Google APIs for positivity and magnitude.

MAIN OUTCOME MEASURES : Physician-patient trust.

RESULTS : Study 1 showed fear and anxiety affected changes in physician-patient trust through media usage, the indirect effect of which was 0.14 (0.03) and the 95% CI was [0.08, 0.19]. Study 2 indicated a more positive image of physicians after the outbreak compared to before [F (2, 3537) = 3.646, p = 0.026, partial η²=0.002].

CONCLUSION : The negative impact of anxiety and fear on physician-patient trust was mediated by media use, which can be explained by the more positive media image during the pandemic.

Chen Yidi, Wu Jianhui, Ma Jinjin, Zhu Huanya, Li Wenju, Gan Yiqun

2021-Mar-23

anxiety, fear, machine learning-based sentiment analysis, physician–patient trust, public health emergency, social media

General

CvDeep-COVID-19 Detection Model.

In SN computer science
COVID-19 (Coronavirus disease) has made world stand still. Detection of COVID-19 positive case immediately is requirement for prevention of its spread and save lives. X-ray images comprises substantial data about the spread of infection through virus in lungs. Advanced assistive tools using machine learning overcome the problem of lack of medical facilities in remote places. In this research, CvDeep, a model for COVID-19 detection using X-ray images as resource is designed. The images are preprocessed for final diagnosis with pertained models. It is observed that it is difficult to detect COVID-19 in early stage using images analysis, but if pre trained deep learning models are used, it can improve the accuracy of detection. This model provides accuracy of 95% for COVID-19 cases. The models used for prediction are AlexNet, SquzeeNet, ResNet and DenseNet. The data set can be shared online to assist radiologists. Patients with COVID-19 (+ ve) can be given instant hospitalization without waiting for lab test result so that survival rate can be increased. Model is evaluated by expert radiologists.
Ingle Vaishali Arjun, Ambad Prashant Mahadev

2021

AlexNet, COVID-19, Corona, Deep learning, DenseNet, ResNet, SquzeeNet, X-ray images

General

COVID_SCREENET: COVID-19 Screening in Chest Radiography Images Using Deep Transfer Stacking.

In Information systems frontiers : a journal of research and innovation
Infectious diseases are highly contagious due to rapid transmission and very challenging to diagnose in the early stage. Artificial Intelligence and Machine Learning now become a strategic weapon in assisting infectious disease prevention, rapid-response in diagnosis, surveillance, and management. In this paper, a bifold COVID_SCREENET architecture is introduced for providing COVID-19 screening solutions using Chest Radiography (CR) images. Transfer learning using nine pre-trained ImageNet models to extract the features of Normal, Pneumonia, and COVID-19 images is adapted in the first fold and classified using baseline Convolutional Neural Network (CNN). A Modified Stacked Ensemble Learning (MSEL) is proposed in the second fold by stacking the top five pre-trained models, and then the predictions resulted. Experimentation is carried out in two folds: In first fold, open-source samples are considered and in second fold 2216 real-time samples collected from Tamilnadu Government Hospitals, India, and the screening results for COVID data is 100% accurate in both the cases. The proposed approach is also validated and blind reviewed with the help of two radiologists at Thanjavur Medical College & Hospitals by collecting 2216 chest X-ray images between the month of April and May. Based on the reports, the measures are calculated for COVID_SCREENET and it showed 100% accuracy in performing multi-class classification.
Elakkiya R, Vijayakumar Pandi, Karuppiah Marimuthu

2021-Mar-17

AI diagnostics tool, COVID-19, Deep learning, Diagnostic radiography, Machine learning, Medical diagnosis, X-rays

General

MANet: A Two-stage Deep Learning Method for Classification of COVID-19 from Chest X-ray Images.

In Neurocomputing
The early detection of infection is significant for the fight against the ongoing COVID-19 pandemic. Chest X-ray (CXR) imaging is an efficient screening technique via which lung infections can be detected. This paper aims to distinguish COVID-19 positive cases from the other four classes, including normal, tuberculosis (TB), bacterial pneumonia (BP), and viral pneumonia (VP), using CXR images. The existing COVID-19 classification researches have achieved some successes with deep learning techniques while sometimes lacking interpretability and generalization ability. Hence, we propose a two-stage classification method MANet to address these issues in computer-aided COVID-19 diagnosis. Particularly, a segmentation model predicts the masks for all CXR images to extract their lung regions at the first stage. A followed classification CNN at the second stage then classifies the segmented CXR images into five classes based only on the preserved lung regions. In this segment-based classification task, we propose the mask attention mechanism (MA) which uses the predicted masks at the first stage as spatial attention maps to adjust the features of the CNN at the second stage. The MA spatial attention maps for features calculate the percentage of masked pixels in their receptive fields, suppressing the feature values based on the overlapping rates between their receptive fields and the segmented lung regions. In evaluation, we segment out the lung regions of all CXR images through a UNet with ResNet backbone, and then perform classification on the segmented CXR images using four classic CNNs with or without MA, including ResNet34, ResNet50, VGG16, and Inceptionv3. The experimental results illustrate that the classification models with MA have higher classification accuracy, more stable training process, and better interpretability and generalization ability than those without MA. Among the evaluated classification models, ResNet50 with MA achieves the highest average test accuracy of 96.32 % in three runs, and the highest one is 97.06 % . Meanwhile, the attention heat maps visualized by Grad-CAM indicate that models with MA make more reliable predictions based on the pathological patterns in lung regions. This further presents the potential of MANet to provide clinicians with diagnosis assistance.
Xu Yujia, Lam Hak-Keung, Jia Guangyu

2021-Mar-18

COVID-19, Chest X-ray images, Convolutional Neural Networks, Segmentation, Spatial Attention, Two-stage

General

Impact of the SARS-COV-2 outbreak on epidemiology and management of major traumain France: a registry-based study (the COVITRAUMA study).

In Scandinavian journal of trauma, resuscitation and emergency medicine ; h5-index 32.0

BACKGROUND : Emerging evidence suggests that the reallocation of health care resources during the COVID-19 pandemic negatively impacts health care system. This study describes the epidemiology and the outcome of major trauma patients admitted to centers in France during the first wave of the COVID-19 outbreak.

METHODS : This retrospective observational study included all consecutive trauma patients aged 15 years and older admitted into 15 centers contributing to the TraumaBase® registry during the first wave of the SARS-CoV-2 pandemic in France. This COVID-19 trauma cohort was compared to historical cohorts (2017-2019).

RESULTS : Over a 4 years-study period, 5762 patients were admitted between the first week of February and mid-June. This cohort was split between patients admitted during the first 2020 pandemic wave in France (pandemic period, 1314 patients) and those admitted during the corresponding period in the three previous years (2017-2019, 4448 patients). Trauma patient demographics changed substantially during the pandemic especially during the lockdown period, with an observed reduction in both the absolute numbers and proportion exposed to road traffic accidents and subsequently admitted to traumacenters (348 annually 2017-2019 [55.4% of trauma admissions] vs 143 [36.8%] in 2020 p < 0.005). The in-hospital observed mortality and predicted mortality during the pandemic period were not different compared to the non-pandemic years.

CONCLUSIONS : During this first wave of COVID-19 in France, and more specifically during lockdown there was a significant reduction of patients admitted to designated trauma centers. Despite the reallocation and reorganization of medical resources this reduction prevented the saturation of the trauma rescue chain and has allowed maintaining a high quality of care for trauma patients.

Moyer Jean-Denis, James Arthur, Gakuba Clément, Boutonnet Mathieu, Angles Emeline, Rozenberg Emmanuel, Bardon Jean, Clavier Thomas, Legros Vincent, Werner Marie, Mathais Quentin, Ramonda Véronique, Le Minh Pierre, Berthelot Yann, Colas Clélia, Pottecher Julien, Gauss Tobias

2021-Mar-22

COVID-19, France, Trauma, Traumacenter

Public Health

Prediction of Sepsis in COVID-19 Using Laboratory Indicators.

In Frontiers in cellular and infection microbiology ; h5-index 53.0

Background : The outbreak of coronavirus disease 2019 (COVID-19) has become a global public health concern. Many inpatients with COVID-19 have shown clinical symptoms related to sepsis, which will aggravate the deterioration of patients' condition. We aim to diagnose Viral Sepsis Caused by SARS-CoV-2 by analyzing laboratory test data of patients with COVID-19 and establish an early predictive model for sepsis risk among patients with COVID-19.

Methods : This study retrospectively investigated laboratory test data of 2,453 patients with COVID-19 from electronic health records. Extreme gradient boosting (XGBoost) was employed to build four models with different feature subsets of a total of 69 collected indicators. Meanwhile, the explainable Shapley Additive ePlanation (SHAP) method was adopted to interpret predictive results and to analyze the feature importance of risk factors.

Findings : The model for classifying COVID-19 viral sepsis with seven coagulation function indicators achieved the area under the receiver operating characteristic curve (AUC) 0.9213 (95% CI, 89.94-94.31%), sensitivity 97.17% (95% CI, 94.97-98.46%), and specificity 82.05% (95% CI, 77.24-86.06%). The model for identifying COVID-19 coagulation disorders with eight features provided an average of 3.68 (±) 4.60 days in advance for early warning prediction with 0.9298 AUC (95% CI, 86.91-99.04%), 82.22% sensitivity (95% CI, 67.41-91.49%), and 84.00% specificity (95% CI, 63.08-94.75%).

Interpretation : We found that an abnormality of the coagulation function was related to the occurrence of sepsis and the other routine laboratory test represented by inflammatory factors had a moderate predictive value on coagulopathy, which indicated that early warning of sepsis in COVID-19 patients could be achieved by our established model to improve the patient's prognosis and to reduce mortality.

Tang Guoxing, Luo Ying, Lu Feng, Li Wei, Liu Xiongcheng, Nan Yucen, Ren Yufei, Liao Xiaofei, Wu Song, Jin Hai, Zomaya Albert Y, Sun Ziyong

2020

COVID-19, artificial intelligence, coagulation function, inflammatory factor, sepsis

Radiology

Deep Learning in the Detection and Diagnosis of COVID-19 Using Radiology Modalities: A Systematic Review.

In Journal of healthcare engineering

Introduction : The early detection and diagnosis of COVID-19 and the accurate separation of non-COVID-19 cases at the lowest cost and in the early stages of the disease are among the main challenges in the current COVID-19 pandemic. Concerning the novelty of the disease, diagnostic methods based on radiological images suffer from shortcomings despite their many applications in diagnostic centers. Accordingly, medical and computer researchers tend to use machine-learning models to analyze radiology images. Material and Methods. The present systematic review was conducted by searching the three databases of PubMed, Scopus, and Web of Science from November 1, 2019, to July 20, 2020, based on a search strategy. A total of 168 articles were extracted and, by applying the inclusion and exclusion criteria, 37 articles were selected as the research population.

Result : This review study provides an overview of the current state of all models for the detection and diagnosis of COVID-19 through radiology modalities and their processing based on deep learning. According to the findings, deep learning-based models have an extraordinary capacity to offer an accurate and efficient system for the detection and diagnosis of COVID-19, the use of which in the processing of modalities would lead to a significant increase in sensitivity and specificity values.

Conclusion : The application of deep learning in the field of COVID-19 radiologic image processing reduces false-positive and negative errors in the detection and diagnosis of this disease and offers a unique opportunity to provide fast, cheap, and safe diagnostic services to patients.

Ghaderzadeh Mustafa, Asadi Farkhondeh

2021

Radiology

Computer-Aided Diagnosis of COVID-19 CT Scans Based on Spatiotemporal Information Fusion.

In Journal of healthcare engineering
Coronavirus disease (COVID-19) is highly contagious and pathogenic. Currently, the diagnosis of COVID-19 is based on nucleic acid testing, but it has false negatives and hysteresis. The use of lung CT scans can help screen and effectively monitor diagnosed cases. The application of computer-aided diagnosis technology can reduce the burden on doctors, which is conducive to rapid and large-scale diagnostic screening. In this paper, we proposed an automatic detection method for COVID-19 based on spatiotemporal information fusion. Using the segmentation network in the deep learning method to segment the lung area and the lesion area, the spatiotemporal information features of multiple CT scans are extracted to perform auxiliary diagnosis analysis. The performance of this method was verified on the collected dataset. We achieved the classification of COVID-19 CT scans and non-COVID-19 CT scans and analyzed the development of the patients' condition through the CT scans. The average accuracy rate is 96.7%, sensitivity is 95.2%, and F1 score is 95.9%. Each scan takes about 30 seconds for detection.
Li Tianyi, Wei Wei, Cheng Lidan, Zhao Shengjie, Xu Chuanjun, Zhang Xia, Zeng Yi, Gu Jihua

2021

General

A new approach for computer-aided detection of coronavirus (COVID-19) from CT and X-ray images using machine learning methods.

In Applied soft computing
The COVID-19 outbreak has been causing a global health crisis since December 2019. Due to this virus declared by the World Health Organization as a pandemic, the health authorities of the countries are constantly trying to reduce the spread rate of the virus by emphasizing the rules of masks, social distance, and hygiene. COVID-19 is highly contagious and spreads rapidly globally and early detection is of paramount importance. Any technological tool that can provide rapid detection of COVID-19 infection with high accuracy can be very useful to medical professionals. The disease findings on COVID-19 images, such as computed tomography (CT) and X-rays, are similar to other lung infections, making it difficult for medical professionals to distinguish COVID-19. Therefore, computer-aided diagnostic solutions are being developed to facilitate the identification of positive COVID-19 cases. The method currently used as a gold standard in detecting the virus is the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. Due to the high false-negative rate of this test and the delays in the test results, alternative solutions are sought. This study was conducted to investigate the contribution of machine learning and image processing to the rapid and accurate detection of COVID-19 from two of the most widely used different medical imaging modes, chest X-ray and CT images. The main purpose of this study is to support early diagnosis and treatment to end the coronavirus epidemic as soon as possible. One of the primary aims of the study is to provide support to medical professionals who are most worn out and working under intense stress during COVID-19 through smart learning methods and image classification models. The proposed approach was applied to three different public COVID-19 data sets and consists of five basic steps: data set acquisition, pre-processing, feature extraction, dimension reduction, and classification stages. Each stage has its sub-operations. The proposed model performs in considerable levels of COVID-19 detection for dataset-1 (CT), dataset-2 (X-ray) and dataset-3 (CT) with the accuracy of 89.41%, 99.02%, 98.11%, respectively. On the other hand, in the X-ray data set, an accuracy of 85.96% was obtained for COVID-19 (+), COVID-19 (-), and those with Pneumonia but not COVID-19 classes. As a result of the study, it has been shown that COVID-19 can be detected with a high success rate in about less than one minute with image processing and classical learning methods. In the light of the findings, it is possible to say that the proposed system will help radiologists in their decisions, will be useful in the early diagnosis of the virus, and can distinguish pneumonia caused by the COVID-19 virus from the pneumonia of other diseases.
Saygılı Ahmet

2021-Jul

CAD, COVID-19, CT, Machine learning, X-ray

Radiology

Machining learning predicts the need for escalated care and mortality in COVID-19 patients from clinical variables.

In International journal of medical sciences
Objective: This study aimed to develop a machine learning algorithm to identify key clinical measures to triage patients more effectively to general admission versus intensive care unit (ICU) admission and to predict mortality in COVID-19 pandemic. Materials and methods: This retrospective study consisted of 1874 persons-under-investigation for COVID-19 between February 7, 2020, and May 27, 2020 at Stony Brook University Hospital, New York. Two primary outcomes were ICU admission and mortality compared to COVID-19 positive patients in general hospital admission. Demographic, vitals, symptoms, imaging findings, comorbidities, and laboratory tests at presentation were collected. Predictions of mortality and ICU admission were made using machine learning with 80% training and 20% testing. Performance was evaluated using receiver operating characteristic (ROC) area under the curve (AUC). Results: A total of 635 patients were included in the analysis (age 60±11, 40.2% female). The top 6 mortality predictors were age, procalcitonin, C-creative protein, lactate dehydrogenase, D-dimer and lymphocytes. The top 6 ICU admission predictors are procalcitonin, lactate dehydrogenase, C-creative protein, pulse oxygen saturation, temperature and ferritin. The best machine learning algorithms predicted mortality with 89% AUC and ICU admission with 79% AUC. Conclusion: This study identifies key independent clinical parameters that predict ICU admission and mortality associated with COVID-19 infection. The predictive model is practical, readily enhanced and retrained using additional data. This approach has immediate translation and may prove useful for frontline physicians in clinical decision making under time-sensitive and resource-constrained environment.
Hou Wei, Zhao Zirun, Chen Anne, Li Haifang, Duong Tim Q

2021

artificial intelligence, coronavirus 2 (SARS-CoV-2), lung infection, pneumonia

General

COVID-19: Automatic Detection from X-ray images by utilizing Deep Learning Methods.

In Expert systems with applications
In recent months, a novel virus named Coronavirus has emerged to become a pandemic. The virus is spreading not only humans, but it is also affecting animals. First ever case of Coronavirus was registered in city of Wuhan, Hubei province of China on 31st of December in 2019. Coronavirus infected patients display very similar symptoms like pneumonia, and it attacks the respiratory organs of the body, causing difficulty in breathing. The disease is diagnosed using a Real-Time Reverse Transcriptase Polymerase Chain reaction (RT-PCR) kit and requires time in the laboratory to confirm the presence of the virus. Due to insufficient availability of the kits, the suspected patients cannot be treated in time, which in turn increases the chance of spreading the disease. To overcome this solution, radiologists observed the changes appearing in the radiological images such as X-ray and CT scans. Using deep learning algorithms, the suspected patients' X-ray or Computed Tomography (CT) scan can differentiate between the healthy person and the patient affected by Coronavirus. In this paper, popular deep learning architectures are used to develop a Coronavirus diagnostic systems. The architectures used in this paper are VGG16, DenseNet121, Xception, NASNet, and EfficientNet. Multiclass classification is performed in this paper. The classes considered are COVID-19 positive patients, normal patients, and other class. In other class, chest X-ray images of pneumonia, influenza, and other illnesses related to the chest region are included. The accuracies obtained for VGG16, DenseNet121, Xception, NASNet, and EfficientNet are 79.01%, 89.96%, 88.03%, 85.03% and 93.48% respectively. The need for deep learning with radiologic images is necessary for this critical condition as this will provide a second opinion to the radiologists fast and accurately. These deep learning Coronavirus detection systems can also be useful in the regions where expert physicians and well-equipped clinics are not easily accessible.
Nigam Bhawna, Nigam Ayan, Jain Rahul, Dodia Shubham, Arora Nidhi, B Annappa

2021-Mar-16

COVID-19, Coronavirus, Deep Learning, Pandemic

General

Domain adaptation based self-correction model for COVID-19 infection segmentation in CT images.

In Expert systems with applications
The capability of generalization to unseen domains is crucial for deep learning models when considering real-world scenarios. However, current available medical image datasets, such as those for COVID-19 CT images, have large variations of infections and domain shift problems. To address this issue, we propose a prior knowledge driven domain adaptation and a dual-domain enhanced self-correction learning scheme. Based on the novel learning schemes, a domain adaptation based self-correction model (DASC-Net) is proposed for COVID-19 infection segmentation on CT images. DASC-Net consists of a novel attention and feature domain enhanced domain adaptation model (AFD-DA) to solve the domain shifts and a self-correction learning process to refine segmentation results. The innovations in AFD-DA include an image-level activation feature extractor with attention to lung abnormalities and a multi-level discrimination module for hierarchical feature domain alignment. The proposed self-correction learning process adaptively aggregates the learned model and corresponding pseudo labels for the propagation of aligned source and target domain information to alleviate the overfitting to noises caused by pseudo labels. Extensive experiments over three publicly available COVID-19 CT datasets demonstrate that DASC-Net consistently outperforms state-of-the-art segmentation, domain shift, and coronavirus infection segmentation methods. Ablation analysis further shows the effectiveness of the major components in our model. The DASC-Net enriches the theory of domain adaptation and self-correction learning in medical imaging and can be generalized to multi-site COVID-19 infection segmentation on CT images for clinical deployment.
Jin Qiangguo, Cui Hui, Sun Changming, Meng Zhaopeng, Wei Leyi, Su Ran

2021-Mar-13

COVID-19 CT segmentation, attention mechanism, domain adaptation, self-correction learning

General

EMR2vec: Bridging the Gap Between Patient Data and Clinical Trial.

In Computers & industrial engineering
The human suffering from diseases caused by life-threatening viruses such as SARS, Ebola, and COVID-19 motivated many of us to study and discover the best means to harness the potential of data integration to assist clinical researchers to curb these viruses. Integrating patients data with clinical trials data is enormously promising as it provides a comprehensive knowledge base that accelerates the clinical research response-ability to tackle emerging infectious disease outbreaks. This work introduces EMR2vec, a platform that customises advanced NLP, machine learning and semantic web techniques to link potential patients to suitable clinical trials. Linking these two different but complementary datasets allows clinicians and researchers to compare patients to clinical research opportunities or to automatically select patients for personalized clinical care. The platform derives a 'bag of medical terms' (BoMT) from eligibility criteria by normalizing extracted entities through SNOMED-CT ontology. With the usage of BoMT, an ontological reasoning method is proposed to represent EMR and clinical trials in a vector space model. The platform presents a matching process that reduces vector dimensionality using a neural network, then applies orthogonality projection to measure the similarity between vectors. Finally, the proposed EMR2vec platform is evaluated with an extendable prototype based on Big data tools.
Dhayne Houssein, Kilany Rima, Haque Rafiqul, Taher Yehia

2021-Mar-15

00-01, 99-00, Clinical Trial, EMR, Medical Data Integration, Neural Network, Semantic Web

General

The Psychological Impact of the COVID-19 Pandemic Affected Decision-Making Processes.

In The Spanish journal of psychology
A sample of 641 participants were presented with four decision-making tasks during the first stages of the COVID-19 lockdown in Spain: The dictator game, framing problems, utilitarian/deontological and altruistic/egoistic moral dilemmas. Participants also completed questionnaires on mental health status and experiences related to the COVID-19 pandemic. We used boosted regression trees (an advanced form of regression analysis based on machine learning) to model relationships between responses to the questionnaires and decision-making tasks. Results showed that the psychological impact of the COVID-19 pandemic predicted participants' responses to the framing problems and utilitarian/deontological and altruistic/egoistic moral dilemmas (but not to the dictator game). More concretely, the more psychological impact participants suffered, the more they were willing to choose the safest response in the framing problems, and the more deontological/altruistic were their responses to moral dilemmas. These results suggest that the psychological impact of the COVID-19 pandemic might prompt automatic processes.
Romero-Rivas Carlos, Rodriguez-Cuadrado Sara

2021-Mar-22

COVID–19, decision-making, dictator game, framing problems, moral dilemmas

Public Health

The impact of contact tracing and household bubbles on deconfinement strategies for COVID-19.

In Nature communications ; h5-index 260.0
The COVID-19 pandemic caused many governments to impose policies restricting social interactions. A controlled and persistent release of lockdown measures covers many potential strategies and is subject to extensive scenario analyses. Here, we use an individual-based model (STRIDE) to simulate interactions between 11 million inhabitants of Belgium at different levels including extended household settings, i.e., "household bubbles". The burden of COVID-19 is impacted by both the intensity and frequency of physical contacts, and therefore, household bubbles have the potential to reduce hospital admissions by 90%. In addition, we find that it is crucial to complete contact tracing 4 days after symptom onset. Assumptions on the susceptibility of children affect the impact of school reopening, though we find that business and leisure-related social mixing patterns have more impact on COVID-19 associated disease burden. An optimal deployment of the mitigation policies under study require timely compliance to physical distancing, testing and self-isolation.
Willem Lander, Abrams Steven, Libin Pieter J K, Coletti Pietro, Kuylen Elise, Petrof Oana, Møgelmose Signe, Wambua James, Herzog Sereina A, Faes Christel, Beutels Philippe, Hens Niel

2021-Mar-09

Public Health

Comparison of public response to containment measures during the initial outbreak and resurgence of COVID-19 epidemic in China: an infodemiology study.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : The COVID-19 cases resurged around the world in the second half of 2020. Not much is known about the change of public responses to containment measures from the initial outbreak to resurgence. Monitoring public responses is crucial to inform policy measures to prepare for COVID-19 resurgence.

OBJECTIVE : To assess and compare public responses towards containment measures during the initial outbreak and resurgence of COVID-19 epidemic in China.

METHODS : We collected all COVID-19 related posts from Sina Weibo (China's Twitter) during the initial outbreak in China and resurgence in Beijing. With a Python script, we constructed subsets of Weibo posts focusing on three containment measures: lockdown, test-trace-isolate, and suspension of gatherings. Baidu's open source sentiment analysis model, and Latent Dirichlet Allocation topic modeling, a widely-used machine learning algorithm, were used to assess the public's engagement, sentiment, and frequently discussed topics on each containment measure.

RESULTS : A total of 8,985,221 Weibo posts were collected. In China, the containment measures evolved from the fully lockdown for general population during the initial outbreak, to a more targeted response strategy for high-risk population during COVID-19 resurgence. Between the initial outbreak and resurgence, average daily proportion of Weibo posts with negative sentiments decreased from 57% to 47% for lockdown, from 56% to 51% for test-trace-isolate, and from 55% to 48% for suspension of gathering. Among the top 3 frequently discussed topics on lockdown measure, discussions on containment measures accounting for around 32% in both periods, but the second top topic shifted from expressing negative emotions (11%) to its impacts on daily lives or work (26%). The public expressed the high level of panic (21%) in the initial outbreak but virtually zero panic (1%) in the resurgence. The more targeted test-trace-isolation measure got the greatest support (60%) among all three containment measures in the initial outbreak, and its supporting rate reached 90% during the resurgence.

CONCLUSIONS : Compared to the initial outbreak, the public expressed less engagement and less negative sentiment on containment measures, and were more supportive towards containment measures during the resurgence. The targeted test-trace-isolate strategies were more acceptable for the public. When COVID-19 resurges, more targeted test-trace-isolate strategies for high-risk population should be promoted to balance epidemic control and its impacts on daily lives and the economy.

CLINICALTRIAL :

Zhou Xinyu, Song Yi, Jiang Hao, Wang Qian, Qu Zhiqiang, Zhou Xiaoyu, Jit Mark, Hou Zhiyuan, Lin Leesa

2021-Mar-11

General

Concerns Discussed on Chinese and French Social Media during the COVID-19 Lockdown: Comparative Infodemiology Study based on Topic Modeling.

In JMIR formative research

BACKGROUND : During the coronavirus disease 2019 (COVID-19) pandemic, numerous countries, including China and France, have implemented lockdown measures that have been shown to be effective in controlling the epidemic. However, little is known about the impact of these measures on the population as expressed on social media from different cultural contexts.

OBJECTIVE : To assess and compare the evolution of the topics discussed on Chinese and French social media during the COVID-19 lockdown.

METHODS : We extracted posts containing "COVID-19"- or "lockdown"-related keywords in the most commonly used micro-blogging social media platforms, i.e., Weibo (China) and Twitter (France), from one week before to the lifting of the lockdown. A topic model was applied independently for three periods: pre-lockdown, early lockdown and mid-to-late lockdown, to assess the evolution of the topics discussed on Chinese and French social media.

RESULTS : 6 395, 23 422 and 141 643 Chinese Weibo messages, and 34 327, 119 919, and 282 965 French tweets were extracted in the pre-lockdown, early lockdown and mid-to-late lockdown periods in China and France, respectively. Four categories of topics were discussed in a continuously evolving way in all three periods: epidemic news and everyday life, scientific information, public measures and solidarity & encouragement. The most represented category over all periods in both countries was epidemic news and everyday life. Scientific information was far more discussed on Weibo than in French tweets. Misinformation circulated through social media in both countries; however, it was more concerned with the virus and epidemic in China, whereas it was more concerned with the lockdown measures in France. Regarding public measures, more criticisms were identified in French tweets than on Weibo. Advantages and data privacy concerns regarding tracing apps were also addressed in French tweets. All these differences were explained by the different use of social media, the different timeline of the epidemic and the different cultural context in these two countries.

CONCLUSIONS : This study is the first to compare the social media content in Eastern and Western countries during the unprecedented COVID-19 lockdown. Using general COVID-19-related social media data, our results describe common and different public reactions, behaviors and concerns in China and France, covering even the fine topics identified in prior studies focusing on specific interests. We believe our study can help characterize country-specific public needs and appropriately address them during an outbreak.

CLINICALTRIAL :

Schück Stéphane, Foulquié Pierre, Mebarki Adel, Faviez Carole, Khadhar Mickaïl, Texier Nathalie, Katsahian Sandrine, Burgun Anita, Chen Xiaoyi

2021-Mar-15

General

Machine Learning Classification Models for COVID-19 Test Prioritization in Brazil.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : controlling the COVID-19 outbreak in Brazil is a challenge of continental proportions due to the population's size and urban density, inefficient maintenance of social distancing and testing strategies, and limited availability of testing resources.

OBJECTIVE : the purpose of this study is to effectively prioritize symptomatic patients for testing to assist the early COVID-19 detection in Brazil, addressing problems related to inefficient testing and control strategies.

METHODS : raw data from 55,676 Brazilians were pre-processed, and the Chi-squared test was used to confirm the relevance of features: Gender, Health Professional, Fever, Sore Throat, Dyspnea, Olfactory Disorders, Cough, Coryza, Taste Disorders, and Headache. Classification models were implemented relying on pre-processed datasets, supervised learning, and the algorithms Multilayer Perceptron (MLP), Gradient Boosting Machine (GBM), Decision Tree (DT), Random Forest (RF), Extreme Gradient Boosting (XGBoost), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Logistic Regression (LR). The models' performances were analyzed using 10-fold cross-validation, classification metrics, and the Friedman and Nemenyi statistical tests. The permutation feature importance method was applied for ranking the features used by the classification models with the highest performances.

RESULTS : Gender, Fever, and Dyspnea are among the highest-ranked features used by classification models. The comparative analysis presents MLP, GBM, DT, RF, XGBoost, and SVM as the highest performance models with similar results. KNN and LR were outperformed by the other algorithms. Applying the easy interpretability as an additional comparison criterion, the DT was considered the most suitable model.

CONCLUSIONS : the DT classification model can effectively (e.g., mean accuracy ≥ 89.12%) assist the COVID-19 test prioritization in Brazil. The model can be applied to recommend the prioritizing of a symptomatic patient for COVID-19 testing.

CLINICALTRIAL :

Viana Dos Santos Santana Íris, C M da Silveira Andressa, Sobrinho Álvaro, Chaves E Silva Lenardo, Dias da Silva Leandro, Freire de Souza Santos Danilo, Candeia Edmar, Perkusich Angelo

2021-Mar-21

Radiology

Machine learning models to identify low adherence to influenza vaccination among Korean adults with cardiovascular disease.

In BMC cardiovascular disorders

BACKGROUND : Annual influenza vaccination is an important public health measure to prevent influenza infections and is strongly recommended for cardiovascular disease (CVD) patients, especially in the current coronavirus disease 2019 (COVID-19) pandemic. The aim of this study is to develop a machine learning model to identify Korean adult CVD patients with low adherence to influenza vaccination METHODS: Adults with CVD (n = 815) from a nationally representative dataset of the Fifth Korea National Health and Nutrition Examination Survey (KNHANES V) were analyzed. Among these adults, 500 (61.4%) had answered "yes" to whether they had received seasonal influenza vaccinations in the past 12 months. The classification process was performed using the logistic regression (LR), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB) machine learning techniques. Because the Ministry of Health and Welfare in Korea offers free influenza immunization for the elderly, separate models were developed for the < 65 and ≥ 65 age groups.

RESULTS : The accuracy of machine learning models using 16 variables as predictors of low influenza vaccination adherence was compared; for the ≥ 65 age group, XGB (84.7%) and RF (84.7%) have the best accuracies, followed by LR (82.7%) and SVM (77.6%). For the < 65 age group, SVM has the best accuracy (68.4%), followed by RF (64.9%), LR (63.2%), and XGB (61.4%).

CONCLUSIONS : The machine leaning models show comparable performance in classifying adult CVD patients with low adherence to influenza vaccination.

Kim Moojung, Kim Young Jae, Park Sung Jin, Kim Kwang Gi, Oh Pyung Chun, Kim Young Saing, Kim Eun Young

2021-Mar-09

Cardiovascular disease, Influenza vaccination, Machine learning

General

A region-specific clustering approach to investigate risk-factors in mortality rate during COVID-19: comprehensive statistical analysis from 208 countries.

In Journal of medical engineering & technology
Since the outbreak of the novel coronavirus, COVID-19 has continuously spread across the globe briskly. However, since its existence, the symptoms of the disease have been varying widely; thus, developing an urgent need to stratify high-risk categories of people who show more propensity to be affected by this deadly virus will be beneficial for health care. Using the open-access data and machine learning algorithms, this paper aims to cluster countries in groups with similar profiles with respect to the country level pre COVID-19 pandemic parameters. The purpose of performing the data analysis is to measure the extent to which these major risk factors determine the mortality rate due to the coronavirus disease 2019. An unsupervised machine learning model (k-means) was employed for two hundred and eight countries to define data-driven clusters based on thirteen country-level parameters. After performing the one-way ANOVA for comparing the clusters in terms of total cases, total deaths, total cases per population, total deaths per population, and death rate, the paradigm with four and seven clusters showed the best ability to stratify the countries according to total cases per population and death rate with p-values of less than 0.05 and 0.001, respectively. However, the model could not stratify countries in total deaths/cases and total deaths per population.
Garg Poojita, Joshi Deepak

2021-Mar-22

COVID-19, K-means, machine learning, risk-factors

General

A super learner ensemble of 14 statistical learning models for predicting COVID-19 severity among patients with cardiovascular conditions.

In Intelligence-based medicine

Background : Cardiovascular and other circulatory system diseases have been implicated in the severity of COVID-19 in adults. This study provides a super learner ensemble of models for predicting COVID-19 severity among these patients.

Method : The Cerner Real-World Database was used for this study. Data on adult patients (18 years or older) with cardiovascular and related circulatory diseases between 2017 and 2019 were retrieved and a total of 13 these conditions were identified. Among these patients, 33,042 admitted with positive diagnoses for COVID-19 between March 2020 and June 2020 (from 59 hospitals) were identified and selected for this study. A total of 14 statistical and machine learning models were developed and combined into a single more powerful super learning model for predicting COVID-19 severity on admission to the hospital.

Result : LASSO regression, a full extreme gradient boosting model with tree depth of 2, and a full logistic regression model were the most predictive with cross-validated AUROCs of 0.7964, 0.7961, and 0.7958 respectively. The resulting super learner ensemble model had a cross validated AUROC of 0.8006 (range: 0.7814, 0.8163). The unbiased AUROC of the super learner model on an independent test set was 0.8057 (95% CI: 0.7954, 0.8159).

Conclusion : Highly predictive models can be built to predict COVID-19 severity of patients with cardiovascular and other circulatory conditions. Super learning ensembles will improve individual and classical ensemble models significantly.

Ehwerhemuepha Louis, Danioko Sidy, Verma Shiva, Marano Rachel, Feaster William, Taraman Sharief, Moreno Tatiana, Zheng Jianwei, Yaghmaei Ehsan, Chang Anthony

2021-Mar-17

COVID-19, COVID-19 severity, Super learning, cardiovascular conditions, ensemble learning, predicting COVID-19 severity

General

Sonographic Diagnosis of COVID-19: A Review of Image Processing for Lung Ultrasound.

In Frontiers in big data
The sustained increase in new cases of COVID-19 across the world and potential for subsequent outbreaks call for new tools to assist health professionals with early diagnosis and patient monitoring. Growing evidence around the world is showing that lung ultrasound examination can detect manifestations of COVID-19 infection. Ultrasound imaging has several characteristics that make it ideally suited for routine use: small hand-held systems can be contained inside a protective sheath, making it easier to disinfect than X-ray or computed tomography equipment; lung ultrasound allows triage of patients in long term care homes, tents or other areas outside of the hospital where other imaging modalities are not available; and it can determine lung involvement during the early phases of the disease and monitor affected patients at bedside on a daily basis. However, some challenges still remain with routine use of lung ultrasound. Namely, current examination practices and image interpretation are quite challenging, especially for unspecialized personnel. This paper reviews how lung ultrasound (LUS) imaging can be used for COVID-19 diagnosis and explores different image processing methods that have the potential to detect manifestations of COVID-19 in LUS images. Then, the paper reviews how general lung ultrasound examinations are performed before addressing how COVID-19 manifests itself in the images. This will provide the basis to study contemporary methods for both segmentation and classification of lung ultrasound images. The paper concludes with a discussion regarding practical considerations of lung ultrasound image processing use and draws parallels between different methods to allow researchers to decide which particular method may be best considering their needs. With the deficit of trained sonographers who are working to diagnose the thousands of people afflicted by COVID-19, a partially or totally automated lung ultrasound detection and diagnosis tool would be a major asset to fight the pandemic at the front lines.
McDermott Conor, Łącki Maciej, Sainsbury Ben, Henry Jessica, Filippov Mihail, Rossa Carlos

2021

COVID-19, classification, diagnosis, image processing, lung ultrasound, machine learning, segmentation

Public Health

Machine Learning Approaches Reveal That the Number of Tests Do Not Matter to the Prediction of Global Confirmed COVID-19 Cases.

In Frontiers in artificial intelligence
Coronavirus disease 2019 (COVID-19) has developed into a global pandemic, affecting every nation and territory in the world. Machine learning-based approaches are useful when trying to understand the complexity behind the spread of the disease and how to contain its spread effectively. The unsupervised learning method could be useful to evaluate the shortcomings of health facilities in areas of increased infection as well as what strategies are necessary to prevent disease spread within or outside of the country. To contribute toward the well-being of society, this paper focusses on the implementation of machine learning techniques for identifying common prevailing public health care facilities and concerns related to COVID-19 as well as attitudes to infection prevention strategies held by people from different countries concerning the current pandemic situation. Regression tree, random forest, cluster analysis and principal component machine learning techniques are used to analyze the global COVID-19 data of 133 countries obtained from the Worldometer website as of April 17, 2020. The analysis revealed that there are four major clusters among the countries. Eight countries having the highest cumulative infected cases and deaths, forming the first cluster. Seven countries, United States, Spain, Italy, France, Germany, United Kingdom, and Iran, play a vital role in explaining the 60% variation of the total variations by us of the first component characterized by all variables except for the rate variables. The remaining countries explain only 20% of the variation of the total variation by use of the second component characterized by only rate variables. Most strikingly, the analysis found that the variable number of tests by the country did not play a vital role in the prediction of the cumulative number of confirmed cases.
Khan Md Hasinur Rahaman, Hossain Ahmed

2020

COVID-19 disease, cluster analysis, machine learning, principal component analysis, regression tree

General

Predicting hosts based on early SARS-CoV-2 samples and analyzing later world-wide pandemic in 2020

bioRxiv Preprint
The SARS-CoV-2 pandemic has raised the concern for identifying hosts of the virus since the early-stage outbreak. To address this problem, we proposed a deep learning method, DeepHoF, based on extracting the viral genomic features automatically, to predict host likelihood scores on five host types, including plant, germ, invertebrate, non-human vertebrate and human, for novel viruses. DeepHoF made up for the lack of an accurate tool applicable to any novel virus and overcame the limitation of the sequence similarity-based methods, reaching a satisfactory AUC of 0.987 on the five-classification. Additionally, to fill the gap in the efficient inference of host species for SARS-CoV-2 using existed tools, we conducted a deep analysis on the host likelihood profile calculated by DeepHoF. Using the isolates sequenced in the earliest stage of COVID-19, we inferred minks, bats, dogs and cats were potential hosts of SARS-CoV-2, while minks might be one of the most noteworthy hosts. Several genes of SARS-CoV-2 demonstrated their significance in determining the host range. Furthermore, the large-scale genome analysis, based on DeepHoF's computation for the later world-wide pandemic in 2020, disclosed the uniformity of host range among SARS-CoV-2 samples and the strong association of SARS-CoV-2 between humans and minks.
Guo, Q.; Li, M.; Wang, C.; Guo, J.; Jiang, X.; Tan, J.; Wu, S.; Wang, P.; Xiao, T.; Zhou, M.; Fang, Z.; Xiao, Y.; Zhu, H.

2021-03-22

General

Modelization of Covid-19 pandemic spreading: A machine learning forecasting with relaxation scenarios of countermeasures.

In Journal of infection and public health

BACKGROUND & OBJECTIVE : Mathematical modeling is the most scientific technique to understand the evolution of natural phenomena, including the spread of infectious diseases. Therefore, these modeling tools have been widely used in epidemiology for predicting risks and decision-making processes. The purpose of this paper is to provide an effective mathematical model for predicting the spread of Covid-19 pandemic.

METHODS : Our mathematical model is performed according to a SIDR model for infectious diseases. Epidemiological data from four countries; Belgium, Morocco, Netherlands and Russia, are used to validate this model. Also, we have evaluated the efficiency of Morocco's Covid-19 countermeasures and simulated the different relaxation plans in order to predict the effects of relaxation countermeasures.

RESULTS AND CONCLUSIONS : In this paper, we developed and validated a new way of data aggregation, modeling and interpretation to predict the spread of Covid-19, evaluate the efficiency of countermeasures and suggest potential scenarios. Our results will be used to keep the spread of Covid-19 under control in the world.

Lmater Moulay A, Eddabbah Mohamed, Elmoussaoui Tariq, Boussaa Samia

2021-Jan-12

Covid-19 pandemic, Machine learning, Mathematical modeling, Simulation

General

Host-dependent molecular factors mediating SARS-CoV-2 infection to gain clinical insights for developing effective targeted therapy.

In Molecular genetics and genomics : MGG
Coronavirus disease 2019 (COVID-19), a recent viral pandemic that first began in December 2019, in Hunan wildlife market, Wuhan, China. The infection is caused by a coronavirus, SARS-CoV-2 and clinically characterized by common symptoms including fever, dry cough, loss of taste/smell, myalgia and pneumonia in severe cases. With overwhelming spikes in infection and death, its pathogenesis yet remains elusive. Since the infection spread rapidly, its healthcare demands are overwhelming with uncontrollable emergencies. Although laboratory testing and analysis are developing at an enormous pace, the high momentum of severe cases demand more rapid strategies for initial screening and patient stratification. Several molecular biomarkers like C-reactive protein, interleukin-6 (IL6), eosinophils and cytokines, and artificial intelligence (AI) based screening approaches have been developed by various studies to assist this vast medical demand. This review is an attempt to collate the outcomes of such studies, thus highlighting the utility of AI in rapid screening of molecular markers along with chest X-rays and other COVID-19 symptoms to enable faster diagnosis and patient stratification. By doing so, we also found that molecular markers such as C-reactive protein, IL-6 eosinophils, etc. showed significant differences between severe and non-severe cases of COVID-19 patients. CT findings in the lungs also showed different patterns like lung consolidation significantly higher in patients with poor recovery and lung lesions and fibrosis being higher in patients with good recovery. Thus, from these evidences we perceive that an initial rapid screening using integrated AI approach could be a way forward in efficient patient stratification.
Shafi Gowhar, Desai Shruti, Srinivasan Krithika, Ramesh Aarthi, Chaturvedi Rupesh, Uttarwar Mohan

2021-Mar-20

Artificial intelligence, COVID-19, Molecular biomarkers, Multiomics, SARS-CoV-2

Ophthalmology

Artificial Intelligence: the unstoppable revolution in ophthalmology.

In Survey of ophthalmology ; h5-index 35.0
Artificial Intelligence (AI) is an unstoppable force that is starting to permeate all aspects of our society as part of the revolution being brought into our lives (and into medicine) by the digital era, and accelerated by the current COVID-19 pandemic. As the population ages and developing countries move forward, AI-based systems may be a key asset in streamlining the screening, staging, and treatment planning of sight-threatening eye conditions, offloading the most tedious tasks from the experts, allowing for a greater population coverage, and bringing the best possible care to every patient. This paper presents a review of the state of the art of AI in the field of ophthalmology, focusing on the strengths and weaknesses of current systems, and defining the vision that will enable us to advance scientifically in this digital era. It starts with a thorough yet accessible introduction to the algorithms underlying all modern AI applications. Then, a critical review of the main AI applications in ophthalmology is presented, including Diabetic Retinopathy, Age-Related Macular Degeneration, Retinopathy of Prematurity, Glaucoma, and other AI-related topics such as image enhancement. The review finishes with a brief discussion on the opportunities and challenges that the future of this field might hold.
Benet David, Pellicer-Valero Oscar J

2021-Mar-16

Age-Related Macular Degeneration, Artificial Intelligence, Deep learning, Diabetic Retinopathy, Glaucoma, Machine Learning, Ophthalmology, Optical Coherence Tomography, Retina, Retinopathy of Prematurity

Radiology

Quantification of COVID-19 Opacities on Chest CT - Evaluation of a Fully Automatic AI-approach to Noninvasively Differentiate Critical Versus Noncritical Patients.

In Academic radiology

OBJECTIVES : To evaluate the potential of a fully automatic artificial intelligence (AI)-driven computed tomography (CT) software prototype to quantify severity of COVID-19 infection on chest CT in relationship with clinical and laboratory data.

METHODS : We retrospectively analyzed 50 patients with laboratory confirmed COVID-19 infection who had received chest CT between March and July 2020. Pulmonary opacifications were automatically evaluated by an AI-driven software and correlated with clinical and laboratory parameters using Spearman-Rho and linear regression analysis. We divided the patients into sub cohorts with or without necessity of intensive care unit (ICU) treatment. Sub cohort differences were evaluated employing Wilcoxon-Mann-Whitney-Test.

RESULTS : We included 50 CT examinations (mean age, 57.24 years), of whom 24 (48%) had an ICU stay. Extent of COVID-19 like opacities on chest CT showed correlations (all p < 0.001 if not otherwise stated) with occurrence of ICU stay (R = 0.74), length of ICU stay (R = 0.81), lethal outcome (R = 0.56) and length of hospital stay (R = 0.33, p < 0.05). The opacities extent was correlated with laboratory parameters: neutrophil count (NEU) (R = 0.60), lactate dehydrogenase (LDH) (R = 0.60), troponin (TNTHS) (R = 0.55) and c-reactive protein (CRP) (R = 0.51). Differences (p < 0.001) between ICU group and non-ICU group concerned longer length of hospital stay (24.04 vs. 10.92 days), higher opacity score (12.50 vs. 4.96) and severity of laboratory data changes such as c-reactive protein (11.64 vs. 5.07 mg/dl, p < 0.01).

CONCLUSIONS : Automatically AI-driven quantification of opacities on chest CT correlates with laboratory and clinical data in patients with confirmed COVID-19 infection and may serve as non-invasive predictive marker for clinical course of COVID-19.

Mader Christoph, Bernatz Simon, Michalik Sabine, Koch Vitali, Martin Simon S, Mahmoudi Scherwin, Basten Lajos, Grünewald Leon D, Bucher Andreas, Albrecht Moritz H, Vogl Thomas J, Booz Christian

2021-Mar-06

Artificial Intelligence, COVID-19, Chest-CT, Pneumonia, SARS-CoV-2 infection, Viral

General

Critical interactions for SARS-CoV-2 spike protein binding to ACE2 identified by machine learning

bioRxiv Preprint
Both SARS-CoV and SARS-CoV-2 bind to the human ACE2 receptor. Based on high-resolution structures, the two viruses bind in practically identical conformations, although several residues of the receptor-binding domain (RBD) differ between them. Here we have used molecular dynamics (MD) simulations, machine learning (ML), and free energy perturbation (FEP) calculations to elucidate the differences in RBD binding by the two viruses. Although only subtle differences were observed from the initial MD simulations of the two RBD-ACE2 complexes, ML identified the individual residues with the most distinctive ACE2 interactions, many of which have been highlighted in previous experimental studies. FEP calculations quantified the corresponding differences in binding free energies to ACE2, and examination of MD trajectories provided structural explanations for these differences. Lastly, the energetics of emerging SARS-CoV-2 mutations were studied, showing that the affinity of the RBD for ACE2 is increased by N501Y and E484K mutations but is slightly decreased by K417N.
Pavlova, A.; Zhang, Z.; Acharya, A.; Lynch, D. L.; Pang, Y. T.; Mou, Z.; Parks, J. M.; Chipot, C.; Gumbart, J. C.

2021-03-21

Radiology

A comparison between manual and artificial intelligence-based automatic positioning in CT imaging for COVID-19 patients.

In European radiology ; h5-index 62.0

OBJECTIVE : To analyze and compare the imaging workflow, radiation dose, and image quality for COVID-19 patients examined using either the conventional manual positioning (MP) method or an AI-based automatic positioning (AP) method.

MATERIALS AND METHODS : One hundred twenty-seven adult COVID-19 patients underwent chest CT scans on a CT scanner using the same scan protocol except with the manual positioning (MP group) for the initial scan and an AI-based automatic positioning method (AP group) for the follow-up scan. Radiation dose, patient positioning time, and off-center distance of the two groups were recorded and compared. Image noise and signal-to-noise ratio (SNR) were assessed by three experienced radiologists and were compared between the two groups.

RESULTS : The AP operation was successful for all patients in the AP group and reduced the total positioning time by 28% compared with the MP group. Compared with the MP group, the AP group had significantly less patient off-center distance (AP 1.56 cm ± 0.83 vs. MP 4.05 cm ± 2.40, p < 0.001) and higher proportion of positioning accuracy (AP 99% vs. MP 92%), resulting in 16% radiation dose reduction (AP 6.1 mSv ± 1.3 vs. MP 7.3 mSv ± 1.2, p < 0.001) and 9% image noise reduction in erector spinae and lower noise and higher SNR for lesions in the pulmonary peripheral areas.

CONCLUSION : The AI-based automatic positioning and centering in CT imaging is a promising new technique for reducing radiation dose and optimizing imaging workflow and image quality in imaging the chest.

KEY POINTS : • The AI-based automatic positioning (AP) operation was successful for all patients in our study. • AP method reduced the total positioning time by 28% compared with the manual positioning (MP). • AP method had less patient off-center distance and higher proportion of positioning accuracy than MP method, resulting in 16% radiation dose reduction and 9% image noise reduction in erector spinae.

Gang Yadong, Chen Xiongfeng, Li Huan, Wang Hanlun, Li Jianying, Guo Ying, Zeng Junjie, Hu Qiang, Hu Jinxiang, Xu Haibo

2021-Mar-19

Artificial intelligence, Coronavirus, Radiation dosage, Tomography

Radiology

Lung Lesion Localization of COVID-19 from Chest CT Image: A Novel Weakly Supervised Learning Method.

In IEEE journal of biomedical and health informatics
Chest computed tomography (CT) image data is necessary for early diagnosis, treatment, and prognosis of Coronavirus Disease 2019(COVID-19). Artificial intelligence has been tried to help clinicians in improving the diagnostic accuracy and working efficiency of CT. Whereas, existing supervised approaches on CT image of COVID-19 pneumonia require voxel-based annotations for training, which take a lot of time and effort. This paper proposed a weakly-supervised method for COVID-19 lesion localization based on generative adversarial network (GAN) with image-level labels only. We first introduced a GAN-based framework to generate normal-looking CT slices from CT slices with COVID-19 lesions. We then developed a novel feature match strategy to improve the reality of generated images by guiding the generator to capture the complex texture of chest CT images. Finally, the localization map of lesions can be easily obtained by subtracting the output image from its corresponding input image. By adding a classifier branch to the GAN-based framework to classify localization maps, we can further develop a diagnosis system with improved classification accuracy. Three CT datasets from hospitals of Sao Paulo, Italian Society of Medical and Interventional Radiology, and China Medical University about COVID-19 were collected in this article for evaluation. Our weakly supervised learning method obtained AUC of 0.883, dice coefficient of 0.575, accuracy of 0.884, sensitivity of 0.647, specificity of 0.929, and F1-score of 0.640, which exceeded other widely used weakly supervised object localization methods by a significant margin. We also compared the proposed method with fully supervised learning methods in COVID-19 lesion segmentation task, the proposed weakly supervised method still leads to a competitive result with dice coefficient of 0.575. Furthermore, we also analyzed the association between illness severity and visual score, we found that the common severity cohort had the largest sample size as well as the highest visual score which suggests our method can help rapid diagnosis of COVID-19 patients, especially in massive common severity cohort. In conclusion, we proposed this novel method can serve as an accurate and efficient tool to alleviate the bottleneck of expert annotation cost and advance the progress of computer-aided COVID-19 diagnosis.
Yang Ziduo, Zhao Lu, Wu Shuyu, Chen Yu-Chian

2021-Mar-19

Radiology

Machine Learning-Based Prediction of COVID-19 Severity and Progression to Critical Illness Using CT Imaging and Clinical Data.

In Korean journal of radiology

OBJECTIVE : To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables.

MATERIALS AND METHODS : Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists.

RESULTS : Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively.

CONCLUSION : CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.

Purkayastha Subhanik, Xiao Yanhe, Jiao Zhicheng, Thepumnoeysuk Rujapa, Halsey Kasey, Wu Jing, Tran Thi My Linh, Hsieh Ben, Choi Ji Whae, Wang Dongcui, Vallières Martin, Wang Robin, Collins Scott, Feng Xue, Feldman Michael, Zhang Paul J, Atalay Michael, Sebro Ronnie, Yang Li, Fan Yong, Liao Wei Hua, Bai Harrison X

2021-Mar-09

COVID-19, CT, Machine learning, Radiomics, Severity

Public Health

Revealing Opinions for COVID-19 Questions Using a Context Retriever, Opinion Aggregator, and Question-Answering Model: Model Development Study.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : COVID-19 has challenged global public health because it is highly contagious and can be lethal. Numerous ongoing and recently published studies about the disease have emerged. However, the research regarding COVID-19 is largely ongoing and inconclusive.

OBJECTIVE : A potential way to accelerate COVID-19 research is to use existing information gleaned from research into other viruses that belong to the coronavirus family. Our objective is to develop a natural language processing method for answering factoid questions related to COVID-19 using published articles as knowledge sources.

METHODS : Given a question, first, a BM25-based context retriever model is implemented to select the most relevant passages from previously published articles. Second, for each selected context passage, an answer is obtained using a pretrained bidirectional encoder representations from transformers (BERT) question-answering model. Third, an opinion aggregator, which is a combination of a biterm topic model and k-means clustering, is applied to the task of aggregating all answers into several opinions.

RESULTS : We applied the proposed pipeline to extract answers, opinions, and the most frequent words related to six questions from the COVID-19 Open Research Dataset Challenge. By showing the longitudinal distributions of the opinions, we uncovered the trends of opinions and popular words in the articles published in the five time periods assessed: before 1990, 1990-1999, 2000-2009, 2010-2018, and since 2019. The changes in opinions and popular words agree with several distinct characteristics and challenges of COVID-19, including a higher risk for senior people and people with pre-existing medical conditions; high contagion and rapid transmission; and a more urgent need for screening and testing. The opinions and popular words also provide additional insights for the COVID-19-related questions.

CONCLUSIONS : Compared with other methods of literature retrieval and answer generation, opinion aggregation using our method leads to more interpretable, robust, and comprehensive question-specific literature reviews. The results demonstrate the usefulness of the proposed method in answering COVID-19-related questions with main opinions and capturing the trends of research about COVID-19 and other relevant strains of coronavirus in recent years.

Lu Zhao-Hua, Wang Jade Xiaoqing, Li Xintong

2021-Mar-19

COVID-19, coronavirus literature, language summarization, life and medical sciences, machine learning, natural language processing, public health, question-answering systems

General

Transfer learning-based ensemble support vector machine model for automated COVID-19 detection using lung computerized tomography scan data.

In Medical & biological engineering & computing ; h5-index 32.0
The novel discovered disease coronavirus popularly known as COVID-19 is caused due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and declared a pandemic by the World Health Organization (WHO). An early-stage detection of COVID-19 is crucial for the containment of the pandemic it has caused. In this study, a transfer learning-based COVID-19 screening technique is proposed. The motivation of this study is to design an automated system that can assist medical staff especially in areas where trained staff are outnumbered. The study investigates the potential of transfer learning-based models for automatically diagnosing diseases like COVID-19 to assist the medical force, especially in times of an outbreak. In the proposed work, a deep learning model, i.e., truncated VGG16 (Visual Geometry Group from Oxford) is implemented to screen COVID-19 CT scans. The VGG16 architecture is fine-tuned and used to extract features from CT scan images. Further principal component analysis (PCA) is used for feature selection. For the final classification, four different classifiers, namely deep convolutional neural network (DCNN), extreme learning machine (ELM), online sequential ELM, and bagging ensemble with support vector machine (SVM) are compared. The best performing classifier bagging ensemble with SVM within 385 ms achieved an accuracy of 95.7%, the precision of 95.8%, area under curve (AUC) of 0.958, and an F1 score of 95.3% on 208 test images. The results obtained on diverse datasets prove the superiority and robustness of the proposed work. A pre-processing technique has also been proposed for radiological data. The study further compares pre-trained CNN architectures and classification models against the proposed technique.
Singh Mukul, Bansal Shrey, Ahuja Sakshi, Dubey Rahul Kumar, Panigrahi Bijaya Ketan, Dey Nilanjan

2021-Mar-18

COVID-19, CT scan data, Ensemble SVM, Transfer learning, VGG16

Internal Medicine

Effects of laser acupuncture tele-therapy for rheumatoid arthritis elderly patients.

In Lasers in medical science
Rheumatoid arthritis (RA) is a progressive common autoimmune disorder and is one of the most functional limiting diseases in elderly. Until recently, its treatment is mainly based on physical locations and meetings while being face to face. However, laser acupuncture tele-therapy approaches can significantly provide the patient with safety during the COVID-19 pandemic as well as changing the disorder's prognosis. Sixty patients were assigned randomly into 2 groups with 1:1 ratio. Patients in group A are treated remotely by laser acupuncture in addition to methotrexate and a tele-rehabilitation program in the form of aerobic exercise training. Patients in group B are treated by methotrexate and a tele-rehabilitation program in the form of aerobic exercise. There was a statistically significant difference in health assessment questionnaire (HAQ) pre- and post-treatment in group A (p < 0.05). The C-reactive protein (CRP) and interleukin-6 (IL-6) inflammatory markers as well as the malondialdehyde (MDA) oxidative marker showed a significant reduction pre- and post-treatment in group A (p < 0.05). Additionally, there was a significant increase in the adenosine tri-phosphate (ATP) antioxidant marker pre- and post-treatment in group A (p < 0.05). The comparison between groups A and B showed a statistically significant post-treatment difference in RAQoL, CRP, IL-6, ATP, and MDA in group A than group B. Considering the significant improvement that was found in the laser acupuncture group, it can be concluded that the use of laser acupuncture as adjunctive was effective in the treatment of elderly patients with RA. ClinicalTrials.gov Identifier: NCT04758689.
Adly Afnan Sedky, Adly Aya Sedky, Adly Mahmoud Sedky, Abdeen Heba Ahmed Ali

2021-Mar-19

Elderly, Geriatrics, Laser acupuncture, Rheumatoid arthritis, Tele-rehabilitation

General

AI detection of mild COVID-19 pneumonia from chest CT scans.

In European radiology ; h5-index 62.0

OBJECTIVES : An artificial intelligence model was adopted to identify mild COVID-19 pneumonia from computed tomography (CT) volumes, and its diagnostic performance was then evaluated.

METHODS : In this retrospective multicenter study, an atrous convolution-based deep learning model was established for the computer-assisted diagnosis of mild COVID-19 pneumonia. The dataset included 2087 chest CT exams collected from four hospitals between 1 January 2019 and 31 May 2020. The true positive rate, true negative rate, receiver operating characteristic curve, area under the curve (AUC) and convolutional feature map were used to evaluate the model.

RESULTS : The proposed deep learning model was trained on 1538 patients and tested on an independent testing cohort of 549 patients. The overall sensitivity was 91.5% (195/213; p < 0.001, 95% CI: 89.2-93.9%), the overall specificity was 90.5% (304/336; p < 0.001, 95% CI: 88.0-92.9%) and the general AUC value was 0.955 (p < 0.001).

CONCLUSIONS : A deep learning model can accurately detect COVID-19 and serve as an important supplement to the COVID-19 reverse transcription-polymerase chain reaction (RT-PCR) test.

KEY POINTS : • The implementation of a deep learning model to identify mild COVID-19 pneumonia was confirmed to be effective and feasible. • The strategy of using a binary code instead of the region of interest label to identify mild COVID-19 pneumonia was verified. • This AI model can assist in the early screening of COVID-19 without interfering with normal clinical examinations.

Yao Jin-Cao, Wang Tao, Hou Guang-Hua, Ou Di, Li Wei, Zhu Qiao-Dan, Chen Wen-Cong, Yang Chen, Wang Li-Jing, Wang Li-Ping, Fan Lin-Yin, Shi Kai-Yuan, Zhang Jie, Xu Dong, Li Ya-Qing

2021-Mar-18

Artificial intelligence, COVID-19, Computer-assisted diagnosis, Deep learning, Volume CT

General

Analysis of the real number of infected people by COVID-19: A system dynamics approach.

In PloS one ; h5-index 176.0
At the beginning of 2020, the COVID-19 pandemic was able to spread quickly in Wuhan and in the province of Hubei due to a lack of experience with this novel virus. Additionally, authories had no proven experience with applying insufficient medical, communication and crisis management tools. For a considerable period of time, the actual number of people infected was unknown. There were great uncertainties regarding the dynamics and spread of the Covid-19 virus infection. In this paper, we develop a system dynamics model for the three connected regions (Wuhan, Hubei excl. Wuhan, China excl. Hubei) to understand the infection and spread dynamics of the virus and provide a more accurate estimate of the number of infected people in Wuhan and discuss the necessity and effectivity of protective measures against this epidemic, such as the quarantines imposed throughout China. We use the statistics of confirmed cases of China excl. Hubei. Also the daily data on travel activity within China was utilized, in order to determine the actual numerical development of the infected people in Wuhan City and Hubei Province. We used a multivariate Monte Carlo optimization to parameterize the model to match the official statistics. In particular, we used the model to calculate the infections, which had already broken out, but were not diagnosed for various reasons.
Hu Bo, Dehmer Matthias, Emmert-Streib Frank, Zhang Bo

2021

General

Machine learning models for image-based diagnosis and prognosis of COVID-19: A systematic review.

In JMIR medical informatics ; h5-index 23.0

BACKGROUND : In order to provide the best possible care for COVID-19 patients and reduce the burden on the health care system, accurate and timely diagnosis and effective prognosis of this disease is important. Machine learning methods can play vital roles in diagnosing COVID-19 by processing chest x-ray images.

OBJECTIVE : Our aim of this study is to summarize information on the use of intelligent models for diagnosing and prognosing the COVID-19 to help early and timely diagnosis of the disease to help with health.

METHODS : A systematic search of the PubMed, Web of Science, IEEE, ProQuest, Scopus, bioRxiv, and medRxiv databases up to 24 May 2020 is performed. To conduct this study, PRISMA guidelines were followed. All original articles applying image processing for predicting and diagnosing the COVID-19 disease were considered. Two reviewers independently assessed original papers to determine eligibility for inclusion. Risk of bias was evaluated by using Prediction Model Risk of Bias Assessment Tool (PROBAST).

RESULTS : Of the 629 articles retrieved, 45studies were included. The review identified 4 prognosis models for calculating prediction of disease severity and estimation of confinement time, for individual patients, and 41 diagnosis models for detecting COVID-19 from normal or other pneumonias. Most articles used deep learning methods based on CNN networks which have been used widely as a classification algorithm The most frequently reported predictors of prognosis in patients with COVID-19 included age, CT data, gender, comorbidities, symptoms and laboratory findings. Deep CNN obtained better results compared with non-Neural Network-based methods. Moreover, all of the models are in high risk of bias due to the lack of information about study population, intended groups and inappropriate reporting.

CONCLUSIONS : Machine learning models for diagnosis and prognosis of COVID-19 showed excellent discriminative performance approximately. However, these models were at high risk of bias, because of various reasons like low information about participants, randomizing process and lack of external validation. Therefore, it leads to optimistic report in their models. In hence this review doesn't recommend any of the current models to be used in practice.

CLINICALTRIAL :

Montazeri Mahdieh, ZahediNasab Roxana, Farahani Ali, Mohseni Hadis, Ghasemian Fahimeh

2021-Jan-16

Public Health

Case Report: Utilizing AI and NLP to Assist with Healthcare and Rehabilitation During the COVID-19 Pandemic.

In Frontiers in artificial intelligence
The COVID-19 pandemic has profoundly affected healthcare systems and healthcare delivery worldwide. Policy makers are utilizing social distancing and isolation policies to reduce the risk of transmission and spread of COVID-19, while the research, development, and testing of antiviral treatments and vaccines are ongoing. As part of these isolation policies, in-person healthcare delivery has been reduced, or eliminated, to avoid the risk of COVID-19 infection in high-risk and vulnerable populations, particularly those with comorbidities. Clinicians, occupational therapists, and physiotherapists have traditionally relied on in-person diagnosis and treatment of acute and chronic musculoskeletal (MSK) and neurological conditions and illnesses. The assessment and rehabilitation of persons with acute and chronic conditions has, therefore, been particularly impacted during the pandemic. This article presents a perspective on how Artificial Intelligence and Machine Learning (AI/ML) technologies, such as Natural Language Processing (NLP), can be used to assist with assessment and rehabilitation for acute and chronic conditions.
Carriere Jay, Shafi Hareem, Brehon Katelyn, Pohar Manhas Kiran, Churchill Katie, Ho Chester, Tavakoli Mahdi

2021

COVID-19, artificial intelligence, natural language processing, neuromusculoskeletal rehabilitation, smart health

Ophthalmology

Artificial Intelligence and Telehealth may Provide Early Warning of Epidemics.

In Frontiers in artificial intelligence
The COVID-19 pandemic produced a very sudden and serious impact on public health around the world, greatly adding to the burden of overloaded professionals and national medical systems. Recent medical research has demonstrated the value of using online systems to predict emerging spatial distributions of transmittable diseases. Concerned internet users often resort to online sources in an effort to explain their medical symptoms. This raises the prospect that incidence of COVID-19 may be tracked online by search queries and social media posts analyzed by advanced methods in data science, such as Artificial Intelligence. Online queries can provide early warning of an impending epidemic, which is valuable information needed to support planning timely interventions. Identification of the location of clusters geographically helps to support containment measures by providing information for decision-making and modeling.
Arslan Janan, Benke Kurt K

2021

COVID-19, artificial intelligence, digital disease detection, epidemiology, pattern recognition, telehealth, virus

Public Health

Forecasting and Evaluating Multiple Interventions for COVID-19 Worldwide.

In Frontiers in artificial intelligence
As the Covid-19 pandemic surges around the world, questions arise about the number of global cases at the pandemic's peak, the length of the pandemic before receding, and the timing of intervention strategies to significantly stop the spread of Covid-19. We have developed artificial intelligence (AI)-inspired methods for modeling the transmission dynamics of the epidemics and evaluating interventions to curb the spread and impact of COVID-19. The developed methods were applied to the surveillance data of cumulative and new COVID-19 cases and deaths reported by WHO as of March 16th, 2020. Both the timing and the degree of intervention were evaluated. The average error of five-step ahead forecasting was 2.5%. The total peak number of cumulative cases, new cases, and the maximum number of cumulative cases in the world with complete intervention implemented 4 weeks later than the beginning date (March 16th, 2020) reached 75,249,909, 10,086,085, and 255,392,154, respectively. However, the total peak number of cumulative cases, new cases, and the maximum number of cumulative cases in the world with complete intervention after 1 week were reduced to 951,799, 108,853 and 1,530,276, respectively. Duration time of the COVID-19 spread was reduced from 356 days to 232 days between later and earlier interventions. We observed that delaying intervention for 1 month caused the maximum number of cumulative cases reduce by -166.89 times that of earlier complete intervention, and the number of deaths increased from 53,560 to 8,938,725. Earlier and complete intervention is necessary to stem the tide of COVID-19 infection.
Hu Zixin, Ge Qiyang, Li Shudi, Boerwinkle Eric, Jin Li, Xiong Momiao

2020

COVID-19, artificial intelligence, auto-encoder, forecasting, time series, transmission dynamics

Pathology

Deep Learning Analysis Improves Specificity of SARS-CoV-2 Real Time PCR.

In Journal of clinical microbiology ; h5-index 74.0
Real time polymerase chain reaction (RT-PCR) is widely used to diagnose human pathogens. RT-PCR data is traditionally analyzed by estimating the threshold cycle (C_T) at which the fluorescence signal produced by emission of a probe crosses a baseline level. Current models used to estimate the C_T value are based on approximations that do not adequately account for the stochastic variation of the fluorescence signal that is detected during RT-PCR. Less common deviations become more apparent as the sample size increases, as is the case in the current SARS-CoV-2 pandemic. In this work we employ a method independent of C_T value to interpret to RT-PCR data. In this novel approach we built and trained a deep learning model, qPCRdeepNet, to analyze the fluorescent readings obtained during RT-PCR. We describe how this model can be deployed as a quality assurance tool to monitor results interpretation in real-time. The model's performance with the TaqPath COVID19 Combo Kit, widely used for SARS-CoV-2 detection, is described. This model can be applied broadly for the primary interpretation of RT-PCR assays and potentially replace the C_T interpretive paradigm.
Alouani David J, Rajapaksha Roshani R P, Jani Mehul, Rhoads Daniel D, Sadri Navid

2021-Mar-17

General

RENET2: High-Performance Full-text Gene-Disease Relation Extraction with Iterative Training Data Expansion

bioRxiv Preprint
Background: Relation extraction is a fundamental task for extracting gene-disease associations from biomedical text. Existing tools have limited capacity, as they can extract gene-disease associations only from single sentences or abstract texts. Results: In this work, we propose RENET2, a deep learning-based relation extraction method, which implements section filtering and ambiguous relations modeling to extract gene-disease associations from full-text articles. We designed a novel iterative training data expansion strategy to build an annotated full-text dataset to resolve the scarcity of labels on full-text articles. In our experiments, RENET2 achieved an F1-score of 72.13% for extracting gene-disease associations from an annotated full-text dataset, which was 27.22%, 30.30% and 29.24% higher than the best existing tools BeFree, DTMiner and BioBERT, respectively. We applied RENET2 to (1) ~1.89M full-text articles from PMC and found ~3.72M gene-disease associations; and (2) the LitCovid articles set and ranked the top 15 proteins associated with COVID-19, supported by recent articles. Conclusion: RENET2 is an efficient and accurate method for full-text gene-disease association extraction. The source-code, manually curated abstract/full-text training data, and results of RENET2 are available at https://github.com/sujunhao/RENET2.
Su, J.; Wu, Y.; Ting, H.-F.; Lam, T.-W.; Luo, R.

2021-03-19

General

Variational Knowledge Distillation for Disease Classification in Chest X-Rays

ArXiv Preprint
Disease classification relying solely on imaging data attracts great interest in medical image analysis. Current models could be further improved, however, by also employing Electronic Health Records (EHRs), which contain rich information on patients and findings from clinicians. It is challenging to incorporate this information into disease classification due to the high reliance on clinician input in EHRs, limiting the possibility for automated diagnosis. In this paper, we propose \textit{variational knowledge distillation} (VKD), which is a new probabilistic inference framework for disease classification based on X-rays that leverages knowledge from EHRs. Specifically, we introduce a conditional latent variable model, where we infer the latent representation of the X-ray image with the variational posterior conditioning on the associated EHR text. By doing so, the model acquires the ability to extract the visual features relevant to the disease during learning and can therefore perform more accurate classification for unseen patients at inference based solely on their X-ray scans. We demonstrate the effectiveness of our method on three public benchmark datasets with paired X-ray images and EHRs. The results show that the proposed variational knowledge distillation can consistently improve the performance of medical image classification and significantly surpasses current methods.
Tom van Sonsbeek, Xiantong Zhen, Marcel Worring, Ling Shao

2021-03-19

Radiology

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

In IEEE/ACM transactions on computational biology and bioinformatics
Due to the shortage of COVID-19 viral testing kits, radiology is used to complement the screening process. Deep learning methods are promising in automatically detecting COVID-19 disease in chest x-ray images. Most of these works first train a Convolutional Neural Network (CNN) on an existing large-scale chest x-ray image dataset and then fine-tune the model on the newly collected COVID-19 chest x-ray dataset, often at a much smaller scale. However, simple fine-tuning may lead to poor performance due to two issues, firstly the large domain shift present in chest x-ray datasets and secondly the relatively small scale of the COVID-19 chest x-ray dataset. In an attempt to address these issues, we formulate the problem of COVID-19 chest x-ray image classification in a semi-supervised open set domain adaptation setting and propose a novel domain adaptation method, Semi-supervised Open set Domain Adversarial network (SODA). SODA is designed to align the data distributions across different domains in the general domain space and also in the common subspace of source and target data. In our experiments, SODA achieves a leading classification performance compared with recent state-of-the-art models in separating COVID-19 with common pneumonia. We also present results showing that SODA produces better pathology localizations.
Zhou Jieli, Jing Baoyu, Wang Zeya, Xin Hongyi, Tong Hanghang

2021-Mar-17

General

An analysis of the development of digital health technologies to fight COVID-19 in Brazil and the world.

In Cadernos de saude publica
The coronavirus pandemic that struck the world in late 2019 continues to break records of new cases and deaths from the disease. Guidelines for clinical management of infected patients and prevention of new cases focus on measures to control symptoms, hygiene habits, social distancing, and decrease in human crowding. This forced a change in the way health services provide care, generating the incorporation of new health technologies. The Essay thus aims to compile and analyze experiences in the use of digital health technologies to minimize the impacts of COVID-19. The authors identified the development of technological solutions for clinical management of patients, imaging diagnosis, use of artificial intelligence for risk analysis, geolocation apps, data analysis and reports, self-diagnosis, and even orientation for decision-making. The great majority of the initiatives listed here prove effective in minimizing the impacts of COVID-19 on health systems and aim to decrease human crowding and thus facilitate access to services, besides contributing to the incorporation of new health practices and modes of care.
Celuppi Ianka Cristina, Lima Geovana Dos Santos, Rossi Elaine, Wazlawick Raul Sidnei, Dalmarco Eduardo Monguilhott

2021

General

IoT enabled depthwise separable convolution neural network with deep support vector machine for COVID-19 diagnosis and classification.

In International journal of machine learning and cybernetics
At present times, the drastic advancements in the 5G cellular and internet of things (IoT) technologies find useful in different applications of the healthcare sector. At the same time, COVID-19 is commonly spread from animals to persons, but today it is transmitting among persons by adapting the structure. It is a severe virus and inappropriately resulted in a global pandemic. Radiologists utilize X-ray or computed tomography (CT) images to diagnose COVID-19 disease. It is essential to identify and classify the disease through the use of image processing techniques. So, a new intelligent disease diagnosis model is in need to identify the COVID-19. In this view, this paper presents a novel IoT enabled Depthwise separable convolution neural network (DWS-CNN) with Deep support vector machine (DSVM) for COVID-19 diagnosis and classification. The proposed DWS-CNN model aims to detect both binary and multiple classes of COVID-19 by incorporating a set of processes namely data acquisition, Gaussian filtering (GF) based preprocessing, feature extraction, and classification. Initially, patient data will be collected in the data acquisition stage using IoT devices and sent to the cloud server. Besides, the GF technique is applied to remove the existence of noise that exists in the image. Then, the DWS-CNN model is employed for replacing default convolution for automatic feature extraction. Finally, the DSVM model is applied to determine the binary and multiple class labels of COVID-19. The diagnostic outcome of the DWS-CNN model is tested against Chest X-ray (CXR) image dataset and the results are investigated interms of distinct performance measures. The experimental results ensured the superior results of the DWS-CNN model by attaining maximum classification performance with the accuracy of 98.54% and 99.06% on binary and multiclass respectively.
Le Dac-Nhuong, Parvathy Velmurugan Subbiah, Gupta Deepak, Khanna Ashish, Rodrigues Joel J P C, Shankar K

2021-Jan-02

COVID-19, Convolutional neural network, Deep learning, Feature extraction, Multilabel classification

General

CheXbreak: Misclassification Identification for Deep Learning Models Interpreting Chest X-rays

ArXiv Preprint
A major obstacle to the integration of deep learning models for chest x-ray interpretation into clinical settings is the lack of understanding of their failure modes. In this work, we first investigate whether there are patient subgroups that chest x-ray models are likely to misclassify. We find that patient age and the radiographic finding of lung lesion or pneumothorax are statistically relevant features for predicting misclassification for some chest x-ray models. Second, we develop misclassification predictors on chest x-ray models using their outputs and clinical features. We find that our best performing misclassification identifier achieves an AUROC close to 0.9 for most diseases. Third, employing our misclassification identifiers, we develop a corrective algorithm to selectively flip model predictions that have high likelihood of misclassification at inference time. We observe F1 improvement on the prediction of Consolidation (0.008 [95\% CI 0.005, 0.010]) and Edema (0.003, [95\% CI 0.001, 0.006]). By carrying out our investigation on ten distinct and high-performing chest x-ray models, we are able to derive insights across model architectures and offer a generalizable framework applicable to other medical imaging tasks.
Emma Chen, Andy Kim, Rayan Krishnan, Jin Long, Andrew Y. Ng, Pranav Rajpurkar

2021-03-18

General

Circular RNA-MicroRNA-MRNA interaction predictions in SARS-CoV-2 infection.

In Journal of integrative bioinformatics
Different types of noncoding RNAs like microRNAs (miRNAs) and circular RNAs (circRNAs) have been shown to take part in various cellular processes including post-transcriptional gene regulation during infection. MiRNAs are expressed by more than 200 organisms ranging from viruses to higher eukaryotes. Since miRNAs seem to be involved in host-pathogen interactions, many studies attempted to identify whether human miRNAs could target severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mRNAs as an antiviral defence mechanism. In this work, a machine learning based miRNA analysis workflow was developed to predict differential expression patterns of human miRNAs during SARS-CoV-2 infection. In order to obtain the graphical representation of miRNA hairpins, 36 features were defined based on the secondary structures. Moreover, potential targeting interactions between human circRNAs and miRNAs as well as human miRNAs and viral mRNAs were investigated.
Demirci Yılmaz Mehmet, Saçar Demirci Müşerref Duygu

2021-Mar-16

SARS-CoV-2, circRNA, gene regulation, machine learning, miRNA

Public Health

Genetic and structural analyses of ssRNA viruses pave the way for the discovery of novel antiviral pharmacological targets.

In Molecular omics
In the era of big data and artificial intelligence, a lot of new discoveries have influenced the fields of antiviral drug design and pharmacophore identification. Viruses have always been a threat to society in terms of public health and economic stability. Viruses not only affect humans but also livestock and agriculture with a direct impact on food safety, economy and environmental imprint. Most recently, with the pandemic of COVID-19, it was made clear that a single virus can have a devastating impact on global well-being and economy. In this direction, there is an emerging need for the identification of promising pharmacological targets in viruses. Herein, an effort has been made to discuss the current knowledge, state-of-the-art applications and future implications for the main pharmacological targets of single-stranded RNA viruses.
Vlachakis Dimitrios

2021-Mar-16

Public Health

Artificial intelligence-enabled analysis of UK and US public attitudes on Facebook and Twitter towards COVID-19 vaccinations.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Global efforts towards the development and deployment of a vaccine for SARS-CoV-2 are rapidly advancing. To achieve herd immunity, widespread administration is required which necessitates significant cooperation from the general public. As such, it is crucial that governments and public health agencies understand public sentiment towards vaccines, which can help guide educational campaigns and other targeted policy interventions.

OBJECTIVE : The aim of this study was to develop and apply an artificial-intelligence (AI)-based approach to analyse social-media public sentiment in the United Kingdom (UK) and the United States (US) towards COVID-19 vaccinations, to better understand public attitude and identify topics of concern.

METHODS : Over 300,000 social-media posts related to COVID-19 vaccinations were extracted, including 23,571 Facebook-posts from the UK and 144,864 from the US, along with 40,268 tweets from the UK and 98,385 from the US respectively, from 1st March - 22nd November 2020. We used natural-language processing and deep learning-based techniques to predict average sentiments, sentiment trends and topics of discussion. These were analysed longitudinally and geo-spatially, and a manual-reading of randomly selected posts around points of interest helped identify underlying themes and validated insights from the analysis.

RESULTS : We found overall averaged positive, negative and neutral sentiment in the UK to be 58%, 22% and 17%, compared to 56%, 24% and 18% in the US, respectively. Public optimism over vaccine development, effectiveness and trials as well as concerns over safety, economic viability and corporation control were identified. We compared our findings to national surveys in both countries and found them to correlate broadly.

CONCLUSIONS : AI-enabled social-media analysis should be considered for adoption by institutions and governments, alongside surveys and other conventional methods of assessing public attitude. This could enable real-time assessment, at scale, of public confidence and trust in COVID-19 vaccinations, help address concerns of vaccine-sceptics and develop more effective policies and communication strategies to maximise uptake.

CLINICALTRIAL :

Hussain Amir, Tahir Ahsen, Hussain Zain, Sheikh Zakariya, Gogate Mandar, Dashtipour Kia, Ali Azhar, Sheikh Aziz

2021-Jan-31

Public Health

Emotional Attitudes of Chinese Citizens on Social Distancing During the COVID-19 Outbreak: Analysis of Social Media Data.

In JMIR medical informatics ; h5-index 23.0

BACKGROUND : Wuhan, China, the epicenter of the COVID-19 pandemic, imposed citywide lockdown measures on January 23, 2020. Neighboring cities in Hubei Province followed suit with the government enforcing social distancing measures to restrict the spread of the disease throughout the province. Few studies have examined the emotional attitudes of citizens as expressed on social media toward the imposed social distancing measures and the factors that affected their emotions.

OBJECTIVE : The aim of this study was twofold. First, we aimed to detect the emotional attitudes of different groups of users on Sina Weibo toward the social distancing measures imposed by the People's Government of Hubei Province. Second, the influencing factors of their emotions, as well as the impact of the imposed measures on users' emotions, was studied.

METHODS : Sina Weibo, one of China's largest social media platforms, was chosen as the primary data source. The time span of selected data was from January 21, 2020, to March 24, 2020, while analysis was completed in late June 2020. Bi-directional long short-term memory (Bi-LSTM) was used to analyze users' emotions, while logistic regression analysis was employed to explore the influence of explanatory variables on users' emotions, such as age and spatial location. Further, the moderating effects of social distancing measures on the relationship between user characteristics and users' emotions were assessed by observing the interaction effects between the measures and explanatory variables.

RESULTS : Based on the 63,169 comments obtained, we identified six topics of discussion-(1) delaying the resumption of work and school, (2) travel restrictions, (3) traffic restrictions, (4) extending the Lunar New Year holiday, (5) closing public spaces, and (6) community containment. There was no multicollinearity in the data during statistical analysis; the Hosmer-Lemeshow goodness-of-fit was 0.24 (χ²₈=10.34, P>.24). The main emotions shown by citizens were negative, including anger and fear. Users located in Hubei Province showed the highest amount of negative emotions in Mainland China. There are statistically significant differences in the distribution of emotional polarity between social distancing measures (χ²₂₀=19,084.73, P<.001), as well as emotional polarity between genders (χ²₄=1784.59, P<.001) and emotional polarity between spatial locations (χ²₄=1659.67, P<.001). Compared with other types of social distancing measures, the measures of delaying the resumption of work and school or travel restrictions mainly had a positive moderating effect on public emotion, while traffic restrictions or community containment had a negative moderating effect on public emotion.

CONCLUSIONS : Findings provide a reference point for the adoption of epidemic prevention and control measures, and are considered helpful for government agencies to take timely actions to alleviate negative emotions during public health emergencies.

Shen Lining, Yao Rui, Zhang Wenli, Evans Richard, Cao Guang, Zhang Zhiguo

2021-Mar-16

COVID-19, Sina Weibo, attitude, deep learning, emotion, emotional analysis, infodemiology, infoveillance, machine learning, moderating effects, social distancing measures, social media

General

Automatic Detection of COVID-19 Based on Short-Duration Acoustic Smartphone Speech Analysis.

In Journal of healthcare informatics research
Currently, there is an increasing global need for COVID-19 screening to help reduce the rate of infection and at-risk patient workload at hospitals. Smartphone-based screening for COVID-19 along with other respiratory illnesses offers excellent potential due to its rapid-rollout remote platform, user convenience, symptom tracking, comparatively low cost, and prompt result processing timeframe. In particular, speech-based analysis embedded in smartphone app technology can measure physiological effects relevant to COVID-19 screening that are not yet digitally available at scale in the healthcare field. Using a selection of the Sonde Health COVID-19 2020 dataset, this study examines the speech of COVID-19-negative participants exhibiting mild and moderate COVID-19-like symptoms as well as that of COVID-19-positive participants with mild to moderate symptoms. Our study investigates the classification potential of acoustic features (e.g., glottal, prosodic, spectral) from short-duration speech segments (e.g., held vowel, pataka phrase, nasal phrase) for automatic COVID-19 classification using machine learning. Experimental results indicate that certain feature-task combinations can produce COVID-19 classification accuracy of up to 80% as compared with using the all-acoustic feature baseline (68%). Further, with brute-forced n-best feature selection and speech task fusion, automatic COVID-19 classification accuracy of upwards of 82-86% was achieved, depending on whether the COVID-19-negative participant had mild or moderate COVID-19-like symptom severity.
Stasak Brian, Huang Zhaocheng, Razavi Sabah, Joachim Dale, Epps Julien

2021-Mar-11

Digital medicine, Machine learning, Remote sensing, Respiratory illness

General

Data science approach to stock prices forecasting in Indonesia during Covid-19 using Long Short-Term Memory (LSTM).

In Journal of big data

Background : Stock market process is full of uncertainty; hence stock prices forecasting very important in finance and business. For stockbrokers, understanding trends and supported by prediction software for forecasting is very important for decision making. This paper proposes a data science model for stock prices forecasting in Indonesian exchange based on the statistical computing based on R language and Long Short-Term Memory (LSTM).

Findings : The first Covid-19 (Coronavirus disease-19) confirmed case in Indonesia is on 2 March 2020. After that, the composite stock price index has plunged 28% since the start of the year and the share prices of cigarette producers and banks in the midst of the corona pandemic reached their lowest value on March 24, 2020. We use the big data from Bank of Central Asia (BCA) and Bank of Mandiri from Indonesia obtained from Yahoo finance. In our experiments, we visualize the data using data science and predict and simulate the important prices called Open, High, Low and Closing (OHLC) with various parameters.

Conclusions : Based on the experiment, data science is very useful for visualization data and our proposed method using Long Short-Term Memory (LSTM) can be used as predictor in short term data with accuracy 94.57% comes from the short term (1 year) with high epoch in training phase rather than using 3 years training data.

Budiharto Widodo

2021

Data science, Deep learning, Finance, Forecasting, LSTM, Stock market

General

Identifying mortality factors from Machine Learning using Shapley values - a case of COVID19.

In Expert systems with applications
In this paper we apply a series of Machine Learning models to a recently published unique dataset on the mortality of COVID19 patients. We use a dataset consisting of blood samples of 375 patients admitted to a hospital in the region of Wuhan, China. There are 201 patients who survived hospitalisation and 174 patients who died whilst in hospital. The focus of the paper is not only on seeing which Machine Learning model is able to obtain the absolute highest accuracy but more on the interpretation of what the Machine Learning models provides. We find that age, days in hospital, Lymphocyte and Neutrophils are important and robust predictors when predicting a patients mortality. Furthermore, the algorithms we use allows us to observe the marginal impact of each variable on a case-by-case patient level, which might help practicioneers to easily detect anomalous patterns. This paper analyses the global and local interpretation of the Machine Learning models on patients with COVID19.
Smith Matthew, Alvarez Francisco

2021-Mar-11

COVID19, Coronavirus, Machine Learning, Shapley Values

General

Physiological parameters of mental health predict the emergence of post-traumatic stress symptoms in physicians treating COVID-19 patients.

In Translational psychiatry ; h5-index 60.0
Lack of established knowledge and treatment strategies, and change in work environment, may altogether critically affect the mental health and functioning of physicians treating COVID-19 patients. Thus, we examined whether treating COVID-19 patients affect the physicians' mental health differently compared with physicians treating non-COVID-19 patients. In this cohort study, an association was blindly computed between physiologically measured anxiety and attention vigilance (collected from 1 May 2014 to 31 May 31 2016) and self-reports of anxiety, mental health aspects, and sleep quality (collected from 20 April to 30 June 2020, and analyzed from 1 July to 1 September 2020), of 91 physicians treating COVID-19 or non-COVID-19 patients. As a priori hypothesized, physicians treating COVID-19 patients showed a relative elevation in both physiological measures of anxiety (95% CI: 2317.69-2453.44 versus 1982.32-2068.46; P < 0.001) and attention vigilance (95% CI: 29.85-34.97 versus 22.84-26.61; P < 0.001), compared with their colleagues treating non-COVID-19 patients. At least 3 months into the pandemic, physicians treating COVID-19 patients reported high anxiety and low quality of sleep. Machine learning showed clustering to the COVID-19 and non-COVID-19 subgroups with a high correlation mainly between physiological and self-reported anxiety, and between physiologically measured anxiety and sleep duration. To conclude, the pattern of attention vigilance, heightened anxiety, and reduced sleep quality findings point the need for mental intervention aimed at those physicians susceptible to develop post-traumatic stress symptoms, owing to the consequences of fighting at the forefront of the COVID-19 pandemic.
Dolev T, Zubedat S, Brand Z, Bloch B, Mader E, Blondheim O, Avital A

2021-Mar-15

General

Identification of Images of COVID-19 from Chest X-rays Using Deep Learning: Comparing COGNEX VisionPro Deep Learning 1.0™ Software with Open Source Convolutional Neural Networks.

In SN computer science
The novel Coronavirus, COVID-19, pandemic is being considered the most crucial health calamity of the century. Many organizations have come together during this crisis and created various Deep Learning models for the effective diagnosis of COVID-19 from chest radiography images. For example, The University of Waterloo, along with Darwin AI-a start-up spin-off of this department, has designed the Deep Learning model 'COVID-Net' and created a dataset called 'COVIDx' consisting of 13,975 images across 13,870 patient cases. In this study, COGNEX's Deep Learning Software, VisionPro Deep Learning™, is used to classify these Chest X-rays from the COVIDx dataset. The results are compared with the results of COVID-Net and various other state-of-the-art Deep Learning models from the open-source community. Deep Learning tools are often referred to as black boxes because humans cannot interpret how or why a model is classifying an image into a particular class. This problem is addressed by testing VisionPro Deep Learning with two settings, first, by selecting the entire image as the Region of Interest (ROI), and second, by segmenting the lungs in the first step, and then doing the classification step on the segmented lungs only, instead of using the entire image. VisionPro Deep Learning results: on the entire image as the ROI it achieves an overall F score of 94.0%, and on the segmented lungs, it gets an F score of 95.3%, which is better than COVID-Net and other state-of-the-art open-source Deep Learning models.
Sarkar Arjun, Vandenhirtz Joerg, Nagy Jozsef, Bacsa David, Riley Mitchell

2021

COVID-19, COVID-net, Classification, Confidence interval, Deep learning, DenseNet, F score, Inception, Positive predictive values, ResNet, Segmentation, Sensitivity, VGG19, VisionPro deep learning, X-ray imaging

Radiology

Quantitative CT imaging and advanced visualization methods: potential application in novel coronavirus disease 2019 (COVID-19) pneumonia.

In BJR open
Increasingly, quantitative lung computed tomography (qCT)-derived metrics are providing novel insights into chronic inflammatory lung diseases, including chronic obstructive pulmonary disease, asthma, interstitial lung disease, and more. Metrics related to parenchymal, airway, and vascular anatomy together with various measures associated with lung function including regional parenchymal mechanics, air trapping associated with functional small airways disease, and dual-energy derived measures of perfused blood volume are offering the ability to characterize disease phenotypes associated with the chronic inflammatory pulmonary diseases. With the emergence of COVID-19, together with its widely varying degrees of severity, its rapid progression in some cases, and the potential for lengthy post-COVID-19 morbidity, there is a new role in applying well-established qCT-based metrics. Based on the utility of qCT tools in other lung diseases, previously validated supervised classical machine learning methods, and emerging unsupervised machine learning and deep-learning approaches, we are now able to provide desperately needed insight into the acute and the chronic phases of this inflammatory lung disease. The potential areas in which qCT imaging can be beneficial include improved accuracy of diagnosis, identification of clinically distinct phenotypes, improvement of disease prognosis, stratification of care, and early objective evaluation of intervention response. There is also a potential role for qCT in evaluating an increasing population of post-COVID-19 lung parenchymal changes such as fibrosis. In this work, we discuss the basis of various lung qCT methods, using case-examples to highlight their potential application as a tool for the exploration and characterization of COVID-19, and offer scanning protocols to serve as templates for imaging the lung such that these established qCT analyses have the best chance at yielding the much needed new insights.
Nagpal Prashant, Guo Junfeng, Shin Kyung Min, Lim Jae-Kwang, Kim Ki Beom, Comellas Alejandro P, Kaczka David W, Peterson Samuel, Lee Chang Hyun, Hoffman Eric A

2021

General

Dank or not? Analyzing and predicting the popularity of memes on Reddit.

In Applied network science
Internet memes have become an increasingly pervasive form of contemporary social communication that attracted a lot of research interest recently. In this paper, we analyze the data of 129,326 memes collected from Reddit in the middle of March, 2020, when the most serious coronavirus restrictions were being introduced around the world. This article not only provides a looking glass into the thoughts of Internet users during the COVID-19 pandemic but we also perform a content-based predictive analysis of what makes a meme go viral. Using machine learning methods, we also study what incremental predictive power image related attributes have over textual attributes on meme popularity. We find that the success of a meme can be predicted based on its content alone moderately well, our best performing machine learning model predicts viral memes with AUC=0.68. We also find that both image related and textual attributes have significant incremental predictive power over each other.
Barnes Kate, Riesenmy Tiernon, Trinh Minh Duc, Lleshi Eli, Balogh Nóra, Molontay Roland

2021

COVID-19, Content-based analysis, Image analysis, Machine learning, Memes, Popularity prediction, Sentiment analysis, Social media, Visual humor

General

Wearable devices as a valid support for diagnostic excellence: lessons from a pandemic going forward.

In Health and technology
Today, the use of wearable devices is continuously increasing with many different application fields. Their low-cost and wide availability make these devices proper instruments for long-term monitoring, potentially useful to detect physiological changes related to influenza or other viruses. The relevance of this aspect and the impact of such technology have become evident particularly in the last year, during COVID-19 emergency; (big) data from wearable devices (already worn by many citizens) together with artificial intelligence techniques gave birth to specific studies dedicated to quickly identify patterns discriminating between healthy and infected people. These evaluations are made on the basis of parameters measured by these devices, among which heart rate, physical activity, and sleep seem to play a dominant role. This could be extremely significant in terms of early detection and limit of contagion risk. However, there is still a lot of research to be conducted in terms of measurement accuracy, data management (privacy and security issues), and results exploitation, in order to reach an accurate and reliable solution helping the whole healthcare system particularly in epidemic events, such as the SARS-CoV-2 pandemic.
Cosoli Gloria, Scalise Lorenzo, Poli Angelica, Spinsante Susanna

2021-Mar-07

Artificial intelligence, COVID-19, Measurement uncertainty, Remote health monitoring, Wearable devices

Radiology

Machine learning models for predicting critical illness risk in hospitalized patients with COVID-19 pneumonia.

In Journal of thoracic disease ; h5-index 52.0

Background : To develop machine learning classifiers at admission for predicting which patients with coronavirus disease 2019 (COVID-19) who will progress to critical illness.

Methods : A total of 158 patients with laboratory-confirmed COVID-19 admitted to three designated hospitals between December 31, 2019 and March 31, 2020 were retrospectively collected. 27 clinical and laboratory variables of COVID-19 patients were collected from the medical records. A total of 201 quantitative CT features of COVID-19 pneumonia were extracted by using an artificial intelligence software. The critically ill cases were defined according to the COVID-19 guidelines. The least absolute shrinkage and selection operator (LASSO) logistic regression was used to select the predictors of critical illness from clinical and radiological features, respectively. Accordingly, we developed clinical and radiological models using the following machine learning classifiers, including naive bayes (NB), linear regression (LR), random forest (RF), extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), K-nearest neighbor (KNN), kernel support vector machine (k-SVM), and back propagation neural networks (BPNN). The combined model incorporating the selected clinical and radiological factors was also developed using the eight above-mentioned classifiers. The predictive efficiency of the models is validated using a 5-fold cross-validation method. The performance of the models was compared by the area under the receiver operating characteristic curve (AUC).

Results : The mean age of all patients was 58.9±13.9 years and 89 (56.3%) were males. 35 (22.2%) patients deteriorated to critical illness. After LASSO analysis, four clinical features including lymphocyte percentage, lactic dehydrogenase, neutrophil count, and D-dimer and four quantitative CT features were selected. The XGBoost-based clinical model yielded the highest AUC of 0.960 [95% confidence interval (CI): 0.913-1.000)]. The XGBoost-based radiological model achieved an AUC of 0.890 (95% CI: 0.757-1.000). However, the predictive efficacy of XGBoost-based combined model was very close to that of the XGBoost-based clinical model, with an AUC of 0.955 (95% CI: 0.906-1.000).

Conclusions : A XGBoost-based based clinical model on admission might be used as an effective tool to identify patients at high risk of critical illness.

Liu Qin, Pang Baoguo, Li Haijun, Zhang Bin, Liu Yumei, Lai Lihua, Le Wenjun, Li Jianyu, Xia Tingting, Zhang Xiaoxian, Ou Changxing, Ma Jianjuan, Li Shenghao, Guo Xiumei, Zhang Shuixing, Zhang Qingling, Jiang Min, Zeng Qingsi

2021-Feb

COVID-19, chest CT, critical illness, machine learning, prediction

General

Education for future biobankers - The state-of-the-art and outlook.

In The EPMA journal
Biobanking as a quickly growing branch of personalised medicine has undergone enormous progress during last two decades. Nowadays it is a well developed and structured multidisciplinary field that reflects developments and advances of biomedical research based on principles of predictive, preventive and personalised medicine (PPPM/3PM). All these trends in PPPM progress have to be translated into practice and education of new generation of scientists and healthcare givers. The importance of biobanks for multitasking research, personalised treatment, and health care systems was emphasised by many scientists and health care experts. As biobanking carries multidisciplinary character currently including more professionals than ten-twenty years ago, new generation of professional biobankers is urgently needed. To create new generation of biobankers who are fully competent to answer more and more scientific and practical questions, new study programmes, novel university curricula, and topic-dedicated courses are essential. The aim of the review is to present basic forms, trends of biobanking education offered by various biobanking related bodies and to highlight future needs. The first step is to cover all activities and duties of biobanks: acquiring, collecting, storageing and sharing biological samples and associated data, using adequate assessment for both - materials and data, taking into consideration ethical, legal, and societal issues (ELSI), responding to all stakeholder needs including pharmaceutical and other related industries, patient organisations and many other interested groups, emerging technologies and innovations as well as current and future requirements of health care systems. To compile educational programmes is a comprehensive task for all actors involved in the field of biobanking who contribute to the harmonised process of creating high educational level for future generation of biobankers. The exchange of experience involving extensive international collaboration is the way how to facilitate the process of creating optimal biobanking education.
Kinkorová Judita

2021-Mar-10

Artificial intelligence, Big data, Biobank, Biomedical research, COVID-19, Education, Ethical legal and societal issues (ELSI), Future health care, Health policy, Healthcare industry, Innovation, International collaboration, Machine learning, Multidisciplinary, Predictive preventive personalised medicine (PPPM/3PM), Training

General

Community detection using unsupervised machine learning techniques on COVID-19 dataset.

In Social network analysis and mining
COVID-19 has been considered to be the most destructive pandemic ever happened in the history of mankind. The worldwide research community has put a tenacious effort to carry out research on the COVID-19 to analyse its impact on economic, medical and sociolgoical fields. They are trying to solve many crucial issues related to this disease and derive strategies to deal with this global pandemic. In this paper, we have analysed the trend, countries affected regionally and the variation of cases at the country level on COVID-19 dataset. We have used the Principal component analysis on the COVID-19 dataset variables to reduce the dimensionality and find the most significant variables. Further, we have unveiled the hidden community structure of countries by applying the unsupervised clustering approach, K-means. We have compared the results with the K-means method. The communities achieved after applying the PCA are more precise. The resulted communities can be beneficial to researchers, scientists, sociologists, different policy makers and managers of health sector.
Chaudhary Laxmi, Singh Buddha

2021

COVID-19, Communities, Coronavirus, K-means, Machine learning, PCA

Dermatology

CXCL17 Is a Specific Diagnostic Biomarker for Severe Pandemic Influenza A(H1N1) That Predicts Poor Clinical Outcome.

In Frontiers in immunology ; h5-index 100.0
The C-X-C motif chemokine ligand 17 (CXCL17) is chemotactic for myeloid cells, exhibits bactericidal activity, and exerts anti-viral functions. This chemokine is constitutively expressed in the respiratory tract, suggesting a role in lung defenses. However, little is known about the participation of CXCL17 against relevant respiratory pathogens in humans. Here, we evaluated the serum levels and lung tissue expression pattern of CXCL17 in a cohort of patients with severe pandemic influenza A(H1N1) from Mexico City. Peripheral blood samples obtained on admission and seven days after hospitalization were processed for determinations of serum CXCL17 levels by enzyme-linked immunosorbent assay (ELISA). The expression of CXCL17 was assessed by immunohistochemistry (IHQ) in lung autopsy specimens from patients that succumbed to the disease. Serum CXCL17 levels were also analyzed in two additional comparative cohorts of coronavirus disease 2019 (COVID-19) and pulmonary tuberculosis (TB) patients. Additionally, the expression of CXCL17 was tested in lung autopsy specimens from COVID-19 patients. A total of 122 patients were enrolled in the study, from which 68 had pandemic influenza A(H1N1), 24 had COVID-19, and 30 with PTB. CXCL17 was detected in post-mortem lung specimens from patients that died of pandemic influenza A(H1N1) and COVID-19. Interestingly, serum levels of CXCL17 were increased only in patients with pandemic influenza A(H1N1), but not COVID-19 and PTB. CXCL17 not only differentiated pandemic influenza A(H1N1) from other respiratory infections but showed prognostic value for influenza-associated mortality and renal failure in machine-learning algorithms and regression analyses. Using cell culture assays, we also identified that human alveolar A549 cells and peripheral blood monocyte-derived macrophages increase their CXCL17 production capacity after influenza A(H1N1) pdm09 virus infection. Our results for the first time demonstrate an induction of CXCL17 specifically during pandemic influenza A(H1N1), but not COVID-19 and PTB in humans. These findings could be of great utility to differentiate influenza and COVID-19 and to predict poor prognosis specially at settings of high incidence of pandemic A(H1N1). Future studies on the role of CXCL17 not only in severe pandemic influenza, but also in seasonal influenza, COVID-19, and PTB are required to validate our results.
Choreño-Parra Jose Alberto, Jiménez-Álvarez Luis Armando, Ramírez-Martínez Gustavo, Sandoval-Vega Montserrat, Salinas-Lara Citlaltepetl, Sánchez-Garibay Carlos, Luna-Rivero Cesar, Hernández-Montiel Erika Mariana, Fernández-López Luis Alejandro, Cabrera-Cornejo María Fernanda, Choreño-Parra Eduardo Misael, Cruz-Lagunas Alfredo, Domínguez Andrea, Márquez-García Eduardo, Cabello-Gutiérrez Carlos, Bolaños-Morales Francina Valezka, Mena-Hernández Lourdes, Delgado-Zaldivar Diego, Rebolledo-García Daniel, Guadarrama-Ortiz Parménides, Regino-Zamarripa Nora E, Mendoza-Milla Criselda, García-Latorre Ethel A, Rodiguez-Reyna Tatiana Sofia, Cervántes-Rosete Diana, Hernández-Cárdenas Carmen M, Khader Shabaana A, Zlotnik Albert, Zúñiga Joaquín

2021

COVID-19, CXCL17, SARS-CoV-2, chemokines, influenza A(H1N1), tuberculosis

General

Biological Perspective of Thiazolide Derivatives against Mpro and MTase of SARS-CoV-2: Molecular Docking, DFT and MD Simulation Investigations.

In Chemical physics letters
Humans around the globe have been severely affected by SARS-CoV-2 and no treatment has yet been authorized for the treatment of this severe condition brought by COVID-19. Here, an in silico research was executed to elucidate the inhibitory potential of selected thiazolides derivatives against SARS-CoV-2 Protease (Mpro) and Methyltransferase (MTase). Based on the analysis; 4 compounds were discovered to have efficacious and remarkable results against the proteins of the interest. Primarily, results obtained through this study not only allude these compounds as potential inhibitors but also pave the way for in vivo and in vitro validation of these compounds.
Rasool Nouman, Yasmin Farkhanda, Sahai Shalini, Hussain Waqar, Inam Hadiqa, Arshad Arooj

2021-Mar-06

COVID-19, MTase, Mpro, SARS-CoV-2, Thiazolide Derivatives

General

Deep learning model for virtual screening of novel 3C-like protease enzyme inhibitors against SARS coronavirus diseases.

In Computers in biology and medicine
In the context of the recently emerging COVID-19 pandemic, we developed a deep learning model that can be used to predict the inhibitory activity of 3CLpro in severe acute respiratory syndrome coronavirus (SARS-CoV) for unknown compounds during the virtual screening process. This paper proposes a novel deep learning-based method to implement virtual screening with convolutional neural network (CNN) architecture. The descriptors represent chemical molecules, and these descriptors are input into the CNN framework to train a model and predict active compounds. When compared to other machine learning methods, including random forest, naive Bayes, decision tree, and support vector machine, the proposed CNN model's evaluation of the test set showed an accuracy of 0.86, a sensitivity of 0.45, a specificity of 0.96, a precision of 0.73, a recall of 0.45, an F-measure of 0.55, and a ROC of 0.71. The CNN model screened 17 out of 918 phytochemical compounds; 60 out of 423 from the natural product NCI divset IV; 17,831 out of 112,267 from the ZINC natural product database; and 315 out of 1556 FDA-approved drugs as anti-SARS-CoV agents. Further, to prioritize drug-like compounds, Lipinski's rule of five was applied to screen anti-SARS-CoV compounds (excluding FDA-approved drugs), resulting in 10, 59, and 14,025 hit molecules. Out of 10 phytochemical compounds, 9 anti-SARS-CoV agents belonged to the flavonoid group. In conclusion, the proposed CNN model can prove useful for developing novel target-specific anti-SARS-CoV compounds.
Kumari Madhulata, Subbarao Naidu

2021-Mar-06

3CLpro, CNN Model, COVID-19, Convolutional neural network, Deep learning, FDA-approved drugs, Phytochemical compounds, SARS-CoV, Virtual screening

General

Personalized prescription of ACEI/ARBs for hypertensive COVID-19 patients.

In Health care management science
The COVID-19 pandemic has prompted an international effort to develop and repurpose medications and procedures to effectively combat the disease. Several groups have focused on the potential treatment utility of angiotensin-converting-enzyme inhibitors (ACEIs) and angiotensin-receptor blockers (ARBs) for hypertensive COVID-19 patients, with inconclusive evidence thus far. We couple electronic medical record (EMR) and registry data of 3,643 patients from Spain, Italy, Germany, Ecuador, and the US with a machine learning framework to personalize the prescription of ACEIs and ARBs to hypertensive COVID-19 patients. Our approach leverages clinical and demographic information to identify hospitalized individuals whose probability of mortality or morbidity can decrease by prescribing this class of drugs. In particular, the algorithm proposes increasing ACEI/ARBs prescriptions for patients with cardiovascular disease and decreasing prescriptions for those with low oxygen saturation at admission. We show that personalized recommendations can improve patient outcomes by 1.0% compared to the standard of care when applied to external populations. We develop an interactive interface for our algorithm, providing physicians with an actionable tool to easily assess treatment alternatives and inform clinical decisions. This work offers the first personalized recommendation system to accurately evaluate the efficacy and risks of prescribing ACEIs and ARBs to hypertensive COVID-19 patients.
Bertsimas Dimitris, Borenstein Alison, Mingardi Luca, Nohadani Omid, Orfanoudaki Agni, Stellato Bartolomeo, Wiberg Holly, Sarin Pankaj, Varelmann Dirk J, Estrada Vicente, Macaya Carlos, Gil Iván J Núñez

2021-Mar-15

ACE inhibitors, ARBs, COVID-19, Machine learning, Prescriptive analytics

Public Health

Public Discourse Against Masks in the COVID-19 Era: Infodemiology Study of Twitter Data.

In JMIR public health and surveillance

BACKGROUND : Despite the presence of scientific evidences supporting the importance of wearing masks to curtail the widespread of the COVID-19 virus, wearing masks has stirred up a significant debate particularly on social media.

OBJECTIVE : To investigate the topics associated with the public discourse against wearing masks in the United States. Further, we studied the relationship between the anti-mask discourse on social media and the number of new COVID-19 cases.

METHODS : Using hashtags against wearing masks, we collected a total of 51,170 English tweets between January 1st, 2020 and October 27th, 2020. We used machine learning techniques to analyze the data collected. We investigated the relationship between the volume of tweets that are against mask-wearing and the daily volume of new COVID-19 cases using the Pearson Correlation between the two-time series.

RESULTS : The results and analysis showed that social media could help identify important insights related to wearing masks. The results of topic mining identified 10 categories/themes of user concerns dominated by 1) constitutional rights and freedom of choice followed by 2) conspiracy theory, population control and big pharma, and 3) Fake news, fake numbers, fake pandemic. Combined, these three categories represent almost 65% of the volume of tweets against masks. The relationship between the volume of tweets against wearing masks and the reported new COVID-19 cases depicted a strong correlation where the rise in the volume of negative tweets is leading the rise in the number of new cases by nine days.

CONCLUSIONS : The findings demonstrated the potential of mining social media for understanding the public discourse about public health issues such as wearing masks during the COVID-19 pandemic. The results emphasized the relationship between the discourse on social media and the potential impact on real events like changing the course of the pandemic. Policy makers are advised to proactively address public perception and work on shaping this perception through raising awareness, debunking negative sentiments, and prioritizing early policy intervention toward the most prevalent topics.

CLINICALTRIAL :

Al-Ramahi Mohammad, Elnoshokaty Ahmed, El-Gayar Omar, Nasralah Tareq, Wahbeh Abdullah

2021-Mar-03

General

HOPES: An Integrative Digital Phenotyping Platform for Data Collection, Monitoring, and Machine Learning.

In Journal of medical Internet research ; h5-index 88.0
The collection of data from a personal digital device to characterize current health conditions and behaviors that determine how an individual's health will evolve has been called digital phenotyping. In this paper, we describe the development of and early experiences with a comprehensive digital phenotyping platform: Health Outcomes through Positive Engagement and Self-Empowerment (HOPES). HOPES is based on the open-source Beiwe platform but adds a wider range of data collection, including the integration of wearable devices and further sensor collection from smartphones. Requirements were partly derived from a concurrent clinical trial for schizophrenia that required the development of significant capabilities in HOPES for security, privacy, ease of use, and scalability, based on a careful combination of public cloud and on-premises operation. We describe new data pipelines to clean, process, present, and analyze data. This includes a set of dashboards customized to the needs of research study operations and clinical care. A test use case for HOPES was described by analyzing the digital behavior of 22 participants during the SARS-CoV-2 pandemic.
Wang Xuancong, Vouk Nikola, Heaukulani Creighton, Buddhika Thisum, Martanto Wijaya, Lee Jimmy, Morris Robert Jt

2021-Mar-15

data collection, digital phenotyping, eHealth, mHealth, machine learning, mobile phone, outpatient monitoring, phenotype

General

Analyzing the attitude of Indian citizens towards COVID-19 vaccine - A text analytics study.

In Diabetes & metabolic syndrome

BACKGROUND AND AIMS : The government of India recently planned to start the process of the mass vaccination program to end the COVID-19 crises. However, the process of vaccination was not made mandatory, and there are a lot of aspects that arise skepticism in the minds of common people regarding COVID-19 vaccines. This study using machine learning techniques analyzes the major concerns Indian citizens voice out about COVID-19 vaccines in social media.

METHODS : For this study, we have used social media posts as data. Using Python, we have scrapped the social media posts of Indian citizens discussing about the COVID- 19 vaccine. In Study 1, we performed a sentimental analysis to determine how the general perception of Indian citizens regarding the COVID-19 vaccine changes over different months of COVID-19 crises. In Study 2, we have performed topic modeling to understand the major issues that concern the general public regarding the COVID- 19 vaccine.

RESULTS : Our results have indicated that 47% of social media posts discussing vaccines were in a neutral tone, and nearly 17% of the social media posts discussing the COVID-19 vaccine were in a negative tone. Fear of health and allergic reactions towards the vaccine are the two prominent issues that concern Indian citizens regarding the COVID-19 vaccine.

CONCLUSION : With the positive sentiments regarding vaccine is just over 35%, the Indian government needs to focus especially on addressing the fear of vaccines before implementing the process of mass vaccination.

Praveen S V, Ittamalla Rajesh, Deepak Gerard

2021-Feb-27

COVID-19, Data analytics, Social media, Text analytics, Vaccine

General

Construction and validation of a machine learning-based nomogram: A tool to predict the risk of getting severe coronavirus disease 2019 (COVID-19).

In Immunity, inflammation and disease

BACKGROUND : Identifying patients who may develop severe coronavirus disease 2019 (COVID-19) will facilitate personalized treatment and optimize the distribution of medical resources.

METHODS : In this study, 590 COVID-19 patients during hospitalization were enrolled (Training set: n = 285; Internal validation set: n = 127; Prospective set: n = 178). After filtered by two machine learning methods in the training set, 5 out of 31 clinical features were selected into the model building to predict the risk of developing severe COVID-19 disease. Multivariate logistic regression was applied to build the prediction nomogram and validated in two different sets. Receiver operating characteristic (ROC) analysis and decision curve analysis (DCA) were used to evaluate its performance.

RESULTS : From 31 potential predictors in the training set, 5 independent predictive factors were identified and included in the risk score: C-reactive protein (CRP), lactate dehydrogenase (LDH), Age, Charlson/Deyo comorbidity score (CDCS), and erythrocyte sedimentation rate (ESR). Subsequently, we generated the nomogram based on the above features for predicting severe COVID-19. In the training cohort, the area under curves (AUCs) were 0.822 (95% CI, 0.765-0.875) and the internal validation cohort was 0.762 (95% CI, 0.768-0.844). Further, we validated it in a prospective cohort with the AUCs of 0.705 (95% CI, 0.627-0.778). The internally bootstrapped calibration curve showed favorable consistency between prediction by nomogram and the actual situation. And DCA analysis also conferred high clinical net benefit.

CONCLUSION : In this study, our predicting model based on five clinical characteristics of COVID-19 patients will enable clinicians to predict the potential risk of developing critical illness and thus optimize medical management.

Yao Zhixian, Zheng Xinyi, Zheng Zhong, Wu Ke, Zheng Junhua

2021-Mar-13

COVID-19, machine learning, nomogram, severe COVID-19 prediction

General

Reservoir Hosts Prediction for COVID-19 by Hybrid Transfer Learning Model.

In Journal of biomedical informatics ; h5-index 55.0
The recent outbreak of COVID-19 has infected millions of people around the world, which is leading to the global emergency. In the event of the virus outbreak, it is crucial to get the carriers of the virus timely and precisely, then the animal origins can be isolated for further infection. Traditional identifications rely on fields and laboratory researches that lag the responses to emerging epidemic prevention. With the development of machine learning, the efficiency of predicting the viral hosts has been demonstrated by recent researchers. However, the problems of the limited annotated virus data and imbalanced hosts information restrict these approaches to obtain a better result. To assure the high reliability of predicting the animal origins on COVID-19, we extend transfer learning and ensemble learning to present a hybrid transfer learning model. When predicting the hosts of newly discovered virus, our model provides a novel solution to utilize the related virus domain as auxiliary to help building a robust model for target virus domain. The simulation results on several UCI benchmarks and viral genome datasets demonstrate that our model outperforms the general classical methods under the condition of limited target training sets and class-imbalance problems. By setting the coronavirus as target domain and other related virus as source domain, the feasibility of our approach is evaluated. Finally, we show the animal reservoirs prediction of the COVID-19 for further analysing.
Yang Yun, Guo Jing, Wang Pei, Wang Yaowei, Yu Minghao, Wang Xiang, Yang Po, Sun Liang

2021-Mar-09

COVID-19, ensemble learning, hosts prediction, machine learning, transfer learning, virus origins

General

Explainable Automated Coding of Clinical Notes using Hierarchical Label-wise Attention Networks and Label Embedding Initialisation.

In Journal of biomedical informatics ; h5-index 55.0

BACKGROUND : Diagnostic or procedural coding of clinical notes aims to derive a coded summary of disease-related information about patients. Such coding is usually done manually in hospitals but could potentially be automated to improve the efficiency and accuracy of medical coding. Recent studies on deep learning for automated medical coding achieved promising performances. However, the explainability of these models is usually poor, preventing them to be used confidently in supporting clinical practice. Another limitation is that these models mostly assume independence among labels, ignoring the complex correlations among medical codes which can potentially be exploited to improve the performance.

METHODS : To address the issues of model explainability and label correlations, we propose a Hierarchical Label-wise Attention Network (HLAN), which aimed to interpret the model by quantifying importance (as attention weights) of words and sentences related to each of the labels. Secondly, we propose to enhance the major deep learning models with a label embedding (LE) initialisation approach, which learns a dense, continuous vector representation and then injects the representation into the final layers and the label-wise attention layers in the models. We evaluated the methods using three settings on the MIMIC-III discharge summaries: full codes, top-50 codes, and the UK NHS (National Health Service) COVID-19 (Coronavirus disease 2019) shielding codes. Experiments were conducted to compare the HLAN model and label embedding initialisation to the state-of-the-art neural network based methods, including variants of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).

RESULTS : HLAN achieved the best Micro-level AUC and F₁ on the top-50 code prediction, 91.9% and 64.1%, respectively; and comparable results on the NHS COVID-19 shielding code prediction to other models: around 97% Micro-level AUC. More importantly, in the analysis of model explanations, by highlighting the most salient words and sentences for each label, HLAN showed more meaningful and comprehensive model interpretation compared to the CNN-based models and its downgraded baselines, HAN and HA-GRU. Label embedding (LE) initialisation significantly boosted the previous state-of-the-art model, CNN with attention mechanisms, on the full code prediction to 52.5% Micro-level F₁. The analysis of the layers initialised with label embeddings further explains the effect of this initialisation approach. The source code of the implementation and the results are openly available at https://github.com/acadTags/Explainable-Automated-Medical-Coding.

CONCLUSION : We draw the conclusion from the evaluation results and analyses. First, with hierarchical label-wise attention mechanisms, HLAN can provide better or comparable results for automated coding to the state-of-the-art, CNN-based models. Second, HLAN can provide more comprehensive explanations for each label by highlighting key words and sentences in the discharge summaries, compared to the n-grams in the CNN-based models and the downgraded baselines, HAN and HA-GRU. Third, the performance of deep learning based multi-label classification for automated coding can be consistently boosted by initialising label embeddings that captures the correlations among labels. We further discuss the advantages and drawbacks of the overall method regarding its potential to be deployed to a hospital and suggest areas for future studies.

Dong Hang, Suárez-Paniagua Víctor, Whiteley William, Wu Honghan

2021-Mar-09

Attention Mechanisms, Automated medical coding, Deep learning, Explainability, Label correlation, Multi-label classification, Natural Language Processing

Public Health

A Machine Learning Explanation of the Pathogen-Immune Relationship of SARS-CoV-2 (COVID-19), and a Model to Predict Immunity and Therapeutic Opportunity: A Comparative Effectiveness Research Study.

In JMIRx med

Background : Approximately 80% of those infected with COVID-19 are immune. They are asymptomatic unknown carriers who can still infect those with whom they come into contact. Understanding what makes them immune could inform public health policies as to who needs to be protected and why, and possibly lead to a novel treatment for those who cannot, or will not, be vaccinated once a vaccine is available.

Objective : The primary objectives of this study were to learn if machine learning could identify patterns in the pathogen-host immune relationship that differentiate or predict COVID-19 symptom immunity and, if so, which ones and at what levels. The secondary objective was to learn if machine learning could take such differentiators to build a model that could predict COVID-19 immunity with clinical accuracy. The tertiary purpose was to learn about the relevance of other immune factors.

Methods : This was a comparative effectiveness research study on 53 common immunological factors using machine learning on clinical data from 74 similarly grouped Chinese COVID-19-positive patients, 37 of whom were symptomatic and 37 asymptomatic. The setting was a single-center primary care hospital in the Wanzhou District of China. Immunological factors were measured in patients who were diagnosed as SARS-CoV-2 positive by reverse transcriptase-polymerase chain reaction (RT-PCR) in the 14 days before observations were recorded. The median age of the 37 asymptomatic patients was 41 years (range 8-75 years); 22 were female, 15 were male. For comparison, 37 RT-PCR test-positive patients were selected and matched to the asymptomatic group by age, comorbidities, and sex. Machine learning models were trained and compared to understand the pathogen-immune relationship and predict who was immune to COVID-19 and why, using the statistical programming language R.

Results : When stem cell growth factor-beta (SCGF-β) was included in the machine learning analysis, a decision tree and extreme gradient boosting algorithms classified and predicted COVID-19 symptom immunity with 100% accuracy. When SCGF-β was excluded, a random-forest algorithm classified and predicted asymptomatic and symptomatic cases of COVID-19 with 94.8% AUROC (area under the receiver operating characteristic) curve accuracy (95% CI 90.17%-100%). In total, 34 common immune factors have statistically significant associations with COVID-19 symptoms (all c<.05), and 19 immune factors appear to have no statistically significant association.

Conclusions : The primary outcome was that asymptomatic patients with COVID-19 could be identified by three distinct immunological factors and levels: SCGF-β (>127,637), interleukin-16 (IL-16) (>45), and macrophage colony-stimulating factor (M-CSF) (>57). The secondary study outcome was the suggestion that stem-cell therapy with SCGF-β may be a novel treatment for COVID-19. Individuals with an SCGF-β level >127,637, or an IL-16 level >45 and an M-CSF level >57, appear to be predictively immune to COVID-19 100% and 94.8% (AUROC) of the time, respectively. Testing levels of these three immunological factors may be a valuable tool at the point of care for managing and preventing outbreaks. Further, stem-cell therapy via SCGF-β and M-CSF appear to be promising novel therapeutics for patients with COVID-19.

Luellen Eric

COVID-19, SARS-CoV-2, immunity, infectious disease, mass vaccinations, public health, stem-cell growth factor-beta, therapeutics

Public Health

PreDTIs: prediction of drug-target interactions based on multiple feature information using gradient boosting framework with data balancing and feature selection techniques.

In Briefings in bioinformatics
Discovering drug-target (protein) interactions (DTIs) is of great significance for researching and developing novel drugs, having a tremendous advantage to pharmaceutical industries and patients. However, the prediction of DTIs using wet-lab experimental methods is generally expensive and time-consuming. Therefore, different machine learning-based methods have been developed for this purpose, but there are still substantial unknown interactions needed to discover. Furthermore, data imbalance and feature dimensionality problems are a critical challenge in drug-target datasets, which can decrease the classifier performances that have not been significantly addressed yet. This paper proposed a novel drug-target interaction prediction method called PreDTIs. First, the feature vectors of the protein sequence are extracted by the pseudo-position-specific scoring matrix (PsePSSM), dipeptide composition (DC) and pseudo amino acid composition (PseAAC); and the drug is encoded with MACCS substructure fingerings. Besides, we propose a FastUS algorithm to handle the class imbalance problem and also develop a MoIFS algorithm to remove the irrelevant and redundant features for getting the best optimal features. Finally, balanced and optimal features are provided to the LightGBM Classifier to identify DTIs, and the 5-fold CV validation test method was applied to evaluate the prediction ability of the proposed method. Prediction results indicate that the proposed model PreDTIs is significantly superior to other existing methods in predicting DTIs, and our model could be used to discover new drugs for unknown disorders or infections, such as for the coronavirus disease 2019 using existing drugs compounds and severe acute respiratory syndrome coronavirus 2 protein sequences.
Mahmud S M Hasan, Chen Wenyu, Liu Yongsheng, Awal Md Abdul, Ahmed Kawsar, Rahman Md Habibur, Moni Mohammad Ali

2021-Mar-12

SARS-CoV-2, data imbalance, drug chemical structure, drug–target interaction, feature selection, protein sequence

Radiology

An Interpretable Model-Based Prediction of Severity and Crucial Factors in Patients with COVID-19.

In BioMed research international ; h5-index 102.0
This study established an interpretable machine learning model to predict the severity of coronavirus disease 2019 (COVID-19) and output the most crucial deterioration factors. Clinical information, laboratory tests, and chest computed tomography (CT) scans at admission were collected. Two experienced radiologists reviewed the scans for the patterns, distribution, and CT scores of lung abnormalities. Six machine learning models were established to predict the severity of COVID-19. After parameter tuning and performance comparison, the optimal model was explained using Shapley Additive explanations to output the crucial factors. This study enrolled and classified 198 patients into mild (n = 162; 46.93 ± 14.49 years old) and severe (n = 36; 60.97 ± 15.91 years old) groups. The severe group had a higher temperature (37.42 ± 0.99°C vs. 36.75 ± 0.66°C), CT score at admission, neutrophil count, and neutrophil-to-lymphocyte ratio than the mild group. The XGBoost model ranked first among all models, with an AUC, sensitivity, and specificity of 0.924, 90.91%, and 97.96%, respectively. The early stage of chest CT, total CT score of the percentage of lung involvement, and age were the top three contributors to the prediction of the deterioration of XGBoost. A higher total score on chest CT had a more significant impact on the prediction. In conclusion, the XGBoost model to predict the severity of COVID-19 achieved excellent performance and output the essential factors in the deterioration process, which may help with early clinical intervention, improve prognosis, and reduce mortality.
Zheng Bowen, Cai Yong, Zeng Fengxia, Lin Min, Zheng Jun, Chen Weiguo, Qin Genggeng, Guo Yi

2021

General

A biomarker-based age, biomarkers, clinical history, sex (ABCS)-mortality risk score for patients with coronavirus disease 2019.

In Annals of translational medicine

Background : Early identification and timely therapeutic strategies for potential critical patients with coronavirus disease 2019 (COVID-19) are of crucial importance to reduce mortality. We aimed to develop and validate a prediction tool for 30-day mortality for these patients on admission.

Methods : Consecutive hospitalized patients admitted to Tongji Hospital and Hubei Xinhua Hospital from January 1 to March 10, 2020, were retrospective analyzed. They were grouped as derivation and external validation set. Multivariate Cox regression was applied to identify the risk factors associated with death, and a nomogram was developed and externally validated by calibration plots, C-index, Kaplan-Meier curves and decision curve.

Results : Data from 1,717 patients at the Tongji Hospital and 188 cases at the Hubei Xinhua Hospital were included in our study. Using multivariate Cox regression with backward stepwise selection of variables in the derivation cohort, age, sex, chronic obstructive pulmonary disease (COPD), as well as seven biomarkers (aspartate aminotransferase, high-sensitivity C-reactive protein, high-sensitivity troponin I, white blood cell count, lymphocyte count, D-dimer, and procalcitonin) were incorporated in the model. An age, biomarkers, clinical history, sex (ABCS)-mortality score was developed, which yielded a higher C-index than the conventional CURB-65 score for predicting 30-day mortality in both the derivation cohort {0.888 [95% confidence interval (CI), 0.869-0.907] vs. 0.696 (95% CI, 0.660-0.731)} and validation cohort [0.838 (95% CI, 0.777-0.899) vs. 0.619 (95% CI, 0.519-0.720)], respectively. Furthermore, risk stratified Kaplan-Meier curves showed good discriminatory capacity of the model for classifying patients into distinct mortality risk groups for both derivation and validation cohorts.

Conclusions : The ABCS-mortality score might be offered to clinicians to strengthen the prognosis-based clinical decision-making, which would be helpful for reducing mortality of COVID-19 patients.

Jiang Meng, Li Changli, Zheng Li, Lv Wenzhi, He Zhigang, Cui Xinwu, Dietrich Christoph F

2021-Feb

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), coronavirus disease 2019 (COVID-19), mortality, nomogram

Radiology

A deep learning-based quantitative computed tomography model for predicting the severity of COVID-19: a retrospective study of 196 patients.

In Annals of translational medicine

Background : The assessment of the severity of coronavirus disease 2019 (COVID-19) by clinical presentation has not met the urgent clinical need so far. We aimed to establish a deep learning (DL) model based on quantitative computed tomography (CT) and initial clinical features to predict the severity of COVID-19.

Methods : One hundred ninety-six hospitalized patients with confirmed COVID-19 were enrolled from January 20 to February 10, 2020 in our centre, and were divided into severe and non-severe groups. The clinico-radiological data on admission were retrospectively collected and compared between the two groups. The optimal clinico-radiological features were determined based on least absolute shrinkage and selection operator (LASSO) logistic regression analysis, and a predictive nomogram model was established by five-fold cross-validation. Receiver operating characteristic (ROC) analyses were conducted, and the areas under the receiver operating characteristic curve (AUCs) of the nomogram model, quantitative CT parameters that were significant in univariate analysis, and pneumonia severity index (PSI) were compared.

Results : In comparison with the non-severe group (151 patients), the severe group (45 patients) had a higher PSI (P<0.001). DL-based quantitative CT indicated that the mass of infection (MOI_CT) and the percentage of infection (POI_CT) in the whole lung were higher in the severe group (both P<0.001). The nomogram model was based on MOI_CT and clinical features, including age, cluster of differentiation 4 (CD4)⁺ T cell count, serum lactate dehydrogenase (LDH), and C-reactive protein (CRP). The AUC values of the model, MOI_CT, POI_CT, and PSI scores were 0.900, 0.813, 0.805, and 0.751, respectively. The nomogram model performed significantly better than the other three parameters in predicting severity (P=0.003, P=0.001, and P<0.001, respectively).

Conclusions : Although quantitative CT parameters and the PSI can well predict the severity of COVID-19, the DL-based quantitative CT model is more efficient.

Shi Weiya, Peng Xueqing, Liu Tiefu, Cheng Zenghui, Lu Hongzhou, Yang Shuyi, Zhang Jiulong, Wang Mei, Gao Yaozong, Shi Yuxin, Zhang Zhiyong, Shan Fei

2021-Feb

Coronavirus disease 2019 (COVID-19), computed tomography, deep learning, multivariate analysis, predicting

General

A novel artificial intelligence-assisted triage tool to aid in the diagnosis of suspected COVID-19 pneumonia cases in fever clinics.

In Annals of translational medicine

Background : Currently, the need to prevent and control the spread of the 2019 novel coronavirus disease (COVID-19) outside of Hubei province in China and internationally has become increasingly critical. We developed and validated a diagnostic model that does not rely on computed tomography (CT) images to aid in the early identification of suspected COVID-19 pneumonia (S-COVID-19-P) patients admitted to adult fever clinics and made the validated model available via an online triage calculator.

Methods : Patients admitted from January 14 to February 26, 2020 with an epidemiological history of exposure to COVID-19 were included in the study [model development group (n=132) and validation group (n=32)]. Candidate features included clinical symptoms, routine laboratory tests, and other clinical information on admission. The features selection and model development were based on the least absolute shrinkage and selection operator (LASSO) regression. The primary outcome was the development and validation of a diagnostic aid model for the early identification of S-COVID-19-P on admission.

Results : The development cohort contained 26 cases of S-COVID-19-P and seven cases of confirmed COVID-19 pneumonia (C-COVID-19-P). The final selected features included one demographic variable, four vital signs, five routine blood values, seven clinical signs and symptoms, and one infection-related biomarker. The model's performance in the testing set and the validation group resulted in area under the receiver operating characteristic (ROC) curves (AUCs) of 0.841 and 0.938, F1 scores of 0.571 and 0.667, recall of 1.000 and 1.000, specificity of 0.727 and 0.778, and precision of 0.400 and 0.500, respectively. The top five most important features were age, interleukin-6 (IL-6), systolic blood pressure (SYS_BP), monocyte ratio (MONO%), and fever classification (FC). Based on this model, an optimized strategy for the early identification of S-COVID-19-P in fever clinics has also been designed.

Conclusions : A machine-learning model based solely on clinical information and not on CT images was able to perform the early identification of S-COVID-19-P on admission in fever clinics with a 100% recall score. This high-performing and validated model has been deployed as an online triage tool, which is available at https://intensivecare.shinyapps.io/COVID19/.

Feng Cong, Wang Lili, Chen Xin, Zhai Yongzhi, Zhu Feng, Chen Hua, Wang Yingchan, Su Xiangzheng, Huang Sai, Tian Lin, Zhu Weixiu, Sun Wenzheng, Zhang Liping, Han Qingru, Zhang Juan, Pan Fei, Chen Li, Zhu Zhihong, Xiao Hongju, Liu Yu, Liu Gang, Chen Wei, Li Tanshi

2021-Feb

Suspected COVID-19 pneumonia (S-COVID-19-P), diagnosis aid model, fever clinics, machine learning

General

Smart homes for the older population: particularly important during the COVID-19 outbreak.

In Reumatologia
Osteoporosis, one of the leading causes of disability in older adults, significantly reduces the quality of life and leads to loss of independence. Dynamic development of "smart" solutions based on artificial intelligence more and more commonly applied in older people's houses may be an answer to the above issues. The aim of this study is to present selected "smart home" solutions for the diagnosis and prevention of falls in the older population through a literature review. The conducted meta-analysis based on a review of the scientific literature available in English and Polish in the Medline/PubMed, Embase, Scopus, and GBL databases was undertaken from 01.01.2015 to 01.10.2020 with the string search method using key words. According to the authors of this study, the development of new technology based on artificial intelligence allows older people to live independently, which contributes to a higher level of life satisfaction and quality.
Gawrońska Karolina, Lorkowski Jacek

2021

artificially intelligent home monitoring, fall prevention, home hazards, innovative assisted living tools

Radiology

Deep metric learning-based image retrieval system for chest radiograph and its clinical applications in COVID-19.

In Medical image analysis
In recent years, deep learning-based image analysis methods have been widely applied in computer-aided detection, diagnosis and prognosis, and has shown its value during the public health crisis of the novel coronavirus disease 2019 (COVID-19) pandemic. Chest radiograph (CXR) has been playing a crucial role in COVID-19 patient triaging, diagnosing and monitoring, particularly in the United States. Considering the mixed and unspecific signals in CXR, an image retrieval model of CXR that provides both similar images and associated clinical information can be more clinically meaningful than a direct image diagnostic model. In this work we develop a novel CXR image retrieval model based on deep metric learning. Unlike traditional diagnostic models which aim at learning the direct mapping from images to labels, the proposed model aims at learning the optimized embedding space of images, where images with the same labels and similar contents are pulled together. The proposed model utilizes multi-similarity loss with hard-mining sampling strategy and attention mechanism to learn the optimized embedding space, and provides similar images, the visualizations of disease-related attention maps and useful clinical information to assist clinical decisions. The model is trained and validated on an international multi-site COVID-19 dataset collected from 3 different sources. Experimental results of COVID-19 image retrieval and diagnosis tasks show that the proposed model can serve as a robust solution for CXR analysis and patient management for COVID-19. The model is also tested on its transferability on a different clinical decision support task for COVID-19, where the pre-trained model is applied to extract image features from a new dataset without any further training. The extracted features are then combined with COVID-19 patient's vitals, lab tests and medical histories to predict the possibility of airway intubation in 72 hours, which is strongly associated with patient prognosis, and is crucial for patient care and hospital resource planning. These results demonstrate our deep metric learning based image retrieval model is highly efficient in the CXR retrieval, diagnosis and prognosis, and thus has great clinical value for the treatment and management of COVID-19 patients.
Zhong Aoxiao, Li Xiang, Wu Dufan, Ren Hui, Kim Kyungsang, Kim Younggon, Buch Varun, Neumark Nir, Bizzo Bernardo, Tak Won Young, Park Soo Young, Lee Yu Rim, Kang Min Kyu, Park Jung Gil, Kim Byung Seok, Chung Woo Jin, Guo Ning, Dayan Ittai, Kalra Mannudeep K, Li Quanzheng

2021-Feb-07

COVID-19, Chest radiograph, Image content query, Image retrieval

General

Development and validation of prediction models for mechanical ventilation, renal replacement therapy, and readmission in COVID-19 patients.

In Journal of the American Medical Informatics Association : JAMIA

OBJECTIVE : Coronavirus disease 2019 (COVID-19) patients are at risk for resource-intensive outcomes including mechanical ventilation (MV), renal replacement therapy (RRT), and readmission. Accurate outcome prognostication could facilitate hospital resource allocation. We develop and validate predictive models for each outcome using retrospective electronic health record data for COVID-19 patients treated between March 2 and May 6, 2020.

MATERIALS AND METHODS : For each outcome, we trained 3 classes of prediction models using clinical data for a cohort of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2)-positive patients (n = 2256). Cross-validation was used to select the best-performing models per the areas under the receiver-operating characteristic and precision-recall curves. Models were validated using a held-out cohort (n = 855). We measured each model's calibration and evaluated feature importances to interpret model output.

RESULTS : The predictive performance for our selected models on the held-out cohort was as follows: area under the receiver-operating characteristic curve-MV 0.743 (95% CI, 0.682-0.812), RRT 0.847 (95% CI, 0.772-0.936), readmission 0.871 (95% CI, 0.830-0.917); area under the precision-recall curve-MV 0.137 (95% CI, 0.047-0.175), RRT 0.325 (95% CI, 0.117-0.497), readmission 0.504 (95% CI, 0.388-0.604). Predictions were well calibrated, and the most important features within each model were consistent with clinical intuition.

DISCUSSION : Our models produce performant, well-calibrated, and interpretable predictions for COVID-19 patients at risk for the target outcomes. They demonstrate the potential to accurately estimate outcome prognosis in resource-constrained care sites managing COVID-19 patients.

CONCLUSIONS : We develop and validate prognostic models targeting MV, RRT, and readmission for hospitalized COVID-19 patients which produce accurate, interpretable predictions. Additional external validation studies are needed to further verify the generalizability of our results.

Rodriguez Victor Alfonso, Bhave Shreyas, Chen Ruijun, Pang Chao, Hripcsak George, Sengupta Soumitra, Elhadad Noemie, Green Robert, Adelman Jason, Metitiri Katherine Schlosser, Elias Pierre, Groves Holden, Mohan Sumit, Natarajan Karthik, Perotte Adler

2021-Mar-11

COVID-19, artificial, patient readmission, renal replacement therapy, respiration, supervised machine learning

Surgery

Automatic deep learning-based pleural effusion classification in lung ultrasound images for respiratory pathology diagnosis.

In Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Lung ultrasound (LUS) imaging as a point-of-care diagnostic tool for lung pathologies has been proven superior to X-ray and comparable to CT, enabling earlier and more accurate diagnosis in real-time at the patient's bedside. The main limitation to widespread use is its dependence on the operator training and experience. COVID-19 lung ultrasound findings predominantly reflect a pneumonitis pattern, with pleural effusion being infrequent. However, pleural effusion is easy to detect and to quantify, therefore it was selected as the subject of this study, which aims to develop an automated system for the interpretation of LUS of pleural effusion. A LUS dataset was collected at the Royal Melbourne Hospital which consisted of 623 videos containing 99,209 2D ultrasound images of 70 patients using a phased array transducer. A standardized protocol was followed that involved scanning six anatomical regions providing complete coverage of the lungs for diagnosis of respiratory pathology. This protocol combined with a deep learning algorithm using a Spatial Transformer Network provides a basis for automatic pathology classification on an image-based level. In this work, the deep learning model was trained using supervised and weakly supervised approaches which used frame- and video-based ground truth labels respectively. The reference was expert clinician image interpretation. Both approaches show comparable accuracy scores on the test set of 92.4% and 91.1%, respectively, not statistically significantly different. However, the video-based labelling approach requires significantly less effort from clinical experts for ground truth labelling.
Tsai Chung-Han, van der Burgt Jeroen, Vukovic Damjan, Kaur Nancy, Demi Libertario, Canty David, Wang Andrew, Royse Alistair, Royse Colin, Haji Kavi, Dowling Jason, Chetty Girija, Fontanarosa Davide

2021-Mar-08

Lung ultrasound, Machine learning, Pleural effusion diagnosis, COVID-19, Weakly supervised deep learning

General

Predicting fear and perceived health during the COVID-19 pandemic using machine learning: A cross-national longitudinal study.

In PloS one ; h5-index 176.0
During medical pandemics, protective behaviors need to be motivated by effective communication, where finding predictors of fear and perceived health is of critical importance. The varying trajectories of the COVID-19 pandemic in different countries afford the opportunity to assess the unique influence of 'macro-level' environmental factors and 'micro-level' psychological variables on both fear and perceived health. Here, we investigate predictors of fear and perceived health using machine learning as lockdown restrictions in response to the COVID-19 pandemic were introduced in Austria, Spain, Poland and Czech Republic. Over a seven-week period, 533 participants completed weekly self-report surveys which measured the target variables subjective fear of the virus and perceived health, in addition to potential predictive variables related to psychological factors, social factors, perceived vulnerability to disease (PVD), and economic circumstances. Viral spread, mortality and governmental responses were further included in the analysis as potential environmental predictors. Results revealed that our models could accurately predict fear of the virus (accounting for approximately 23% of the variance) using predictive factors such as worrying about shortages in food supplies and perceived vulnerability to disease (PVD), where interestingly, environmental factors such as spread of the virus and governmental restrictions did not contribute to this prediction. Furthermore, our results revealed that perceived health could be predicted using PVD, physical exercise, attachment anxiety and age as input features, albeit with smaller effect sizes. Taken together, our results emphasize the importance of 'micro-level' psychological factors, as opposed to 'macro-level' environmental factors, when predicting fear and perceived health, and offer a starting point for more extensive research on the influences of pathogen threat and governmental restrictions on the psychology of fear and health.
Eder Stephanie Josephine, Steyrl David, Stefanczyk Michal Mikolaj, Pieniak Michał, Martínez Molina Judit, Pešout Ondra, Binter Jakub, Smela Patrick, Scharnowski Frank, Nicholson Andrew A

2021

General

Deep learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) with CT images.

In IEEE/ACM transactions on computational biology and bioinformatics
A novel coronavirus (COVID-19) has emerged recently as an acute respiratory syndrome. The outbreak was originally reported in Wuhan, China, but has subsequently been spread world-widely. As the COVID-19 continues to spread rapidly across the world, computed tomography (CT) has become essentially important for fast diagnoses. Thus, it is urgent to develop an accurate computer-aided method to assist clinicians to identify COVID-19-infected patients by CT images. We collected chest CT scans of 88 patients diagnosed with the COVID-19 from hospitals of two provinces in China, 101 patients infected with bacteria pneumonia, and 86 healthy persons for comparison and modeling. A deep learning-based CT diagnosis system was developed to identify patients with COVID-19. The experimental results showed that our model can accurately identify the COVID-19 patients from the healthy with an AUC of 0.99, recall (sensitivity) of 0.93, and precision of 0.96. When integrating three types of CT images, our model achieved a recall of 0.93 with precision of 0.86 for discriminating COVID-19 patients from others. Moreover, our model could extract main lesion features, especially the ground-glass opacity (GGO) that is visually helpful for assisted diagnoses by doctors. An online server is available for online diagnoses with CT images by http://biomed.nscc-gz.cn/model.php.
Song Ying, Zheng Shuangjia, Li Liang, Zhang Xiang, Zhang Xiaodong, Huang Ziwang, Chen Jianwen, Wang Ruixuan, Zhao Huiying, Zha Yunfei, Shen Jun, Chong Yutian, Yang Yuedong

2021-Mar-11

Pathology

Longitudinal proteomic profiling of dialysis patients with COVID-19 reveals markers of severity and predictors of death.

In eLife
End-stage kidney disease (ESKD) patients are at high risk of severe COVID-19. We measured 436 circulating proteins in serial blood samples from hospitalised and non-hospitalised ESKD patients with COVID-19 (n=256 samples from 55 patients). Comparison to 51 non-infected patients revealed 221 differentially expressed proteins, with consistent results in a separate subcohort of 46 COVID-19 patients. 203 proteins were associated with clinical severity, including IL6, markers of monocyte recruitment (e.g. CCL2, CCL7), neutrophil activation (e.g. proteinase-3) and epithelial injury (e.g. KRT19). Machine learning identified predictors of severity including IL18BP, CTSD, GDF15, and KRT19. Survival analysis with joint models revealed 69 predictors of death. Longitudinal modelling with linear mixed models uncovered 32 proteins displaying different temporal profiles in severe versus non-severe disease, including integrins and adhesion molecules. These data implicate epithelial damage, innate immune activation, and leucocyte-endothelial interactions in the pathology of severe COVID-19 and provide a resource for identifying drug targets.
Gisby Jack, Clarke Candice L, Medjeral-Thomas Nicholas, Malik Talat H, Papadaki Artemis, Mortimer Paige M, Buang Norzawani B, Lewis Shanice, Pereira Marie, Toulza Frederic, Fagnano Ester, Mawhin Marie-Anne, Dutton Emma E, Tapeng Lunnathaya, Richard Arianne C, Kirk Paul Dw, Behmoaras Jacques, Sandhu Eleanor, McAdoo Stephen P, Prendecki Maria F, Pickering Matthew C, Botto Marina, Willicombe Michelle, Thomas David C, Peters James Edward

2021-Mar-11

human, immunology, inflammation, medicine

Public Health

Why do people oppose mask wearing? A comprehensive analysis of US tweets during the COVID-19 pandemic.

In Journal of the American Medical Informatics Association : JAMIA

OBJECTIVE : Facial masks are an essential personal protective measure to fight the COVID-19 pandemic. However, the mask adoption rate in the US is still less than optimal. This study aims to understand the beliefs held by individuals who oppose the use of facial masks, and the evidence that they use to support these beliefs, to inform the development of targeted public health communication strategies.

MATERIALS AND METHODS : We analyzed a total of 771,268 US-based tweets between January to October 2020. We developed machine-learning classifiers to identify and categorize relevant tweets, followed by a qualitative content analysis of a subset of the tweets to understand the rationale of those opposed mask wearing.

RESULTS : We identified 267,152 tweets that contained personal opinions about wearing facial masks to prevent the spread of COVID-19. While the majority of the tweets supported mask wearing, the proportion of anti-mask tweets stayed constant at about 10% level throughout the study period. Common reasons for opposition included physical discomfort and negative effects, lack of effectiveness, and being unnecessary or inappropriate for certain people or under certain circumstances. The opposing tweets were significantly less likely to cite external sources of information such as public health agencies' websites to support the arguments.

DISCUSSION AND CONCLUSION : Combining machine learning and qualitative content analysis is an effective strategy for identifying public attitudes toward mask wearing and the reasons for opposition. The results may inform better communication strategies to improve the public perception of wearing masks and, in particular, to specifically address common anti-mask beliefs.

He Lu, He Changyang, Reynolds Tera L, Bai Qiushi, Huang Yicong, Li Chen, Zheng Kai, Chen Yunan

2021-Mar-04

Coronavirus [B04.820.504.540.150], Health Communication [L01.143.350], Machine Learning [G17.035.250.500], Masks [E07.325.877.500], Natural Language Processing [L01.224.050.375.580], Personal Protective Equipment [E07.700.560], Public Health [H02.403.720], Social Media [L01.178.751]

General

Assessing the feasibility and effectiveness of household-pooled universal testing to control COVID-19 epidemics.

In PLoS computational biology
Outbreaks of SARS-CoV-2 are threatening the health care systems of several countries around the world. The initial control of SARS-CoV-2 epidemics relied on non-pharmaceutical interventions, such as social distancing, teleworking, mouth masks and contact tracing. However, as pre-symptomatic transmission remains an important driver of the epidemic, contact tracing efforts struggle to fully control SARS-CoV-2 epidemics. Therefore, in this work, we investigate to what extent the use of universal testing, i.e., an approach in which we screen the entire population, can be utilized to mitigate this epidemic. To this end, we rely on PCR test pooling of individuals that belong to the same households, to allow for a universal testing procedure that is feasible with the limited testing capacity. We evaluate two isolation strategies: on the one hand pool isolation, where we isolate all individuals that belong to a positive PCR test pool, and on the other hand individual isolation, where we determine which of the individuals that belong to the positive PCR pool are positive, through an additional testing step. We evaluate this universal testing approach in the STRIDE individual-based epidemiological model in the context of the Belgian COVID-19 epidemic. As the organisation of universal testing will be challenging, we discuss the different aspects related to sample extraction and PCR testing, to demonstrate the feasibility of universal testing when a decentralized testing approach is used. We show through simulation, that weekly universal testing is able to control the epidemic, even when many of the contact reductions are relieved. Finally, our model shows that the use of universal testing in combination with stringent contact reductions could be considered as a strategy to eradicate the virus.
Libin Pieter J K, Willem Lander, Verstraeten Timothy, Torneri Andrea, Vanderlocht Joris, Hens Niel

2021-Mar

General

Effects of climate variables on the COVID-19 outbreak in Spain.

In International journal of hygiene and environmental health ; h5-index 50.0
An outbreak of the novel COVID-19 virus occurred during February 2020 onwards in almost all the European countries, including Spain. This study covers the correlation found between weather variables (Maximum Temperature, Minimum Temperature, Mean Temperature, Atmospheric Pressure, Daily Rainfall, Daily Sun hours) and the coronavirus propagation in Spain. A strong relationship is found when correlating the virus spread to the mean temperature, minimum temperature, and atmospheric pressure in different Spanish provinces. In this analysis we have used the ratio of the PCR COVID-19 positives with respect to the population size. A linear regression model using the mean temperature is implemented. Moreover, an analysis of variance is used to confirm the influence of mean temperature on the spread of virus. As a second measurement of the COVID-19 outbreak we have used the results of the antibodies tests carried out in Spain that provide an estimation of the heard immunity achieved. Based on this analysis, an estimation of the asymptomatic population is performed. All these results exhibit significant correlation with weather variables. The most affected provinces were Soria, Segovia and Ciudad Real, which are the coldest. On the opposite side, places such as Southern Spain, the Baleares, and Canary Islands showed a lower rate of spread. This might be related to the warmer climate and the insularity of these islands. Besides, the coastal influence and the daily sun hours might also influence the lower rates in the east and west regions in Spain. This analysis provides a deeper insight of the influence of weather variables onto the COVID-19 spread in Spain.
Loché Fernández-Ahúja José María, Fernández Martínez Juan Luis

2021-Feb-27

COVID-19, Climate variables, Correlation, IgG, PCR

General

SWIFT: A Deep Learning Approach to Prediction of Hypoxemic Events in Critically-Ill Patients Using SpO 2 Waveform Prediction.

In medRxiv : the preprint server for health sciences
Hypoxemia is a significant driver of mortality and poor clinical outcomes in conditions such as brain injury and cardiac arrest in critically ill patients, including COVID-19 patients. Given the host of negative clinical outcomes attributed to hypoxemia, identifying patients likely to experience hypoxemia would offer valuable opportunities for early and thus more effective intervention. We present SWIFT (SpO ₂ W aveform I CU F orecasting T echnique), a deep learning model that predicts blood oxygen saturation (SpO ₂ ) waveforms 5 and 30 minutes in the future using only prior SpO ₂ values as inputs. When tested on novel data, SWIFT predicts more than 80% and 60% of hypoxemic events in critically ill and COVID-19 patients, respectively. SWIFT also predicts SpO ₂ waveforms with average MSE below .0007. SWIFT provides information on both occurrence and magnitude of potential hypoxemic events 30 minutes in advance, allowing it to be used to inform clinical interventions, patient triaging, and optimal resource allocation. SWIFT may be used in clinical decision support systems to inform the management of critically ill patients during the COVID-19 pandemic and beyond.
Annapragada Akshaya V, Greenstein Joseph L, Bose Sanjukta N, Winters Bradford D, Sarma Sridevi V, Winslow Raimond L

2021-Mar-05

General

Development and External Validation of a Delirium Prediction Model for Hospitalized Patients With Coronavirus Disease 2019.

In Journal of the Academy of Consultation-Liaison Psychiatry

Background : The coronavirus disease 2019 pandemic has placed unprecedented stress on health systems and has been associated with elevated risk for delirium. The convergence of pandemic resource limitation and clinical demand associated with delirium requires careful risk stratification for targeted prevention efforts.

Objectives : To develop an incident delirium predictive model among coronavirus disease 2019 patients.

Methods : We applied supervised machine learning to electronic health records data available at the start coronavirus disease 2019 inpatients admissions at three hospitals to build an incident delirium predictive model. We validated this model in three different hospitals. Both hospital cohorts included academic and community settings.

Results : Among 2907 patients across 6 hospitals, 488 (16.8%) developed delirium. Applying the predictive model in the external validation cohort of 755 patients, the c-index was 0.75 (0.71-0.79) and the lift in the top quintile was 2.1. At a sensitivity of 80%, the specificity was 56%, negative predictive value 92%, and positive predictive value 30%. Equivalent model performance was observed in subsamples stratified by age, sex, race, need for critical care and care at community vs. academic hospitals.

Conclusion : Machine learning applied to electronic health records available at the time of inpatient admission can be used to risk-stratify patients with coronavirus disease 2019 for incident delirium. Delirium is common among patients with coronavirus disease 2019, and resource constraints during a pandemic demand careful attention to the optimal application of predictive models.

Castro Victor M, Sacks Chana A, Perlis Roy H, McCoy Thomas H

2021-Mar-05

COVID-19, cohort study, crisis standard of care, delirium, electronic health records, machine learning, predictive modeling

General

Convolution Neural Network Based Infection Transmission Analysis on Covid -19 Using GIS and Covid Data Materials.

In Materials today. Proceedings
Towards the improvement of predicting and analyzing the infection transmission, a novel CNN (Convolution Neural Network) based Covid Infection Transmission Analysis (CNN-CITA) is presented in this article. The method works based on both GIS data set and the Covid data set. The method reads all the data from the data sets. From the remote sensing data, the method extracts different climate conditions like temperature, humidity, and rainfall. Similarly from Global Information System data set, the locations of the peoples are fetched and merged. The merged data has been split into number of time frame, at each condition, the data sets are merged. Such merged data has been trained with deep learning networks which support the search of person location and mobility. Based on the result and the data set maintained by the governments, the infection transmission rate has been measured on region basis. In each region of movement performed by any person, the method computes the infection Transmission Rate (ITR) in two time window as before and after. According to the infection rate and ITR value of different region, a subset of sources are selected as vulnerable sources. The method produces higher performance in predicting the vulnerable sources and supports the reduction of infection rate. Index Terms: CNN, CNN-CITA, Regional Transmission, Infection Rate, ITA, ITS, GIS, Remote Sensing Data.
Jadhav Jagannath, Rao Surampudi Srinivasa, Alagirisamy Mukil

2021-Mar-04

Public Health

How Does Railway Respond to the Spread of COVID-19? Countermeasure Analysis and Evaluation Around the World.

In Urban rail transit
The global COVID-19 pandemic is having a significant impact on the development of many aspects all over the world. As an important part of public services, rail transit requires effective response countermeasures to control the spread of COVID-19. Considering the current development of the epidemic situation, this article discusses the characteristics of COVID-19 transmission and identifies vulnerable areas to target in order to prevent and control the spread of the epidemic in the rail transit system. Countermeasures adopted to prevent the spread of COVID-19 are analyzed in terms of external and internal categories, which were classified into six groups: passenger service, case care, information, staff, equipment and operation management. An evaluation architecture was also constructed, which was established from the perspective of effectiveness, economic efficiency, acceptability, privacy and so on. The effect of implementing the measures was evaluated by a social survey, and their advantages and shortcomings were analyzed, which can be used to guide future epidemic prevention and control for rail transit systems around the world. It is important to formulate a reasonable work schedule according to local conditions, providing a reference for rapid response to future public health emergencies of international concern.
Yin Yonghao, Li Dewei, Zhang Songliang, Wu Lifu

2021-Mar-04

COVID-19 transmission, Countermeasure evaluation, Public health, Railway response

General

COVID-19 Infection Detection from Chest X-Ray Images Using Hybrid Social Group Optimization and Support Vector Classifier.

In Cognitive computation
A novel strain of Coronavirus, identified as the Severe Acute Respiratory Syndrome-2 (SARS-CoV-2), outbroke in December 2019 causing the novel Corona Virus Disease (COVID-19). Since its emergence, the virus has spread rapidly and has been declared a global pandemic. As of the end of January 2021, there are almost 100 million cases worldwide with over 2 million confirmed deaths. Widespread testing is essential to reduce further spread of the disease, but due to a shortage of testing kits and limited supply, alternative testing methods are being evaluated. Recently researchers have found that chest X-Ray (CXR) images provide salient information about COVID-19. An intelligent system can help the radiologists to detect COVID-19 from these CXR images which can come in handy at remote locations in many developing nations. In this work, we propose a pipeline that uses CXR images to detect COVID-19 infection. The features from the CXR images were extracted and the relevant features were then selected using Hybrid Social Group Optimization algorithm. The selected features were then used to classify the CXR images using a number of classifiers. The proposed pipeline achieves a classification accuracy of 99.65% using support vector classifier, which outperforms other state-of-the-art deep learning algorithms for binary and multi-class classification.
Singh Asu Kumar, Kumar Anupam, Mahmud Mufti, Kaiser M Shamim, Kishore Akshat

2021-Mar-04

Computer-aided detection system, Evolutionary computing, Feature reduction, Social group optimization

General

AI support for ethical decision-making around resuscitation: proceed with care.

In Journal of medical ethics ; h5-index 34.0
Artificial intelligence (AI) systems are increasingly being used in healthcare, thanks to the high level of performance that these systems have proven to deliver. So far, clinical applications have focused on diagnosis and on prediction of outcomes. It is less clear in what way AI can or should support complex clinical decisions that crucially depend on patient preferences. In this paper, we focus on the ethical questions arising from the design, development and deployment of AI systems to support decision-making around cardiopulmonary resuscitation and the determination of a patient's Do Not Attempt to Resuscitate status (also known as code status). The COVID-19 pandemic has made us keenly aware of the difficulties physicians encounter when they have to act quickly in stressful situations without knowing what their patient would have wanted. We discuss the results of an interview study conducted with healthcare professionals in a university hospital aimed at understanding the status quo of resuscitation decision processes while exploring a potential role for AI systems in decision-making around code status. Our data suggest that (1) current practices are fraught with challenges such as insufficient knowledge regarding patient preferences, time pressure and personal bias guiding care considerations and (2) there is considerable openness among clinicians to consider the use of AI-based decision support. We suggest a model for how AI can contribute to improve decision-making around resuscitation and propose a set of ethically relevant preconditions-conceptual, methodological and procedural-that need to be considered in further development and implementation efforts.
Biller-Andorno Nikola, Ferrario Andrea, Joebges Susanne, Krones Tanja, Massini Federico, Barth Phyllis, Arampatzis Georgios, Krauthammer Michael

2021-Mar-09

artificial intelligence, clinical ethics, decision-making, emergency medicine, end-of-life, patient perspective

General

Automatic Social Distance Estimation From Images: Performance Evaluation, Test Benchmark, and Algorithm

ArXiv Preprint
The COVID-19 virus has caused a global pandemic since March 2020. The World Health Organization (WHO) has provided guidelines on how to reduce the spread of the virus and one of the most important measures is social distancing. Maintaining a minimum of one meter distance from other people is strongly suggested to reduce the risk of infection. This has created a strong interest in monitoring the social distances either as a safety measure or to study how the measures have affected human behavior and country-wise differences in this. The need for automatic social distance estimation algorithms is evident, but there is no suitable test benchmark for such algorithms. Collecting images with measured ground-truth pair-wise distances between all the people using different camera settings is cumbersome. Furthermore, performance evaluation for social distance estimation algorithms is not straightforward and there is no widely accepted evaluation protocol. In this paper, we provide a dataset of varying images with measured pair-wise social distances under different camera positionings and focal length values. We suggest a performance evaluation protocol and provide a benchmark to easily evaluate social distance estimation algorithms. We also propose a method for automatic social distance estimation. Our method takes advantage of object detection and human pose estimation. It can be applied on any single image as long as focal length and sensor size information are known. The results on our benchmark are encouraging with 92% human detection rate and only 28.9% average error in distance estimation among the detected people.
Mert Seker, Anssi Männistö, Alexandros Iosifidis, Jenni Raitoharju

2021-03-11

Public Health

How Much Does the (Social) Environment Matter? Using Artificial Intelligence to Predict COVID-19 Outcomes with Socio-demographic Data.

In Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
While the coronavirus pandemic has affected all demographic brackets and geographies, certain areas have been more adversely affected than others. This paper focuses on Veterans as a potentially vulnerable group that might be systematically more exposed to infection than others because of their co-morbidities, i.e., greater incidence of physical and mental health challenges. Using data on 122 Veteran Healthcare Systems (HCS), this paper tests three machine learning models for predictive analysis. The combined LASSO and ridge regression with five-fold cross validation performs the best. We find that socio-demographic features are highly predictive of both cases and deaths-even more important than any hospital-specific characteristics. These results suggest that socio-demographic and social capital characteristics are important determinants of public health outcomes, especially for vulnerable groups, like Veterans, and they should be investigated further.
Makridis Christos A, Mudide Anish, Alterovitz Gil

2021

Dermatology

TrueImage: A Machine Learning Algorithm to Improve the Quality of Telehealth Photos.

In Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Telehealth is an increasingly critical component of the health care ecosystem, especially due to the COVID-19 pandemic. Rapid adoption of telehealth has exposed limitations in the existing infrastructure. In this paper, we study and highlight photo quality as a major challenge in the telehealth workflow. We focus on teledermatology, where photo quality is particularly important; the framework proposed here can be generalized to other health domains. For telemedicine, dermatologists request that patients submit images of their lesions for assessment. However, these images are often of insufficient quality to make a clinical diagnosis since patients do not have experience taking clinical photos. A clinician has to manually triage poor quality images and request new images to be submitted, leading to wasted time for both the clinician and the patient. We propose an automated image assessment machine learning pipeline, TrueImage, to detect poor quality dermatology photos and to guide patients in taking better photos. Our experiments indicate that TrueImage can reject ~50% of the sub-par quality images, while retaining ~80% of good quality images patients send in, despite heterogeneity and limitations in the training data. These promising results suggest that our solution is feasible and can improve the quality of teledermatology care.
Vodrahalli Kailas, Daneshjou Roxana, Novoa Roberto A, Chiou Albert, Ko Justin M, Zou James

2021

General

Protein sequence models for prediction and comparative analysis of the SARS-CoV-2 -human interactome.

In Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Viruses such as the novel coronavirus, SARS-CoV-2, that is wreaking havoc on the world, depend on interactions of its own proteins with those of the human host cells. Relatively small changes in sequence such as between SARS-CoV and SARS-CoV-2 can dramatically change clinical phenotypes of the virus, including transmission rates and severity of the disease. On the other hand, highly dissimilar virus families such as Coronaviridae, Ebola, and HIV have overlap in functions. In this work we aim to analyze the role of protein sequence in the binding of SARS-CoV-2 virus proteins towards human proteins and compare it to that of the above other viruses. We build supervised machine learning models, using Generalized Additive Models to predict interactions based on sequence features and find that our models perform well with an AUC-PR of 0.65 in a class-skew of 1:10. Analysis of the novel predictions using an independent dataset showed statistically significant enrichment. We further map the importance of specific amino-acid sequence features in predicting binding and summarize what combinations of sequences from the virus and the host is correlated with an interaction. By analyzing the sequence-based embeddings of the interactomes from different viruses and clustering them together we find some functionally similar proteins from different viruses. For example, vif protein from HIV-1, vp24 from Ebola and orf3b from SARS-CoV all function as interferon antagonists. Furthermore, we can differentiate the functions of similar viruses, for example orf3a's interactions are more diverged than orf7b interactions when comparing SARS-CoV and SARS-CoV-2.
Kshirsagar Meghana, Tasnina Nure, Ward Michael D, Law Jeffrey N, Murali T M, Lavista Ferres Juan M, Bowman Gregory R, Klein-Seetharaman Judith

2021

General

Teledentistry Platforms for Orthodontics.

In The Journal of clinical pediatric dentistry
Technology has transformed almost every aspect of our lives. Smartphones enable patients to request, receive, and transmit information irrespective of the time and place. The global pandemic has forced healthcare providers to employ technology to aid in 'flattening the curve. The Novel Coronavirus, which is responsible for COVID-19, is transmitted primarily through person-to-person contact but may also be spread through aerosol generating procedures, so many clinics have severely limited interpersonal interactions. The purpose of this article is to provide helpful information for those orthodontists considering some form of remote practice. Various HIPAA-compliant telecommunication or teledentistry systems that can be used for orthodontic treatment are introduced and discussed. Detailed information about each platform that can potentially be used for orthodontics is provided in Figure 1. The authors do not endorse any of the products listed and the included software is not all inclusive but instead is a glimpse into the options available.
Park Jae Hyun, Rogowski Leah, Kim Janet H, Al Shami Sumayah, Howell Scott E I

2021-Jan-01

Artificial Intelligence (AI) assisted, Patient Management, Telecommunication, Teleconsultation

Public Health

The broader societal impacts of COVID-19 and the growing importance of capturing these in health economic analyses.

In International journal of technology assessment in health care
AbstractThe rapid spread of the current COVID-19 pandemic has affected societies worldwide, leading to excess mortality, long-lasting health consequences, strained healthcare systems, and additional strains and spillover effects on other sectors outside health (i.e., intersectoral costs and benefits). In this perspective piece, we demonstrate the broader societal impacts of COVID-19 on other sectors outside the health sector and the growing importance of capturing these in health economic analyses. These broader impacts include, for instance, the effects on the labor market and productivity, education, criminal justice, housing, consumption, and environment. The current pandemic highlights the importance of adopting a societal perspective to consider these broader impacts of public health issues and interventions and only omit these where it can be clearly justified as appropriate to do so. Furthermore, we explain how the COVID-19 pandemic exposed and exacerbated existing deep-rooted structural inequalities that contribute to the wider societal impacts of the pandemic.
Schnitzler Lena, Janssen Luca M M, Evers Silvia M A A, Jackson Louise J, Paulus Aggie T G, Roberts Tracy E, Pokhilenko Irina

2021-Mar-09

COVID-19, Economic evaluation, Intersectoral costs and benefits, Societal perspective

Radiology

Prediction of Patient Management in COVID-19 Using Deep Learning-Based Fully Automated Extraction of Cardiothoracic CT Metrics and Laboratory Findings.

In Korean journal of radiology

OBJECTIVE : To extract pulmonary and cardiovascular metrics from chest CTs of patients with coronavirus disease 2019 (COVID-19) using a fully automated deep learning-based approach and assess their potential to predict patient management.

MATERIALS AND METHODS : All initial chest CTs of patients who tested positive for severe acute respiratory syndrome coronavirus 2 at our emergency department between March 25 and April 25, 2020, were identified (n = 120). Three patient management groups were defined: group 1 (outpatient), group 2 (general ward), and group 3 (intensive care unit [ICU]). Multiple pulmonary and cardiovascular metrics were extracted from the chest CT images using deep learning. Additionally, six laboratory findings indicating inflammation and cellular damage were considered. Differences in CT metrics, laboratory findings, and demographics between the patient management groups were assessed. The potential of these parameters to predict patients' needs for intensive care (yes/no) was analyzed using logistic regression and receiver operating characteristic curves. Internal and external validity were assessed using 109 independent chest CT scans.

RESULTS : While demographic parameters alone (sex and age) were not sufficient to predict ICU management status, both CT metrics alone (including both pulmonary and cardiovascular metrics; area under the curve [AUC] = 0.88; 95% confidence interval [CI] = 0.79-0.97) and laboratory findings alone (C-reactive protein, lactate dehydrogenase, white blood cell count, and albumin; AUC = 0.86; 95% CI = 0.77-0.94) were good classifiers. Excellent performance was achieved by a combination of demographic parameters, CT metrics, and laboratory findings (AUC = 0.91; 95% CI = 0.85-0.98). Application of a model that combined both pulmonary CT metrics and demographic parameters on a dataset from another hospital indicated its external validity (AUC = 0.77; 95% CI = 0.66-0.88).

CONCLUSION : Chest CT of patients with COVID-19 contains valuable information that can be accessed using automated image analysis. These metrics are useful for the prediction of patient management.

Weikert Thomas, Rapaka Saikiran, Grbic Sasa, Re Thomas, Chaganti Shikha, Winkel David J, Anastasopoulos Constantin, Niemann Tilo, Wiggli Benedikt J, Bremerich Jens, Twerenbold Raphael, Sommer Gregor, Comaniciu Dorin, Sauter Alexander W

2021-Feb-24

Artificial intelligence, COVID-19, Computed tomography, Deep learning, Patient management

Internal Medicine

Estimating Baseline Incidence of Conditions Potentially Associated with Vaccine Adverse Events: a Call for Surveillance System Using the Korean National Health Insurance Claims Data.

In Journal of Korean medical science

BACKGROUND : Vaccines against coronavirus disease 2019 (COVID-19) are raising concerns about vaccine safety, particularly in the context of large-scale immunization. To address public concerns, we measured the baseline incidence rates of major conditions potentially related to vaccine-related adverse events (VAEs). We aimed to provide a basis for evaluating VAEs and verifying causality.

METHODS : Conditions of interest were selected from the US Vaccine Adverse Event Reporting System Table of Reportable Events and a recent report from a European consortium on vaccine surveillance. We used the National Health Insurance Service database in Korea to identify the monthly numbers of cases with these conditions. Data from January 2006 to June 2020 were included. Prediction models were constructed from the observed incidences using an autoregressive integrated moving average. We predicted the incidences of the conditions and their respective 95% confidence intervals (CIs) for January through December 2021. In addition, subgroup analysis for the expected vaccination population was conducted.

RESULTS : Mean values (95% CIs) of the predicted monthly incidence of vasovagal syncope, anaphylaxis, brachial neuritis, acute disseminated encephalomyelitis, Bell's palsy, Guillain-Barré syndrome, encephalopathy, optic neuritis, transverse myelitis, immune thrombocytopenic purpura, and systemic lupus erythematosus in 2021 were 23.89 (19.81-27.98), 4.72 (3.83-5.61), 57.62 (51.37-63.88), 0.03 (0.01-0.04), 8.58 (7.90-9.26), 0.26 (0.18-0.34), 2.13 (1.42-2.83), 1.65 (1.17-2.13), 0.19 (0.14-0.25), 0.75 (0.61-0.90), and 3.40 (2.79-4.01) cases per 100,000 respectively. The majority of the conditions showed an increasing trend with seasonal variations in their incidences.

CONCLUSION : We measured the incidence of a total of 11 conditions that could potentially be associated with VAEs to predict the monthly incidence in 2021. In Korea, conditions that could potentially be related to VAEs occur on a regular basis, and an increasing trend is observed with seasonality.

Huh Kyungmin, Kim Young Eun, Radnaabaatar Munkhzul, Lee Dae Ho, Kim Dong Wook, Shin Soon Ae, Jung Jaehun

2021-Mar-08

COVID-19, Vaccination, Vaccine Adverse Events, Vaccine Hesitancy

General

Novel coronavirus (COVID-19) diagnosis using computer vision and artificial intelligence techniques: a review.

In Multimedia tools and applications
The universal transmission of pandemic COVID-19 (Coronavirus) causes an immediate need to commit in the fight across the whole human population. The emergencies for human health care are limited for this abrupt outbreak and abandoned environment. In this situation, inventive automation like computer vision (machine learning, deep learning, artificial intelligence), medical imaging (computed tomography, X-Ray) has developed an encouraging solution against COVID-19. In recent months, different techniques using image processing are done by various researchers. In this paper, a major review on image acquisition, segmentation, diagnosis, avoidance, and management are presented. An analytical comparison of the various proposed algorithm by researchers for coronavirus has been carried out. Also, challenges and motivation for research in the future to deal with coronavirus are indicated. The clinical impact and use of computer vision and deep learning were discussed and we hope that dermatologists may have better understanding of these areas from the study.
Bhargava Anuja, Bansal Atul

2021-Mar-03

COVID-19, Computed tomography, Computer vision, Coronavirus, Machine learning

General

COVID-19 in early 2021: current status and looking forward.

In Signal transduction and targeted therapy
Since the first description of a coronavirus-related pneumonia outbreak in December 2019, the virus SARS-CoV-2 that causes the infection/disease (COVID-19) has evolved into a pandemic, and as of today, >100 million people globally in over 210 countries have been confirmed to have been infected and two million people have died of COVID-19. This brief review summarized what we have hitherto learned in the following areas: epidemiology, virology, and pathogenesis, diagnosis, use of artificial intelligence in assisting diagnosis, treatment, and vaccine development. As there are a number of parallel developments in each of these areas and some of the development and deployment were at unprecedented speed, we also provided some specific dates for certain development and milestones so that the readers can appreciate the timing of some of these critical events. Of note is the fact that there are diagnostics, antiviral drugs, and vaccines developed and approved by a regulatory within 1 year after the virus was discovered. As a number of developments were conducted in parallel, we also provided the specific dates of a number of critical events so that readers can appreciate the evolution of these research data and our understanding. The world is working together to combat this pandemic. This review also highlights the research and development directions in these areas that will evolve rapidly in the near future.
Wang Chengdi, Wang Zhoufeng, Wang Guangyu, Lau Johnson Yiu-Nam, Zhang Kang, Li Weimin

2021-Mar-08

General

COVID-19 classification using deep feature concatenation technique.

In Journal of ambient intelligence and humanized computing
Detecting COVID-19 from medical images is a challenging task that has excited scientists around the world. COVID-19 started in China in 2019, and it is still spreading even now. Chest X-ray and Computed Tomography (CT) scan are the most important imaging techniques for diagnosing COVID-19. All researchers are looking for effective solutions and fast treatment methods for this epidemic. To reduce the need for medical experts, fast and accurate automated detection techniques are introduced. Deep learning convolution neural network (DL-CNN) technologies are showing remarkable results for detecting cases of COVID-19. In this paper, deep feature concatenation (DFC) mechanism is utilized in two different ways. In the first one, DFC links deep features extracted from X-ray and CT scan using a simple proposed CNN. The other way depends on DFC to combine features extracted from either X-ray or CT scan using the proposed CNN architecture and two modern pre-trained CNNs: ResNet and GoogleNet. The DFC mechanism is applied to form a definitive classification descriptor. The proposed CNN architecture consists of three deep layers to overcome the problem of large time consumption. For each image type, the proposed CNN performance is studied using different optimization algorithms and different values for the maximum number of epochs, the learning rate (LR), and mini-batch (M-B) size. Experiments have demonstrated the superiority of the proposed approach compared to other modern and state-of-the-art methodologies in terms of accuracy, precision, recall and f_score.
Saad Waleed, Shalaby Wafaa A, Shokair Mona, El-Samie Fathi Abd, Dessouky Moawad, Abdellatef Essam

2021-Mar-02

COVID-19, Convolutional neural networks (CNNs), Deep feature concatenation

General

One-shot Cluster-Based Approach for the Detection of COVID-19 from Chest X-ray Images.

In Cognitive computation
Coronavirus disease (COVID-19) has infected over more than 28.3 million people around the globe and killed 913K people worldwide as on 11 September 2020. With this pandemic, to combat the spreading of COVID-19, effective testing methodologies and immediate medical treatments are much required. Chest X-rays are the widely available modalities for immediate diagnosis of COVID-19. Hence, automation of detection of COVID-19 from chest X-ray images using machine learning approaches is of greater demand. A model for detecting COVID-19 from chest X-ray images is proposed in this paper. A novel concept of cluster-based one-shot learning is introduced in this work. The introduced concept has an advantage of learning from a few samples against learning from many samples in case of deep leaning architectures. The proposed model is a multi-class classification model as it classifies images of four classes, viz., pneumonia bacterial, pneumonia virus, normal, and COVID-19. The proposed model is based on ensemble of Generalized Regression Neural Network (GRNN) and Probabilistic Neural Network (PNN) classifiers at decision level. The effectiveness of the proposed model has been demonstrated through extensive experimentation on a publicly available dataset consisting of 306 images. The proposed cluster-based one-shot learning has been found to be more effective on GRNN and PNN ensembled model to distinguish COVID-19 images from that of the other three classes. It has also been experimentally observed that the model has a superior performance over contemporary deep learning architectures. The concept of one-shot cluster-based learning is being first of its kind in literature, expected to open up several new dimensions in the field of machine learning which require further researching for various applications.
Aradhya V N Manjunath, Mahmud Mufti, Guru D S, Agarwal Basant, Kaiser M Shamim

2021-Mar-02

COVID-19, Chest X-rays, Classification, GRNN, Machine learning, Neural networks, PNN

General

Deep Learning-Driven Automated Detection of COVID-19 from Radiography Images: a Comparative Analysis.

In Cognitive computation
The COVID-19 pandemic has wreaked havoc on the whole world, taking over half a million lives and capsizing the world economy in unprecedented magnitudes. With the world scampering for a possible vaccine, early detection and containment are the only redress. Existing diagnostic technologies with high accuracy like RT-PCRs are expensive and sophisticated, requiring skilled individuals for specimen collection and screening, resulting in lower outreach. So, methods excluding direct human intervention are much sought after, and artificial intelligence-driven automated diagnosis, especially with radiography images, captured the researchers' interest. This survey marks a detailed inspection of the deep learning-based automated detection of COVID-19 works done to date, a comparison of the available datasets, methodical challenges like imbalanced datasets and others, along with probable solutions with different preprocessing methods, and scopes of future exploration in this arena. We also benchmarked the performance of 315 deep models in diagnosing COVID-19, normal, and pneumonia from X-ray images of a custom dataset created from four others. The dataset is publicly available at https://github.com/rgbnihal2/COVID-19-X-ray-Dataset. Our results show that DenseNet201 model with Quadratic SVM classifier performs the best (accuracy: 98.16%, sensitivity: 98.93%, specificity: 98.77%) and maintains high accuracies in other similar architectures as well. This proves that even though radiography images might not be conclusive for radiologists, but it is so for deep learning algorithms for detecting COVID-19. We hope this extensive review will provide a comprehensive guideline for researchers in this field.
Rahman Sejuti, Sarker Sujan, Miraj Md Abdullah Al, Nihal Ragib Amin, Nadimul Haque A K M, Noman Abdullah Al

2021-Mar-02

Automated detection, COVID-19, Deep learning, Medical imaging, Radiography, SARS-CoV-2

General

COVID-19 in Iran: Forecasting Pandemic Using Deep Learning.

In Computational and mathematical methods in medicine
COVID-19 has led to a pandemic, affecting almost all countries in a few months. In this work, we applied selected deep learning models including multilayer perceptron, random forest, and different versions of long short-term memory (LSTM), using three data sources to train the models, including COVID-19 occurrences, basic information like coded country names, and detailed information like population, and area of different countries. The main goal is to forecast the outbreak in nine countries (Iran, Germany, Italy, Japan, Korea, Switzerland, Spain, China, and the USA). The performances of the models are measured using four metrics, including mean average percentage error (MAPE), root mean square error (RMSE), normalized RMSE (NRMSE), and R². The best performance was found for a modified version of LSTM, called M-LSTM (winner model), to forecast the future trajectory of the pandemic in the mentioned countries. For this purpose, we collected the data from January 22 till July 30, 2020, for training, and from 1 August 2020 to 31 August 2020, for the testing phase. Through experimental results, the winner model achieved reasonably accurate predictions (MAPE, RMSE, NRMSE, and R² are 0.509, 458.12, 0.001624, and 0.99997, respectively). Furthermore, we stopped the training of the model on some dates related to main country actions to investigate the effect of country actions on predictions by the model.
Kafieh Rahele, Arian Roya, Saeedizadeh Narges, Amini Zahra, Serej Nasim Dadashi, Minaee Shervin, Yadav Sunil Kumar, Vaezi Atefeh, Rezaei Nima, Haghjooy Javanmard Shaghayegh

2021

General

Is COVID-19 pushing us to the Fifth Industrial Revolution (Society 5.0)?

In Pakistan journal of medical sciences
The coronavirus disease 2019 (COVID-19) pandemic may further promote the development of Industry 4.0 leading to the fifth industrial revolution (Society 5.0). Industry 4.0 technology such as Big Data (BD) and Artificial Intelligence (AI) may lead to a personalized system of healthcare in Pakistan. The final bridge between humans and machines is Society 5.0, also known as the super-smart society that employs AI in healthcare manufacturing and logistics. In this communication, we review various Industry 4.0 and Society 5.0 technologies including robotics and AI being inspected to control the rate of transmission of COVID-19 globally. We demonstrate the applicability of advanced information technologies including AI, BD, and Information of Technology (IoT) to healthcare. Lastly, we discuss the evolution of Industry 4.0 to Society 5.0 given the impact of the COVID-19 pandemic in accordance with the technological strategies being considered and employed.
Sarfraz Zouina, Sarfraz Azza, Iftikar Hamza Mohammad, Akhund Ramsha

COVID-19, Development, Industrial, Revolution, Society 5.0

General

Prediction of muscular paralysis disease based on hybrid feature extraction with machine learning technique for COVID-19 and post-COVID-19 patients.

In Personal and ubiquitous computing
Many Coronavirus disease 2019 (COVID-19) and post-COVID-19 patients experience muscle fatigues. Early detection of muscle fatigue and muscular paralysis helps in the diagnosis, prediction, and prevention of COVID-19 and post-COVID-19 patients. Nowadays, the biomedical and clinical domains widely used the electromyography (EMG) signal due to its ability to differentiate various neuromuscular diseases. In general, nerves or muscles and the spinal cord influence numerous neuromuscular disorders. The clinical examination plays a major role in early finding and diagnosis of these diseases; this research study focused on the prediction of muscular paralysis using EMG signals. Machine learning-based diagnosis of the diseases has been widely used due to its efficiency and the hybrid feature extraction (FE) methods with deep learning classifier are used for the muscular paralysis disease prediction. The discrete wavelet transform (DWT) method is applied to decompose the EMG signal and reduce feature degradation. The proposed hybrid FE method consists of Yule-Walker, Burg's method, Renyi entropy, mean absolute value, min-max voltage FE, and other 17 conventional features for prediction of muscular paralysis disease. The hybrid FE method has the advantage of extract the relevant features from the signals and the Relief-F feature selection (FS) method is applied to select the optimal relevant feature for the deep learning classifier. The University of California, Irvine (UCI), EMG-Lower Limb Dataset is used to determine the performance of the proposed classifier. The evaluation shows that the proposed hybrid FE method achieved 88% of precision, while the existing neural network (NN) achieved 65% of precision and the support vector machine (SVM) achieved 35% of precision on whole EMG signal.
Subramani Prabu, K Srinivas, B Kavitha Rani, R Sujatha, B D Parameshachari

2021-Mar-03

Deep learning, Discrete wavelet transform, Electromyography, Hybrid feature extraction, Muscular paralysis disease, Neural network, Relief-F selection algorithm, Support vector machine

General

An analysis model of diagnosis and treatment for COVID-19 pandemic based on medical information fusion.

In An international journal on information fusion
Exploring the complicated relationships underlying the clinical information is essential for the diagnosis and treatment of the Coronavirus Disease 2019 (COVID-19). Currently, few approaches are mature enough to show operational impact. Based on electronic medical records (EMRs) of 570 COVID-19 inpatients, we proposed an analysis model of diagnosis and treatment for COVID-19 based on the machine learning algorithms and complex networks. Introducing the medical information fusion, we constructed the heterogeneous information network to discover the complex relationships among the syndromes, symptoms, and medicines. We generated the numerical symptom (medicine) embeddings and divided them into seven communities (syndromes) using the combination of Skip-Gram model and Spectral Clustering (SC) algorithm. After analyzing the symptoms and medicine networks, we identified the key factors using six evaluation metrics of node centrality. The experimental results indicate that the proposed analysis model is capable of discovering the critical symptoms and symptom distribution for diagnosis; the key medicines and medicine combinations for treatment. Based on the latest COVID-19 clinical guidelines, this model could result in the higher accuracy results than the other representative clustering algorithms. Furthermore, the proposed model is able to provide tremendously valuable guidance and help the physicians to combat the COVID-19.
Hu Fang, Huang Mingfang, Sun Jing, Zhang Xiong, Liu Jifen

2021-Sep

Analysis model, Coronavirus Disease 2019 (COVID-19), Diagnosis and treatment, Medical information fusion

General

Face mask detection using YOLOv3 and faster R-CNN models: COVID-19 environment.

In Multimedia tools and applications
There are many solutions to prevent the spread of the COVID-19 virus and one of the most effective solutions is wearing a face mask. Almost everyone is wearing face masks at all times in public places during the coronavirus pandemic. This encourages us to explore face mask detection technology to monitor people wearing masks in public places. Most recent and advanced face mask detection approaches are designed using deep learning. In this article, two state-of-the-art object detection models, namely, YOLOv3 and faster R-CNN are used to achieve this task. The authors have trained both the models on a dataset that consists of images of people of two categories that are with and without face masks. This work proposes a technique that will draw bounding boxes (red or green) around the faces of people, based on whether a person is wearing a mask or not, and keeps the record of the ratio of people wearing face masks on the daily basis. The authors have also compared the performance of both the models i.e., their precision rate and inference time.
Singh Sunil, Ahuja Umang, Kumar Munish, Kumar Krishan, Sachdeva Monika

2021-Mar-01

COVID-19, Deep learning, Face mask detection, Faster R-CNN, YOLO v3

Internal Medicine

Overweight and Obesity are Risk Factors for Coronavirus Disease 2019: A Propensity Score-Matched Case-Control Study.

In Endocrinology and metabolism (Seoul, Korea)
Although obesity is a risk factor for infection, whether it has the same effect on coronavirus disease 2019 (COVID-19) need confirming. We conducted a retrospective propensity score matched case-control study to examine the association between obesity and COVID-19. This study included data from the Nationwide COVID-19 Registry and the Biennial Health Checkup database, until May 30, 2020. We identified 2,231 patients with confirmed COVID-19 and 10-fold-matched negative test controls. Overweight (body mass index [BMI] 23 to 24.9 kg/m2; adjusted odds ratio [aOR], 1.16; 95% confidence interval [CI], 1.1.03 to 1.30) and class 1 obesity (BMI 25 to 29.9 kg/m2; aOR, 1.27; 95% CI, 1.14 to 1.42) had significantly increased COVID-19 risk, while classes 2 and 3 obesity (BMI ≥30 kg/m2) showed similar but non-significant trend. Females and those <50 years had more robust association pattern. Overweight and obesity are possible risk factors of COVID-19.
Ji Wonjun, Lee Rugyeom, Huh Kyungmin, Kang Minsun, Hwang In Cheol, Radnaabaatar Munkhzul, Lee Dae Ho, Jung Jaehun

2021-Feb

COVID-19, Infections, Obesity, Overweight

General

QIBA guidance: Computed tomography imaging for COVID-19 quantitative imaging applications.

In Clinical imaging
As the COVID-19 pandemic impacts global populations, computed tomography (CT) lung imaging is being used in many countries to help manage patient care as well as to rapidly identify potentially useful quantitative COVID-19 CT imaging biomarkers. Quantitative COVID-19 CT imaging applications, typically based on computer vision modeling and artificial intelligence algorithms, include the potential for better methods to assess COVID-19 extent and severity, assist with differential diagnosis of COVID-19 versus other respiratory conditions, and predict disease trajectory. To help accelerate the development of robust quantitative imaging algorithms and tools, it is critical that CT imaging is obtained following best practices of the quantitative lung CT imaging community. Toward this end, the Radiological Society of North America's (RSNA) Quantitative Imaging Biomarkers Alliance (QIBA) CT Lung Density Profile Committee and CT Small Lung Nodule Profile Committee developed a set of best practices to guide clinical sites using quantitative imaging solutions and to accelerate the international development of quantitative CT algorithms for COVID-19. This guidance document provides quantitative CT lung imaging recommendations for COVID-19 CT imaging, including recommended CT image acquisition settings for contemporary CT scanners. Additional best practice guidance is provided on scientific publication reporting of quantitative CT imaging methods and the importance of contributing COVID-19 CT imaging datasets to open science research databases.
Avila Ricardo S, Fain Sean B, Hatt Chuck, Armato Samuel G, Mulshine James L, Gierada David, Silva Mario, Lynch David A, Hoffman Eric A, Ranallo Frank N, Mayo John R, Yankelevitz David, Estepar Raul San Jose, Subramaniam Raja, Henschke Claudia I, Guimaraes Alex, Sullivan Daniel C

2021-Feb-25

Artificial intelligence, COVID-19, Computed tomography, Quantitative imaging

General

An integrated autoencoder-based hybrid CNN-LSTM model for COVID-19 severity prediction from lung ultrasound.

In Computers in biology and medicine
The COVID-19 pandemic has become one of the biggest threats to the global healthcare system, creating an unprecedented condition worldwide. The necessity of rapid diagnosis calls for alternative methods to predict the condition of the patient, for which disease severity estimation on the basis of Lung Ultrasound (LUS) can be a safe, radiation-free, flexible, and favorable option. In this paper, a frame-based 4-score disease severity prediction architecture is proposed with the integration of deep convolutional and recurrent neural networks to consider both spatial and temporal features of the LUS frames. The proposed convolutional neural network (CNN) architecture implements an autoencoder network and separable convolutional branches fused with a modified DenseNet-201 network to build a vigorous, noise-free classification model. A five-fold cross-validation scheme is performed to affirm the efficacy of the proposed network. In-depth result analysis shows a promising improvement in the classification performance by introducing the Long Short-Term Memory (LSTM) layers after the proposed CNN architecture by an average of 7-12%, which is approximately 17% more than the traditional DenseNet architecture alone. From an extensive analysis, it is found that the proposed end-to-end scheme is very effective in detecting COVID-19 severity scores from LUS images.
Dastider Ankan Ghosh, Sadik Farhan, Fattah Shaikh Anowarul

2021-Feb-28

COVID-19, Deep learning, Lung ultrasound, Neural network, Severity score

General

How to spot Covid-19 patients: speech & sound audio analysis for preliminary diagnosis of SARS-COV-2 corona patients.

In International journal of clinical practice

BACKGROUND : The global cases of Covid-19 increasing day by day. On Nov. 25, 2020, a total of 59,850,910 cases reported globally with a 1,411,216 global death. In India, total cases in the country now stand at 91,77,841 including 86,04,955 recoveries and 4,38,667 active cases as on Nov. 24, 2020, as per the data issued by ICMR. A new generation of voice/audio analysis application which can tell whether the person is suffering from COVID-19 or not.

AIMS : To describe how to established a new generation of voice/audio analysis application to identify the suspected covid-19 hidden cases in hotspot areas with the help of an audio sample of the general public.

MATERIALS & METHODS : The different patents and data available as literature on the internet are evaluated to make a new generation of voice/audio analysis application with the help of an audio sample of the general public.

RESULTS : The collection of the audio sample will be done from the already suffered covid-19 patients in (.Wave files) personally or through phone calls. The audio samples like the sound of the cough, the pattern of breathing, respiration rate and way of speech will be recorded. The parameters will be evaluated for loudness, articulation, tempo, rhythm, melody and timbre. The analysis and interpretation of the parameters can be made through machine learning and artificial intelligence to detect corona cases with an audio sample.

DISCUSSION : The voice/audio application current project can be merged with a mobile App called "AarogyaSetu" by Govt. of India. The project can be implemented in the high-risk area of Covid-19 in the country.

CONCLUSION : This new method of detecting cases will decrease the workload in the covid-19 laboratory.

Sharma Amit, Baldi Ashish, Kumar Sharma Dinesh

2021-Mar-08

Artificial intelligence, Audio/voice, Corona, Covid-19, Machine learning

Radiology

Temporal changes of quantitative CT findings from 102 patients with COVID-19 in Wuhan, China: A longitudinal study.

In Technology and health care : official journal of the European Society for Engineering and Medicine

BACKGROUND : Computed tomography (CT) imaging combined with artificial intelligence is important in the diagnosis and prognosis of lung diseases.

OBJECTIVE : This study aimed to investigate temporal changes of quantitative CT findings in patients with COVID-19 in three clinic types, including moderate, severe, and non-survivors, and to predict severe cases in the early stage from the results.

METHODS : One hundred and two patients with confirmed COVID-19 were included in this study. Based on the time interval between onset of symptoms and the CT scan, four stages were defined in this study: Stage-1 (0 ∼7 days); Stage-2 (8 ∼ 14 days); Stage-3 (15 ∼ 21days); Stage-4 (> 21 days). Eight parameters, the infection volume and percentage of the whole lung in four different Hounsfield (HU) ranges, ((-, -750), [-750, -300), [-300, 50) and [50, +)), were calculated and compared between different groups.

RESULTS : The infection volume and percentage of four HU ranges peaked in Stage-2. The highest proportion of HU [-750, 50) was found in the infected regions in non-survivors among three groups.

CONCLUSIONS : The findings indicate rapid deterioration in the first week since the onset of symptoms in non-survivors. Higher proportions of HU [-750, 50) in e lesion area might be a potential bio-marker for poor prognosis in patients with COVID-19.

Chen Xiaohui, Sun Wenbo, Xu Dan, Ma Jiaojiao, Xiao Feng, Xu Haibo

2021-Feb-26

COVID-19, early detection, quantitative CT parameters, temporal changes

General

Video-Based Analyses of Parkinson's Disease Severity: A Brief Review.

In Journal of Parkinson's disease
Remote and objective assessment of the motor symptoms of Parkinson's disease is an area of great interest particularly since the COVID-19 crisis emerged. In this paper, we focus on a) the challenges of assessing motor severity via videos and b) the use of emerging video-based Artificial Intelligence (AI)/Machine Learning techniques to quantitate human movement and its potential utility in assessing motor severity in patients with Parkinson's disease. While we conclude that video-based assessment may be an accessible and useful way of monitoring motor severity of Parkinson's disease, the potential of video-based AI to diagnose and quantify disease severity in the clinical context is dependent on research with large, diverse samples, and further validation using carefully considered performance standards.
Sibley Krista G, Girges Christine, Hoque Ehsan, Foltynie Thomas

2021-Mar-01

Parkinson’s disease, artificial intelligence, machine learning, video

Internal Medicine

Low awareness of past SARS-CoV-2 infection in healthy plasma donors.

In Cell reports. Medicine
Awareness of infection with SARS-CoV-2 is crucial for the effectiveness of COVID-19 control measures. Here, we investigate awareness of infection and symptoms in relation to antibodies against SARS-CoV-2 in healthy plasma donors. We ask individuals donating plasma across the Netherlands between May 11^th and 18^th 2020 to report COVID-19 related symptoms and we test for antibodies indicative of a past infection with SARS-CoV-2. Among 3,676 with antibody and questionnaire data 239 (6.5%) are positive for SARS-CoV-2 antibodies. Of those, 48% suspect no COVID-19 despite the majority reporting symptoms. 11% of seropositive individuals report no, and 27% very mild symptoms at any time during the first peak of the epidemic. Anosmia/ageusia and fever are most strongly associated with seropositivity. Almost half of seropositive individuals do not suspect SARS-CoV-2 infection. Improved recognition of COVID-19 symptoms, in particular anosmia/ageusia and fever, is needed to reduce widespread SARS-CoV-2 transmission.
van den Hurk Katja, Merz Eva-Maria, Prinsze Femmeke J, Spekman Marloes L C, Quee Franke A, Ramondt Steven, Slot Ed, Vrielink Hans, Huis In ‘t Veld Elisabeth M J, Zaaijer Hans L, Hogema Boris M

2021-Mar-01

General

Understanding Demographic Risk Factors for Adverse Outcomes in COVID-19 Patients: Explanation of a Deep Learning Model.

In Journal of healthcare informatics research
This study was to understand the impacts of three key demographic variables, age, gender, and race, on the adverse outcome of all-cause hospitalization or all-cause mortality in patients with COVID-19, using a deep neural network (DNN) analysis. We created a cohort of Veterans who were tested positive for COVID-19, extracted data on age, gender, and race, and clinical characteristics from their electronic health records, and trained a DNN model for predicting the adverse outcome. Then, we analyzed the association of the demographic variables with the risks of the adverse outcome using the impact scores and interaction scores for explaining DNN models. The results showed that, on average, older age and African American race were associated with higher risks while female gender was associated with lower risks. However, individual-level impact scores of age showed that age was a more impactful risk factor in younger patients and in older patients with fewer comorbidities. The individual-level impact scores of gender and race variables had a wide span covering both positive and negative values. The interaction scores between the demographic variables showed that the interaction effects were minimal compared to the impact scores associated with them. In conclusion, the DNN model is able to capture the non-linear relationship between the risk factors and the adverse outcome, and the impact scores and interaction scores can help explain the complicated non-linear effects between the demographic variables and the risk of the outcome.
Shao Yijun, Ahmed Ali, Liappis Angelike P, Faselis Charles, Nelson Stuart J, Zeng-Treitler Qing

2021-Feb-27

Artificial intelligence, Coronavirus disease, Deep neural network, Explainable AI

General

Classification of covid related articles using machine learning.

In Materials today. Proceedings
Covid 19 pandemic has placed the entire world in a precarious condition. Earlier it was a serious issue in china whereas now it is being witnessed by citizens all over the world. Scientists are working hard to find treatment and vaccines for the coronavirus, also termed as covid. With the growing literature, it has become a major challenge for the medical community to find answers to questions related to covid-19. We have proposed a machine learning-based system that uses text classification applications of NLP to extract information from the scientific literature. Classification of large textual data makes the searching process easier thus useful for scientists. The main aim of our system is to classify the abstracts related to covid with their respective journals so that a researcher can refer to articles of his interest from the required journals instead of searching all the articles. In this paper, we describe our methodology needed to build such a system. Our system experiments on the COVID-19 open research dataset and the performance is evaluated using classifiers like KNN, MLP, etc. An explainer was also built using XGBoost to show the model predictions.
Godavarthi Deepthi, A Mary Sowjanya

2021-Feb-28

COVID-19, Explainability, KNN, MLP, Text classification, XGBoost

General

An Intelligent and Energy-Efficient Wireless Body Area Network to Control Coronavirus Outbreak.

In Arabian journal for science and engineering
The coronaviruses are a deadly family of epidemic viruses that can spread from one individual to another very quickly, infecting masses. The literature on epidemics indicates that the early diagnosis of a coronavirus infection can lead to a reduction in mortality rates. To prevent coronavirus disease 2019 (COVID-19) from spreading, the regular identification and monitoring of infected patients are needed. In this regard, wireless body area networks (WBANs) can be used in conjunction with machine learning and the Internet of Things (IoT) to identify and monitor the human body for health-related information, which in turn can aid in the early diagnosis of diseases. This paper proposes a novel coronavirus-body area network (CoV-BAN) model based on IoT technology as a real-time health monitoring system for the detection of the early stages of coronavirus infection using a number of wearable biosensors to examine the health status of the patient. The proposed CoV-BAN model is tested with five machine learning-based classification methods, including random forest, logistic regression, Naive Bayes, support vector machine and multi-layer perceptron classifiers, to optimize the accuracy of the diagnosis of COVID-19. For the long-term sustainability of the sensor devices, the development of energy-efficient WBAN is critical. To address this issue, a long-range (LoRa)-based IoT program is used to receive biosensor signals from the patient and transmit them to the cloud directly for monitoring. The experimental results indicate that the proposed model using the random forest classifier outperforms models using the other classifiers, with an average accuracy of 88.6%. In addition, power consumption is reduced when LoRa technology is used as a relay node.
Bilandi Naveen, Verma Harsh K, Dhir Renu

2021-Feb-26

Biosensors, COVID-19, Coronavirus, IoT, Machine learning, WBAN

General

Deep learning for COVID-19 chest CT (computed tomography) image analysis: a lesson from lung cancer.

In Computational and structural biotechnology journal
As a recent global health emergency, the quick and reliable diagnosis of COVID-19 is urgently needed. Thus, many artificial intelligence (AI)-base methods are proposed for COVID-19 chest CT (computed tomography) image analysis. However, there are very limited COVID-19 chest CT images publicly available to evaluate those deep neural networks. On the other hand, a huge amount of CT images from lung cancer are publicly available. To build a reliable deep learning model trained and tested with a larger scale dataset, the proposed model builds a public COVID-19 CT dataset, containing 1186 CT images synthesized from lung cancer CT images using CycleGAN. Additionally, various deep learning models are tested with synthesized or real chest CT images for COVID-19 and Non-COVID-19 classification. In comparison, all models achieve excellent results (over than 90%) in accuracy, precision, recall and F1 score for both synthesized and real COVID-19 CT images, demonstrating the reliable of the synthesized dataset. The public dataset and deep learning models can facilitate the development of accurate and efficient diagnostic testing for COVID-19.
Jiang Hao, Tang Shiming, Liu Weihuang, Zhang Yang

2021-Mar-02

COVID-19, Chest CT image, Classification, CycleGAN, Image synthesis, Lung cancer, Style transfer

General

COVID-19 SignSym: a fast adaptation of a general clinical NLP tool to identify and normalize COVID-19 signs and symptoms to OMOP common data model.

In Journal of the American Medical Informatics Association : JAMIA
The COVID-19 pandemic swept across the world rapidly, infecting millions of people. An efficient tool that can accurately recognize important clinical concepts of COVID-19 from free text in electronic health records (EHRs) will be valuable to accelerate COVID-19 clinical research. To this end, this study aims at adapting the existing CLAMP natural language processing tool to quickly build COVID-19 SignSym, which can extract COVID-19 signs/symptoms and their 8 attributes (body location, severity, temporal expression, subject, condition, uncertainty, negation, and course) from clinical text. The extracted information is also mapped to standard concepts in the Observational Medical Outcomes Partnership common data model. A hybrid approach of combining deep learning-based models, curated lexicons, and pattern-based rules was applied to quickly build the COVID-19 SignSym from CLAMP, with optimized performance. Our extensive evaluation using 3 external sites with clinical notes of COVID-19 patients, as well as the online medical dialogues of COVID-19, shows COVID-19 SignSym can achieve high performance across data sources. The workflow used for this study can be generalized to other use cases, where existing clinical natural language processing tools need to be customized for specific information needs within a short time. COVID-19 SignSym is freely accessible to the research community as a downloadable package (https://clamp.uth.edu/covid/nlp.php) and has been used by 16 healthcare organizations to support clinical research of COVID-19.
Wang Jingqi, Abu-El-Rub Noor, Gray Josh, Pham Huy Anh, Zhou Yujia, Manion Frank J, Liu Mei, Song Xing, Xu Hua, Rouhizadeh Masoud, Zhang Yaoyun

2021-Mar-01

General

Individualized prediction of COVID-19 adverse outcomes with MLHO.

In Scientific reports ; h5-index 158.0
The COVID-19 pandemic has devastated the world with health and economic wreckage. Precise estimates of adverse outcomes from COVID-19 could have led to better allocation of healthcare resources and more efficient targeted preventive measures, including insight into prioritizing how to best distribute a vaccination. We developed MLHO (pronounced as melo), an end-to-end Machine Learning framework that leverages iterative feature and algorithm selection to predict Health Outcomes. MLHO implements iterative sequential representation mining, and feature and model selection, for predicting patient-level risk of hospitalization, ICU admission, need for mechanical ventilation, and death. It bases this prediction on data from patients' past medical records (before their COVID-19 infection). MLHO's architecture enables a parallel and outcome-oriented model calibration, in which different statistical learning algorithms and vectors of features are simultaneously tested to improve prediction of health outcomes. Using clinical and demographic data from a large cohort of over 13,000 COVID-19-positive patients, we modeled the four adverse outcomes utilizing about 600 features representing patients' pre-COVID health records and demographics. The mean AUC ROC for mortality prediction was 0.91, while the prediction performance ranged between 0.80 and 0.81 for the ICU, hospitalization, and ventilation. We broadly describe the clusters of features that were utilized in modeling and their relative influence for predicting each outcome. Our results demonstrated that while demographic variables (namely age) are important predictors of adverse outcomes after a COVID-19 infection, the incorporation of the past clinical records are vital for a reliable prediction model. As the COVID-19 pandemic unfolds around the world, adaptable and interpretable machine learning frameworks (like MLHO) are crucial to improve our readiness for confronting the potential future waves of COVID-19, as well as other novel infectious diseases that may emerge.
Estiri Hossein, Strasser Zachary H, Murphy Shawn N

2021-Mar-05

General

Stay-at-home policy is a case of exception fallacy: an internet-based ecological study.

In Scientific reports ; h5-index 158.0
A recent mathematical model has suggested that staying at home did not play a dominant role in reducing COVID-19 transmission. The second wave of cases in Europe, in regions that were considered as COVID-19 controlled, may raise some concerns. Our objective was to assess the association between staying at home (%) and the reduction/increase in the number of deaths due to COVID-19 in several regions in the world. In this ecological study, data from www.google.com/covid19/mobility/ , ourworldindata.org and covid.saude.gov.br were combined. Countries with > 100 deaths and with a Healthcare Access and Quality Index of ≥ 67 were included. Data were preprocessed and analyzed using the difference between number of deaths/million between 2 regions and the difference between the percentage of staying at home. The analysis was performed using linear regression with special attention to residual analysis. After preprocessing the data, 87 regions around the world were included, yielding 3741 pairwise comparisons for linear regression analysis. Only 63 (1.6%) comparisons were significant. With our results, we were not able to explain if COVID-19 mortality is reduced by staying at home in ~ 98% of the comparisons after epidemiological weeks 9 to 34.
Savaris R F, Pumi G, Dalzochio J, Kunst R

2021-Mar-05

Public Health

Using artificial intelligence to improve COVID-19 rapid diagnostic test result interpretation.

In Proceedings of the National Academy of Sciences of the United States of America
Serological rapid diagnostic tests (RDTs) are widely used across pathologies, often providing users a simple, binary result (positive or negative) in as little as 5 to 20 min. Since the beginning of the COVID-19 pandemic, new RDTs for identifying SARS-CoV-2 have rapidly proliferated. However, these seemingly easy-to-read tests can be highly subjective, and interpretations of the visible "bands" of color that appear (or not) in a test window may vary between users, test models, and brands. We developed and evaluated the accuracy/performance of a smartphone application (xRCovid) that uses machine learning to classify SARS-CoV-2 serological RDT results and reduce reading ambiguities. Across 11 COVID-19 RDT models, the app yielded 99.3% precision compared to reading by eye. Using the app replaces the uncertainty from visual RDT interpretation with a smaller uncertainty of the image classifier, thereby increasing confidence of clinicians and laboratory staff when using RDTs, and creating opportunities for patient self-testing.
Mendels David-A, Dortet Laurent, Emeraud Cécile, Oueslati Saoussen, Girlich Delphine, Ronat Jean-Baptiste, Bernabeu Sandrine, Bahi Silvestre, Atkinson Gary J H, Naas Thierry

2021-Mar-23

SARS-CoV-2, machine learning, smartphone application

Pathology

Development of a convolutional neural network to differentiate among the etiology of similar appearing pathological B lines on lung ultrasound: a deep learning study.

In BMJ open

OBJECTIVES : Lung ultrasound (LUS) is a portable, low-cost respiratory imaging tool but is challenged by user dependence and lack of diagnostic specificity. It is unknown whether the advantages of LUS implementation could be paired with deep learning (DL) techniques to match or exceed human-level, diagnostic specificity among similar appearing, pathological LUS images.

DESIGN : A convolutional neural network (CNN) was trained on LUS images with B lines of different aetiologies. CNN diagnostic performance, as validated using a 10% data holdback set, was compared with surveyed LUS-competent physicians.

SETTING : Two tertiary Canadian hospitals.

PARTICIPANTS : 612 LUS videos (121 381 frames) of B lines from 243 distinct patients with either (1) COVID-19 (COVID), non-COVID acute respiratory distress syndrome (NCOVID) or (3) hydrostatic pulmonary edema (HPE).

RESULTS : The trained CNN performance on the independent dataset showed an ability to discriminate between COVID (area under the receiver operating characteristic curve (AUC) 1.0), NCOVID (AUC 0.934) and HPE (AUC 1.0) pathologies. This was significantly better than physician ability (AUCs of 0.697, 0.704, 0.967 for the COVID, NCOVID and HPE classes, respectively), p<0.01.

CONCLUSIONS : A DL model can distinguish similar appearing LUS pathology, including COVID-19, that cannot be distinguished by humans. The performance gap between humans and the model suggests that subvisible biomarkers within ultrasound images could exist and multicentre research is merited.

Arntfield Robert, VanBerlo Blake, Alaifan Thamer, Phelps Nathan, White Matthew, Chaudhary Rushil, Ho Jordan, Wu Derek

2021-Mar-05

COVID-19, adult intensive & critical care, chest imaging, respiratory infections, ultrasound

Radiology

On the Adoption of Radiomics and Formal Methods for COVID-19 Coronavirus Diagnosis.

In Diagnostics (Basel, Switzerland)
Considering the current pandemic, caused by the spreading of the novel Coronavirus disease, there is the urgent need for methods to quickly and automatically diagnose infection. To assist pathologists and radiologists in the detection of the novel coronavirus, in this paper we propose a two-tiered method, based on formal methods (to the best of authors knowledge never previously introduced in this context), aimed to (i) detect whether the patient lungs are healthy or present a generic pulmonary infection; (ii) in the case of the previous tier, a generic pulmonary disease is detected to identify whether the patient under analysis is affected by the novel Coronavirus disease. The proposed approach relies on the extraction of radiomic features from medical images and on the generation of a formal model that can be automatically checked using the model checking technique. We perform an experimental analysis using a set of computed tomography medical images obtained by the authors, achieving an accuracy of higher than 81% in disease detection.
Santone Antonella, Belfiore Maria Paola, Mercaldo Francesco, Varriano Giulia, Brunese Luca

2021-Feb-12

COVID-19, CT, Coronavirus, HRCT, artificial intelligence, diagnosis, formal methods, radiology, radiomics

General

Changing Dental Profession-Modern Forms and Challenges in Dental Practice.

In International journal of environmental research and public health ; h5-index 73.0
In the last two decades, an increasing trend towards new forms of dental practice was observed [...].
Wolf Thomas Gerhard, Campus Guglielmo

2021-Feb-17

COVID-19, SARS-CoV-2, artificial intelligence, big data, challenges, changing dental profession, cross-border use of services, dental personnel, dental practice, dentist, digitalization, e-health, expectations, future career prospects, green dentistry, health care, modern forms of practice, one health approach, oral health policy, pandemic, sustainability, tele-dentistry, worldwide, young dentists

Oncology

Mitochondriopathies as a Clue to Systemic Disorders-Analytical Tools and Mitigating Measures in Context of Predictive, Preventive, and Personalized (3P) Medicine.

In International journal of molecular sciences ; h5-index 102.0
The mitochondrial respiratory chain is the main site of reactive oxygen species (ROS) production in the cell. Although mitochondria possess a powerful antioxidant system, an excess of ROS cannot be completely neutralized and cumulative oxidative damage may lead to decreasing mitochondrial efficiency in energy production, as well as an increasing ROS excess, which is known to cause a critical imbalance in antioxidant/oxidant mechanisms and a "vicious circle" in mitochondrial injury. Due to insufficient energy production, chronic exposure to ROS overproduction consequently leads to the oxidative damage of life-important biomolecules, including nucleic acids, proteins, lipids, and amino acids, among others. Different forms of mitochondrial dysfunction (mitochondriopathies) may affect the brain, heart, peripheral nervous and endocrine systems, eyes, ears, gut, and kidney, among other organs. Consequently, mitochondriopathies have been proposed as an attractive diagnostic target to be investigated in any patient with unexplained progressive multisystem disorder. This review article highlights the pathomechanisms of mitochondriopathies, details advanced analytical tools, and suggests predictive approaches, targeted prevention and personalization of medical services as instrumental for the overall management of mitochondriopathy-related cascading pathologies.
Liskova Alena, Samec Marek, Koklesova Lenka, Kudela Erik, Kubatka Peter, Golubnitschaja Olga

2021-Feb-18

ATP synthesis, COVID-19, DNA repair, ROS overproduction, antioxidant mechanisms, apoptosis, biomarker panels, cancer, chronic inflammation, diagnostic tools, dietary habits, disease predisposition, dysfunction, energy metabolism, health policy, individualised patient profile, injury, life-style, liquid biopsy, mitochondrial function, mitochondriopathy, multi-parametric analysis and machine learning, neurodegeneration, oxidative damage, pathology, predictive, preventive, and personalized medicine (PPPM/3PM), socio-economic burden, suboptimal health conditions, systemic disorders, tumorigenesis, vasoconstriction, vicious circle

General

The Social Robot in Rehabilitation and Assistance: What Is the Future?

In Healthcare (Basel, Switzerland)
This commentary aims to address the field of social robots both in terms of the global situation and research perspectives. It has four polarities. First, it revisits the evolutions in robotics, which, starting from collaborative robotics, has led to the diffusion of social robots. Second, it illustrates the main fields in the employment of social robots in rehabilitation and assistance in the elderly and handicapped and in further emerging sectors. Third, it takes a look at the future directions of the research development both in terms of clinical and technological aspects. Fourth, it discusses the opportunities and limits, starting from the development and clinical use of social robots during the COVID-19 pandemic to the increase of ethical discussion on their use.
Giansanti Daniele

2021-Feb-25

artificial intelligence, collaborative robots, e-health, electronic surveys, m-health, medical devices, organization models, rehabilitation, robotics, social robots

General

Clinical presentation of COVID-19 - a model derived by a machine learning algorithm.

In Journal of integrative bioinformatics
COVID-19 pandemic has flooded all triage stations, making it difficult to carefully select those most likely infected. Data on total patients tested, infected, and hospitalized is fragmentary making it difficult to easily select those most likely to be infected. The Israeli Ministry of Health made public its registry of immediate clinical data and the respective status of infected/not infected for all viral DNA tests performed up to Apr. 18th, 2020 including almost 120,000 tests. We used a machine-learning algorithm to find out which immediate clinical elements mattered the most in identifying the true status of the tested persons including age or gender matter, to enable future better allocation of surveillance policy for those belonging to high-risk groups. In addition to the analyses applied on the first batch of the available data (Apr. 11th), we further tested the algorithm on the independent second batch (Apr. 12th to 18th). Fever, cough and headache were the most diagnostic, differing in degree of importance in different subgroups. Higher percentage of men were found positive (9.3 vs. 7.3%), but gender did not matter for the clinical presentation. The prediction power of the model was high, with accuracy of 0.84 and area under the curve 0.92. We provide a hand-held short checklist with verbal description of importance for the leading symptoms, which should expedite the triage and enable proper selection of people for further follow-up.
Yousef Malik, Showe Louise C, Ben Shlomo Izhar

2021-Mar-04

COVID_19, clinical presentation, machine learning, national registry, risk allocation

General

Mining topic and sentiment dynamics in physician rating websites during the early wave of the COVID-19 pandemic: Machine learning approach.

In International journal of medical informatics ; h5-index 49.0

INTRODUCTION : An increasing number of patients are voicing their opinions and expectations about the quality of care in online forums and on physician rating websites (PRWs). This paper analyzes patient online reviews (PORs) to identify emerging and fading topics and sentiment trends in PRWs during the early stage of the COVID-19 outbreak.

METHODS : Text data were collected, including 55,612 PORs of 3430 doctors from three popular PRWs in the United States (RateMDs, HealthGrades, and Vitals) from March 01 to June 27, 2020. An improved latent Dirichlet allocation (LDA)-based topic modeling (topic coherence-based LDA [TCLDA]), manual annotation, and sentiment analysis tool were applied to extract a suitable number of topics, generate corresponding keywords, assign topic names, and determine trends in the extracted topics and specific emotions.

RESULTS : According to the coherence value and manual annotation, the identified taxonomy includes 30 topics across high-rank and low-rank disease categories. The emerging topics in PRWs focus mainly on themes such as treatment experience, policy implementation regarding epidemic control measures, individuals' attitudes toward the pandemic, and mental health across high-rank diseases. In contrast, the treatment process and experience during COVID-19, awareness and COVID-19 control measures, and COVID-19 deaths, fear, and stress were the most popular themes for low-rank diseases. Panic buying and daily life impact, treatment processes, and bedside manner were the fading themes across high-rank diseases. In contrast, provider attitude toward patients during the pandemic, detection at public transportation, passenger, travel bans and warnings, and materials supplies and society support during COVID-19 were the most fading themes across low-rank diseases. Regarding sentiment analysis, negative emotions (fear, anger, and sadness) prevail during the early wave of the COVID-19.

CONCLUSION : Mining topic dynamics and sentiment trends in PRWs may provide valuable knowledge of patients' opinions during the COVID-19 crisis. Policymakers should consider these PORs and develop global healthcare policies and surveillance systems through monitoring PRWs. The findings of this study identify research gaps in the areas of e-health and text mining and offer future research directions.

Shah Adnan Muhammad, Yan Xiangbin, Qayyum Abdul, Naqvi Rizwan Ali, Shah Syed Jamal

2021-Feb-26

COVID-19, Discrete emotions, Dynamics of healthcare topics, LDA, Text mining, Topic modeling

General

The nexus between COVID-19 deaths, air pollution and economic growth in New York state: Evidence from Deep Machine Learning.

In Journal of environmental management
The aim of this paper is to assess the relationship between COVID-19-related deaths, economic growth, PM₁₀, PM_2.5, and NO₂ concentrations in New York state using city-level daily data through two Machine Learning experiments. PM_2.5 and NO₂ are the most significant pollutant agents responsible for facilitating COVID-19 attributed death rates. Besides, we found only six out of many tested causal inferences to be significant and true within the AUPRC analysis. In line with the causal findings, a unidirectional causal effect is found from PM_2.5 to Deaths, NO₂ to Deaths, and economic growth to both PM_2.5 and NO₂. Corroborating the first experiment, the causal results confirmed the capability of polluting variables (PM_2.5 to Deaths, NO₂ to Deaths) to accelerate COVID-19 deaths. In contrast, we found evidence that unsustainable economic growth predicts the dynamics of air pollutants. This shows how unsustainable economic growth could increase environmental pollution by escalating emissions of pollutant agents (PM_2.5 and NO₂) in New York state.
Magazzino Cosimo, Mele Marco, Sarkodie Samuel Asumadu

2021-Mar-02

Air pollution, COVID-19, Economic growth, Machine learning, New York state

Public Health

A computational framework of host-based drug repositioning for broad-spectrum antivirals against RNA viruses.

In iScience
RNA viruses are responsible for many zoonotic diseases that post great challenges for public health. Effective therapeutics against these viral infections remain limited. Here, we deployed a computational framework for host-based drug repositioning to predict potential antiviral drugs from 2,352 approved drugs and 1,062 natural compounds embedded in herbs of traditional Chinese medicine. By systematically interrogating public genetic screening data, we comprehensively cataloged host dependency genes (HDGs) that are indispensable for successful viral infection corresponding to 10 families and 29 species of RNA viruses. We then utilized these HDGs as potential drug targets and interrogated extensive drug-target interactions through database retrieval, literature mining, and de novo prediction using artificial intelligence-based algorithms. Repurposed drugs or natural compounds were proposed against many viral pathogens such as coronaviruses including severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), flaviviruses, and influenza viruses. This study helps to prioritize promising drug candidates for in-depth evaluation against these virus-related diseases.
Li Zexu, Yao Yingjia, Cheng Xiaolong, Chen Qing, Zhao Wenchang, Ma Shixin, Li Zihan, Zhou Hu, Li Wei, Fei Teng

2021-Mar-19

Bioinformatics, Molecular Biology, Pharmacoinformatics

General

Low compositions of human toll-like receptor 7/8-stimulating RNA motifs in the MERS-CoV, SARS-CoV and SARS-CoV-2 genomes imply a substantial ability to evade human innate immunity.

In PeerJ

Background : The innate immune system especially Toll-like receptor (TLR) 7/8 and the interferon pathway, constitutes an important first line of defense against single-stranded RNA viruses. However, large-scale, systematic comparisons of the TLR 7/8-stimulating potential of genomic RNAs of single-stranded RNA viruses are rare. In this study, a computational method to evaluate the human TLR 7/8-stimulating ability of single-stranded RNA virus genomes based on their human TLR 7/8-stimulating trimer compositions was used to analyze 1,002 human coronavirus genomes.

Results : The human TLR 7/8-stimulating potential of coronavirus genomic (positive strand) RNAs followed the order of NL63-CoV > HKU1-CoV >229E-CoV ≅ OC63-CoV > SARS-CoV-2 > MERS-CoV > SARS-CoV. These results suggest that among these coronaviruses, MERS-CoV, SARS-CoV and SARS-CoV-2 may have a higher ability to evade the human TLR 7/8-mediated innate immune response. Analysis with a logistic regression equation derived from human coronavirus data revealed that most of the 1,762 coronavirus genomic (positive strand) RNAs isolated from bats, camels, cats, civets, dogs and birds exhibited weak human TLR 7/8-stimulating potential equivalent to that of the MERS-CoV, SARS-CoV and SARS-CoV-2 genomic RNAs.

Conclusions : Prediction of the human TLR 7/8-stimulating potential of viral genomic RNAs may be useful for surveillance of emerging coronaviruses from nonhuman mammalian hosts.

Yang Chu-Wen, Chen Mei-Fang

2021

Immunostimulating RNA motifs, SARS-CoV-2, Toll-like receptor 7/8

General

Potential neutralizing antibodies discovered for novel corona virus using machine learning.

In Scientific reports ; h5-index 158.0
The fast and untraceable virus mutations take lives of thousands of people before the immune system can produce the inhibitory antibody. The recent outbreak of COVID-19 infected and killed thousands of people in the world. Rapid methods in finding peptides or antibody sequences that can inhibit the viral epitopes of SARS-CoV-2 will save the life of thousands. To predict neutralizing antibodies for SARS-CoV-2 in a high-throughput manner, in this paper, we use different machine learning (ML) model to predict the possible inhibitory synthetic antibodies for SARS-CoV-2. We collected 1933 virus-antibody sequences and their clinical patient neutralization response and trained an ML model to predict the antibody response. Using graph featurization with variety of ML methods, like XGBoost, Random Forest, Multilayered Perceptron, Support Vector Machine and Logistic Regression, we screened thousands of hypothetical antibody sequences and found nine stable antibodies that potentially inhibit SARS-CoV-2. We combined bioinformatics, structural biology, and Molecular Dynamics (MD) simulations to verify the stability of the candidate antibodies that can inhibit SARS-CoV-2.
Magar Rishikesh, Yadav Prakarsh, Barati Farimani Amir

2021-Mar-04

General

Does non-COVID-19 lung lesion help? investigating transferability in COVID-19 CT image segmentation.

In Computer methods and programs in biomedicine

BACKGROUND AND OBJECTIVE : Coronavirus disease 2019 (COVID-19) is a highly contagious virus spreading all around the world. Deep learning has been adopted as an effective technique to aid COVID-19 detection and segmentation from computed tomography (CT) images. The major challenge lies in the inadequate public COVID-19 datasets. Recently, transfer learning has become a widely used technique that leverages the knowledge gained while solving one problem and applying it to a different but related problem. However, it remains unclear whether various non-COVID19 lung lesions could contribute to segmenting COVID-19 infection areas and how to better conduct this transfer procedure. This paper provides a way to understand the transferability of non-COVID19 lung lesions and a better strategy to train a robust deep learning model for COVID-19 infection segmentation.

METHODS : Based on a publicly available COVID-19 CT dataset and three public non-COVID19 datasets, we evaluate four transfer learning methods using 3D U-Net as a standard encoder-decoder method. i) We introduce the multi-task learning method to get a multi-lesion pre-trained model for COVID-19 infection. ii) We propose and compare four transfer learning strategies with various performance gains and training time costs. Our proposed Hybrid-encoder Learning strategy introduces a Dedicated-encoder and an Adapted-encoder to extract COVID-19 infection features and general lung lesion features, respectively. An attention-based Selective Fusion unit is designed for dynamic feature selection and aggregation.

RESULTS : Experiments show that trained with limited data, proposed Hybrid-encoder strategy based on multi-lesion pre-trained model achieves a mean DSC, NSD, Sensitivity, F1-score, Accuracy and MCC of 0.704, 0.735, 0.682, 0.707, 0.994 and 0.716, respectively, with better genetalization and lower over-fitting risks for segmenting COVID-19 infection.

CONCLUSIONS : The results reveal the benefits of transferring knowledge from non-COVID19 lung lesions, and learning from multiple lung lesion datasets can extract more general features, leading to accurate and robust pre-trained models. We further show the capability of the encoder to learn feature representations of lung lesions, which improves segmentation accuracy and facilitates training convergence. In addition, our proposed Hybrid-encoder learning method incorporates transferred lung lesion features from non-COVID19 datasets effectively and achieves significant improvement. These findings promote new insights into transfer learning for COVID-19 CT image segmentation, which can also be further generalized to other medical tasks.

Wang Yixin, Zhang Yao, Liu Yang, Tian Jiang, Zhong Cheng, Shi Zhongchao, Zhang Yang, He Zhiqiang

2021-Feb-23

COVID-19, CT image, Segmentation, Transfer learning

Radiology

Estimating COVID-19 Pneumonia Extent and Severity From Chest Computed Tomography.

In Frontiers in physiology

Background : COVID-19 pneumonia extension is assessed by computed tomography (CT) with the ratio between the volume of abnormal pulmonary opacities (PO) and CT-estimated lung volume (CT_LV). CT-estimated lung weight (CT_LW) also correlates with pneumonia severity. However, both CT_LV and CT_LW depend on demographic and anthropometric variables.

Purposes : To estimate the extent and severity of COVID-19 pneumonia adjusting the volume and weight of abnormal PO to the predicted CT_LV (pCT_LV) and CT_LW (pCT_LW), respectively, and to evaluate their possible association with clinical and radiological outcomes.

Methods : Chest CT from 103 COVID-19 and 86 healthy subjects were examined retrospectively. In controls, predictive equations for estimating pCT_LV and pCT_LW were assessed. COVID-19 pneumonia extent and severity were then defined as the ratio between the volume and the weight of abnormal PO expressed as a percentage of the pCT_LV and pCT_LW, respectively. A ROC analysis was used to test differential diagnosis ability of the proposed method in COVID-19 and controls. The degree of pneumonia extent and severity was assessed with Z-scores relative to the average volume and weight of PO in controls. Accordingly, COVID-19 patients were classified as with limited, moderate and diffuse pneumonia extent and as with mild, moderate and severe pneumonia severity.

Results : In controls, CT_LV could be predicted by sex and height (adjusted R² = 0.57; P < 0.001) while CT_LW by age, sex, and height (adjusted R² = 0.6; P < 0.001). The cutoff of 20% (AUC = 0.91, 95%CI 0.88-0.93) for pneumonia extent and of 50% (AUC = 0.91, 95%CI 0.89-0.92) for pneumonia severity were obtained. Pneumonia extent were better correlated when expressed as a percentage of the pCT_LV and pCT_LW (r = 0.85, P < 0.001), respectively. COVID-19 patients with diffuse and severe pneumonia at admission presented significantly higher CRP concentration, intra-hospital mortality, ICU stay and ventilatory support necessity, than those with moderate and limited/mild pneumonia. Moreover, pneumonia severity, but not extent, was positively and moderately correlated with age (r = 0.46) and CRP concentration (r = 0.44).

Conclusion : The proposed estimation of COVID-19 pneumonia extent and severity might be useful for clinical and radiological patient stratification.

Carvalho Alysson Roncally Silva, Guimarães Alan, Garcia Thiego de Souza Oliveira, Madeira Werberich Gabriel, Ceotto Victor Fraga, Bozza Fernando Augusto, Rodrigues Rosana Souza, Pinto Joana Sofia F, Schmitt Willian Rebouças, Zin Walter Araujo, França Manuela

2021

COVID-19, CT-estimated lung volume, CT-estimated lung weight, computed tomography, deep learning

General

Artificial Intelligence Clinicians Can Use Chest Computed Tomography Technology to Automatically Diagnose Coronavirus Disease 2019 (COVID-19) Pneumonia and Enhance Low-Quality Images.

In Infection and drug resistance

Purpose : Nowadays, the number of patients with COVID-19 pneumonia worldwide is still increasing. The clinical diagnosis of COVID-19 pneumonia faces challenges, such as the difficulty to perform RT-PCR tests in real time, the lack of experienced radiologists, clinical low-quality images, and the similarity of imaging features of community-acquired pneumonia and COVID-19. Therefore, we proposed an artificial intelligence model GARCD that uses chest CT images to assist in the diagnosis of COVID-19 in real time. It can show better diagnostic performance even facing low-quality CT images.

Methods : We used 14,129 CT images from 104 patients. A total of 12,929 samples were used to build artificial intelligence models, and 1200 samples were used to test its performance. The image quality improvement module is based on the generative adversarial structure. It improves the quality of the input image under the joint drive of feature loss and content loss. The enhanced image is sent to the disease diagnosis model based on residual convolutional network. It automatically extracts the semantic features of the image and then infers the probability that the sample belongs to COVID-19. The ROC curve is used to evaluate the performance of the model.

Results : This model can effectively enhance the low-quality image and make the image that is difficult to be recognized become recognizable. The model proposed in this paper reached 97.8% AUC, 96.97% sensitivity and 91.16% specificity in an independent test set. ResNet, GADCD, CNN, and DenseNet achieved 80.9%, 97.3%, 70.7% and 85.7% AUC in the same test set, respectively. By comparing the performance with related works, it is proved that the model proposed has stronger clinical usability.

Conclusion : The method proposed can effectively assist doctors in real-time detection of suspected cases of COVID-19 pneumonia even faces unclear image. It can quickly isolate patients in a targeted manner, which is of positive significance for preventing the further spread of COVID-19 pneumonia.

Zhang Quan, Chen Zhuo, Liu Guohua, Zhang Wenjia, Du Qian, Tan Jiayuan, Gao Qianqian

2021

artificial intelligence, auxiliary diagnosis, coronavirus disease 2019, deep learning, low-quality image enhancement

Radiology

Epidemiological Surveillance of the Impact of the COVID-19 Pandemic on Stroke Care Using Artificial Intelligence.

In Stroke ; h5-index 83.0

BACKGROUND AND PURPOSE : The degree to which the coronavirus disease 2019 (COVID-19) pandemic has affected systems of care, in particular, those for time-sensitive conditions such as stroke, remains poorly quantified. We sought to evaluate the impact of COVID-19 in the overall screening for acute stroke utilizing a commercial clinical artificial intelligence platform.

METHODS : Data were derived from the Viz Platform, an artificial intelligence application designed to optimize the workflow of patients with acute stroke. Neuroimaging data on suspected patients with stroke across 97 hospitals in 20 US states were collected in real time and retrospectively analyzed with the number of patients undergoing imaging screening serving as a surrogate for the amount of stroke care. The main outcome measures were the number of computed tomography (CT) angiography, CT perfusion, large vessel occlusions (defined according to the automated software detection), and severe strokes on CT perfusion (defined as those with hypoperfusion volumes >70 mL) normalized as number of patients per day per hospital. Data from the prepandemic (November 4, 2019 to February 29, 2020) and pandemic (March 1 to May 10, 2020) periods were compared at national and state levels. Correlations were made between the inter-period changes in imaging screening, stroke hospitalizations, and thrombectomy procedures using state-specific sampling.

RESULTS : A total of 23 223 patients were included. The incidence of large vessel occlusion on CT angiography and severe strokes on CT perfusion were 11.2% (n=2602) and 14.7% (n=1229/8328), respectively. There were significant declines in the overall number of CT angiographies (-22.8%; 1.39-1.07 patients/day per hospital, P<0.001) and CT perfusion (-26.1%; 0.50-0.37 patients/day per hospital, P<0.001) as well as in the incidence of large vessel occlusion (-17.1%; 0.15-0.13 patients/day per hospital, P<0.001) and severe strokes on CT perfusion (-16.7%; 0.12-0.10 patients/day per hospital, P<0.005). The sampled cohort showed similar declines in the rates of large vessel occlusions versus thrombectomy (18.8% versus 19.5%, P=0.9) and comprehensive stroke center hospitalizations (18.8% versus 11.0%, P=0.4).

CONCLUSIONS : A significant decline in stroke imaging screening has occurred during the COVID-19 pandemic. This analysis underscores the broader application of artificial intelligence neuroimaging platforms for the real-time monitoring of stroke systems of care.

Nogueira Raul G, Davies Jason M, Gupta Rishi, Hassan Ameer E, Devlin Thomas, Haussen Diogo C, Mohammaden Mahmoud H, Kellner Christopher P, Arthur Adam, Elijovich Lucas, Owada Kumiko, Begun Dina, Narayan Mukund, Mordenfeld Nadia, Tekle Wondwossen G, Nahab Fadi, Jovin Tudor G, Frei Don, Siddiqui Adnan H, Frankel Michael R, Mocco J

2021-Mar-04

artificial intelligence, hospitalization, incidence, pandemic, perfusion

Public Health

COVID-19: Role of Robotics, Artificial Intelligence, and Machine learning during pandemic.

In Current medical imaging
The outbreak of COVID-19 has led to a global heath emergency. Emerging from China, it has now been declared as a pandemic. Owing to the fast pace at which it spreads, its control and prevention has now become the greatest challenge. The inner structural analysis of the virus is an important area of research for the invention of the potential drug. The Countries are following different strategies and policies to fight against COVID-19, various schemes have also been employed to cope up with the economic crisis. While the government is struggling to balance between the public health sector and the economic collapse, the researchers and medicine practitioners are inclined towards obtaining treatment and early detection of the deadly disease. Further, the impact of COVID-19 on Dentistry is alarming and posing severe threats to the professionals as well. Now, the technology is helping the countries fight against the disease. ML and AI based applications are substantially aiding the process for detection and diagnosis of novel corona virus. Science of Robotics is another approach followed with an aim to improve patient care.
Sodhi Gurpreet Kour, Kaur Simarpreet, Gaba Gurjot Singh, Kansal Lavish, Sharma Ashutosh, Dhiman Gaurav

2021-Feb-23

Artificial Intelligence, Coronavirus, Coronavirus disease 2019 (COVID-19), Pandemic, Respiratory Syndrome

General

Group IIA Secreted Phospholipase A 2 Plays a Central Role in the Pathobiology of COVID-19.

In medRxiv : the preprint server for health sciences
There is an urgent need to identify cellular and molecular mechanisms responsible for severe COVID-19 disease accompanied by multiple organ failure and high mortality rates. Here, we performed untargeted/targeted lipidomics and focused biochemistry on 127 patient plasma samples, and showed high levels of circulating, enzymatically active secreted phospholipase A ₂ Group IIA (sPLA ₂ -IIA) in severe and fatal COVID-19 disease compared with uninfected patients or mild illness. Machine learning demonstrated that sPLA ₂ -IIA effectively stratifies severe from fatal COVID-19 disease. We further introduce a PLA-BUN index that combines sPLA ₂ -IIA and blood urea nitrogen (BUN) threshold levels as a critical risk factor for mitochondrial dysfunction, sustained inflammatory injury and lethal COVID-19. With the availability of clinically tested inhibitors of sPLA ₂ -IIA, our study opens the door to a precision intervention using indices discovered here to reduce COVID-19 mortality.
Snider Justin M, You Jeehyun Karen, Wang Xia, Snider Ashley J, Hallmark Brian, Seeds Michael C, Sergeant Susan, Johnstone Laurel, Wang Qiuming, Sprissler Ryan, Zhang Hao Helen, Luberto Chiara, Kew Richard R, Hannun Yusuf A, McCall Charles E, Yao Guang, Del Poeta Maurizio, Chilton Floyd H

2021-Feb-23

General

Informing patients that they are at high risk for serious complications of viral infection increases vaccination rates.

In medRxiv : the preprint server for health sciences
For many vaccine-preventable diseases like influenza, vaccination rates are lower than optimal to achieve community protection. Those at high risk for infection and serious complications are especially advised to be vaccinated to protect themselves. Using influenza as a model, we studied one method of increasing vaccine uptake: informing high-risk patients, identified by a machine learning model, about their risk status. Patients (N=39,717) were evenly randomized to (1) a control condition (exposure only to standard direct mail or patient portal vaccine promotion efforts) or to be told via direct mail, patient portal, and/or SMS that they were (2) at high risk for influenza and its complications if not vaccinated; (3) at high risk according to a review of their medical records; or (4) at high risk according to a computer algorithm analysis of their medical records. Patients in the three treatment conditions were 5.7% more likely to get vaccinated during the 112 days post-intervention (p < .001), and did so 1.4 days earlier (p < .001), on average, than those in the control group. There were no significant differences among risk messages, suggesting that patients are neither especially averse to nor uniquely appreciative of learning their records had been reviewed or that computer algorithms were involved. Similar approaches should be considered for COVID-19 vaccination campaigns.
Shermohammed Maheen, Goren Amir, Lanyado Alon, Yesharim Rachel, Wolk Donna M, Doyle Joseph, Meyer Michelle N, Chabris Christopher F

2021-Feb-23

General

Diagnosis and combating COVID-19 using wearable Oura smart ring with deep learning methods.

In Personal and ubiquitous computing
Since the coronavirus (COVID-19) outbreak keeps on spreading all through the world, scientists have been crafting varied technologies mainly focusing on AI for an approach to acknowledge the difficulties of the epidemic. In this current worldwide emergency, the clinical business is searching for new advancements to screen and combat COVID-19 contamination. Strategies used by artificial intelligence can stretch screen the spread of the infection, distinguish highly infected patients, and be compelling in supervising the illness continuously. The artificial intelligence anticipation can further be used for passing dangers by sufficiently dissecting information from past sufferers. International patient support with recommendations for population testing, medical care, notification, and infection control can help fight this deadly virus. We proposed the hybrid deep learning method to diagnose COVID-19. The layered approach is used here to measure the symptom level of the patients and to analyze the patient image data whether he/she is positive with COVID-19. This work utilizes smart AI techniques to predict and diagnose the coronavirus rapidly by the Oura smart ring within 24 h. In the laboratory, a coronavirus rapid test is prepared with the help of a deep learning model using the RNN and CNN algorithms to diagnose the coronavirus rapidly and accurately. The result shows the value 0 or 1. The result 1 indicates the person is affected with coronavirus and the result 0 indicates the person is not affected with coronavirus. X-Ray and CT image classifications are considered here so that the threshold value is utilized for identifying an individual's health condition from the initial stage to a severe stage. Threshold value 0.5 is used to identify coronavirus initial stage condition and 1 is used to identify the coronavirus severe condition of the patient. The proposed methods are utilized for four weighting parameters to reduce both false positive and false negative image classification results for rapid and accurate diagnosis of COVID-19.
Poongodi M, Hamdi Mounir, Malviya Mohit, Sharma Ashutosh, Dhiman Gaurav, Vimal S

2021-Feb-26

Artificial intelligence, COVID-19, Diagnosis, Drug, Image acquisition, Machine learning

General

COVID-19 salivary Raman fingerprint: innovative approach for the detection of current and past SARS-CoV-2 infections.

In Scientific reports ; h5-index 158.0
The pandemic of COVID-19 is continuously spreading, becoming a worldwide emergency. Early and fast identification of subjects with a current or past infection must be achieved to slow down the epidemiological widening. Here we report a Raman-based approach for the analysis of saliva, able to significantly discriminate the signal of patients with a current infection by COVID-19 from healthy subjects and/or subjects with a past infection. Our results demonstrated the differences in saliva biochemical composition of the three experimental groups, with modifications grouped in specific attributable spectral regions. The Raman-based classification model was able to discriminate the signal collected from COVID-19 patients with accuracy, precision, sensitivity and specificity of more than 95%. In order to translate this discrimination from the signal-level to the patient-level, we developed a Deep Learning model obtaining accuracy in the range 89-92%. These findings have implications for the creation of a potential Raman-based diagnostic tool, using saliva as minimal invasive and highly informative biofluid, demonstrating the efficacy of the classification model.
Carlomagno C, Bertazioli D, Gualerzi A, Picciolini S, Banfi P I, Lax A, Messina E, Navarro J, Bianchi L, Caronni A, Marenco F, Monteleone S, Arienti C, Bedoni M

2021-Mar-02

General

MP Twitter Engagement and Abuse Post-first COVID-19 Lockdown in the UK: White Paper

ArXiv Preprint
The UK has had a volatile political environment for some years now, with Brexit and leadership crises marking the past five years. With this work, we wanted to understand more about how the global health emergency, COVID-19, influences the amount, type or topics of abuse that UK politicians receive when engaging with the public. With this work, we wanted to understand more about how the global health emergency, COVID-19, influences the amount, type or topics of abuse that UK politicians receive when engaging with the public. This work covers the period of June to December 2020 and analyses Twitter abuse in replies to UK MPs. This work is a follow-up from our analysis of online abuse during the first four months of the COVID-19 pandemic in the UK. The paper examines overall abuse levels during this new seven month period, analyses reactions to members of different political parties and the UK government, and the relationship between online abuse and topics such as Brexit, government's COVID-19 response and policies, and social issues. In addition, we have also examined the presence of conspiracy theories posted in abusive replies to MPs during the period. We have found that abuse levels toward UK MPs were at an all-time high in December 2020 (5.4% of all reply tweets sent to MPs). This is almost 1% higher that the two months preceding the General Election. In a departure from the trend seen in the first four months of the pandemic, MPs from the Tory party received the highest percentage of abusive replies from July 2020 onward, which stays above 5% starting from September 2020 onward, as the COVID-19 crisis deepened and the Brexit negotiations with the EU started nearing completion.
Tracie Farrell, Mehmet Bakir, Kalina Bontcheva

2021-03-04

Radiology

Prognostic Implications of CT Feature Analysis in Patients with COVID-19: a Nationwide Cohort Study.

In Journal of Korean medical science

BACKGROUND : Few studies have classified chest computed tomography (CT) findings of coronavirus disease 2019 (COVID-19) and analyzed their correlations with prognosis. The present study aimed to evaluate retrospectively the clinical and chest CT findings of COVID-19 and to analyze CT findings and determine their relationships with clinical severity.

METHODS : Chest CT and clinical features of 271 COVID-19 patients were assessed. The presence of CT findings and distribution of parenchymal abnormalities were evaluated, and CT patterns were classified as bronchopneumonia, organizing pneumonia (OP), or diffuse alveolar damage (DAD). Total extents were assessed using a visual scoring system and artificial intelligence software. Patients were allocated to two groups based on clinical outcomes, that is, to a severe group (requiring O₂ therapy or mechanical ventilation, n = 55) or a mild group (not requiring O₂ therapy or mechanical ventilation, n = 216). Clinical and CT features of these two groups were compared and univariate and multivariate logistic regression analyses were performed to identify independent prognostic factors.

RESULTS : Age, lymphocyte count, levels of C-reactive protein, and procalcitonin were significantly different in the two groups. Forty-five of the 271 patients had normal chest CT findings. The most common CT findings among the remaining 226 patients were ground-glass opacity (98%), followed by consolidation (53%). CT findings were classified as OP (93%), DAD (4%), or bronchopneumonia (3%) and all nine patients with DAD pattern were included in the severe group. Uivariate and multivariate analyses showed an elevated procalcitonin (odds ratio [OR], 2.521; 95% confidence interval [CI], 1.001-6.303, P = 0.048), and higher visual CT scores (OR, 1.137; 95% CI, 1.042-1.236; P = 0.003) or higher total extent by AI measurement (OR, 1.048; 95% CI, 1.020-1.076; P < 0.001) were significantly associated with a severe clinical course.

CONCLUSION : CT findings of COVID-19 pneumonia can be classified into OP, DAD, or bronchopneumonia patterns and all patients with DAD pattern were included in severe group. Elevated inflammatory markers and higher CT scores were found to be significant predictors of poor prognosis in patients with COVID-19 pneumonia.

Jeong Yeon Joo, Nam Bo Da, Yoo Jin Young, Kim Kun Il, Kang Hee, Hwang Jung Hwa, Kim Yun Hyeon, Lee Kyung Soo

2021-Mar-01

COVID-19, Chest, Coronavirus, Pneumonia, Prognosis, Tomography, X-Ray Computed

Public Health

Automated Detection of Covid-19 from Chest X-ray scans using an optimized CNN architecture.

In Applied soft computing
The novel coronavirus termed as covid-19 has taken the world by its crutches affecting innumerable lives with devastating impact on the global economy and public health. One of the major ways to control the spread of this disease is identification in the initial stage, so that isolation and treatment could be initiated. Due to the lack of automated auxiliary diagnostic medical tools, availability of lesser sensitivity testing kits, and limited availability of healthcare professionals, the pandemic has spread like wildfire across the world. Certain recent findings state that chest X-ray scans contain salient information regarding the onset of the virus, the information can be analyzed so that the diagnosis and treatment can be initiated at an earlier stage. This is where artificial intelligence meets the diagnostic capabilities of experienced clinicians. The objective of the proposed research is to contribute towards fighting the global pandemic by developing an automated image analysis module for identifying covid-19 affected chest X-ray scans by employing an optimized Convolution Neural Network (CNN) model. The aforementioned objective is achieved in the following manner by developing three classification models, (i) ensemble of ResNet 50-Error Correcting Output Code (ECOC) model, (ii) CNN optimized using Grey Wolf Optimizer (GWO) and, (iii) CNN optimized using Whale Optimization + BAT algorithm. The novelty of the proposed method lies in the automatic tuning of hyper parameters considering a hierarchy of MultiLayer Perceptron (MLP), feature extraction, and optimization ensemble. A 100% classification accuracy was obtained in classifying covid-19 images. Classification accuracy of 98.8% and 96% were obtained for dataset 1 and dataset 2 respectively for classification into covid-19, normal, and viral pneumonia cases. The proposed method can be adopted in a clinical setting for assisting radiologists and it can also be employed in remote areas to facilitate the faster screening of affected patients.
Pathan Sameena, Siddalingaswamy P C, Ali Tanweer

2021-Jun

CNN, Covid-19, ECOC, Ensemble, GWO, SVM, WOA

General

Triage of potential COVID-19 patients from chest X-ray images using hierarchical convolutional networks.

In Neural computing & applications
The current COVID-19 pandemic has motivated the researchers to use artificial intelligence techniques for a potential alternative to reverse transcription-polymerase chain reaction due to the limited scale of testing. The chest X-ray (CXR) is one of the alternatives to achieve fast diagnosis, but the unavailability of large-scale annotated data makes the clinical implementation of machine learning-based COVID detection difficult. Another issue is the usage of ImageNet pre-trained networks which does not extract reliable feature representations from medical images. In this paper, we propose the use of hierarchical convolutional network (HCN) architecture to naturally augment the data along with diversified features. The HCN uses the first convolution layer from COVIDNet followed by the convolutional layers from well-known pre-trained networks to extract the features. The use of the convolution layer from COVIDNet ensures the extraction of representations relevant to the CXR modality. We also propose the use of ECOC for encoding multiclass problems to binary classification for improving the recognition performance. Experimental results show that HCN architecture is capable of achieving better results in comparison with the existing studies. The proposed method can accurately triage potential COVID-19 patients through CXR images for sharing the testing load and increasing the testing capacity.
Dev Kapal, Khowaja Sunder Ali, Bist Ankur Singh, Saini Vaibhav, Bhatia Surbhi

2021-Feb-25

COVID-19, Chest X-ray images, ECOC, Fusion strategies, Hierarchical convolutional network

General

A pharmacophore model for SARS-CoV-2 3CLpro small molecule inhibitors and in vitro experimental validation of computationally screened inhibitors

bioRxiv Preprint
Among the biomedical efforts in response to the current coronavirus (COVID-19) pandemic, pharmacological strategies to reduce viral load in patients with severe forms of the disease are being studied intensively. One of the main drug target proteins proposed so far is the SARS-CoV-2 viral protease 3CLpro (also called Mpro), an essential component for viral replication. Ongoing ligand- and receptor-based computational screening efforts would be facilitated by an improved understanding of the electrostatic, hydrophobic and steric features that characterize small molecule inhibitors binding stably to 3CLpro, as well as by an extended collection of known binders. Here, we present combined virtual screening, molecular dynamics simulation, machine learning and in vitro experimental validation analyses which have led to the identification of small molecule inhibitors of 3CLpro with micromolar activity, and to a pharmacophore model that describes functional chemical groups associated with the molecular recognition of ligands by the 3CLpro binding pocket. Experimentally validated inhibitors using a ligand activity assay include natural compounds with available prior knowledge on safety and bioavailability properties, such as the natural compound rottlerin (IC50 = 37 mcM), and synthetic compounds previously not characterized (e.g. compound CID 46897844, IC50 = 31 mcM). In combination with the developed pharmacophore model, these and other confirmed 3CLpro inhibitors may provide a basis for further similarity-based screening in independent compound databases and structural design optimization efforts, to identify 3CLpro ligands with improved potency and selectivity. Overall, this study suggests that the integration of virtual screening, molecular dynamics simulations and machine learning can facilitate 3CLpro-targeted small molecule screening investigations. Different receptor-, ligand- and machine learning-based screening strategies provided complementary information, helping to increase the number and diversity of identified active compounds. Finally, the resulting pharmacophore model and experimentally validated small molecule inhibitors for 3CLpro provide resources to support follow-up computational screening efforts for this drug target.
Glaab, E.; Manoharan, G. B.; Abankwa, D.

2021-03-03

General

Can social media data be used to evaluate the risk of human interactions during the COVID-19 pandemic?

In International journal of disaster risk reduction : IJDRR
The United States has taken multiple measures to contain the spread of COVID-19, including the implementation of lockdown orders and social distancing practices. Evaluating social distancing is critical since it reflects the frequency of close human interactions. While questionnaire surveys or mobility data-based systems have provided valuable insights, social media data can contribute as an additional instrument to help monitor the risk of human interactions during the pandemic. For this reason, this study introduced a social media-based approach that quantifies the pro/anti-lockdown ratio as an indicator of the risk of human interactions. With the aid of natural language processing and machine learning techniques, this study classified the lockdown-related tweets and quantified the pro/anti-lockdown ratio for each state over time. The anti-lockdown ratio showed a moderate and negative correlation with the state-level social distancing index on a weekly basis, suggesting that people are more likely to travel out of the state where the higher anti-lockdown level is observed. The study further showed that the perception expressed on social media could reflect people's behaviors. The findings of the study are of significance for government agencies to assess the risk of close human interactions and to evaluate their policy effectiveness in the context of social distancing and lockdown.
Li Lingyao, Ma Zihui, Lee Hyesoo, Lee Sangyu

2021-Feb-24

COVID-19, lockdown, social distancing, social media, text classification

General

Design ensemble deep learning model for pneumonia disease classification.

In International journal of multimedia information retrieval
With the recent spread of the SARS-CoV-2 virus, computer-aided diagnosis (CAD) has received more attention. The most important CAD application is to detect and classify pneumonia diseases using X-ray images, especially, in a critical period as pandemic of covid-19 that is kind of pneumonia. In this work, we aim to evaluate the performance of single and ensemble learning models for the pneumonia disease classification. The ensembles used are mainly based on fined-tuned versions of (InceptionResNet_V2, ResNet50 and MobileNet_V2). We collected a new dataset containing 6087 chest X-ray images in which we conduct comprehensive experiments. As a result, for a single model, we found out that InceptionResNet_V2 gives 93.52% of F1 score. In addition, ensemble of 3 models (ResNet50 with MobileNet_V2 with InceptionResNet_V2) shows more accurate than other ensembles constructed (94.84% of F1 score).
El Asnaoui Khalid

2021-Feb-20

Computer-aided diagnosis, Covid-19, Deep learning, Ensemble deep learning, Pneumonia disease, Pneumonia multiclass classification, X-ray images

General

Classification of COVID-19 pneumonia from chest CT images based on reconstructed super-resolution images and VGG neural network.

In Health information science and systems
The COVID-19 coronavirus has spread rapidly around the world and has caused global panic. Chest CT images play a major role in confirming positive COVID-19 patients. The computer aided diagnosis of COVID-19 from CT images based on artificial intelligence have been developed and deployed in some hospitals. But environmental influences and the movement of lung will affect the image quality, causing the lung parenchyma and pneumonia area unclear in CT images. Therefore, the performance of COVID-19's artificial intelligence diagnostic algorithm is reduced. If chest CT images are reconstructed, the accuracy and performance of the aided diagnostic algorithm may be improved. In this paper, a new aided diagnostic algorithm for COVID-19 based on super-resolution reconstructed images and convolutional neural network is presented. Firstly, the SRGAN neural network is used to reconstruct super-resolution images from original chest CT images. Then COVID-19 images and Non-COVID-19 images are classified from super-resolution chest CT images by VGG16 neural network. Finally, the performance of this method is verified by the pubic COVID-CT dataset and compared with other aided diagnosis methods of COVID-19. The experimental results show that improving the data quality through SRGAN neural network can greatly improve the final classification accuracy when the data quality is low. This proves that this method can obtain high accuracy, sensitivity and specificity in the examined test image datasets and has similar performance to other state-of-the-art deep learning aided algorithms.
Tan Wenjun, Liu Pan, Li Xiaoshuo, Liu Yao, Zhou Qinghua, Chen Chao, Gong Zhaoxuan, Yin Xiaoxia, Zhang Yanchun

2021-Dec

COVID-19, Chest CT images, Computer aided diagnosis, Convolutional neural network, Super-resolution images

Public Health

The challenge of privacy and security when using technology to track people in times of COVID-19 pandemic.

In Procedia computer science
Since the start of the Coronavirus disease 2019 (COVID-19) governments and health authorities across the world have find it very difficult in controlling infections. Digital technologies such as artificial intelligence (AI), big data, cloud computing, blockchain and 5G have effectively improved the efficiency of efforts in epidemic monitoring, virus tracking, prevention, control and treatment. Surveillance to halt COVID-19 has raised privacy concerns, as many governments are willing to overlook privacy implications to save lives. The purpose of this paper is to conduct a focused Systematic Literature Review (SLR), to explore the potential benefits and implications of using digital technologies such as AI, big data and cloud to track COVID-19 amongst people in different societies. The aim is to highlight the risks of security and privacy to personal data when using technology to track COVID-19 in societies and identify ways to govern these risks. The paper uses the SLR approach to examine 40 articles published during 2020, ultimately down selecting to the most relevant 24 studies. In this SLR approach we adopted the following steps; formulated the problem, searched the literature, gathered information from studies, evaluated the quality of studies, analysed and integrated the outcomes of studies while concluding by interpreting the evidence and presenting the results. Papers were classified into different categories such as technology use, impact on society and governance. The study highlighted the challenge for government to balance the need of what is good for public health versus individual privacy and freedoms. The findings revealed that although the use of technology help governments and health agencies reduce the spread of the COVID-19 virus, government surveillance to halt has sparked privacy concerns. We suggest some requirements for government policy to be ethical and capable of commanding the trust of the public and present some research questions for future research.
Smidt Hermanus J, Jokonya Osden

2021

COVID 19, privacy, society, technology, tracking

General

Predict Mortality in Patients Infected with COVID-19 Virus Based on Observed Characteristics of the Patient using Logistic Regression.

In Procedia computer science
The spread of COVID-19 has made the world a mess. Up to this day, 5,235,452 cases confirmed worldwide with 338,612 death. One of the methods to predict mortality risk is machine learning algorithm using medical features, which means it takes time. Therefore, in this study, Logistic Regression is modeled by training 114 data and used to create a prediction over the patient's mortality using nonmedical features. The model can help hospitals and doctors to prioritize who has a high probability of death and triage patients especially when the hospital is overrun by patients. The model can accurately predict with more than 90% accuracy achieved. Further analysis found that age is the most important predictor in the patient's mortality rate. Using this model, the death rate caused by COVID-19 could be reduced.
Josephus Bernhard O, Nawir Ardianto H, Wijaya Evelyn, Moniaga Jurike V, Ohyver Margaretha

2021

Covid-19, Logistic Regression, Mortality

General

Synthesis of COVID-19 chest X-rays using unpaired image-to-image translation.

In Social network analysis and mining
Motivated by the lack of publicly available datasets of chest radiographs of positive patients with coronavirus disease 2019 (COVID-19), we build the first-of-its-kind open dataset of synthetic COVID-19 chest X-ray images of high fidelity using an unsupervised domain adaptation approach by leveraging class conditioning and adversarial training. Our contributions are twofold. First, we show considerable performance improvements on COVID-19 detection using various deep learning architectures when employing synthetic images as additional training set. Second, we show how our image synthesis method can serve as a data anonymization tool by achieving comparable detection performance when trained only on synthetic data. In addition, the proposed data generation framework offers a viable solution to the COVID-19 detection in particular, and to medical image classification tasks in general. Our publicly available benchmark dataset (https://github.com/hasibzunair/synthetic-covid-cxr-dataset.) consists of 21,295 synthetic COVID-19 chest X-ray images. The insights gleaned from this dataset can be used for preventive actions in the fight against the COVID-19 pandemic.
Zunair Hasib, Hamza A Ben

2021

COVID-19, Chest X-rays, Deep learning, Image classification, Image synthesis, Imbalanced data

General

COVID-DeepPredictor: Recurrent Neural Network to Predict SARS-CoV-2 and Other Pathogenic Viruses.

In Frontiers in genetics ; h5-index 62.0
The COVID-19 disease for Novel coronavirus (SARS-CoV-2) has turned out to be a global pandemic. The high transmission rate of this pathogenic virus demands an early prediction and proper identification for the subsequent treatment. However, polymorphic nature of this virus allows it to adapt and sustain in different kinds of environment which makes it difficult to predict. On the other hand, there are other pathogens like SARS-CoV-1, MERS-CoV, Ebola, Dengue, and Influenza as well, so that a predictor is highly required to distinguish them with the use of their genomic information. To mitigate this problem, in this work COVID-DeepPredictor is proposed on the framework of deep learning to identify an unknown sequence of these pathogens. COVID-DeepPredictor uses Long Short Term Memory as Recurrent Neural Network for the underlying prediction with an alignment-free technique. In this regard, k-mer technique is applied to create Bag-of-Descriptors (BoDs) in order to generate Bag-of-Unique-Descriptors (BoUDs) as vocabulary and subsequently embedded representation is prepared for the given virus sequences. This predictor is not only validated for the dataset using K -fold cross-validation but also for unseen test datasets of SARS-CoV-2 sequences and sequences from other viruses as well. To verify the efficacy of COVID-DeepPredictor, it has been compared with other state-of-the-art prediction techniques based on Linear Discriminant Analysis, Random Forests, and Gradient Boosting Method. COVID-DeepPredictor achieves 100% prediction accuracy on validation dataset while on test datasets, the accuracy ranges from 99.51 to 99.94%. It shows superior results over other prediction techniques as well. In addition to this, accuracy and runtime of COVID-DeepPredictor are considered simultaneously to determine the value of k in k-mer, a comparative study among k values in k-mer, Bag-of-Descriptors (BoDs), and Bag-of-Unique-Descriptors (BoUDs) and a comparison between COVID-DeepPredictor and Nucleotide BLAST have also been performed. The code, training, and test datasets used for COVID-DeepPredictor are available at http://www.nitttrkol.ac.in/indrajit/projects/COVID-DeepPredictor/.
Saha Indrajit, Ghosh Nimisha, Maity Debasree, Seal Arjit, Plewczynski Dariusz

2021

SARS-CoV-2, genomic information, long-short term memory, sequence analysis, virus prediction

General

Forcing the Digital Economy: How will the Structure of Digital Markets Change as a Result of the COVID-19 Pandemic.

In Studies on Russian economic development
Could the forced digitalization of multiple spheres of human life caused by the coronavirus pandemic lead to radical changes in the global and Russian economies? How and to what extent have ubiquitous lockdowns affected the digital transformation? The new model of the digital economy growth, formed during the ongoing crisis, actually contributes to the accelerated development of secondary digital infrastructure (platforms and artificial intelligence technologies) through the creation of mass markets, the noticeably higher consumption in the field of ICT services, and the redistribution of a significant part of resources from other sectors. However, this digital forcing, within the framework of which traditional industries were placed in a deliberately losing situation due to artificially created circumstances, is taking place during a fundamental structural crisis of the global economy. Therefore, unlike the technological revolutions of the past, this one will have serious objective limitations associated with narrowed opportunities for the development of the primary digital infrastructure, without which extensive development of digital services and markets is impossible. In addition, further implementation of the adopted model of building a digital economy, based on the collection and processing of big data, is fundamentally impossible outside globalization processes and implies a significant imbalance between the new "world technological center" (the United States and China, who, however, are in a state of trade war) and the "world technological periphery." For most other countries, including Russia, it means the need to "fit" into one of the two currently possible peripheral contours of the global digital transformation.
Ganichev N A, Koshovets O B

2021

5G networks, COVID-19 pandemic, ICT, artificial intelligence, coronavirus, digital economy, digital platforms, digital transformation, microelectronics

General

XCOVNet: Chest X-ray Image Classification for COVID-19 Early Detection Using Convolutional Neural Networks.

In New generation computing
COVID-19 (also known as SARS-COV-2) pandemic has spread in the entire world. It is a contagious disease that easily spreads from one person in direct contact to another, classified by experts in five categories: asymptomatic, mild, moderate, severe, and critical. Already more than 66 million people got infected worldwide with more than 22 million active patients as of 5 December 2020 and the rate is accelerating. More than 1.5 million patients (approximately 2.5% of total reported cases) across the world lost their life. In many places, the COVID-19 detection takes place through reverse transcription polymerase chain reaction (RT-PCR) tests which may take longer than 48 h. This is one major reason of its severity and rapid spread. We propose in this paper a two-phase X-ray image classification called XCOVNet for early COVID-19 detection using convolutional neural Networks model. XCOVNet detects COVID-19 infections in chest X-ray patient images in two phases. The first phase pre-processes a dataset of 392 chest X-ray images of which half are COVID-19 positive and half are negative. The second phase trains and tunes the neural network model to achieve a 98.44% accuracy in patient classification.
Madaan Vishu, Roy Aditya, Gupta Charu, Agrawal Prateek, Sharma Anand, Bologa Cristian, Prodan Radu

2021-Feb-24

COVID-19 disease diagnosis, Coronavirus, Image classification, Machine learning, SARS-COV-2