Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

General General

Improving the performance of CNN to predict the likelihood of COVID-19 using chest X-ray images with preprocessing algorithms.

In International journal of medical informatics ; h5-index 49.0

OBJECTIVE : This study aims to develop and test a new computer-aided diagnosis (CAD) scheme of chest X-ray images to detect coronavirus (COVID-19) infected pneumonia.

METHOD : CAD scheme first applies two image preprocessing steps to remove the majority of diaphragm regions, process the original image using a histogram equalization algorithm, and a bilateral low-pass filter. Then, the original image and two filtered images are used to form a pseudo color image. This image is fed into three input channels of a transfer learning-based convolutional neural network (CNN) model to classify chest X-ray images into 3 classes of COVID-19 infected pneumonia, other community-acquired no-COVID-19 infected pneumonia, and normal (non-pneumonia) cases. To build and test the CNN model, a publicly available dataset involving 8474 chest X-ray images is used, which includes 415, 5179 and 2,880 cases in three classes, respectively. Dataset is randomly divided into 3 subsets namely, training, validation, and testing with respect to the same frequency of cases in each class to train and test the CNN model.

RESULTS : The CNN-based CAD scheme yields an overall accuracy of 94.5 % (2404/2544) with a 95 % confidence interval of [0.93,0.96] in classifying 3 classes. CAD also yields 98.4 % sensitivity (124/126) and 98.0 % specificity (2371/2418) in classifying cases with and without COVID-19 infection. However, without using two preprocessing steps, CAD yields a lower classification accuracy of 88.0 % (2239/2544).

CONCLUSION : This study demonstrates that adding two image preprocessing steps and generating a pseudo color image plays an important role in developing a deep learning CAD scheme of chest X-ray images to improve accuracy in detecting COVID-19 infected pneumonia.

Heidari Morteza, Mirniaharikandehei Seyedehnafiseh, Khuzani Abolfazl Zargari, Danala Gopichandh, Qiu Yuchen, Zheng Bin

2020-Sep-23

COVID-19 diagnosis, Computer-aided diagnosis, Convolution neural network (CNN), Coronavirus, Disease classification, VGG16 network

General General

Leveraging Computational Modeling to Understand Infectious Diseases.

In Current pathobiology reports

Purpose of Review : Computational and mathematical modeling have become a critical part of understanding in-host infectious disease dynamics and predicting effective treatments. In this review, we discuss recent findings pertaining to the biological mechanisms underlying infectious diseases, including etiology, pathogenesis, and the cellular interactions with infectious agents. We present advances in modeling techniques that have led to fundamental disease discoveries and impacted clinical translation.

Recent Findings : Combining mechanistic models and machine learning algorithms has led to improvements in the treatment of Shigella and tuberculosis through the development of novel compounds. Modeling of the epidemic dynamics of malaria at the within-host and between-host level has afforded the development of more effective vaccination and antimalarial therapies. Similarly, in-host and host-host models have supported the development of new HIV treatment modalities and an improved understanding of the immune involvement in influenza. In addition, large-scale transmission models of SARS-CoV-2 have furthered the understanding of coronavirus disease and allowed for rapid policy implementations on travel restrictions and contract tracing apps.

Summary : Computational modeling is now more than ever at the forefront of infectious disease research due to the COVID-19 pandemic. This review highlights how infectious diseases can be better understood by connecting scientists from medicine and molecular biology with those in computer science and applied mathematics.

Jenner Adrianne L, Aogo Rosemary A, Davis Courtney L, Smith Amber M, Craig Morgan

2020-Sep-24

Bacteria, Computational modeling, Infectious diseases, Mathematics, Parasites, Viruses

General General

Learning distinctive filters for COVID-19 detection from chest X-ray using shuffled residual CNN.

In Applied soft computing

COVID-19 is a deadly viral infection that has brought a significant threat to human lives. Automatic diagnosis of COVID-19 from medical imaging enables precise medication, helps to control community outbreak, and reinforces coronavirus testing methods in place. While there exist several challenges in manually inferring traces of this viral infection from X-ray, Convolutional Neural Network (CNN) can mine data patterns that capture subtle distinctions between infected and normal X-rays. To enable automated learning of such latent features, a custom CNN architecture has been proposed in this research. It learns unique convolutional filter patterns for each kind of pneumonia. This is achieved by restricting certain filters in a convolutional layer to maximally respond only to a particular class of pneumonia/COVID-19. The CNN architecture integrates different convolution types to aid better context for learning robust features and strengthen gradient flow between layers. The proposed work also visualizes regions of saliency on the X-ray that have had the most influence on CNN's prediction outcome. To the best of our knowledge, this is the first attempt in deep learning to learn custom filters within a single convolutional layer for identifying specific pneumonia classes. Experimental results demonstrate that the proposed work has significant potential in augmenting current testing methods for COVID-19. It achieves an F1-score of 97.20% and an accuracy of 99.80% on the COVID-19 X-ray set.

Karthik R, Menaka R, M Hariharan

2020-Sep-23

CNN, COVID-19, Chest X-ray, Deep learning, Pneumonia

General General

Clinical features of COVID-19 mortality: development and validation of a clinical prediction model.

In The Lancet. Digital health

Background : The COVID-19 pandemic has affected millions of individuals and caused hundreds of thousands of deaths worldwide. Predicting mortality among patients with COVID-19 who present with a spectrum of complications is very difficult, hindering the prognostication and management of the disease. We aimed to develop an accurate prediction model of COVID-19 mortality using unbiased computational methods, and identify the clinical features most predictive of this outcome.

Methods : In this prediction model development and validation study, we applied machine learning techniques to clinical data from a large cohort of patients with COVID-19 treated at the Mount Sinai Health System in New York City, NY, USA, to predict mortality. We analysed patient-level data captured in the Mount Sinai Data Warehouse database for individuals with a confirmed diagnosis of COVID-19 who had a health system encounter between March 9 and April 6, 2020. For initial analyses, we used patient data from March 9 to April 5, and randomly assigned (80:20) the patients to the development dataset or test dataset 1 (retrospective). Patient data for those with encounters on April 6, 2020, were used in test dataset 2 (prospective). We designed prediction models based on clinical features and patient characteristics during health system encounters to predict mortality using the development dataset. We assessed the resultant models in terms of the area under the receiver operating characteristic curve (AUC) score in the test datasets.

Findings : Using the development dataset (n=3841) and a systematic machine learning framework, we developed a COVID-19 mortality prediction model that showed high accuracy (AUC=0·91) when applied to test datasets of retrospective (n=961) and prospective (n=249) patients. This model was based on three clinical features: patient's age, minimum oxygen saturation over the course of their medical encounter, and type of patient encounter (inpatient vs outpatient and telehealth visits).

Interpretation : An accurate and parsimonious COVID-19 mortality prediction model based on three features might have utility in clinical settings to guide the management and prognostication of patients affected by this disease. External validation of this prediction model in other populations is needed.

Funding : National Institutes of Health.

Yadaw Arjun S, Li Yan-Chak, Bose Sonali, Iyengar Ravi, Bunyavanich Supinda, Pandey Gaurav

2020-Oct

Radiology Radiology

Deep learning-based triage and analysis of lesion burden for COVID-19: a retrospective study with external validation.

In The Lancet. Digital health

Background : Prompt identification of patients suspected to have COVID-19 is crucial for disease control. We aimed to develop a deep learning algorithm on the basis of chest CT for rapid triaging in fever clinics.

Methods : We trained a U-Net-based model on unenhanced chest CT scans obtained from 2447 patients admitted to Tongji Hospital (Wuhan, China) between Feb 1, 2020, and March 3, 2020 (1647 patients with RT-PCR-confirmed COVID-19 and 800 patients without COVID-19) to segment lung opacities and alert cases with COVID-19 imaging manifestations. The ability of artificial intelligence (AI) to triage patients suspected to have COVID-19 was assessed in a large external validation set, which included 2120 retrospectively collected consecutive cases from three fever clinics inside and outside the epidemic centre of Wuhan (Tianyou Hospital [Wuhan, China; area of high COVID-19 prevalence], Xianning Central Hospital [Xianning, China; area of medium COVID-19 prevalence], and The Second Xiangya Hospital [Changsha, China; area of low COVID-19 prevalence]) between Jan 22, 2020, and Feb 14, 2020. To validate the sensitivity of the algorithm in a larger sample of patients with COVID-19, we also included 761 chest CT scans from 722 patients with RT-PCR-confirmed COVID-19 treated in a makeshift hospital (Guanggu Fangcang Hospital, Wuhan, China) between Feb 21, 2020, and March 6, 2020. Additionally, the accuracy of AI was compared with a radiologist panel for the identification of lesion burden increase on pairs of CT scans obtained from 100 patients with COVID-19.

Findings : In the external validation set, using radiological reports as the reference standard, AI-aided triage achieved an area under the curve of 0·953 (95% CI 0·949-0·959), with a sensitivity of 0·923 (95% CI 0·914-0·932), specificity of 0·851 (0·842-0·860), a positive predictive value of 0·790 (0·777-0·803), and a negative predictive value of 0·948 (0·941-0·954). AI took a median of 0·55 min (IQR: 0·43-0·63) to flag a positive case, whereas radiologists took a median of 16·21 min (11·67-25·71) to draft a report and 23·06 min (15·67-39·20) to release a report. With regard to the identification of increases in lesion burden, AI achieved a sensitivity of 0·962 (95% CI 0·947-1·000) and a specificity of 0·875 (95 %CI 0·833-0·923). The agreement between AI and the radiologist panel was high (Cohen's kappa coefficient 0·839, 95% CI 0·718-0·940).

Interpretation : A deep learning algorithm for triaging patients with suspected COVID-19 at fever clinics was developed and externally validated. Given its high accuracy across populations with varied COVID-19 prevalence, integration of this system into the standard clinical workflow could expedite identification of chest CT scans with imaging indications of COVID-19.

Funding : Special Project for Emergency of the Science and Technology Department of Hubei Province, China.

Wang Minghuan, Xia Chen, Huang Lu, Xu Shabei, Qin Chuan, Liu Jun, Cao Ying, Yu Pengxin, Zhu Tingting, Zhu Hui, Wu Chaonan, Zhang Rongguo, Chen Xiangyu, Wang Jianming, Du Guang, Zhang Chen, Wang Shaokang, Chen Kuan, Liu Zheng, Xia Liming, Wang Wei

2020-Oct

General General

Artificial intelligence in COVID-19 drug repurposing.

In The Lancet. Digital health

Drug repurposing or repositioning is a technique whereby existing drugs are used to treat emerging and challenging diseases, including COVID-19. Drug repurposing has become a promising approach because of the opportunity for reduced development timelines and overall costs. In the big data era, artificial intelligence (AI) and network medicine offer cutting-edge application of information science to defining disease, medicine, therapeutics, and identifying targets with the least error. In this Review, we introduce guidelines on how to use AI for accelerating drug repurposing or repositioning, for which AI approaches are not just formidable but are also necessary. We discuss how to use AI models in precision medicine, and as an example, how AI models can accelerate COVID-19 drug repurposing. Rapidly developing, powerful, and innovative AI and network medicine technologies can expedite therapeutic development. This Review provides a strong rationale for using AI-based assistive tools for drug repurposing medications for human disease, including during the COVID-19 pandemic.

Zhou Yadi, Wang Fei, Tang Jian, Nussinov Ruth, Cheng Feixiong

2020-Sep-18

General General

Challenges and Opportunities of Preclinical Medical Education: COVID-19 Crisis and Beyond.

In SN comprehensive clinical medicine

COVID-19 pandemic has disrupted face-to-face teaching in medical schools globally. The use of remote learning as an emergency measure has affected students, faculty, support staff, and administrators. The aim of this narrative review paper is to examine the challenges and opportunities faced by medical schools in implementing remote learning for basic science teaching in response to the COVID-19 crisis. We searched relevant literature in PubMed, Scopus, and Google Scholar using specific keywords, e.g., "COVID-19 pandemic," "preclinical medical education," "online learning," "remote learning," "challenges," and "opportunities." The pandemic has posed several challenges to premedical education (e.g., suspension of face-to-face teaching, lack of cadaveric dissections, and practical/laboratory sessions) but has provided many opportunities as well, such as the incorporation of online learning in the curriculum and upskilling and reskilling in new technologies. To date, many medical schools have successfully transitioned their educational environment to emergency remote teaching and assessments. During COVID-19 crisis, the preclinical phase of medical curricula has successfully introduced the novel culture of "online home learning" using technology-oriented innovations, which may extend to post-COVID era to maintain teaching and learning in medical education. However, the lack of hands-on training in the preclinical years may have serious implications on the training of the current cohort of students, and they may struggle later in the clinical years. The use of emergent technology (e.g., artificial intelligence for adaptive learning, virtual simulation, and telehealth) for education is most likely to be indispensable components of the transformative change and post-COVID medical education.

Gaur Uma, Majumder Md Anwarul Azim, Sa Bidyadhar, Sarkar Sankalan, Williams Arlene, Singh Keerti

2020-Sep-22

COVID-19 pandemic, Challenges, Online learning, Opportunities, Preclinical medical education, Remote learning

General General

Machine Learning and Image Analysis Applications in the Fight against COVID-19 Pandemic: Datasets, Research Directions, Challenges and Opportunities.

In Materials today. Proceedings

COVID-19 pandemic has become the most devastating disease of the current century and spread over 216 countries around the world. The disease is spreading through outbreaks despite the availability of modern sophisticated medical treatment. Machine Learning and Image Analysis research has been making great progress in many directions in the healthcare field for providing support to subsequent medical diagnosis. In this paper, we have propose three research directions with methodologies in the fight against the pandemic namely: Chest X-Ray (CXR) images classification using deep convolution neural networks with transfer learning to assist diagnosis; Patient Risk prediction of pandemic based on risk factors such as patient characteristics, comorbidities, initial symptoms, vital signs for prognosis of disease; and forecasting of disease spread & case fatality rate using deep neural networks. Further, some of the challenges, open datasets and opportunities are discussed for researchers.

Somasekar J, Pavan Kumar Visulaization P, Sharma Avinash, Ramesh G

2020-Sep-22

COVID-19, Chest X-Ray Images, Classification, Diagnosis, Machine Learning, medical image analysis

General General

Mitigating the Impact of the Novel Coronavirus Pandemic on Neuroscience and Music Research Protocols in Clinical Populations.

In Frontiers in psychology ; h5-index 92.0

The COVID-19 disease and the systemic responses to it has impacted lives, routines and procedures at an unprecedented level. While medical care and emergency response present immediate needs, the implications of this pandemic will likely be far-reaching. Most practices that the clinical research within neuroscience and music field rely on, take place in hospitals or closely connected clinical settings which have been hit hard by the contamination. So too have its preventive and treatment measures. This means that clinical research protocols may have been altered, postponed or put in complete jeopardy. In this context, we would like to present and discuss the problems arising under the current crisis. We do so by critically approaching an online discussion facilitated by an expert panel in the field of music and neuroscience. This effort is hoped to provide an efficient basis to orient ourselves as we begin to map the needs and elements in this field of research as we further propose ideas and solutions on how to overcome, or at least ease the problems and questions we encounter or will encounter, with foresight. Among others, we hope to answer questions on technical or social problems that can be expected, possible solutions and preparatory steps to take in order to improve or ease research implementation, ethical implications and funding considerations. Finally, we further hope to facilitate the process of creating new protocols in order to minimize the impact of this crisis on essential research which may have the potential to relieve health systems.

Papatzikis Efthymios, Zeba Fathima, Särkämö Teppo, Ramirez Rafael, Grau-Sánchez Jennifer, Tervaniemi Mari, Loewy Joanne

2020

COVID-19, music and neuroscience, music and neuroscience research protocols, music therapy, research crisis response

General General

An optimized deep learning architecture for the diagnosis of COVID-19 disease based on gravitational search optimization.

In Applied soft computing

In this paper, a novel approach called GSA-DenseNet121-COVID-19 based on a hybrid convolutional neural network (CNN) architecture is proposed using an optimization algorithm. The CNN architecture that was used is called DenseNet121, and the optimization algorithm that was used is called the gravitational search algorithm (GSA). The GSA is used to determine the best values for the hyperparameters of the DenseNet121 architecture. To help this architecture to achieve a high level of accuracy in diagnosing COVID-19 through chest x-ray images. The obtained results showed that the proposed approach could classify 98.38% of the test set correctly. To test the efficacy of the GSA in setting the optimum values for the hyperparameters of DenseNet121. The GSA was compared to another approach called SSD-DenseNet121, which depends on the DenseNet121 and the optimization algorithm called social ski driver (SSD). The comparison results demonstrated the efficacy of the proposed GSA-DenseNet121-COVID-19. As it was able to diagnose COVID-19 better than SSD-DenseNet121 as the second was able to diagnose only 94% of the test set. The proposed approach was also compared to another method based on a CNN architecture called Inception-v3 and manual search to quantify hyperparameter values. The comparison results showed that the GSA-DenseNet121-COVID-19 was able to beat the comparison method, as the second was able to classify only 95% of the test set samples. The proposed GSA-DenseNet121-COVID-19 was also compared with some related work. The comparison results showed that GSA-DenseNet121-COVID-19 is very competitive.

Ezzat Dalia, Hassanien Aboul Ella, Ella Hassan Aboul

2020-Sep-22

Convolutional neural networks, Deep learning, Gravitational search algorithm, Hyperparameters optimization, SARS-CoV-2, Transfer learning

General General

Predicting Psychological State Among Chinese Undergraduate Students in the COVID-19 Epidemic: A Longitudinal Study Using a Machine Learning.

In Neuropsychiatric disease and treatment

Background : The outbreak of the 2019 novel coronavirus disease (COVID-19) not only caused physical abnormalities, but also caused psychological distress, especially for undergraduate students who are facing the pressure of academic study and work. We aimed to explore the prevalence rate of probable anxiety and probable insomnia and to find the risk factors among a longitudinal study of undergraduate students using the approach of machine learning.

Methods : The baseline data (T1) were collected from freshmen who underwent psychological evaluation at two months after entering the university. At T2 stage (February 10th to 13th, 2020), we used a convenience cluster sampling to assess psychological state (probable anxiety was assessed by general anxiety disorder-7 and probable insomnia was assessed by insomnia severity index-7) based on a web survey. We integrated information attained at T1 stage to predict probable anxiety and probable insomnia at T2 stage using a machine learning algorithm (XGBoost).

Results : Finally, we included 2009 students (response rate: 80.36%). The prevalence rate of probable anxiety and probable insomnia was 12.49% and 16.87%, respectively. The XGBoost algorithm predicted 1954 out of 2009 students (translated into 97.3% accuracy) and 1932 out of 2009 students (translated into 96.2% accuracy) who suffered anxiety and insomnia symptoms, respectively. The most relevant variables in predicting probable anxiety included romantic relationship, suicidal ideation, sleep symptoms, and a history of anxiety symptoms. The most relevant variables in predicting probable insomnia included aggression, psychotic experiences, suicidal ideation, and romantic relationship.

Conclusion : Risks for probable anxiety and probable insomnia among undergraduate students can be identified at an individual level by baseline data. Thus, timely psychological intervention for anxiety and insomnia symptoms among undergraduate students is needed considering the above factors.

Ge Fenfen, Zhang Di, Wu Lianhai, Mu Hongwei

2020

COVID-19, anxiety, cohort, insomnia, machine learning

General General

Morphological Cell Profiling of SARS-CoV-2 Infection Identifies Drug Repurposing Candidates for COVID-19

bioRxiv Preprint

The global spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and the associated disease COVID-19, requires therapeutic interventions that can be rapidly translated to clinical care. Unfortunately, traditional drug discovery methods have a >90% failure rate and can take 10-15 years from target identification to clinical use. In contrast, drug repurposing can significantly accelerate translation. We developed a quantitative high-throughput screen to identify efficacious single agents and combination therapies against SARS-CoV-2. Quantitative high-content morphological profiling was coupled with an AI-based machine learning strategy to classify features of cells for infection and stress. This assay detected multiple antiviral mechanisms of action (MOA), including inhibition of viral entry, propagation, and modulation of host cellular responses. From a library of 1,425 FDA-approved compounds and clinical candidates, we identified 16 dose-responsive compounds with antiviral effects. In particular, we discovered that lactoferrin is an effective inhibitor of SARS-CoV-2 infection with an IC50 of 308 nM and that it potentiates the efficacy of both remdesivir and hydroxychloroquine. Lactoferrin also stimulates an antiviral host cell response and retains inhibitory activity in iPSC-derived alveolar epithelial cells, a model for the primary site of infection. Given its safety profile in humans, these data suggest that lactoferrin is a readily translatable therapeutic adjunct for COVID-19. Additionally, several commonly prescribed drugs were found to exacerbate viral infection and warrant clinical investigation. We conclude that morphological profiling for drug repurposing is an effective strategy for the selection and optimization of drugs and drug combinations as viable therapeutic options for COVID-19 pandemic and other emerging infectious diseases.

Mirabelli, C.; Wotring, J. W.; Zhang, C. J.; McCarty, S. M.; Fursmidt, R.; Frum, T.; Kadambi, N. S.; Amin, A. T.; O’Meara, T. R.; Pretto-Kernahan, C. D.; Spence, J. R.; Huang, J.; Alysandratos, K. D.; Kotton, D. N.; Handelman, S. K.; Wobus, C. E.; Weatherwax, K. J.; Mashour, G. A.; O’Meara, M. J.; Sexton, J. Z.

2020-09-28

General General

Public discourse and sentiment during the COVID 19 pandemic: Using Latent Dirichlet Allocation for topic modeling on Twitter.

In PloS one ; h5-index 176.0

The study aims to understand Twitter users' discourse and psychological reactions to COVID-19. We use machine learning techniques to analyze about 1.9 million Tweets (written in English) related to coronavirus collected from January 23 to March 7, 2020. A total of salient 11 topics are identified and then categorized into ten themes, including "updates about confirmed cases," "COVID-19 related death," "cases outside China (worldwide)," "COVID-19 outbreak in South Korea," "early signs of the outbreak in New York," "Diamond Princess cruise," "economic impact," "Preventive measures," "authorities," and "supply chain." Results do not reveal treatments and symptoms related messages as prevalent topics on Twitter. Sentiment analysis shows that fear for the unknown nature of the coronavirus is dominant in all topics. Implications and limitations of the study are also discussed.

Xue Jia, Chen Junxiang, Chen Chen, Zheng Chengda, Li Sijia, Zhu Tingshao

2020

General General

predCOVID-19: A Systematic Study of Clinical Predictive Models for Coronavirus Disease 2019.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Coronavirus Disease 2019 (COVID-19) is a rapidly emerging respiratory disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Due to the rapid human-to-human transmission of SARS-CoV-2, many healthcare systems are at risk of exceeding their healthcare capacities, in particular in terms of SARS-CoV-2 tests, hospital and intensive care unit (ICU) beds and mechanical ventilators. Predictive algorithms could potentially ease the strain on healthcare systems by identifying those who are most likely to receive a positive SARS-CoV-2 test, be hospitalised or admitted to the ICU.

OBJECTIVE : To develop, study and evaluate clinical predictive models that estimate, using machine learning and based on routinely collected clinical data, which patients are likely to receive a positive SARS-CoV-2 test, require hospitalisation or intensive care.

METHODS : Using a systematic approach to model development and optimisation, we train and compare various types of machine learning models, including logistic regression, neural networks, support vector machines, random forests, and gradient boosting. To evaluate the developed models, we perform a retrospective evaluation on demographic, clinical and blood analysis data from a cohort of 5644 patients. In addition, we determine which clinical features are predictive to what degree for each of the aforementioned clinical tasks using causal explanations.

RESULTS : Our experimental results indicate that our predictive models identify (i) patients that test positive for SARS-CoV-2 a priori at a sensitivity of 75% (95% confidence interval [CI]: 67%, 81%) and a specificity of 49% (95% CI: 46%, 51%), (ii) SARS-CoV-2 positive patients that require hospitalisation with 0.92 area under the receiver operator characteristic curve [AUC] (95% CI: 0.81, 0.98), and (iii) SARS-CoV-2 positive patients that require critical care with 0.98 AUC (95% CI: 0.95, 1.00).

CONCLUSIONS : Our results indicate that predictive models trained on routinely collected clinical data could be used to predict clinical pathways for COVID-19, and therefore help inform care and prioritise resources.

CLINICALTRIAL :

Schwab Patrick, Schütte DuMont August, Dietz Benedikt, Bauer Stefan

2020-Sep-14

Surgery Surgery

Lung Mechanics of Mechanically Ventilated Patients With COVID-19: Analytics With High-Granularity Ventilator Waveform Data.

In Frontiers in medicine

Background: Lung mechanics during invasive mechanical ventilation (IMV) for both prognostic and therapeutic implications; however, the full trajectory lung mechanics has never been described for novel coronavirus disease 2019 (COVID-19) patients requiring IMV. The study aimed to describe the full trajectory of lung mechanics of mechanically ventilated COVID-19 patients. The clinical and ventilator setting that can influence patient-ventilator asynchrony (PVA) and compliance were explored. Post-extubation spirometry test was performed to assess the pulmonary function after COVID-19 induced ARDS. Methods: This was a retrospective study conducted in a tertiary care hospital. All patients with IMV due to COVID-19 induced ARDS were included. High-granularity ventilator waveforms were analyzed with deep learning algorithm to obtain PVAs. Asynchrony index (AI) was calculated as the number of asynchronous events divided by the number of ventilator cycles and wasted efforts. Mortality was recorded as the vital status on hospital discharge. Results: A total of 3,923,450 respiratory cycles in 2,778 h were analyzed (average: 24 cycles/min) for seven patients. Higher plateau pressure (Coefficient: -0.90; 95% CI: -1.02 to -0.78) and neuromuscular blockades (Coefficient: -6.54; 95% CI: -9.92 to -3.16) were associated with lower AI. Survivors showed increasing compliance over time, whereas non-survivors showed persistently low compliance. Recruitment maneuver was not able to improve lung compliance. Patients were on supine position in 1,422 h (51%), followed by prone positioning (499 h, 18%), left positioning (453 h, 16%), and right positioning (404 h, 15%). As compared with supine positioning, prone positioning was associated with 2.31 ml/cmH2O (95% CI: 1.75 to 2.86; p < 0.001) increase in lung compliance. Spirometry tests showed that pulmonary functions were reduced to one third of the predicted values after extubation. Conclusions: The study for the first time described full trajectory of lung mechanics of patients with COVID-19. The result showed that prone positioning was associated with improved compliance; higher plateau pressure and use of neuromuscular blockades were associated with lower risk of AI.

Ge Huiqing, Pan Qing, Zhou Yong, Xu Peifeng, Zhang Lingwei, Zhang Junli, Yi Jun, Yang Changming, Zhou Yuhan, Liu Limin, Zhang Zhongheng

2020

COVID-19, asynchonized, asynchrony, lung mechanics, mechanical ventilation, prone positioning

oncology Oncology

Unveiling COVID-19 from CHEST X-Ray with Deep Learning: A Hurdles Race with Small Data.

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

The possibility to use widespread and simple chest X-ray (CXR) imaging for early screening of COVID-19 patients is attracting much interest from both the clinical and the AI community. In this study we provide insights and also raise warnings on what is reasonable to expect by applying deep learning to COVID classification of CXR images. We provide a methodological guide and critical reading of an extensive set of statistical results that can be obtained using currently available datasets. In particular, we take the challenge posed by current small size COVID data and show how significant can be the bias introduced by transfer-learning using larger public non-COVID CXR datasets. We also contribute by providing results on a medium size COVID CXR dataset, just collected by one of the major emergency hospitals in Northern Italy during the peak of the COVID pandemic. These novel data allow us to contribute to validate the generalization capacity of preliminary results circulating in the scientific community. Our conclusions shed some light into the possibility to effectively discriminate COVID using CXR.

Tartaglione Enzo, Barbano Carlo Alberto, Berzovini Claudio, Calandri Marco, Grangetto Marco

2020-Sep-22

COVID-19, chest X-ray, classification, deep learning

Radiology Radiology

Imaging Diagnostics and Pathology in SARS-CoV-2-Related Diseases.

In International journal of molecular sciences ; h5-index 102.0

In December 2019, physicians reported numerous patients showing pneumonia of unknown origin in the Chinese region of Wuhan. Following the spreading of the infection over the world, The World Health Organization (WHO) on 11 March 2020 declared the novel severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) outbreak a global pandemic. The scientific community is exerting an extraordinary effort to elucidate all aspects related to SARS-CoV-2, such as the structure, ultrastructure, invasion mechanisms, replication mechanisms, or drugs for treatment, mainly through in vitro studies. Thus, the clinical in vivo data can provide a test bench for new discoveries in the field of SARS-CoV-2, finding new solutions to fight the current pandemic. During this dramatic situation, the normal scientific protocols for the development of new diagnostic procedures or drugs are frequently not completely applied in order to speed up these processes. In this context, interdisciplinarity is fundamental. Specifically, a great contribution can be provided by the association and interpretation of data derived from medical disciplines based on the study of images, such as radiology, nuclear medicine, and pathology. Therefore, here, we highlighted the most recent histopathological and imaging data concerning the SARS-CoV-2 infection in lung and other human organs such as the kidney, heart, and vascular system. In addition, we evaluated the possible matches among data of radiology, nuclear medicine, and pathology departments in order to support the intense scientific work to address the SARS-CoV-2 pandemic. In this regard, the development of artificial intelligence algorithms that are capable of correlating these clinical data with the new scientific discoveries concerning SARS-CoV-2 might be the keystone to get out of the pandemic.

Scimeca Manuel, Urbano Nicoletta, Bonfiglio Rita, Montanaro Manuela, Bonanno Elena, Schillaci Orazio, Mauriello Alessandro

2020-Sep-22

SARS-CoV-2, artificial intelligence, imaging diagnostic, pandemic, pathology

Radiology Radiology

Detection of COVID-19 Using Deep Learning Algorithms on Chest Radiographs.

In Journal of thoracic imaging

PURPOSE : To evaluate the performance of a deep learning (DL) algorithm for the detection of COVID-19 on chest radiographs (CXR).

MATERIALS AND METHODS : In this retrospective study, a DL model was trained on 112,120 CXR images with 14 labeled classifiers (ChestX-ray14) and fine-tuned using initial CXR on hospital admission of 509 patients, who had undergone COVID-19 reverse transcriptase-polymerase chain reaction (RT-PCR). The test set consisted of a CXR on presentation of 248 individuals suspected of COVID-19 pneumonia between February 16 and March 3, 2020 from 4 centers (72 RT-PCR positives and 176 RT-PCR negatives). The CXR were independently reviewed by 3 radiologists and using the DL algorithm. Diagnostic performance was compared with radiologists' performance and was assessed by area under the receiver operating characteristics (AUC).

RESULTS : The median age of the subjects in the test set was 61 (interquartile range: 39 to 79) years (51% male). The DL algorithm achieved an AUC of 0.81, sensitivity of 0.85, and specificity of 0.72 in detecting COVID-19 using RT-PCR as the reference standard. On subgroup analyses, the model achieved an AUC of 0.79, sensitivity of 0.80, and specificity of 0.74 in detecting COVID-19 in patients presented with fever or respiratory systems and an AUC of 0.87, sensitivity of 0.85, and specificity of 0.81 in distinguishing COVID-19 from other forms of pneumonia. The algorithm significantly outperforms human readers (P<0.001 using DeLong test) with higher sensitivity (P=0.01 using McNemar test).

CONCLUSIONS : A DL algorithm (COV19NET) for the detection of COVID-19 on chest radiographs can potentially be an effective tool in triaging patients, particularly in resource-stretched health-care systems.

Chiu Wan Hang Keith, Vardhanabhuti Varut, Poplavskiy Dmytro, Yu Philip Leung Ho, Du Richard, Yap Alistair Yun Hee, Zhang Sailong, Fong Ambrose Ho-Tung, Chin Thomas Wing-Yan, Lee Jonan Chun Yin, Leung Siu Ting, Lo Christine Shing Yen, Lui Macy Mei-Sze, Fang Benjamin Xin Hao, Ng Ming-Yen, Kuo Michael D

2020-Sep-22

Radiology Radiology

Advancing COVID-19 differentiation with a robust preprocessing and integration of multi-institutional open-repository computer tomography datasets for deep learning analysis.

In Experimental and therapeutic medicine

The coronavirus pandemic and its unprecedented consequences globally has spurred the interest of the artificial intelligence research community. A plethora of published studies have investigated the role of imaging such as chest X-rays and computer tomography in coronavirus disease 2019 (COVID-19) automated diagnosis. Οpen repositories of medical imaging data can play a significant role by promoting cooperation among institutes in a world-wide scale. However, they may induce limitations related to variable data quality and intrinsic differences due to the wide variety of scanner vendors and imaging parameters. In this study, a state-of-the-art custom U-Net model is presented with a dice similarity coefficient performance of 99.6% along with a transfer learning VGG-19 based model for COVID-19 versus pneumonia differentiation exhibiting an area under curve of 96.1%. The above was significantly improved over the baseline model trained with no segmentation in selected tomographic slices of the same dataset. The presented study highlights the importance of a robust preprocessing protocol for image analysis within a heterogeneous imaging dataset and assesses the potential diagnostic value of the presented COVID-19 model by comparing its performance to the state of the art.

Trivizakis Eleftherios, Tsiknakis Nikos, Vassalou Evangelia E, Papadakis Georgios Z, Spandidos Demetrios A, Sarigiannis Dimosthenis, Tsatsakis Aristidis, Papanikolaou Nikolaos, Karantanas Apostolos H, Marias Kostas

2020-Nov

COVID-19, artificial intelligence, deep learning analysis, multi-institutional data

General General

Pandemic number five - Latest insights into the COVID-19 crisis.

In Biomedical journal

About nine months after the emergence of SARS-CoV-2, this special issue of the Biomedical Journal takes stock of its evolution into a pandemic. We acquire an elaborate overview of the history and virology of SARS-CoV-2, the epidemiology of COVID-19, and the development of therapies and vaccines, based on useful tools such as a pseudovirus system, artificial intelligence, and repurposing of existing drugs. Moreover, we learn about a potential link between COVID-19 and oral health, and some of the strategies that allowed Taiwan to handle the outbreak exceptionally well, including a COVID-19 biobank establishment, online tools for contact tracing, and the efficient management of emergency departments.

Häfner Sophia Julia

2020-Aug-27

COVID-19, Contact tracing, Pseudovirus system, Repurposing drugs, SARS-CoV-2

General General

Ethical dilemmas in COVID-19 times: how to decide who lives and who dies?

In Revista da Associacao Medica Brasileira (1992)

The respiratory disease caused by the coronavirus SARS-CoV-2 (COVID-19) is a pandemic that produces a large number of simultaneous patients with severe symptoms and in need of special hospital care, overloading the infrastructure of health services. All of these demands generate the need to ration equipment and interventions. Faced with this imbalance, how, when, and who decides, there is the impact of the stressful systems of professionals who are at the front line of care and, in the background, issues inherent to human subjectivity. Along this path, the idea of using artificial intelligence algorithms to replace health professionals in the decision-making process also arises. In this context, there is the ethical question of how to manage the demands produced by the pandemic. The objective of this work is to reflect, from the point of view of medical ethics, on the basic principles of the choices made by the health teams, during the COVID-19 pandemic, whose resources are scarce and decisions cause anguish and restlessness. The ethical values for the rationing of health resources in an epidemic must converge to some proposals based on fundamental values such as maximizing the benefits produced by scarce resources, treating people equally, promoting and recommending instrumental values, giving priority to critical situations. Naturally, different judgments will occur in different circumstances, but transparency is essential to ensure public trust. In this way, it is possible to develop prioritization guidelines using well-defined values and ethical recommendations to achieve fair resource allocation.

Neves Nedy M B C, Bitencourt Flávia B C S N, Bitencourt Almir G V

2020

General General

Artificial intelligence technology for diagnosing COVID-19 cases: a review of substantial issues.

In European review for medical and pharmacological sciences

Today, the world suffers from the rapid spread of COVID-19, which has claimed thousands of lives. Unfortunately, its treatment is yet to be developed. Nevertheless, this phenomenon can be decelerated by diagnosing and quarantining patients with COVID-19 at early stages, thereby saving numerous lives. In this study, the early diagnosis of this disease through artificial intelligence (AI) technology is explored. AI is a revolutionizing technology that drives new research opportunities in various fields. Although this study does not provide a final solution, it highlights the most promising lines of research on AI technology for the diagnosis of COVID-19. The major contribution of this work is a discussion on the following substantial issues of AI technology for preventing the severe effects of COVID-19: (1) rapid diagnosis and detection, (2) outbreak and prediction of virus spread, and (3) potential treatments. This study profoundly investigates these controversial research topics to achieve a precise, concrete, and concise conclusion. Thus, this study provides significant recommendations on future research directions related to COVID-19.

Alsharif M H, Alsharif Y H, Chaudhry S A, Albreem M A, Jahid A, Hwang E

2020-Sep

oncology Oncology

Development and validation of a machine learning-based prediction model for near-term in-hospital mortality among patients with COVID-19.

In BMJ supportive & palliative care ; h5-index 29.0

OBJECTIVES : To develop and validate a model for prediction of near-term in-hospital mortality among patients with COVID-19 by application of a machine learning (ML) algorithm on time-series inpatient data from electronic health records.

METHODS : A cohort comprised of 567 patients with COVID-19 at a large acute care healthcare system between 10 February 2020 and 7 April 2020 observed until either death or discharge. Random forest (RF) model was developed on randomly drawn 70% of the cohort (training set) and its performance was evaluated on the rest of 30% (the test set). The outcome variable was in-hospital mortality within 20-84 hours from the time of prediction. Input features included patients' vital signs, laboratory data and ECG results.

RESULTS : Patients had a median age of 60.2 years (IQR 26.2 years); 54.1% were men. In-hospital mortality rate was 17.0% and overall median time to death was 6.5 days (range 1.3-23.0 days). In the test set, the RF classifier yielded a sensitivity of 87.8% (95% CI: 78.2% to 94.3%), specificity of 60.6% (95% CI: 55.2% to 65.8%), accuracy of 65.5% (95% CI: 60.7% to 70.0%), area under the receiver operating characteristic curve of 85.5% (95% CI: 80.8% to 90.2%) and area under the precision recall curve of 64.4% (95% CI: 53.5% to 75.3%).

CONCLUSIONS : Our ML-based approach can be used to analyse electronic health record data and reliably predict near-term mortality prediction. Using such a model in hospitals could help improve care, thereby better aligning clinical decisions with prognosis in critically ill patients with COVID-19.

Parchure Prathamesh, Joshi Himanshu, Dharmarajan Kavita, Freeman Robert, Reich David L, Mazumdar Madhu, Timsina Prem, Kia Arash

2020-Sep-22

end of life care, hospital care, prognosis, supportive care, terminal care

General General

Viral pandemic preparedness: A pluripotent stem cell-based machine-learning platform for simulating SARS-CoV-2 infection to enable drug discovery and repurposing.

In Stem cells translational medicine

Infection with the SARS-CoV-2 virus has rapidly become a global pandemic for which we were not prepared. Several clinical trials using previously approved drugs and drug combinations are urgently underway to improve our current situation. Unfortunately, a vaccine option is optimistically at least a year away. It is imperative that for future viral pandemic preparedness, we have a rapid screening technology for drug discovery and repurposing. The primary purpose of this research project was to evaluate the DeepNEU stem-cell based platform by creating and validating computer simulations of artificial lung cells infected with SARS-CoV-2 to enable the rapid identification of antiviral therapeutic targets and drug repurposing. The data generated from this project indicate that (a) human alveolar type lung cells can be simulated by DeepNEU (v5.0), (b) these simulated cells can then be infected with simulated SARS-CoV-2 virus, (c) the unsupervised learning system performed well in all simulations based on available published wet lab data, and (d) the platform identified potentially effective anti-SARS-CoV2 combinations of known drugs for urgent clinical study. The data also suggest that DeepNEU can identify potential therapeutic targets for expedited vaccine development. We conclude that based on published data plus current DeepNEU results, continued development of the DeepNEU platform will improve our preparedness for and response to future viral outbreaks. This can be achieved through rapid identification of potential therapeutic options for clinical testing as soon as the viral genome has been confirmed.

Esmail Sally, Danter Wayne R

2020-Sep-22

DeepNEU, SARS-CoV-2, antiviral, drug discovery and repurposing, pandemic preparedness, unsupervised learning

Cardiology Cardiology

A machine learning algorithm to increase COVID-19 inpatient diagnostic capacity.

In PloS one ; h5-index 176.0

Worldwide, testing capacity for SARS-CoV-2 is limited and bottlenecks in the scale up of polymerase chain reaction (PCR-based testing exist. Our aim was to develop and evaluate a machine learning algorithm to diagnose COVID-19 in the inpatient setting. The algorithm was based on basic demographic and laboratory features to serve as a screening tool at hospitals where testing is scarce or unavailable. We used retrospectively collected data from the UCLA Health System in Los Angeles, California. We included all emergency room or inpatient cases receiving SARS-CoV-2 PCR testing who also had a set of ancillary laboratory features (n = 1,455) between 1 March 2020 and 24 May 2020. We tested seven machine learning models and used a combination of those models for the final diagnostic classification. In the test set (n = 392), our combined model had an area under the receiver operator curve of 0.91 (95% confidence interval 0.87-0.96). The model achieved a sensitivity of 0.93 (95% CI 0.85-0.98), specificity of 0.64 (95% CI 0.58-0.69). We found that our machine learning algorithm had excellent diagnostic metrics compared to SARS-CoV-2 PCR. This ensemble machine learning algorithm to diagnose COVID-19 has the potential to be used as a screening tool in hospital settings where PCR testing is scarce or unavailable.

Goodman-Meza David, Rudas Akos, Chiang Jeffrey N, Adamson Paul C, Ebinger Joseph, Sun Nancy, Botting Patrick, Fulcher Jennifer A, Saab Faysal G, Brook Rachel, Eskin Eleazar, An Ulzee, Kordi Misagh, Jew Brandon, Balliu Brunilda, Chen Zeyuan, Hill Brian L, Rahmani Elior, Halperin Eran, Manuel Vladimir

2020

Public Health Public Health

Correction: Real-Time Forecasting of the COVID-19 Outbreak in Chinese Provinces: Machine Learning Approach Using Novel Digital Data and Estimates From Mechanistic Models.

In Journal of medical Internet research ; h5-index 88.0

[This corrects the article DOI: 10.2196/20285.].

Liu Dianbo, Clemente Leonardo, Poirier Canelle, Ding Xiyu, Chinazzi Matteo, Davis Jessica, Vespignani Alessandro, Santillana Mauricio

2020-Sep-22

Public Health Public Health

Machine Learning Maps Research Needs in COVID-19 Literature.

In Patterns (New York, N.Y.)

As of August 2020, thousands of COVID-19 (coronavirus disease 2019) publications have been produced. Manual assessment of their scope is an overwhelming task, and shortcuts through metadata analysis (e.g., keywords) assume that studies are properly tagged. However, machine learning approaches can rapidly survey the actual text of publication abstracts to identify research overlap between COVID-19 and other coronaviruses, research hotspots, and areas warranting exploration. We propose a fast, scalable, and reusable framework to parse novel disease literature. When applied to the COVID-19 Open Research Dataset (CORD-19), dimensionality reduction suggests that COVID-19 studies to date are primarily clinical-, modeling- or field-based, in contrast to the vast quantity of laboratory-driven research for other (non-COVID-19) coronavirus diseases. Furthermore, topic modeling indicates that COVID-19 publications have focused on public health, outbreak reporting, clinical care, and testing for coronaviruses, as opposed to the more limited number focused on basic microbiology, including pathogenesis and transmission.

Doanvo Anhvinh, Qian Xiaolu, Ramjee Divya, Piontkivska Helen, Desai Angel, Majumder Maimuna

2020-Sep-16

2019-nCoV, COVID-19, LDA, PCA, SARS-CoV-2, artificial intelligence, coronavirus, data science, dimensionality reduction, machine learning, natural language processing, topic modeling

Radiology Radiology

Discrimination of pulmonary ground-glass opacity changes in COVID-19 and non-COVID-19 patients using CT radiomics analysis.

In European journal of radiology open

Purpose : The coronavirus disease 2019 (COVID-19) has evolved into a worldwide pandemic. CT although sensitive in detecting changes suffers from poor specificity in discrimination from other causes of ground glass opacities (GGOs). We aimed to develop and validate a CT-based radiomics model to differentiate COVID-19 from other causes of pulmonary GGOs.

Methods : We retrospectively included COVID-19 patients between 24/01/2020 and 31/03/2020 as case group and patients with pulmonary GGOs between 04/02/2012 and 31/03/2020 as a control group. Radiomics features were extracted from contoured GGOs by PyRadiomics. The least absolute shrinkage and selection operator method was used to establish the radiomics model. We assessed the performance using the area under the curve of the receiver operating characteristic curve (AUC).

Results : A total of 301 patients (age mean ± SD: 64 ± 15 years; male: 52.8 %) from three hospitals were enrolled, including 33 COVID-19 patients in the case group and 268 patients with malignancies or pneumonia in the control group. Thirteen radiomics features out of 474 were selected to build the model. This model achieved an AUC of 0.905, accuracy of 89.5 %, sensitivity of 83.3 %, specificity of 90.0 % in the testing set.

Conclusion : We developed a noninvasive radiomics model based on CT imaging for the diagnosis of COVID-19 based on GGO lesions, which could be a promising supplementary tool for improving specificity for COVID-19 in a population confounded by ground glass opacity changes from other etiologies.

Xie Chenyi, Ng Ming-Yen, Ding Jie, Leung Siu Ting, Lo Christine Shing Yen, Wong Ho Yuen Frank, Vardhanabhuti Varut

2020

COVID-19, Computed tomography, Infections, Machine learning, Severe acute respiratory syndrome coronavirus 2

General General

Accessing Covid19 Epidemic Outbreak in Tamilnadu and the Impact of Lockdown through Epidemiological Models and Dynamic systems.

In Measurement : journal of the International Measurement Confederation

Despite having a small footprint origin, COVID-19 has expanded its clutches to being a global pandemic with severe consequences threatening the survival of the human species. Despite international communities closing their corridors to reduce the exponential spread of the coronavirus. The need to study the patterns of transmission and spread gains utmost importance at the grass-root level of the social structure. To determine the impact of lockdown and social distancing in Tamilnadu through epidemiological models in forecasting the "effective reproductive number" (R0) determining the significance in transmission rate in Tamilnadu after first Covid19 case confirmation on March 07, 2020. Utilizing web scraping techniques to extract data from different online sources to determine the probable transmission rate in Tamilnadu from the rest of the Indian states. Comparing the different epidemiological models (SIR, SIER) in forecasting and assessing the current and future spread of COVID-19. R0 value has a high spike in densely populated districts with the probable flattening of the curve due to lockdown and the rapid rise after the relaxation of lockdown. As of June 03, 2020, there were 25,872 confirmed cases and 208 deaths in Tamilnadu after two and a half months of lockdown with minimal exceptions. As on June 03, 2020, the information published online by the Tamilnadu state government the fatality is at 1.8% (208/11345=1.8%) spread with those aged (0-12) at 1437 and 13-60 at 21899 and 60+ at 2536 the risk of symptomatic infection increases with age and comorbid conditions.

Rajendran Sukumar, Jayagopal Prabhu

2020-Sep-17

Covid-19, Machine learning, SIR model, Tamilnadu

General General

COVID-CAPS: A Capsule Network-based Framework for Identification of COVID-19 cases from X-ray Images.

In Pattern recognition letters

Novel Coronavirus disease (COVID-19) has abruptly and undoubtedly changed the world as we know it at the end of the 2nd decade of the 21st century. COVID-19 is extremely contagious and quickly spreading globally making its early diagnosis of paramount importance. Early diagnosis of COVID-19 enables health care professionals and government authorities to break the chain of transition and flatten the epidemic curve. The common type of COVID-19 diagnosis test, however, requires specific equipment and has relatively low sensitivity. Computed tomography (CT) scans and X-ray images, on the other hand, reveal specific manifestations associated with this disease. Overlap with other lung infections makes human-centered diagnosis of COVID-19 challenging. Consequently, there has been an urgent surge of interest to develop Deep Neural Network (DNN)-based diagnosis solutions, mainly based on Convolutional Neural Networks (CNNs), to facilitate identification of positive COVID-19 cases. CNNs, however, are prone to lose spatial information between image instances and require large datasets. The paper presents an alternative modeling framework based on Capsule Networks, referred to as the COVID-CAPS, being capable of handling small datasets, which is of significant importance due to sudden and rapid emergence of COVID-19. Our results based on a dataset of X-ray images show that COVID-CAPS has advantage over previous CNN-based models. COVID-CAPS achieved an Accuracy of 95.7%, Sensitivity of 90%, Specificity of 95.8%, and Area Under the Curve (AUC) of 0.97, while having far less number of trainable parameters in comparison to its counterparts. To potentially and further improve diagnosis capabilities of the COVID-CAPS, pre-training and transfer learning are utilized based on a new dataset constructed from an external dataset of X-ray images. This is in contrary to existing works on COVID-19 detection where pre-training is performed based on natural images. Pre-training with a dataset of similar nature further improved accuracy to 98.3% and specificity to 98.6%.

Afshar Parnian, Heidarian Shahin, Naderkhani Farnoosh, Oikonomou Anastasia, Plataniotis Konstantinos N, Mohammadi Arash

2020-Sep-16

COVID-19 Pandemic, Capsule Network, Deep Learning, X-ray Images

General General

Trends and targets of various types of stem cell derived transfusable RBC substitution therapy: Obstacles that need to be converted to opportunity.

In Transfusion and apheresis science : official journal of the World Apheresis Association : official journal of the European Society for Haemapheresis

A shortage of blood during the pandemic outbreak of COVID-19 is a typical example in which the maintenance of a safe and adequate blood supply becomes difficult and highly demanding. So far, human RBCs have been produced in vitro using diverse sources: hematopoietic stem cells (SCs), embryonic SCs and induced pluripotent SCs. The existing, even safest core of conventional cellular bioproducts destined for transfusion have some shortcoming in respects to: donor -dependency variability in terms of hematological /immunological and process/ storage period issues. SCs-derived transfusable RBC bioproducts, as one blood group type for all, were highly complex to work out. Moreover, the strategies for their successful production are often dependent upon the right selection of starting source materials and the composition and the stability of the right expansion media and the strict compliance to GMP regulatory processes. In this mini-review we highlight some model studies, which showed that the efficiency and the functionality of RBCs that could be produced by the various types of SCs, in relation to the in-vitro culture procedures are such that they may, potentially, be used at an industrial level. However, all cultured products do not have an unlimited life due to the critical metabolic pathways or the metabolites produced. New bioreactors are needed to remove these shortcomings and the development of a new mouse model is required. Modern clinical trials based on the employment of regenerative medicine approaches in combination with novel large-scale bioengineering tools, could overcome the current obstacles in artificial RBC substitution, possibly allowing an efficient RBC industrial production.

Lanza Francesxco, Seghatchian Jerard

2020-Sep-06

Artificial intelligence, Bioreactors, Expansion media, GMP regulatory processes, Induced pluripotent stem cell, Stem cells, Transfusable RBC bioproducts

General General

Drug Repurposing for COVID-19 using Graph Neural Network with Genetic, Mechanistic, and Epidemiological Validation

ArXiv Preprint

Amid the pandemic of 2019 novel coronavirus disease (COVID-19) infected by SARS-CoV-2, a vast amount of drug research for prevention and treatment has been quickly conducted, but these efforts have been unsuccessful thus far. Our objective is to prioritize repurposable drugs using a drug repurposing pipeline that systematically integrates multiple SARS-CoV-2 and drug interactions, deep graph neural networks, and in-vitro/population-based validations. We first collected all the available drugs (n= 3,635) involved in COVID-19 patient treatment through CTDbase. We built a SARS-CoV-2 knowledge graph based on the interactions among virus baits, host genes, pathways, drugs, and phenotypes. A deep graph neural network approach was used to derive the candidate representation based on the biological interactions. We prioritized the candidate drugs using clinical trial history, and then validated them with their genetic profiles, in vitro experimental efficacy, and electronic health records. We highlight the top 22 drugs including Azithromycin, Atorvastatin, Aspirin, Acetaminophen, and Albuterol. We further pinpointed drug combinations that may synergistically target COVID-19. In summary, we demonstrated that the integration of extensive interactions, deep neural networks, and rigorous validation can facilitate the rapid identification of candidate drugs for COVID-19 treatment.

Kanglin Hsieh, Yinyin Wang, Luyao Chen, Zhongming Zhao, Sean Savitz, Xiaoqian Jiang, Jing Tang, Yejin Kim

2020-09-23

General General

COVID-19 detection in CT images with deep learning: A voting-based scheme and cross-datasets analysis.

In Informatics in medicine unlocked

Early detection and diagnosis are critical factors to control the COVID-19 spreading. A number of deep learning-based methodologies have been recently proposed for COVID-19 screening in CT scans as a tool to automate and help with the diagnosis. These approaches, however, suffer from at least one of the following problems: (i) they treat each CT scan slice independently and (ii) the methods are trained and tested with sets of images from the same dataset. Treating the slices independently means that the same patient may appear in the training and test sets at the same time which may produce misleading results. It also raises the question of whether the scans from the same patient should be evaluated as a group or not. Moreover, using a single dataset raises concerns about the generalization of the methods. Different datasets tend to present images of varying quality which may come from different types of CT machines reflecting the conditions of the countries and cities from where they come from. In order to address these two problems, in this work, we propose an Efficient Deep Learning Technique for the screening of COVID-19 with a voting-based approach. In this approach, the images from a given patient are classified as group in a voting system. The approach is tested in the two biggest datasets of COVID-19 CT analysis with a patient-based split. A cross dataset study is also presented to assess the robustness of the models in a more realistic scenario in which data comes from different distributions. The cross-dataset analysis has shown that the generalization power of deep learning models is far from acceptable for the task since accuracy drops from 87.68% to 56.16% on the best evaluation scenario. These results highlighted that the methods that aim at COVID-19 detection in CT-images have to improve significantly to be considered as a clinical option and larger and more diverse datasets are needed to evaluate the methods in a realistic scenario.

Silva Pedro, Luz Eduardo, Silva Guilherme, Moreira Gladston, Silva Rodrigo, Lucio Diego, Menotti David

2020

COVID-19, Chest radiography, Deep learning, EfficientNet, Pneumonia

Radiology Radiology

Temporal changes of COVID-19 pneumonia by mass evaluation using CT: a retrospective multi-center study.

In Annals of translational medicine

Background : Coronavirus disease 2019 (COVID-19) has widely spread worldwide and caused a pandemic. Chest CT has been found to play an important role in the diagnosis and management of COVID-19. However, quantitatively assessing temporal changes of COVID-19 pneumonia over time using CT has still not been fully elucidated. The purpose of this study was to perform a longitudinal study to quantitatively assess temporal changes of COVID-19 pneumonia.

Methods : This retrospective and multi-center study included patients with laboratory-confirmed COVID-19 infection from 16 hospitals between January 19 and March 27, 2020. Mass was used as an approach to quantitatively measure dynamic changes of pulmonary involvement in patients with COVID-19. Artificial intelligence (AI) was employed as image segmentation and analysis tool for calculating the mass of pulmonary involvement.

Results : A total of 581 confirmed patients with 1,309 chest CT examinations were included in this study. The median age was 46 years (IQR, 35-55; range, 4-87 years), and 311 (53.5%) patients were male. The mass of pulmonary involvement peaked on day 10 after the onset of initial symptoms. Furthermore, the mass of pulmonary involvement of older patients (>45 years) was significantly severer (P<0.001) and peaked later (day 11 vs. day 8) than that of younger patients (≤45 years). In addition, there were no significant differences in the peak time (day 10 vs. day 10) and median mass (P=0.679) of pulmonary involvement between male and female.

Conclusions : Pulmonary involvement peaked on day 10 after the onset of initial symptoms in patients with COVID-19. Further, pulmonary involvement of older patients was severer and peaked later than that of younger patients. These findings suggest that AI-based quantitative mass evaluation of COVID-19 pneumonia hold great potential for monitoring the disease progression.

Wang Chao, Huang Peiyu, Wang Lihua, Shen Zhujing, Lin Bin, Wang Qiyuan, Zhao Tongtong, Zheng Hanpeng, Ji Wenbin, Gao Yuantong, Xia Junli, Cheng Jianmin, Ma Jianbing, Liu Jun, Liu Yongqiang, Su Miaoguang, Ruan Guixiang, Shu Jiner, Ren Dawei, Zhao Zhenhua, Yao Weigen, Yang Yunjun, Liu Bo, Zhang Minming

2020-Aug

Coronavirus disease 2019 (COVID-19), artificial intelligence (AI), chest CT, temporal changes

General General

Modeling and forecasting the spread tendency of the COVID-19 in China.

In Advances in difference equations

To forecast the spread tendency of the COVID-19 in China and provide effective strategies to prevent the disease, an improved SEIR model was established. The parameters of our model were estimated based on collected data that were issued by the National Health Commission of China (NHCC) from January 10 to March 3. The model was used to forecast the spread tendency of the disease. The key factors influencing the epidemic were explored through modulation of the parameters, including the removal rate, the average number of the infected contacting the susceptible per day and the average number of the exposed contacting the susceptible per day. The correlation of the infected is 99.9% between established model data in this study and issued data by NHCC from January 10 to February 15. The correlation of the removed, the death and the cured are 99.8%, 99.8% and 99.6%, respectively. The average forecasting error rates of the infected, the removed, the death and the cured are 0.78%, 0.75%, 0.35% and 0.83%, respectively, from February 16 to March 3. The peak time of the epidemic forecast by our established model coincided with the issued data by NHCC. Therefore, our study established a mathematical model with high accuracy. The aforementioned parameters significantly affected the trend of the epidemic, suggesting that the exposed and the infected population should be strictly isolated. If the removal rate increases to 0.12, the epidemic will come to an end on May 25. In conclusion, the proposed mathematical model accurately forecast the spread tendency of COVID-19 in China and the model can be applied for other countries with appropriate modifications.

Sun Deshun, Duan Li, Xiong Jianyi, Wang Daping

2020

COVID-19, Control strategy, Forecasting, Mathematical modeling, Parameter estimation

General General

The relationship between air pollution and COVID-19-related deaths: An application to three French cities.

In Applied energy ; h5-index 131.0

Being heavily dependent to oil products (mainly gasoline and diesel), the French transport sector is the main emitter of Particulate Matter (PMs) whose critical levels induce harmful health effects for urban inhabitants. We selected three major French cities (Paris, Lyon, and Marseille) to investigate the relationship between the Coronavirus Disease 19 (COVID-19) outbreak and air pollution. Using Artificial Neural Networks (ANNs) experiments, we have determined the concentration of PM2.5 and PM10 linked to COVID-19-related deaths. Our focus is on the potential effects of Particulate Matter (PM) in spreading the epidemic. The underlying hypothesis is that a pre-determined particulate concentration can foster COVID-19 and make the respiratory system more susceptible to this infection. The empirical strategy used an innovative Machine Learning (ML) methodology. In particular, through the so-called cutting technique in ANNs, we found new threshold levels of PM2.5 and PM10 connected to COVID-19: 17.4 µg/m3 (PM2.5) and 29.6 µg/m3 (PM10) for Paris; 15.6 µg/m3 (PM2.5) and 20.6 µg/m3 (PM10) for Lyon; 14.3 µg/m3 (PM2.5) and 22.04 µg/m3 (PM10) for Marseille. Interestingly, all the threshold values identified by the ANNs are higher than the limits imposed by the European Parliament. Finally, a Causal Direction from Dependency (D2C) algorithm is applied to check the consistency of our findings.

Magazzino Cosimo, Mele Marco, Schneider Nicolas

2020-Dec-01

ANNs, Artificial Neural Networks, Air pollution, Artificial neural networks, CH4, Methane, CMAQ, Community Multiscale Air Quality, CO, Carbon Monoxide, COVID-19, COVID-19, Coronavirus Disease 19, D2C, Causal Direction from Dependency, GAM, Generalized Additive Model, GHG, Greenhouse Gas, ML, Machine Learning, Machine learning, NO2, Nitrogen Dioxide, NOx, Nitrogen Oxides, O3, Ozone, PM10, Particulate Matter with an aerodynamic diameter < 10.0 µm, PM2.5, Particulate Matter with an aerodynamic diameter < 2.5 µm, Particulate matter, SO2, Sulfur Dioxide, SO3, Sulphur Trioxide, SOx, Sulphur Oxides, VOC, Volatile Organic Compounds

General General

Short-term forecasting of the coronavirus pandemic.

In International journal of forecasting

We have been publishing real-time forecasts of confirmed cases and deaths for COVID-19 from mid-March 2020 onwards, published at www.doornik.com/COVID-19. These forecasts are short-term statistical extrapolations of past and current data. They assume that the underlying trend is informative of short term developments, without requiring other assumptions of how the SARS-CoV-2 virus is spreading, or whether preventative policies are effective. As such they are complementary to forecasts from epidemiological models. The forecasts are based on extracting trends from windows of the data, applying machine learning, and then computing forecasts by applying some constraints to this flexible extracted trend. The methods have previously been applied to various other time series data and have performed well. They are also effective in this setting, providing better forecasts in the earlier stages than some epidemiological models.

Doornik Jurgen A, Castle Jennifer L, Hendry David F

2020-Sep-12

Automatic forecasting, COVID-19, Epidemiology, Forecast averaging, Forecasting, Machine learning, Smoothing, Time series, Trend indicator saturation

General General

Impact of lung segmentation on the diagnosis and explanation of COVID-19 in chest X-ray images

ArXiv Preprint

The COVID-19 pandemic is undoubtedly one of the biggest public health crises our society has ever faced. This paper's main objectives are to demonstrate the impact of lung segmentation in COVID-19 automatic identification using CXR images and evaluate which contents of the image decisively contribute to the identification. We have performed lung segmentation using a U-Net CNN architecture, and the classification using three well-known CNN architectures: VGG, ResNet, and Inception. To estimate the impact of lung segmentation, we applied some Explainable Artificial Intelligence (XAI), such as LIME and Grad-CAM. To evaluate our approach, we built a database named RYDLS-20-v2, following our previous publication and the COVIDx database guidelines. We evaluated the impact of creating a COVID-19 CXR image database from different sources, called database bias, and the COVID-19 generalization from one database to another, representing our less biased scenario. The experimental results of the segmentation achieved a Jaccard distance of 0.034 and a Dice coefficient of 0.982. In the best and more realistic scenario, we achieved an F1-Score of 0.74 and an area under the ROC curve of 0.9 for COVID-19 identification using segmented CXR images. Further testing and XAI techniques suggest that segmented CXR images represent a much more realistic and less biased performance. More importantly, the experiments conducted show that even after segmentation, there is a strong bias introduced by underlying factors from the data sources, and more efforts regarding the creation of a more significant and comprehensive database still need to be done.

Lucas O. Teixeira, Rodolfo M. Pereira, Diego Bertolini, Luiz S. Oliveira, Loris Nanni, Yandre M. G. Costa

2020-09-21

General General

Detection of COVID-19 from Chest X-Ray Images Using Convolutional Neural Networks.

In SLAS technology

The detection of severe acute respiratory syndrome coronavirus 2 (SARS CoV-2), which is responsible for coronavirus disease 2019 (COVID-19), using chest X-ray images has life-saving importance for both patients and doctors. In addition, in countries that are unable to purchase laboratory kits for testing, this becomes even more vital. In this study, we aimed to present the use of deep learning for the high-accuracy detection of COVID-19 using chest X-ray images. Publicly available X-ray images (1583 healthy, 4292 pneumonia, and 225 confirmed COVID-19) were used in the experiments, which involved the training of deep learning and machine learning classifiers. Thirty-eight experiments were performed using convolutional neural networks, 10 experiments were performed using five machine learning models, and 14 experiments were performed using the state-of-the-art pre-trained networks for transfer learning. Images and statistical data were considered separately in the experiments to evaluate the performances of models, and eightfold cross-validation was used. A mean sensitivity of 93.84%, mean specificity of 99.18%, mean accuracy of 98.50%, and mean receiver operating characteristics-area under the curve scores of 96.51% are achieved. A convolutional neural network without pre-processing and with minimized layers is capable of detecting COVID-19 in a limited number of, and in imbalanced, chest X-ray images.

Sekeroglu Boran, Ozsahin Ilker

2020-Sep-18

COVID-19, X-ray, convolutional neural networks, coronavirus, pneumonia

Radiology Radiology

Development and Validation of Risk Prediction Models for COVID-19 Positivity in a Hospital Setting.

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

OBJECTIVES : To develop:(1) two validated risk prediction models for COVID-19 positivity using readily available parameters in a general hospital setting; (2) nomograms and probabilities to allow clinical utilisation.

METHODS : Patients with and without COVID-19 were included from 4 Hong Kong hospitals. Database was randomly split 2:1 for model development database (n = 895) and validation database (n = 435). Multivariable logistic regression was utilised for model creation and validated with the Hosmer-Lemeshow (H-L) test and calibration plot. Nomograms and probabilities set at 0.1, 0.2, 0.4, 0.6 were calculated to determine sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV).

RESULTS : 1330 patients (mean age 58.2 ± 24.5 years; 50.7% males; 296 COVID-19 positive) were recruited. First prediction model developed had age, total white blood cell count, chest x-ray appearances and contact history as significant predictors (AUC = 0.911 [CI = 0.880-0.941]). Second model developed has same variables except contact history (AUC = 0.880 [CI = 0.844-0.916]). Both were externally validated on H-L test (p = 0.781 and 0.155 respectively) and calibration plot. Models were converted to nomograms. Lower probabilities give higher sensitivity and NPV; higher probabilities give higher specificity and PPV.

CONCLUSION : Two simple-to-use validated nomograms were developed with excellent AUCs based on readily available parameters and can be considered for clinical utilisation.

Ng Ming-Yen, Wan Eric Yuk Fai, Wong Ho Yuen Frank, Leung Siu Ting, Lee Jonan Chun Yin, Chin Thomas Wing-Yan, Lo Christine Shing Yen, Lui Macy Mei-Sze, Chan Edward Hung Tat, Fong Ambrose Ho-Tung, Yung Fung Sau, Ching On Hang, Chiu Keith Wan-Hang, Chung Tom Wai Hin, Vardhanbhuti Varut, Lam Hiu Yin Sonia, To Kelvin Kai Wang, Chiu Jeffrey Long Fung, Lam Tina Poy Wing, Khong Pek Lan, Liu Raymond Wai To, Man Chan Johnny Wai, Ka Lun Alan Wu, Lung Kwok-Cheung, Hung Ivan Fan Ngai, Lau Chak Sing, Kuo Michael D, Ip Mary Sau-Man

2020-Sep-15

COVID-19, Nomogram, Prediction Model, chest x-ray, white cell count

Pathology Pathology

A Single-Cell RNA Expression Map of Human Coronavirus Entry Factors.

In Cell reports ; h5-index 119.0

To predict the tropism of human coronaviruses, we profile 28 SARS-CoV-2 and coronavirus-associated receptors and factors (SCARFs) using single-cell transcriptomics across various healthy human tissues. SCARFs include cellular factors both facilitating and restricting viral entry. Intestinal goblet cells, enterocytes, and kidney proximal tubule cells appear highly permissive to SARS-CoV-2, consistent with clinical data. Our analysis also predicts non-canonical entry paths for lung and brain infections. Spermatogonial cells and prostate endocrine cells also appear to be permissive to SARS-CoV-2 infection, suggesting male-specific vulnerabilities. Both pro- and anti-viral factors are highly expressed within the nasal epithelium, with potential age-dependent variation, predicting an important battleground for coronavirus infection. Our analysis also suggests that early embryonic and placental development are at moderate risk of infection. Lastly, SCARF expression appears broadly conserved across a subset of primate organs examined. Our study establishes a resource for investigations of coronavirus biology and pathology.

Singh Manvendra, Bansal Vikas, Feschotte Cédric

2020-Sep-03

COVID-19, SARS-CoV-2, coronaviruses, restriction factors, scRNA-seq, viral receptors

Public Health Public Health

Telepsychiatry and other cutting edge technologies in Covid-19 pandemic: bridging the distance in mental health assistance.

In International journal of clinical practice

At the end of 2019 a novel coronavirus (COVID-19) was identified in China. The high potential of human to human transmission led to subsequent COVID-19 global pandemic. Public health strategies including reduced social contact and lockdown have been adopted in many countries. Nonetheless, social distancing and isolation could also represent risk factors for mental disorders, resulting in loneliness, reduced social support and under-detection of mental health needs. Along with this, social distancing determines a relevant obstacle for direct access to psychiatric care services. The pandemic generates the urgent need for integrating technology into innovative models of mental healthcare. In this paper we discuss the potential role of telepsychiatry and other cutting-edge technologies in the management of mental health assistance. We narratively review the literature to examine advantages and risks related to the extensive application of these new therapeutic settings, along with the possible limitations and ethical concerns. Telemental health services may be particularly feasible and appropriate for the support of patients, family members and health-care providers during this COVID-19 pandemic. The integration of telepsychiatry with other technological innovations (e.g., mobile apps, virtual reality, big data and artificial intelligence) opens up interesting future perspectives for the improvement of mental health assistance. Telepsychiatry is a promising and growing way to deliver mental health services but is still underused. The COVID-19 pandemic may serve as an opportunity to introduce and promote, among numerous mental health professionals, the knowledge of the possibilities offered by the digital era.

Di Carlo Francesco, Sociali Antonella, Picutti Elena, Pettorruso Mauro, Vellante Federica, Verrastro Valeria, Martinotti Giovanni, di Giannantonio Massimo

2020-Sep-18

Public Health Public Health

Artificial Intelligence for COVID-19: A Rapid Review.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Coronavirus Disease 2019 (COVID-19) was first discovered in December 2019 and has since evolved into a pandemic.

OBJECTIVE : To address this global health crisis, artificial intelligence (AI) has been deployed at various levels of the healthcare system. However, AI has both potential benefits and limitations. We therefore conducted a review of AI applications for COVID-19.

METHODS : We performed an extensive search of the PubMed and Embase databases for COVID-19-related English-language studies published between 1/12/2019 and 31/3/2020. We supplemented the database search with reference list checks. Thematic analysis and narrative review of AI applications for COVID-19 documented was conducted.

RESULTS : 11 papers were included for review. AI was applied to COVID-19 in four areas: diagnosis, public health, clinical decision-making, and therapeutics. We identified several limitations including insufficient data, omission of multimodal methods of AI-based assessment, delay in realization of benefits, poor internal/external validation, inability to be used by laypersons, inability to be used in resource-poor settings, presence of ethical pitfalls and presence of legal barriers. AI could potentially be explored in four other areas: surveillance, combination with big data, operation of other core clinical services, and management of COVID-19 patients.

CONCLUSIONS : In view of the continuing increase in infected cases, and given that multiple waves of infections may occur, there is need for effective methods to help control the COVID-19 pandemic. Despite its shortcomings, AI holds the potential to greatly augment existing human efforts, which may otherwise be overwhelmed by large patient numbers.

CLINICALTRIAL :

Chen Jiayang, See Kay Choong

2020-Sep-15

Radiology Radiology

Initial chest radiographs and artificial intelligence (AI) predict clinical outcomes in COVID-19 patients: analysis of 697 Italian patients.

In European radiology ; h5-index 62.0

OBJECTIVE : To evaluate whether the initial chest X-ray (CXR) severity assessed by an AI system may have prognostic utility in patients with COVID-19.

METHODS : This retrospective single-center study included adult patients presenting to the emergency department (ED) between February 25 and April 9, 2020, with SARS-CoV-2 infection confirmed on real-time reverse transcriptase polymerase chain reaction (RT-PCR). Initial CXRs obtained on ED presentation were evaluated by a deep learning artificial intelligence (AI) system and compared with the Radiographic Assessment of Lung Edema (RALE) score, calculated by two experienced radiologists. Death and critical COVID-19 (admission to intensive care unit (ICU) or deaths occurring before ICU admission) were identified as clinical outcomes. Independent predictors of adverse outcomes were evaluated by multivariate analyses.

RESULTS : Six hundred ninety-seven 697 patients were included in the study: 465 males (66.7%), median age of 62 years (IQR 52-75). Multivariate analyses adjusting for demographics and comorbidities showed that an AI system-based score ≥ 30 on the initial CXR was an independent predictor both for mortality (HR 2.60 (95% CI 1.69 - 3.99; p < 0.001)) and critical COVID-19 (HR 3.40 (95% CI 2.35-4.94; p < 0.001)). Other independent predictors were RALE score, older age, male sex, coronary artery disease, COPD, and neurodegenerative disease.

CONCLUSION : AI- and radiologist-assessed disease severity scores on CXRs obtained on ED presentation were independent and comparable predictors of adverse outcomes in patients with COVID-19.

TRIAL REGISTRATION : ClinicalTrials.gov NCT04318366 ( https://clinicaltrials.gov/ct2/show/NCT04318366 ).

KEY POINTS : • AI system-based score ≥ 30 and a RALE score ≥ 12 at CXRs performed at ED presentation are independent and comparable predictors of death and/or ICU admission in COVID-19 patients. • Other independent predictors are older age, male sex, coronary artery disease, COPD, and neurodegenerative disease. • The comparable performance of the AI system in relation to a radiologist-assessed score in predicting adverse outcomes may represent a game-changer in resource-constrained settings.

Mushtaq Junaid, Pennella Renato, Lavalle Salvatore, Colarieti Anna, Steidler Stephanie, Martinenghi Carlo M A, Palumbo Diego, Esposito Antonio, Rovere-Querini Patrizia, Tresoldi Moreno, Landoni Giovanni, Ciceri Fabio, Zangrillo Alberto, De Cobelli Francesco

2020-Sep-18

Artificial intelligence, COVID-19, Prognosis, Radiography, Severe acute respiratory syndrome

General General

Screening for obstructive sleep apnea with novel hybrid acoustic smartphone app technology.

In Journal of thoracic disease ; h5-index 52.0

Background : Obstructive sleep apnea (OSA) has a high prevalence, with an estimated 425 million adults with apnea hypopnea index (AHI) of ≥15 events/hour, and is significantly underdiagnosed. This presents a significant pain point for both the sufferers, and for healthcare systems, particularly in a post COVID-19 pandemic world. As such, it presents an opportunity for new technologies that can enable screening in both developing and developed countries. In this work, the performance of a non-contact OSA screener App that can run on both Apple and Android smartphones is presented.

Methods : The subtle breathing patterns of a person in bed can be measured via a smartphone using the "Firefly" app technology platform [and underpinning software development kit (SDK)], which utilizes advanced digital signal processing (DSP) technology and artificial intelligence (AI) algorithms to identify detailed sleep stages, respiration rate, snoring, and OSA patterns. The smartphone is simply placed adjacent to the subject, such as on a bedside table, night stand or shelf, during the sleep session. The system was trained on a set of 128 overnights recorded at a sleep laboratory, where volunteers underwent simultaneous full polysomnography (PSG), and "Firefly" smartphone app analysis. A separate independent test set of 120 recordings was collected across a range of Apple iOS and Android smartphones, and withheld for performance evaluation by a different team. An operating point tuned for mid-sensitivity (i.e., balancing sensitivity and specificity) was chosen for the screener.

Results : The performance on the test set is comparable to ambulatory OSA screeners, and other smartphone screening apps, with a sensitivity of 88.3% and specificity of 80.0% [with receiver operating characteristic (ROC) area under the curve (AUC) of 0.92], for a clinical threshold for the AHI of ≥15 events/hour of detected sleep time.

Conclusions : The "Firefly" app based sensing technology offers the potential to significantly lower the barrier of entry to OSA screening, as no hardware (other than the user's personal smartphone) is required. Additionally, multi-night analysis is possible in the home environment, without requiring the wearing of a portable PSG or other home sleep test (HST).

Tiron Roxana, Lyon Graeme, Kilroy Hannah, Osman Ahmed, Kelly Nicola, O’Mahony Niall, Lopes Cesar, Coffey Sam, McMahon Stephen, Wren Michael, Conway Kieran, Fox Niall, Costello John, Shouldice Redmond, Lederer Katharina, Fietze Ingo, Penzel Thomas

2020-Aug

Sleep-disordered breathing (SDB), apnea hypopnea index (AHI), obstructive sleep apnea (OSA), screening, smartphone

Radiology Radiology

Lung ultrasonography for risk stratification in patients with COVID-19: a prospective observational cohort study.

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

BACKGROUND : Point-of-care lung ultrasound (LUS) is a promising pragmatic risk stratification tool in COVID-19. This study describes and compares LUS characteristics between patients with different clinical outcomes.

METHODS : Prospective observational study of PCR-confirmed COVID-19 adults with symptoms of lower respiratory tract infection in the emergency department (ED) of Lausanne University Hospital. A trained physician recorded LUS images using a standardized protocol. Two experts reviewed images blinded to patient outcome. We describe and compare early LUS findings (acquired within 24hours of presentation to the ED) between patient groups based on their outcome at 7 days after inclusion: 1) outpatients, 2) hospitalised and 3) intubated/death. Normalized LUS score was used to discriminate between groups.

RESULTS : Between March 6 and April 3 2020, we included 80 patients (17 outpatients, 42 hospitalized and 21 intubated/dead). 73 patients (91%) had abnormal LUS (70% outpatients, 95% hospitalised and 100% intubated/death; p=0.003). The proportion of involved zones was lower in outpatients compared with other groups (median 30% [IQR 0-40%], 44% [31-70%] and 70% [50-88%], p<0.001). Predominant abnormal patterns were bilateral and multifocal spread thickening of the pleura with pleural line irregularities (70%), confluent B lines (60%) and pathologic B lines (50%). Posterior inferior zones were more often affected. Median normalized LUS score had a good level of discrimination between outpatients and others with area under the ROC of 0.80 (95% CI 0.68-0.92).

CONCLUSIONS : Systematic LUS has potential as a reliable, cheap and easy-to-use triage tool for the early risk stratification in COVID-19 patients presenting in EDs.

Brahier Thomas, Meuwly Jean-Yves, Pantet Olivier, Brochu Vez Marie-Josée, Gerhard Donnet Hélène, Hartley Mary-Anne, Hugli Olivier, Boillat-Blanco Noémie

2020-Sep-17

COVID-19, LUS score, Lung ultrasound, Triage tool

General General

Investigating the Capabilities of Information Technologies to support Policymaking in COVID-19 Crisis Management; A Systematic Review and Expert opinions.

In European journal of clinical investigation

BACKGROUND : Today, numerous countries are fighting to protect themselves against the Covid-19 crisis, while the policymakers are confounded and empty-handed in dealing with this chaotic circumstance. The infection and its impacts have made it difficult to make optimal and suitable decisions. New information technologies play significant roles in such critical situations to address and relieve stress during the coronavirus crisis. This article endeavors to recognize the challenges policymakers have typically experienced during pandemic diseases, including Covid-19, and, accordingly, new information technology capabilities to encounter with them.

MATERIAL AND METHODS : The current study utilizes the synthesis of findings of experts' opinions within the systematic review process as the research method to recognize the best available evidence drawn from text and opinion to offer practical guidance for policymakers.

RESULTS : The results illustrate that the challenges fall into two categories including; encountering the disease and reducing the results of the disease. Furthermore, Internet of Things, cloud computing, machine learning, and social networking play the most significant roles to address these challenges.

Lagzian Mohammad, Dadkhah Mehdi, Mehraeen AmirReza

2020-Sep-16

Covid-19, Crisis management policies, Informational Technology (IT) capabilities, Pandemic management

Radiology Radiology

Development of a volumetric pancreas segmentation CT dataset for AI applications through trained technologists: a study during the COVID 19 containment phase.

In Abdominal radiology (New York)

PURPOSE : To evaluate the performance of trained technologists vis-à-vis radiologists for volumetric pancreas segmentation and to assess the impact of supplementary training on their performance.

METHODS : In this IRB-approved study, 22 technologists were trained in pancreas segmentation on portal venous phase CT through radiologist-led interactive videoconferencing sessions based on an image-rich curriculum. Technologists segmented pancreas in 188 CTs using freehand tools on custom image-viewing software. Subsequent supplementary training included multimedia videos focused on common errors, which were followed by second batch of 159 segmentations. Two radiologists reviewed all cases and corrected inaccurate segmentations. Technologists' segmentations were compared against radiologists' segmentations using Dice-Sorenson coefficient (DSC), Jaccard coefficient (JC), and Bland-Altman analysis.

RESULTS : Corrections were made in 71 (38%) cases from first batch [26 (37%) oversegmentations and 45 (63%) undersegmentations] and in 77 (48%) cases from second batch [12 (16%) oversegmentations and 65 (84%) undersegmentations]. DSC, JC, false positive (FP), and false negative (FN) [mean (SD)] in first versus second batches were 0.63 (0.15) versus 0.63 (0.16), 0.48 (0.15) versus 0.48 (0.15), 0.29 (0.21) versus 0.21 (0.10), and 0.36 (0.20) versus 0.43 (0.19), respectively. Differences were not significant (p > 0.05). However, range of mean pancreatic volume difference reduced in the second batch [- 2.74 cc (min - 92.96 cc, max 87.47 cc) versus - 23.57 cc (min - 77.32, max 30.19)].

CONCLUSION : Trained technologists could perform volumetric pancreas segmentation with reasonable accuracy despite its complexity. Supplementary training further reduced range of volume difference in segmentations. Investment into training technologists could augment and accelerate development of body imaging datasets for AI applications.

Suman Garima, Panda Ananya, Korfiatis Panagiotis, Edwards Marie E, Garg Sushil, Blezek Daniel J, Chari Suresh T, Goenka Ajit H

2020-Sep-16

Artificial intelligence, COVID-19, Data curation, Deep learning

General General

Natural Language Processing Reveals Vulnerable Mental Health Support Groups and Heightened Health Anxiety on Reddit during COVID-19: An Observational Study.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : The COVID-19 pandemic is exerting a devastating impact on mental health, but it is not clear how people with different types of mental health problems were differentially impacted as the initial wave of cases hit.

OBJECTIVE : We leverage natural language processing (NLP) with the goal of characterizing changes in fifteen of the world's largest mental health support groups (e.g., r/schizophrenia, r/SuicideWatch, r/Depression) found on the website Reddit, along with eleven non-mental health groups (e.g., r/PersonalFinance, r/conspiracy) during the initial stage of the pandemic.

METHODS : We create and release the Reddit Mental Health Dataset including posts from 826,961 unique users from 2018 to 2020. Using regression, we analyze trends from 90 text-derived features such as sentiment analysis, personal pronouns, and a "guns" semantic category. Using supervised machine learning, we classify posts into their respective support group and interpret important features to understand how different problems manifest in language. We apply unsupervised methods such as topic modeling and unsupervised clustering to uncover concerns throughout Reddit before and during the pandemic.

RESULTS : We find that the r/HealthAnxiety forum showed spikes in posts about COVID-19 early on in January, approximately two months before other support groups started posting about the pandemic. There were many features that significantly increased during COVID-19 for specific groups including the categories "economic stress", "isolation", and "home" while others such as "motion" significantly decreased. We find that support groups related to attention deficit hyperactivity disorder (ADHD), eating disorders (ED), and anxiety showed the most negative semantic change during the pandemic out of all mental health groups. Health anxiety emerged as a general theme across Reddit through independent supervised and unsupervised machine learning analyses. For instance, we provide evidence that the concerns of a diverse set of individuals are converging in this unique moment of history; we discover that the more users posted about COVID-19, the more linguistically similar (less distant) the mental health support groups became to r/HealthAnxiety (ρ = -0.96, P<.001). Using unsupervised clustering, we find the Suicidality and Loneliness clusters more than doubled in amount of posts during the pandemic. Specifically, the support groups for borderline personality disorder and post-traumatic stress disorder became significantly associated with the Suicidality cluster. Furthermore, clusters surrounding Self-Harm and Entertainment emerged.

CONCLUSIONS : By using a broad set of NLP techniques and analyzing a baseline of pre-pandemic posts, we uncover patterns of how specific mental health problems manifest in language, identify at-risk users, and reveal the distribution of concerns across Reddit which could help provide better resources to its millions of users. We then demonstrate that textual analysis is sensitive to uncover mental health complaints as they arise in real time, identifying vulnerable groups and alarming themes during COVID-19, and thus may have utility during the ongoing pandemic and other world-changing events such as elections and protests from the present or the past.

CLINICALTRIAL :

Low Daniel M, Rumker Laurie, Talker Tanya, Torous John, Cecchi Guillermo, Ghosh Satrajit S

2020-Sep-13

General General

Identification of risk factors and symptoms of SARS-CoV-2 (COVID-19) using biomedical literature and social media data: Integrative and Consensus study.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : In December 2019, Coronavirus disease 2019 (COVID-19) outbreak started in China and rapidly spread around the world. Lack of any vaccine or optimized intervention raised the importance of characterizing risk factors and symptoms for the early identification and successful treatment of COVID-19 patients.

OBJECTIVE : This study aims to investigate and analyze biomedical literature and public social media data to understand the association of risk factors and symptoms with various outcomes of COVID-19 patients.

METHODS : Through semantic analysis, we collected 45 retrospective cohort studies, which evaluated 303 clinical and demographic variables across 13 different outcomes of COVID-19 patients, and 84,140 Twitter posts from 1,036 COVID-19 positive users. Machine-learning tools to extract biomedical information were introduced to identify uncommon or novel symptoms mentioning in social media. We then examined and compared two datasets to expand our landscape of risk factors and symptoms related to COVID-19.

RESULTS : From the biomedical literature, approximately 90% of clinical and demographic variables showed inconsistent associations with COVID-19 outcomes. Consensus analysis identified 72 risk factors that were specifically associated with individual outcomes. From the social media data, 51 symptoms were characterized and analyzed. By comparing social media data with biomedical literature, we identified 25 novel symptoms that were specifically mentioned in social media but have been not previously well characterized. Furthermore, there were certain combinations of symptoms that were frequently mentioned together in social media.

CONCLUSIONS : Identified outcome-specific risk factors, symptoms, and combinations of symptoms may serve as surrogate indicators to identify COVID-19 patients and predict their clinical outcomes providing appropriate treatments.

CLINICALTRIAL :

Jeon Jouhyun, Baruah Gaurav, Sarabadani Sarah, Palanica Adam

2020-Sep-13

Public Health Public Health

Infodemiological study to understand the community risk perceptions of COVID-19 outbreak in South Korea.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : South Korea is among the best-performing countries in tackling the coronavirus pandemic by utilizing mass drive-through testing, facemasks use, and extensive social distancing. However, understanding the patterns of risk perception could also facilitate effective risk communication to minimize the impacts of disease spread during this crisis.

OBJECTIVE : We attempted to explore patterns of community health risk perceptions of COVID-19 in South Korea using Internet search data.

METHODS : Google Trends (GT) and NAVER relative search volumes (RSVs) data were collected using COVID-19-related terms in the Korean language and were retrieved according to time, gender, age groups, types of device, and location. Online queries were compared to the number of daily new COVID-19 cases and tests reported in the Kaggle open-access dataset for time period of December 5, 2019 to May 31, 2020. Spearman's rank correlation coefficients were employed to assess whether correlations between new COVID-19 cases and Internet searches were affected by time. We also constructed a prediction model of new COVID-19 cases using the number of COVID-19 cases, tests, GT, and NAVER RSVs in lag periods (of 3 to 1 days). Single and multiple regressions were employed using backward elimination and a variance inflation factor (VIF) of <5.

RESULTS : Numbers of COVID-19-related queries in South Korea increased during local events including local transmission, approval of coronavirus test kits, implementation of coronavirus drive-through tests, a facemask shortage, and a widespread campaign for social distancing as well as during international events such as the announcement of a Public Health Emergency of International Concern by the World Health Organization. Online queries were also stronger in women (r=0.763~0.823; p<0.05), and age groups of ≤29 (r=0.726~0.821; p<0.05), 30~44 (r=0.701~0.826; p<0.05), and ≥50 years (r=0.706~0.725; p<0.05). In terms of spatial distribution, GT and NAVER RSVs were higher in affected areas. Moreover, greater correlations were found in mobile searches (r=0.704~0.804; p<0.05) compared to those of desktop searches (r=0.705~0.717; p<0.05), indicating changing behaviors in searching for online health information during the outbreak. Those varied Internet searches related to COVID-19 represented community health risk perceptions. In addition, as a country with a high number of coronavirus tests, results showed that adults perceived coronavirus test-related information as being more important than disease-related knowledge. Meanwhile, younger and older age groups had different perceptions. Moreover, NAVER RSVs can potentially be used for health risk perception assessments and disease predictions. Adding COVID-19-related searches provided by NAVER could increase the performance of the model compared to that of the COVID-19 case-based model and potentially be used to predict epidemic curves.

CONCLUSIONS : The use of both GT and NAVER RSVs to explore patterns of community health risk perceptions could be beneficial for targeting risk communication from several perspectives, including time, population characteristics, and location.

CLINICALTRIAL :

Husnayain Atina, Shim Eunha, Fuad Anis, Su Emily Chia-Yu

2020-Sep-14

Internal Medicine Internal Medicine

COVID-19 risk and outcomes in patients with substance use disorders: analyses from electronic health records in the United States.

In Molecular psychiatry ; h5-index 103.0

The global pandemic of COVID-19 is colliding with the epidemic of opioid use disorders (OUD) and other substance use disorders (SUD) in the United States (US). Currently, there is limited data on risks, disparity, and outcomes for COVID-19 in individuals suffering from SUD. This is a retrospective case-control study of electronic health records (EHRs) data of 73,099,850 unique patients, of whom 12,030 had a diagnosis of COVID-19. Patients with a recent diagnosis of SUD (within past year) were at significantly increased risk for COVID-19 (adjusted odds ratio or AOR = 8.699 [8.411-8.997], P < 10-30), an effect that was strongest for individuals with OUD (AOR = 10.244 [9.107-11.524], P < 10-30), followed by individuals with tobacco use disorder (TUD) (AOR = 8.222 ([7.925-8.530], P < 10-30). Compared to patients without SUD, patients with SUD had significantly higher prevalence of chronic kidney, liver, lung diseases, cardiovascular diseases, type 2 diabetes, obesity and cancer. Among patients with recent diagnosis of SUD, African Americans had significantly higher risk of COVID-19 than Caucasians (AOR = 2.173 [2.01-2.349], P < 10-30), with strongest effect for OUD (AOR = 4.162 [3.13-5.533], P < 10-25). COVID-19 patients with SUD had significantly worse outcomes (death: 9.6%, hospitalization: 41.0%) than general COVID-19 patients (death: 6.6%, hospitalization: 30.1%) and African Americans with COVID-19 and SUD had worse outcomes (death: 13.0%, hospitalization: 50.7%) than Caucasians (death: 8.6%, hospitalization: 35.2%). These findings identify individuals with SUD, especially individuals with OUD and African Americans, as having increased risk for COVID-19 and its adverse outcomes, highlighting the need to screen and treat individuals with SUD as part of the strategy to control the pandemic while ensuring no disparities in access to healthcare support.

Wang Quan Qiu, Kaelber David C, Xu Rong, Volkow Nora D

2020-Sep-14

Radiology Radiology

Development and clinical implementation of tailored image analysis tools for COVID-19 in the midst of the pandemic: The synergetic effect of an open, clinically embedded software development platform and machine learning.

In European journal of radiology ; h5-index 47.0

PURPOSE : During the emerging COVID-19 pandemic, radiology departments faced a substantial increase in chest CT admissions coupled with the novel demand for quantification of pulmonary opacities. This article describes how our clinic implemented an automated software solution for this purpose into an established software platform in 10 days. The underlying hypothesis was that modern academic centers in radiology are capable of developing and implementing such tools by their own efforts and fast enough to meet the rapidly increasing clinical needs in the wake of a pandemic.

METHOD : Deep convolutional neural network algorithms for lung segmentation and opacity quantification on chest CTs were trained using semi-automatically and manually created ground-truth (Ntotal = 172). The performance of the in-house method was compared to an externally developed algorithm on a separate test subset (N = 66).

RESULTS : The final algorithm was available at day 10 and achieved human-like performance (Dice coefficient = 0.97). For opacity quantification, a slight underestimation was seen both for the in-house (1.8 %) and for the external algorithm (0.9 %). In contrast to the external reference, the underestimation for the in-house algorithm showed no dependency on total opacity load, making it more suitable for follow-up.

CONCLUSIONS : The combination of machine learning and a clinically embedded software development platform enabled time-efficient development, instant deployment, and rapid adoption in clinical routine. The algorithm for fully automated lung segmentation and opacity quantification that we developed in the midst of the COVID-19 pandemic was ready for clinical use within just 10 days and achieved human-level performance even in complex cases.

Anastasopoulos Constantin, Weikert Thomas, Yang Shan, Abdulkadir Ahmed, Schmülling Lena, Bühler Claudia, Paciolla Fabiano, Sexauer Raphael, Cyriac Joshy, Nesic Ivan, Twerenbold Raphael, Bremerich Jens, Stieltjes Bram, Sauter Alexander W, Sommer Gregor

2020-Aug-28

COVID-19, Computed tomography, Machine learning, Software

General General

Using Machine Learning to Generate Novel Hypotheses: Increasing Optimism About COVID-19 Makes People Less Willing to Justify Unethical Behaviors.

In Psychological science ; h5-index 93.0

How can we nudge people to not engage in unethical behaviors, such as hoarding and violating social-distancing guidelines, during the COVID-19 pandemic? Because past research on antecedents of unethical behavior has not provided a clear answer, we turned to machine learning to generate novel hypotheses. We trained a deep-learning model to predict whether or not World Values Survey respondents perceived unethical behaviors as justifiable, on the basis of their responses to 708 other items. The model identified optimism about the future of humanity as one of the top predictors of unethicality. A preregistered correlational study (N = 218 U.S. residents) conceptually replicated this finding. A preregistered experiment (N = 294 U.S. residents) provided causal support: Participants who read a scenario conveying optimism about the COVID-19 pandemic were less willing to justify hoarding and violating social-distancing guidelines than participants who read a scenario conveying pessimism. The findings suggest that optimism can help reduce unethicality, and they document the utility of machine-learning methods for generating novel hypotheses.

Sheetal Abhishek, Feng Zhiyu, Savani Krishna

2020-Sep-14

COVID-19, machine learning, neural network, open data, open materials, optimism, preregistered, unethical behavior

oncology Oncology

The future is now? Clinical and translational aspects of "Omics" technologies.

In Immunology and cell biology

Big data has become a central part of medical research, as well as modern life generally. "Omics" technologies include genomics, proteomics, microbiomics, and increasingly other omics. These have been driven by rapid advances in laboratory techniques and equipment. Crucially, improved information handling capabilities have allowed concepts such as artificial intelligence and machine learning to enter the research world. The Covid-19 pandemic has shown how quickly information can be generated and analysed using such approaches, but also showed its limitations. This review will look at how "omics" has begun to be translated into clinical practice. While there appears almost limitless potential in using big data for "precision" or "personalised" medicine, the reality is that this remains largely aspirational. Oncology is the only field of medicine that is widely adopting such technologies, and even in this field uptake is irregular. There are practical and ethical reasons for this lack of translation of increasingly affordable techniques into the clinic. Undoubtedly there will be increasing use of large datasets from traditional (e.g. tumour samples, patient genomics) and non-traditional (e.g. smartphone) sources. It is perhaps the greatest challenge of the healthcare sector over the coming decade to integrate these resources in an effective, practical and ethical way.

D’Adamo Gemma L, Widdop James T, Giles Edward M

2020-Sep-13

Genomics, artificial intelligence, machine learning, microbiome, translational immunology

Public Health Public Health

Impact of COVID-19 on Acute Stroke Presentation at a Comprehensive Stroke Center.

In Frontiers in neurology

Background: COVID-19 has impacted healthcare in many ways, including presentation of acute stroke. Since time-sensitive thrombolysis is essential for reducing morbidity and mortality in acute stroke, any delays due to the pandemic can have serious consequences. Methods: We retrospectively reviewed the electronic medical records for patients presenting with acute ischemic stroke at a comprehensive stroke center in March-April 2020 (the early months of COVID-19) and compared to the same time period in 2019. Stroke metrics such as incidence, time to arrival, and immediate outcomes were assessed. Results: There were 48 acute ischemic strokes (of which 7 were transfers) in March-April 2020 compared to 64 (of which 12 were transfers) in 2019. The average last known well to arrival time (±SD) for stroke codes was 1,041 (±1682.1) min in 2020 and 554 (±604.9) min in 2019. Of the patients presenting directly to the ED with a known last known well time, 27.8% (10/36) presented in the first 4.5 h in 2020, in contrast to 40.5% (15/37) in 2019. Patients who died comprised 10.4% of the stroke cohort in 2020 (5/48) compared to 6.3% in 2019 (4/64). Conclusions: During the first 2 months of COVID-19, there were fewer overall stroke cases who presented to our hospital, and of these cases, there was delayed presentation in comparison to the same time period in 2019. Recognizing how stroke presentation may be affected by COVID-19 would allow for optimization of established stroke triage algorithms in order to ensure safe and timely delivery of stroke care during a pandemic.

Nagamine Masaki, Chow Daniel S, Chang Peter D, Boden-Albala Bernadette, Yu Wengui, Soun Jennifer E

2020

COVID-19, public health, stroke, stroke triage, thrombolytics

General General

CVDNet: A novel deep learning architecture for detection of coronavirus (Covid-19) from chest x-ray images.

In Chaos, solitons, and fractals

The COVID-19 pandemic is an emerging respiratory infectious disease, also known as coronavirus 2019. It appears in November 2019 in Hubei province (in China), and more specifically in the city of Wuhan, then spreads in the whole world. As the number of cases increases with unprecedented speed, many parts of the world are facing a shortage of resources and testing. Faced with this problem, physicians, scientists and engineers, including specialists in Artificial Intelligence (AI), have encouraged the development of a Deep Learning model to help healthcare professionals to detect COVID-19 from chest X-ray images and to determine the severity of the infection in a very short time, with low cost. In this paper, we propose CVDNet, a Deep Convolutional Neural Network (CNN) model to classify COVID-19 infection from normal and other pneumonia cases using chest X-ray images. The proposed architecture is based on the residual neural network and it is constructed by using two parallel levels with different kernel sizes to capture local and global features of the inputs. This model is trained on a dataset publically available containing a combination of 219 COVID-19, 1341 normal and 1345 viral pneumonia chest x-ray images. The experimental results reveal that our CVDNet. These results represent a promising classification performance on a small dataset which can be further achieve better results with more training data. Overall, our CVDNet model can be an interesting tool to help radiologists in the diagnosis and early detection of COVID-19 cases.

Ouchicha Chaimae, Ammor Ouafae, Meknassi Mohammed

2020-Nov

COVID-19, Chest X-ray images, Classification, Convolutional neural network, Coronavirus, Deep learning

General General

A deep learning approach to detect Covid-19 coronavirus with X-Ray images.

In Biocybernetics and biomedical engineering

Rapid and accurate detection of COVID-19 coronavirus is necessity of time to prevent and control of this pandemic by timely quarantine and medical treatment in absence of any vaccine. Daily increase in cases of COVID-19 patients worldwide and limited number of available detection kits pose difficulty in identifying the presence of disease. Therefore, at this point of time, necessity arises to look for other alternatives. Among already existing, widely available and low-cost resources, X-ray is frequently used imaging modality and on the other hand, deep learning techniques have achieved state-of-the-art performances in computer-aided medical diagnosis. Therefore, an alternative diagnostic tool to detect COVID-19 cases utilizing available resources and advanced deep learning techniques is proposed in this work. The proposed method is implemented in four phases, viz., data augmentation, preprocessing, stage-I and stage-II deep network model designing. This study is performed with online available resources of 1215 images and further strengthen by utilizing data augmentation techniques to provide better generalization of the model and to prevent the model overfitting by increasing the overall length of dataset to 1832 images. Deep network implementation in two stages is designed to differentiate COVID-19 induced pneumonia from healthy cases, bacterial and other virus induced pneumonia on X-ray images of chest. Comprehensive evaluations have been performed to demonstrate the effectiveness of the proposed method with both (i) training-validation-testing and (ii) 5-fold cross validation procedures. High classification accuracy as 97.77%, recall as 97.14% and precision as 97.14% in case of COVID-19 detection shows the efficacy of proposed method in present need of time. Further, the deep network architecture showing averaged accuracy/sensitivity/specificity/precision/F1-score of 98.93/98.93/98.66/96.39/98.15 with 5-fold cross validation makes a promising outcome in COVID-19 detection using X-ray images.

Jain Govardhan, Mittal Deepti, Thakur Daksh, Mittal Madhup K

Computer-aided diagnosis, Coronavirus detection, Covid-19, Deep learning, Pneumonia, X-ray

General General

Beyond Infection: Integrating Competence into Reservoir Host Prediction.

In Trends in ecology & evolution

Most efforts to predict novel reservoirs of zoonotic pathogens use information about host exposure and infection rather than competence, defined as the ability to transmit pathogens. Better obtaining and integrating competence data into statistical models as covariates, as the response variable, and through postmodel validation should improve predictive research.

Becker Daniel J, Seifert Stephanie N, Carlson Colin J

2020-Sep-10

SARS-CoV-2, machine learning, vector-borne disease, within-host, zoonoses

General General

Contrastive Cross-site Learning with Redesigned Net for COVID-19 CT Classification

ArXiv Preprint

The pandemic of coronavirus disease 2019 (COVID-19) has lead to a global public health crisis spreading hundreds of countries. With the continuous growth of new infections, developing automated tools for COVID-19 identification with CT image is highly desired to assist the clinical diagnosis and reduce the tedious workload of image interpretation. To enlarge the datasets for developing machine learning methods, it is essentially helpful to aggregate the cases from different medical systems for learning robust and generalizable models. This paper proposes a novel joint learning framework to perform accurate COVID-19 identification by effectively learning with heterogeneous datasets with distribution discrepancy. We build a powerful backbone by redesigning the recently proposed COVID-Net in aspects of network architecture and learning strategy to improve the prediction accuracy and learning efficiency. On top of our improved backbone, we further explicitly tackle the cross-site domain shift by conducting separate feature normalization in latent space. Moreover, we propose to use a contrastive training objective to enhance the domain invariance of semantic embeddings for boosting the classification performance on each dataset. We develop and evaluate our method with two public large-scale COVID-19 diagnosis datasets made up of CT images. Extensive experiments show that our approach consistently improves the performances on both datasets, outperforming the original COVID-Net trained on each dataset by 12.16% and 14.23% in AUC respectively, also exceeding existing state-of-the-art multi-site learning methods.

Zhao Wang, Quande Liu, Qi Dou

2020-09-15

General General

The Coronavirus Network Explorer: Mining a large-scale knowledge graph for effects of SARS-CoV-2 on host cell function

bioRxiv Preprint

Building on recent work that identified human host proteins that interact with SARS-CoV-2 viral proteins in the context of an affinity-purification mass spectrometry screen, we use a machine learning-based approach to connect the viral proteins to relevant biological functions and diseases in a large-scale knowledge graph derived from the biomedical literature. Our aim is to explore how SARS-CoV-2 could interfere with various host cell functions, and also to identify additional drug targets amongst the host genes that could potentially be modulated against COVID-19. Results are presented in the form of interactive network visualizations, that allow exploration of underlying experimental evidence. A selection of networks is discussed in the context of recent clinical observations.

Krämer, A.; Billaud, J.-N.; Tugendreich, S.; Shiffman, D.; Jones, M.; Green, J.

2020-09-14

Public Health Public Health

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

In PloS one ; h5-index 176.0

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

Debnath Ramit, Bardhan Ronita

2020

Public Health Public Health

Contrastive Cross-site Learning with Redesigned Net for COVID-19 CT Classification.

In IEEE journal of biomedical and health informatics

The pandemic of coronavirus disease 2019 (COVID-19) has lead to a global public health crisis spreading hundreds of countries. With the continuous growth of new infections, developing automated tools for COVID-19 identification with CT image is highly desired to assist the clinical diagnosis and reduce the tedious workload of image interpretation. To enlarge the datasets for developing machine learning methods, it is essentially helpful to aggregate the cases from different medical systems for learning robust and generalizable models. This paper proposes a novel joint learning framework to perform accurate COVID-19 identification by effectively learning with heterogeneous datasets with distribution descrepancy.We build a powerful backbone by redesigning the recently proposed COVID-Net in aspects of network architecture and learning strategy to improve the prediction accuracy and learning efficiency. On top of our improved backbone, we further explicitly tackle the cross-site domain shift by conducting separate feature normalization in latent space. Moreover, we propose a contrastive training objective to enhance the domain invariance of semantic embeddings for boosting the classification performance on each dataset. We develop and evaluate our method with two public large-scale COVID-19 diagnosis datasets from real CT images. Extensive experiments show that our approach consistently improves the performanceson both datasets, outperforming the original COVID-Net trained on each dataset by 12.16% and 14.23% in AUC respectively, also exceeding existing state-of-the-art multi-site learning methods.

Wang Zhao, Liu Quande, Dou Qi

2020-Sep-10

Public Health Public Health

Medical Student Training in eHealth: Scoping Review.

In JMIR medical education

BACKGROUND : eHealth is the use of information and communication technologies to enable and improve health and health care services. It is crucial that medical students receive adequate training in eHealth as they will work in clinical environments that are increasingly being enabled by technology. This trend is especially accelerated by the COVID-19 pandemic as it complicates traditional face-to-face medical consultations and highlights the need for innovative approaches in health care.

OBJECTIVE : This review aims to evaluate the extent and nature of the existing literature on medical student training in eHealth. In detail, it aims to examine what this education consists of, the barriers, enhancing factors, and propositions for improving the medical curriculum. This review focuses primarily on some key technologies such as mobile health (mHealth), the internet of things (IoT), telehealth, and artificial intelligence (AI).

METHODS : Searches were performed on 4 databases, and articles were selected based on the eligibility criteria. Studies had to be related to the training of medical students in eHealth. The eligibility criteria were studies published since 2014, from a peer-reviewed journal, and written in either English or French. A grid was used to extract and chart data.

RESULTS : The search resulted in 25 articles. The most studied aspect was mHealth. eHealth as a broad concept, the IoT, AI, and programming were least covered. A total of 52% (13/25) of all studies contained an intervention, mostly regarding mHealth, electronic health records, web-based medical resources, and programming. The findings included various barriers, enhancing factors, and propositions for improving the medical curriculum.

CONCLUSIONS : Trends have emerged regarding the suboptimal present state of eHealth training and barriers, enhancing factors, and propositions for optimal training. We recommend that additional studies be conducted on the following themes: barriers, enhancing factors, propositions for optimal training, competencies that medical students should acquire, learning outcomes from eHealth training, and patient care outcomes from this training. Additional studies should be conducted on eHealth and each of its aspects, especially on the IoT, AI, programming, and eHealth as a broad concept. Training in eHealth is critical to medical practice in clinical environments that are increasingly being enabled by technology. The need for innovative approaches in health care during the COVID-19 pandemic further highlights the relevance of this training.

Echelard Jean-François, Méthot François, Nguyen Hue-Anh, Pomey Marie-Pascale

2020-Sep-11

artificial intelligence, digital health, eHealth, electronic health records, health apps, internet of things, mHealth, medical education, programming, telehealth

Public Health Public Health

Culture versus Policy: More Global Collaboration to Effectively Combat COVID-19.

In Innovation (New York, N.Y.)

The outbreak of COVID-19 seriously challenges every government with regard to capacity and management of public health systems facing the catastrophic emergency. Culture and anti-epidemic policy do not necessarily conflict with each other. All countries and governments should be more tolerant to each other in seeking cultural and political consensus to overcome this historically tragic pandemic together.

Li Jianping, Guo Kun, Viedma Enrique Herrera, Lee Heesoek, Liu Jiming, Zhong Ning, Autran Monteiro Gomes Luiz Flavio, Filip Florin Gheorghe, Fang Shu-Cherng, Özdemir Mujgan Sagir, Liu Xiaohui, Lu Guoqing, Shi Yong

2020-Aug-28

General General

The impact of community containment implementation timing on the spread of COVID-19: A simulation study.

In F1000Research

Background: Community containment is one of the common methods used to mitigate infectious disease outbreaks. The effectiveness of such a method depends on how strictly it is applied and the timing of its implementation. An early start and being strict is very effective; however, at the same time, it impacts freedom and economic opportunity. Here we created a simulation model to understand the effect of the starting day of community containment on the final outcome, that is, the number of those infected, hospitalized and those that died, as we followed the dynamics of COVID-19 pandemic. Methods: We used a stochastic recursive simulation method to apply disease outbreak dynamics measures of COVID-19 as an example to simulate disease spread. Parameters are allowed to be randomly assigned between higher and lower values obtained from published COVID-19 literature. Results: We simulated the dynamics of COVID-19 spread, calculated the number of active infections, hospitalizations and deaths as the outcome of our simulation and compared these results with real world data. We also represented the details of the spread in a network graph structure, and shared the code for the simulation model to be used for examining other variables. Conclusions: Early implementation of community containment has a big impact on the final outcome of an outbreak.

Mohsen Attayeb, Alarabi Ahmed

2020

COVID-19, Community containment, Outbreak, Simulation, Social distancing

Cardiology Cardiology

Common cardiovascular risk factors and in-hospital mortality in 3,894 patients with COVID-19: survival analysis and machine learning-based findings from the multicentre Italian CORIST Study.

In Nutrition, metabolism, and cardiovascular diseases : NMCD

BACKGROUND AND AIMS : There is poor knowledge on characteristics, comorbidities and laboratory measures associated with risk for adverse outcomes and in-hospital mortality in European Countries. We aimed at identifying baseline characteristics predisposing COVID-19 patients to in-hospital death.

METHODS AND RESULTS : Retrospective observational study on 3894 patients with SARS-CoV-2 infection hospitalized from February 19th to May 23rd, 2020 and recruited in 30 clinical centres distributed throughout Italy. Machine learning (random forest)-based and Cox survival analysis. 61.7% of participants were men (median age 67 years), followed up for a median of 13 days. In-hospital mortality exhibited a geographical gradient, Northern Italian regions featuring more than twofold higher death rates as compared to Central/Southern areas (15.6% vs 6.4%, respectively). Machine learning analysis revealed that the most important features in death classification were impaired renal function, elevated C reactive protein and advanced age. These findings were confirmed by multivariable Cox survival analysis (hazard ratio (HR): 8.2; 95% confidence interval (CI) 4.6-14.7 for age ≥85 vs 18-44 y); HR = 4.7; 2.9-7.7 for estimated glomerular filtration rate levels <15 vs ≥ 90 mL/min/1.73 m2; HR = 2.3; 1.5-3.6 for C-reactive protein levels ≥10 vs ≤ 3 mg/L). No relation was found with obesity, tobacco use, cardiovascular disease and related-comorbidities. The associations between these variables and mortality were substantially homogenous across all sub-groups analyses.

CONCLUSIONS : Impaired renal function, elevated C-reactive protein and advanced age were major predictors of in-hospital death in a large cohort of unselected patients with COVID-19, admitted to 30 different clinical centres all over Italy.

Di Castelnuovo Augusto, Bonaccio Marialaura, Costanzo Simona, Gialluisi Alessandro, Antinori Andrea, Berselli Nausicaa, Blandi Lorenzo, Bruno Raffaele, Cauda Roberto, Guaraldi Giovanni, My Ilaria, Menicanti Lorenzo, Parruti Giustino, Patti Giuseppe, Perlini Stefano, Santilli Francesca, Signorelli Carlo, Stefanini Giulio G, Vergori Alessandra, Abdeddaim Amina, Ageno Walter, Agodi Antonella, Agostoni Piergiuseppe, Aiello Luca, Al Moghazi Samir, Aucella Filippo, Barbieri Greta, Bartoloni Alessandro, Bologna Carolina, Bonfanti Paolo, Brancati Serena, Cacciatore Francesco, Caiano Lucia, Cannata Francesco, Carrozzi Laura, Cascio Antonio, Cingolani Antonella, Cipollone Francesco, Colomba Claudia, Crisetti Annalisa, Crosta Francesca, Danzi Gian B, D’Ardes Damiano, de Gaetano Donati Katleen, Di Gennaro Francesco, Di Palma Gisella, Di Tano Giuseppe, Fantoni Massimo, Filippini Tommaso, Fioretto Paola, Fusco Francesco M, Gentile Ivan, Grisafi Leonardo, Guarnieri Gabriella, Landi Francesco, Larizza Giovanni, Leone Armando, Maccagni Gloria, Maccarella Sandro, Mapelli Massimo, Maragna Riccardo, Marcucci Rossella, Maresca Giulio, Marotta Claudia, Marra Lorenzo, Mastroianni Franco, Mengozzi Alessandro, Menichetti Francesco, Milic Jovana, Murri Rita, Montineri Arturo, Mussinelli Roberta, Mussini Cristina, Musso Maria, Odone Anna, Olivieri Marco, Pasi Emanuela, Petri Francesco, Pinchera Biagio, Pivato Carlo A, Pizzi Roberto, Poletti Venerino, Raffaelli Francesca, Ravaglia Claudia, Righetti Giulia, Rognoni Andrea, Rossato Marco, Rossi Marianna, Sabena Anna, Salinaro Francesco, Sangiovanni Vincenzo, Sanrocco Carlo, Scarafino Antonio, Scorzolini Laura, Sgariglia Raffaella, Simeone Paola G, Spinoni Enrico, Torti Carlo, Trecarichi Enrico M, Vezzani Francesca, Veronesi Giovanni, Vettor Roberto, Vianello Andrea, Vinceti Marco, De Caterina Raffaele, Iacoviello Licia

2020-Jul-31

COVID-19, Epidemiology, In-hospital mortality, Risk factors

General General

Customer experiences in the age of artificial intelligence.

In Computers in human behavior ; h5-index 125.0

Artificial intelligence (AI) is revolutionising the way customers interact with brands. There is a lack of empirical research into AI-enabled customer experiences. Hence, this study aims to analyse how the integration of AI in shopping can lead to an improved AI-enabled customer experience. We propose a theoretical model drawing on the trust-commitment theory and service quality model. An online survey was distributed to customers who have used an AI- enabled service offered by a beauty brand. A total of 434 responses were analysed using partial least squares-structural equation modelling. The findings indicate the significant role of trust and perceived sacrifice as factors mediating the effects of perceived convenience, personalisation and AI-enabled service quality. The findings also reveal the significant effect of relationship commitment on AI-enabled customer experience. This study contributes to the existing literature by revealing the mediating effects of trust and perceived sacrifice and the direct effect of relationship commitment on AI-enabled customer experience. In addition, the study has practical implications for retailers deploying AI in services offered to their customers.

Ameen Nisreen, Tarhini Ali, Reppel Alexander, Anand Amitabh

2021-Jan

Artificial intelligence, Beauty brands, COVID 19, Customer experience, Trust-commitment theory, trust

General General

COVID-19 pandemic changes the food consumption patterns.

In Trends in food science & technology

Background : The COVID-19 pandemic affects all aspects of human life including their food consumption. The changes in the food production and supply processes introduce changes to the global dietary patterns.

Scope and Approach : To study the COVID-19 impact on food consumption process, we have analyzed two data sets that consist of food preparation recipes published before (69,444) and during the quarantine (10,009) period. Since working with large data sets is a time-consuming task, we have applied a recently proposed artificial intelligence approach called DietHub. The approach uses the recipe preparation description (i.e. text) and automatically provides a list of main ingredients annotated using the Hansard semantic tags. After extracting the semantic tags of the ingredients for every recipe, we have compared the food consumption patterns between the two data sets by comparing the relative frequency of the ingredients that compose the recipes.

Key Findings and Conclusions : Using the AI methodology, the changes in the food consumption patterns before and during the COVID-19 pandemic are obvious. The highest positive difference in the food consumption can be found in foods such as "Pulses/ plants producing pulses", "Pancake/Tortilla/Outcake", and "Soup/pottage", which increase by 300%, 280%, and 100%, respectively. Conversely, the largest decrease in consumption can be food for food such as "Order Perciformes (type of fish)", "Corn/cereals/grain", and "Wine-making", with a reduction of 50%, 40%, and 30%, respectively. This kind of analysis is valuable in times of crisis and emergencies, which is a very good example of the scientific support that regulators require in order to take quick and appropriate response.

Eftimov Tome, Popovski Gorjan, Petković Matej, Seljak Barbara Koroušić, Kocev Dragi

2020-Sep-02

Artificial intelligence, COVID-19 pandemic, Dietary habits analysis, Food consumption, Food semantic annotation

General General

Should air pollution health effects assumptions be tested? Fine particulate matter and COVID-19 mortality as an example.

In Global epidemiology

In the first half of 2020, much excitement in news media and some peer reviewed scientific articles was generated by the discovery that fine particulate matter (PM2.5) concentrations and COVID-19 mortality rates are statistically significantly positively associated in some regression models. This article points out that they are non-significantly negatively associated in other regression models, once omitted confounders (such as latitude and longitude) are included. More importantly, positive regression coefficients can and do arise when (generalized) linear regression models are applied to data with strong nonlinearities, including data on PM2.5, population density, and COVID-19 mortality rates, due to model specification errors. In general, statistical modeling accompanied by judgments about causal interpretations of statistical associations and regression coefficients - the current weight-of-evidence (WoE) approach favored in much current regulatory risk analysis for air pollutants - is not a valid basis for determining whether or to what extent risk of harm to human health would be reduced by reducing exposure. The traditional scientific method based on testing predictive generalizations against data remains a more reliable paradigm for risk analysis and risk management.

Cox Louis Anthony, Popken Douglas A

2020-Sep-02

Air pollution, Bayesian networks, CART trees, COVID-19 mortality risk, Causation, Health effects, Machine learning, Model specification error, PM2.5, Random forest, Regression, Scientific method

General General

Classification of Coronavirus (COVID-19) from X-ray and CT images using shrunken features.

In International journal of imaging systems and technology

Necessary screenings must be performed to control the spread of the COVID-19 in daily life and to make a preliminary diagnosis of suspicious cases. The long duration of pathological laboratory tests and the suspicious test results led the researchers to focus on different fields. Fast and accurate diagnoses are essential for effective interventions for COVID-19. The information obtained by using X-ray and Computed Tomography (CT) images is vital in making clinical diagnoses. Therefore it is aimed to develop a machine learning method for the detection of viral epidemics by analyzing X-ray and CT images. In this study, images belonging to six situations, including coronavirus images, are classified using a two-stage data enhancement approach. Since the number of images in the dataset is deficient and unbalanced, a shallow image augmentation approach was used in the first phase. It is more convenient to analyze these images with hand-crafted feature extraction methods because the dataset newly created is still insufficient to train a deep architecture. Therefore, the Synthetic minority over-sampling technique algorithm is the second data enhancement step of this study. Finally, the feature vector is reduced in size by using a stacked auto-encoder and principal component analysis methods to remove interconnected features in the feature vector. According to the obtained results, it is seen that the proposed method has leveraging performance, especially to make the diagnosis of COVID-19 in a short time and effectively. Also, it is thought to be a source of inspiration for future studies for deficient and unbalanced datasets.

Öztürk Şaban, Özkaya Umut, Barstuğan Mücahid

2020-Aug-18

COVID‐19, classification, coronavirus, feature extraction, hand‐crafted features, sAE

Surgery Surgery

Enhancing India's Health Care during COVID Era: Role of Artificial Intelligence and Algorithms.

In Indian journal of otolaryngology and head and neck surgery : official publication of the Association of Otolaryngologists of India

Computerization of health care is the only model to sustain safe health care in this COVID era particularly in overpopulated nations with limited health care providers/systems like India. Accordingly incorporation of computer-based algorithms and artificial intelligence seems very robust and practical models to assist the physician. The advantages of Computerized algorithms to facilitate better screening, diagnosis or follow-up and use of Artificial Intelligence (AI) to aid in medical diagnosis are discussed.

Katyayan Angira, Katyayan Adri, Mishra Anupam

2020-Sep-01

Artificial intelligence, COVID-19, Computer algorithms

General General

Is it safe to lift COVID-19 travel bans? The Newfoundland story.

In Computational mechanics

A key strategy to prevent a local outbreak during the COVID-19 pandemic is to restrict incoming travel. Once a region has successfully contained the disease, it becomes critical to decide when and how to reopen the borders. Here we explore the impact of border reopening for the example of Newfoundland and Labrador, a Canadian province that has enjoyed no new cases since late April, 2020. We combine a network epidemiology model with machine learning to infer parameters and predict the COVID-19 dynamics upon partial and total airport reopening, with perfect and imperfect quarantine conditions. Our study suggests that upon full reopening, every other day, a new COVID-19 case would enter the province. Under the current conditions, banning air travel from outside Canada is more efficient in managing the pandemic than fully reopening and quarantining 95% of the incoming population. Our study provides quantitative insights of the efficacy of travel restrictions and can inform political decision making in the controversy of reopening.

Linka Kevin, Rahman Proton, Goriely Alain, Kuhl Ellen

2020-Aug-29

COVID-19, Epidemiology, Machine learning, Reproduction number, SEIR model

General General

Early diagnosis of COVID-19-affected patients based on X-ray and computed tomography images using deep learning algorithm.

In Soft computing

The novel coronavirus infection (COVID-19) that was first identified in China in December 2019 has spread across the globe rapidly infecting over ten million people. The World Health Organization (WHO) declared it as a pandemic on March 11, 2020. What makes it even more critical is the lack of vaccines available to control the disease, although many pharmaceutical companies and research institutions all over the world are working toward developing effective solutions to battle this life-threatening disease. X-ray and computed tomography (CT) images scanning is one of the most encouraging exploration zones; it can help in finding and providing early diagnosis to diseases and gives both quick and precise outcomes. In this study, convolution neural networks method is used for binary classification pneumonia-based conversion of VGG-19, Inception_V2 and decision tree model on X-ray and CT scan images dataset, which contains 360 images. It can infer that fine-tuned version VGG-19, Inception_V2 and decision tree model show highly satisfactory performance with a rate of increase in training and validation accuracy (91%) other than Inception_V2 (78%) and decision tree (60%) models.

Dansana Debabrata, Kumar Raghvendra, Bhattacharjee Aishik, Hemanth D Jude, Gupta Deepak, Khanna Ashish, Castillo Oscar

2020-Aug-28

CNN, COVID-19, CT scan, Decision tree, Inception_V2, VGG-16, X-ray images

General General

COVID CT-Net: Predicting Covid-19 From Chest CT Images Using Attentional Convolutional Network

ArXiv Preprint

The novel corona-virus disease (COVID-19) pandemic has caused a major outbreak in more than 200 countries around the world, leading to a severe impact on the health and life of many people globally. As of Aug 25th of 2020, more than 20 million people are infected, and more than 800,000 death are reported. Computed Tomography (CT) images can be used as a as an alternative to the time-consuming "reverse transcription polymerase chain reaction (RT-PCR)" test, to detect COVID-19. In this work we developed a deep learning framework to predict COVID-19 from CT images. We propose to use an attentional convolution network, which can focus on the infected areas of chest, enabling it to perform a more accurate prediction. We trained our model on a dataset of more than 2000 CT images, and report its performance in terms of various popular metrics, such as sensitivity, specificity, area under the curve, and also precision-recall curve, and achieve very promising results. We also provide a visualization of the attention maps of the model for several test images, and show that our model is attending to the infected regions as intended. In addition to developing a machine learning modeling framework, we also provide the manual annotation of the potentionally infected regions of chest, with the help of a board-certified radiologist, and make that publicly available for other researchers.

Shakib Yazdani, Shervin Minaee, Rahele Kafieh, Narges Saeedizadeh, Milan Sonka

2020-09-10

General General

Predicting the Time Period of Extension of Lockdown due to Increase in Rate of COVID-19 Cases in India using Machine Learning.

In Materials today. Proceedings

The research paper proposes a methodology to predict the extension of lockdown in order to eradicate COVID-19 from India. All the concepts related to Coronavirus, its history, prevention and cure is explained in the research paper. Concept used to predict the number of active cases, deaths and recovery is Linear Regression which is an application of machine learning. Extension of lockdown is predicted on the basis of predicted number of active cases, deaths and recovery all over India. To predict the number of active cases, deaths and recovery, date wise analysis of current data was done and necessary parameters like daily recovery, daily deaths, increase rate of covid-19 cases were included. Graphical representation of each analysis and prediction was done in order to make predicted results more understandable. The combined analysis was performed at the end which included the final result of total cases of coronavirus in India. Combined analysis included the no. of cases from start of COVID-19 to the predicted end of cases all over India. [copyright information to be updated in production process].

Wadhwa Parth, Aishwarya Tripathi, Amrendra Singh, Prabhishek Diwakar, Manoj Kumar

2020-Aug-28

COVID-19, COVID-19 pandemic, Coronavirus India, Coronavirus pandemic, coronavirus, lockdown prediction

General General

COVID-19 Pandemic Cyclic Lockdown Optimization Using Reinforcement Learning

ArXiv Preprint

This work examines the use of reinforcement learning (RL) to optimize cyclic lockdowns, which is one of the methods available for control of the COVID-19 pandemic. The problem is structured as an optimal control system for tracking a reference value, corresponding to the maximum usage level of a critical resource, such as ICU beds. However, instead of using conventional optimal control methods, RL is used to find optimal control policies. A framework was developed to calculate optimal cyclic lockdown timings using an RL-based on-off controller. The RL-based controller is implemented as an RL agent that interacts with an epidemic simulator, implemented as an extended SEIR epidemic model. The RL agent learns a policy function that produces an optimal sequence of open/lockdown decisions such that goals specified in the RL reward function are optimized. Two concurrent goals were used: the first one is a public health goal that minimizes overshoots of ICU bed usage above an ICU bed threshold, and the second one is a socio-economic goal that minimizes the time spent under lockdowns. It is assumed that cyclic lockdowns are considered as a temporary alternative to extended lockdowns when a region faces imminent danger of overpassing resource capacity limits and when imposing an extended lockdown would cause severe social and economic consequences due to lack of necessary economic resources to support its affected population during an extended lockdown.

Mauricio Arango, Lyudmil Pelov

2020-09-10

Surgery Surgery

Endothelial Injury and Glycocalyx Degradation in Critically Ill Coronavirus Disease 2019 Patients: Implications for Microvascular Platelet Aggregation.

In Critical care explorations

Objectives : Coronavirus disease 2019 is caused by the novel severe acute respiratory syndrome coronavirus 2 virus. Patients admitted to the ICU suffer from microvascular thrombosis, which may contribute to mortality. Our aim was to profile plasma thrombotic factors and endothelial injury markers in critically ill coronavirus disease 2019 ICU patients to help understand their thrombotic mechanisms.

Design : Daily blood coagulation and thrombotic factor profiling with immunoassays and in vitro experiments on human pulmonary microvascular endothelial cells.

Setting : Tertiary care ICU and academic laboratory.

Subjects : All patients admitted to the ICU suspected of being infected with severe acute respiratory syndrome coronavirus 2, using standardized hospital screening methodologies, had daily blood samples collected until testing was confirmed coronavirus disease 2019 negative on either ICU day 3 or ICU day 7 if the patient was coronavirus disease 2019 positive.

Interventions : None.

Measurement and Main Results : Age- and sex-matched healthy control subjects and ICU patients that were either coronavirus disease 2019 positive or coronavirus disease 2019 negative were enrolled. Cohorts were well balanced with the exception that coronavirus disease 2019 positive patients were more likely than coronavirus disease 2019 negative patients to suffer bilateral pneumonia. Mortality rate for coronavirus disease 2019 positive ICU patients was 40%. Compared with healthy control subjects, coronavirus disease 2019 positive patients had higher plasma von Willebrand factor (p < 0.001) and glycocalyx-degradation products (chondroitin sulfate and syndecan-1; p < 0.01). When compared with coronavirus disease 2019 negative patients, coronavirus disease 2019 positive patients had persistently higher soluble P-selectin, hyaluronic acid, and syndecan-1 (p < 0.05), particularly on ICU day 3 and thereafter. Thrombosis profiling on ICU days 1-3 predicted coronavirus disease 2019 status with 85% accuracy and patient mortality with 86% accuracy. Surface hyaluronic acid removal from human pulmonary microvascular endothelial cells with hyaluronidase treatment resulted in depressed nitric oxide, an instigating mechanism for platelet adhesion to the microvascular endothelium.

Conclusions : Thrombosis profiling identified endothelial activation and glycocalyx degradation in coronavirus disease 2019 positive patients. Our data suggest that medications to protect and/or restore the endothelial glycocalyx, as well as platelet inhibitors, should be considered for further study.

Fraser Douglas D, Patterson Eric K, Slessarev Marat, Gill Sean E, Martin Claudio, Daley Mark, Miller Michael R, Patel Maitray A, Dos Santos Claudia C, Bosma Karen J, O’Gorman David B, Cepinskas Gediminas

2020-Sep

coronavirus disease 2019, endothelial injury, glycocalyx degradation, platelet adhesion, thrombosis

General General

Classification and Specific Primer Design for Accurate Detection of SARS-CoV-2 Using Deep Learning

bioRxiv Preprint

In this paper, deep learning is coupled with explainable artificial intelligence techniques for the discovery of representative genomic sequences in SARS-CoV-2. A convolutional neural network classifier is first trained on 553 sequences from available repositories, separating the genome of different virus strains from the Coronavirus family with considerable accuracy. The networks behavior is then analyzed, to discover sequences used by the model to identify SARS-CoV-2, ultimately uncovering sequences exclusive to it. The discovered sequences are first validated on samples from other repositories, and proven able to separate SARS-CoV-2 from different virus strains with near-perfect accuracy. Next, one of the sequences is selected to generate a primer set, and tested against other state-of-the-art primer sets on existing datasets, obtaining competitive results. Finally, the primer is synthesized and tested on patient samples (n=6 previously tested positive), delivering a sensibility similar to routine diagnostic methods, and 100% specificity. In this paper, deep learning is coupled with explainable artificial intelligence techniques for the discovery of representative genomic sequences in SARS-CoV-2. A convolutional neural network classifier is first trained on 553 sequences from NGDC, separating the genome of different virus strains from the Coronavirus family with accuracy 98.73%. The networks behavior is then analyzed, to discover sequences used by the model to identify SARS-CoV-2, ultimately uncovering sequences exclusive to it. The discovered sequences are validated on samples from NCBI and GISAID, and proven able to separate SARS-CoV-2 from different virus strains with near-perfect accuracy. Next, one of the sequences is selected to generate a primer set, and tested against other state-of-the-art primer sets, obtaining competitive results. Finally, the primer is synthesized and tested on patient samples (n=6 previously tested positive), delivering a sensibility similar to routine diagnostic methods, and 100% specificity. The proposed methodology has a substantial added value over existing methods, as it is able to both identify promising primer sets for a virus from a limited amount of data, and deliver effective results in a minimal amount of time. Considering the possibility of future pandemics, these characteristics are invaluable to promptly create specific detection methods for diagnostics.

Lopez-Rincon, A.; Tonda, A.; Mendoza-Maldonado, L.; Mulders, D.; Molenkamp, R.; Perez-Romero, C. A.; Claassen, E.; Garssen, J.; Kraneveld, A. D.

2020-09-10

Radiology Radiology

RadLex Normalization in Radiology Reports

ArXiv Preprint

Radiology reports have been widely used for extraction of various clinically significant information about patients' imaging studies. However, limited research has focused on standardizing the entities to a common radiology-specific vocabulary. Further, no study to date has attempted to leverage RadLex for standardization. In this paper, we aim to normalize a diverse set of radiological entities to RadLex terms. We manually construct a normalization corpus by annotating entities from three types of reports. This contains 1706 entity mentions. We propose two deep learning-based NLP methods based on a pre-trained language model (BERT) for automatic normalization. First, we employ BM25 to retrieve candidate concepts for the BERT-based models (re-ranker and span detector) to predict the normalized concept. The results are promising, with the best accuracy (78.44%) obtained by the span detector. Additionally, we discuss the challenges involved in corpus construction and propose new RadLex terms.

Surabhi Datta, Jordan Godfrey-Stovall, Kirk Roberts

2020-09-10

Ophthalmology Ophthalmology

Digital technology, tele-medicine and artificial intelligence in ophthalmology: A global perspective.

In Progress in retinal and eye research ; h5-index 55.0

The simultaneous maturation of multiple digital and telecommunications technologies in 2020 has created an unprecedented opportunity for ophthalmology to adapt to new models of care using tele-health supported by digital innovations. These digital innovations include artificial intelligence (AI), 5th generation (5G) telecommunication networks and the Internet of Things (IoT), creating an inter-dependent ecosystem offering opportunities to develop new models of eye care addressing the challenges of COVID-19 and beyond. Ophthalmology has thrived in some of these areas partly due to its many image-based investigations. Tele-health and AI provide synchronous solutions to challenges facing ophthalmologists and healthcare providers worldwide. This article reviews how countries across the world have utilised these digital innovations to tackle diabetic retinopathy, retinopathy of prematurity, age-related macular degeneration, glaucoma, refractive error correction, cataract and other anterior segment disorders. The review summarises the digital strategies that countries are developing and discusses technologies that may increasingly enter the clinical workflow and processes of ophthalmologists. Furthermore as countries around the world have initiated a series of escalating containment and mitigation measures during the COVID-19 pandemic, the delivery of eye care services globally has been significantly impacted. As ophthalmic services adapt and form a "new normal", the rapid adoption of some of telehealth and digital innovation during the pandemic is also discussed. Finally, challenges for validation and clinical implementation are considered, as well as recommendations on future directions.

Olivia Li Ji-Peng, Liu Hanruo, Ting Darren S J, Jeon Sohee, Chan R V Paul, Kim Judy E, Sim Dawn A, Thomas Peter B M, Lin Haotian, Chen Youxin, Sakomoto Taiji, Loewenstein Anat, Lam Dennis S C, Pasquale Louis R, Wong Tien Y, Lam Linda A, Ting Daniel S W

2020-Sep-05

Artificial intelligence, COVID-19, Deep learning, Diabetic retinopathy screening, Digital innovations, Digital technology, Digital transformation, Tele-ophthalmology, Tele-screening, Telemedicine

Public Health Public Health

Social Listening as a Rapid Approach to Collecting and Analyzing COVID-19 Symptoms and Disease Natural Histories Reported by Large Numbers of Individuals.

In Population health management

Given the severe and rapid impact of COVID-19, the pace of information sharing has been accelerated. However, traditional methods of disseminating and digesting medical information can be time-consuming and cumbersome. In a pilot study, the authors used social listening to quickly extract information from social media channels to explore what people with COVID-19 are talking about regarding symptoms and disease progression. The goal was to determine whether, by amplifying patient voices, new information could be identified that might have been missed through other sources. Two data sets from social media groups of people with or presumed to have COVID-19 were analyzed: a Facebook group poll, and conversation data from a Reddit group including detailed disease natural history-like posts. Content analysis and a customized analytics engine that incorporates machine learning and natural language processing were used to quickly identify symptoms mentioned. Key findings include more than 20 symptoms in the data sets that were not listed in online lists of symptoms from 4 respected medical information sources. The disease natural history-like posts revealed that people can experience symptoms for many weeks and that some symptoms change over time. This study demonstrates that social media can offer novel insights into patient experiences as a source of real-world data. This inductive research approach can quickly generate descriptive information that can be used to develop hypotheses and new research questions. Also, the method allows rapid assessments of large numbers of social media conversations that could be applied to monitor public health for emerging and rapidly spreading diseases such as COVID-19.

Picone Maria, Inoue Sarah, DeFelice Christopher, Naujokas Marisa F, Sinrod Jay, Cruz Vanessa A, Stapleton Jessica, Sinrod Emily, Diebel Sarah E, Wassman Edward Robert

2020-Sep-03

COVID-19, content analysis, data mining, disease natural histories, social listening, social media

General General

COVID-19 and Stock Market Volatility: An Industry Level Analysis.

In Finance research letters

COVID-19 has had significant impact on US stock market volatility. This study focuses on understanding the regime change from lower to higher volatility identified with a Markov Switching AR model. Utilizing machine learning feature selection methods, economic indicators are chosen to best explain changes in volatility. Results show that volatility is affected by specific economic indicators and is sensitive to COVID-19 news. Both negative and positive COVID-19 information is significant, though negative news is more impactful, suggesting a negativity bias. Significant increases in total and idiosyncratic risk are observed across all industries, while changes in systematic risk vary across industry.

Baek Seungho, Mohanty Sunil K, Mina Glambosky

2020-Sep-03

COVID-19, Idiosyncratic Risk, Industry, Machine Learning Feature Selection, Stock Market Volatility, Total Risk

General General

Computer-aided detection of COVID-19 from X-ray images using multi-CNN and Bayesnet classifier.

In Biocybernetics and biomedical engineering

Corona virus disease-2019 (COVID-19) is a pandemic caused by novel coronavirus. COVID-19 is spreading rapidly throughout the world. The gold standard for diagnosing COVID-19 is reverse transcription-polymerase chain reaction (RT-PCR) test. However, the facility for RT-PCR test is limited, which causes early diagnosis of the disease difficult. Easily available modalities like X-ray can be used to detect specific symptoms associated with COVID-19. Pre-trained convolutional neural networks are widely used for computer-aided detection of diseases from smaller datasets. This paper investigates the effectiveness of multi-CNN, a combination of several pre-trained CNNs, for the automated detection of COVID-19 from X-ray images. The method uses a combination of features extracted from multi-CNN with correlation based feature selection (CFS) technique and Bayesnet classifier for the prediction of COVID-19. The method was tested using two public datasets and achieved promising results on both the datasets. In the first dataset consisting of 453 COVID-19 images and 497 non-COVID images, the method achieved an AUC of 0.963 and an accuracy of 91.16%. In the second dataset consisting of 71 COVID-19 images and 7 non-COVID images, the method achieved an AUC of 0.911 and an accuracy of 97.44%. The experiments performed in this study proved the effectiveness of pre-trained multi-CNN over single CNN in the detection of COVID-19.

Abraham Bejoy, Nair Madhu S

2020-Sep-02

Bayesnet, CNN, COVID-19, Multi-CNN, X-ray

General General

A drone-based networked system and methods for combating coronavirus disease (COVID-19) pandemic.

In Future generations computer systems : FGCS

Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. It is similar to influenza viruses and raises concerns through alarming levels of spread and severity resulting in an ongoing pandemic worldwide. Within eight months (by August 2020), it infected 24.0 million persons worldwide and over 824 thousand have died. Drones or Unmanned Aerial Vehicles (UAVs) are very helpful in handling the COVID-19 pandemic. This work investigates the drone-based systems, COVID-19 pandemic situations, and proposes an architecture for handling pandemic situations in different scenarios using real-time and simulation-based scenarios. The proposed architecture uses wearable sensors to record the observations in Body Area Networks (BANs) in a push-pull data fetching mechanism. The proposed architecture is found to be useful in remote and highly congested pandemic areas where either the wireless or Internet connectivity is a major issue or chances of COVID-19 spreading are high. It collects and stores the substantial amount of data in a stipulated period and helps to take appropriate action as and when required. In real-time drone-based healthcare system implementation for COVID-19 operations, it is observed that a large area can be covered for sanitization, thermal image collection, and patient identification within a short period (2 KMs within 10 min approx.) through aerial route. In the simulation, the same statistics are observed with an addition of collision-resistant strategies working successfully for indoor and outdoor healthcare operations. Further, open challenges are identified and promising research directions are highlighted.

Kumar Adarsh, Sharma Kriti, Singh Harvinder, Naugriya Sagar Gupta, Gill Sukhpal Singh, Buyya Rajkumar

2021-Feb

Artificial intelligence, COVID-19, Collision avoidance, Drones, Internet of Things, Pandemic

General General

Tracking SARS-CoV-2 T cells with epitope T-cell receptor recognition models

bioRxiv Preprint

Much is still not understood about the human adaptive immune response to SARS-CoV-2, the causative agent of COVID-19. In this paper, we demonstrate the use of machine learning to classify SARS-CoV-2 epitope specific T-cell clonotypes in T-cell receptor (TCR) sequencing data. We apply these models to public TCR data and show how they can be used to study T-cell longitudinal profiles in COVID-19 patients to characterize how the adaptive immune system reacts to the SARS-CoV-2 virus. Our findings confirm prior knowledge that SARS-CoV-2 reactive T-cell diversity increases over the course of disease progression. However our results show a difference between those T cells that react to epitope unique to SARS-CoV-2, which show a more prominent increase, and those T cells that react to epitopes common to other coronaviruses, which begin at a higher baseline.

Meysman, P.; Postovskaya, A.; De Neuter, N.; Ogunjimi, B.; Laukens, K.

2020-09-09

General General

Health-behaviors associated with the growing risk of adolescent suicide attempts: A data-driven cross-sectional study

ArXiv Preprint

Purpose: Identify and examine the associations between health behaviors and increased risk of adolescent suicide attempts, while controlling for socioeconomic and demographic differences. Design: A data-driven analysis using cross-sectional data. Setting: Communities in the state of Montana from 1999 to 2017. Subjects: Selected 22,447 adolescents of whom 1,631 adolescents attempted suicide at least once. Measures: Overall 29 variables (predictors) accounting for psychological behaviors, illegal substances consumption, daily activities at schools and demographic backgrounds, were considered. Analysis: A library of machine learning algorithms along with the traditionally-used logistic regression were used to model and predict suicide attempt risk. Model performances (goodness-of-fit and predictive accuracy) were measured using accuracy, precision, recall and F-score metrics. Results: The non-parametric Bayesian tree ensemble model outperformed all other models, with 80.0% accuracy in goodness-of-fit (F-score:0.802) and 78.2% in predictive accuracy (F-score:0.785). Key health-behaviors identified include: being sad/hopeless, followed by safety concerns at school, physical fighting, inhalant usage, illegal drugs consumption at school, current cigarette usage, and having first sex at an early age (below 15 years of age). Additionally, the minority groups (American Indian/Alaska Natives, Hispanics/Latinos), and females are also found to be highly vulnerable to attempting suicides. Conclusion: Significant contribution of this work is understanding the key health-behaviors and health disparities that lead to higher frequency of suicide attempts among adolescents, while accounting for the non-linearity and complex interactions among the outcome and the exposure variables.

Zhiyuan Wei, Sayanti Mukherjee

2020-09-08

General General

Team Alex at CLEF CheckThat! 2020: Identifying Check-Worthy Tweets With Transformer Models

CLEF-2020

While misinformation and disinformation have been thriving in social media for years, with the emergence of the COVID-19 pandemic, the political and the health misinformation merged, thus elevating the problem to a whole new level and giving rise to the first global infodemic. The fight against this infodemic has many aspects, with fact-checking and debunking false and misleading claims being among the most important ones. Unfortunately, manual fact-checking is time-consuming and automatic fact-checking is resource-intense, which means that we need to pre-filter the input social media posts and to throw out those that do not appear to be check-worthy. With this in mind, here we propose a model for detecting check-worthy tweets about COVID-19, which combines deep contextualized text representations with modeling the social context of the tweet. We further describe a number of additional experiments and comparisons, which we believe should be useful for future research as they provide some indication about what techniques are effective for the task. Our official submission to the English version of CLEF-2020 CheckThat! Task 1, system Team_Alex, was ranked second with a MAP score of 0.8034, which is almost tied with the wining system, lagging behind by just 0.003 MAP points absolute.

Alex Nikolov, Giovanni Da San Martino, Ivan Koychev, Preslav Nakov

2020-09-07

General General

Managing gestational diabetes mellitus using a smartphone application with artificial intelligence (SineDie) during the COVID-19 pandemic: Much more than just telemedicine.

In Diabetes research and clinical practice ; h5-index 50.0

We describe our experience in the remote management of women with gestational diabetes mellitus during the COVID-19 pandemic. We used a mobile phone application with artificial intelligence that automatically classifies and analyses the data (ketonuria, diet transgressions, and blood glucose values), making adjustment recommendations regarding the diet or insulin treatment.

Albert Lara, Capel Ismael, García-Sáez Gema, Martín-Redondo Pablo, Hernando M Elena, Rigla Mercedes

2020-Sep-03

Artificial intelligence, Gestational diabetes mellitus, Mobile phone application, Telemedicine, eHealth

oncology Oncology

Scholarly Publishing in the Wake of COVID-19.

In International journal of radiation oncology, biology, physics

The speed at which the COVID-19 pandemic spread across the globe and the accompanying need to rapidly disseminate knowledge have highlighted the inadequacies of the traditional research/publication cycle, particularly the slowness and the fragmentary access globally to manuscripts and their findings. Scholarly communication has slowly been undergoing transformational changes since the introduction of the Internet in the 1990s. The pandemic response has created an urgency that has accelerated these trends in some areas. The magnitude of the global emergency has strongly bolstered calls to make the entire research and publishing lifecycle transparent and open. The global scientific community has collaborated in rapid, open, and transparent means that are unprecedented. The general public has been reminded of the important of science, and trusted communication of scientific findings, in everyday life. In addition to COVID-19-driven innovation in scholarly communication, alternative bibliometrics and artificial intelligence tools will further transform academic publishing in the near future.

Miller Robert C, Tsai C Jillian

2020-Oct-01

General General

Machine learning-based mortality rate prediction using optimized hyper-parameter.

In Computer methods and programs in biomedicine

OBJECTIVE AND BACKGROUND : The current scenario of the Pandemic of COVID-19 demands multi-channel investigations and predictions. A variety of prediction models are available in the literature. The majority of these models are based on extrapolating by the parameters related to the diseases, which are history-oriented. Instead, the current research is designed to predict the mortality rate of COVID-19 by Regression techniques in comparison to the models followed by five countries.

METHODS : The Regression method with an optimized hyper-parameter is used to develop these models under training data by Machine Learning Technique.

RESULTS : The validity of the proposed model is endorsed by considering the case study on the data for Pakistan. Five distinct models for mortality rate prediction are built using Confirmed cases data as a predictor variable for France, Spain, Turkey, Sweden, and Pakistan, respectively. The results evidenced that Sweden has a fewer death case over 20,000 confirmed cases without observing lockdown. Hence, by following the strategy adopted by Sweden, the chosen entity will control the death rate despite the increase of the confirmed cases.

CONCLUSION : The evaluated results notice the high mortality rate and low RMSE for Pakistan by the GPR method based Mortality model. Therefore, the morality rate based MRP model is selected for the COVID-19 death rate in Pakistan. Hence, the best-fit is the Sweden model to control the mortality rate.

Khan Y A, Abbas S Z, Truong Buu-Chau

2020-Aug-18

Covid-19 deaths rate, Hyper-parameter, Mortality rate, Optimization, Prediction

General General

Pollution, economic growth, and COVID-19 deaths in India: a machine learning evidence.

In Environmental science and pollution research international

This study uses two different approaches to explore the relationship between pollution emissions, economic growth, and COVID-19 deaths in India. Using a time series approach and annual data for the years from 1980 to 2018, stationarity and Toda-Yamamoto causality tests were performed. The results highlight unidirectional causality between economic growth and pollution. Then, a D2C algorithm on proportion-based causality is applied, implementing the Oryx 2.0.8 protocol in Apache. The underlying hypothesis is that a predetermined pollution concentration, caused by economic growth, could foster COVID-19 by making the respiratory system more susceptible to infection. We use data (from January 29 to May 18, 2020) on confirmed deaths (total and daily) and air pollution concentration levels for 25 major Indian cities. We verify a ML causal link between PM2.5, CO2, NO2, and COVID-19 deaths. The implications require careful policy design.

Mele Marco, Magazzino Cosimo

2020-Sep-04

COVID-19, Economic growth, India, Machine learning, Pollution, Time series

General General

A model for the effective COVID-19 identification in uncertainty environment using primary symptoms and CT scans.

In Health informatics journal ; h5-index 25.0

The rapid spread of the COVID-19 virus around the world poses a real threat to public safety. Some COVID-19 symptoms are similar to other viral chest diseases, which makes it challenging to develop models for effective detection of COVID-19 infection. This article advocates a model to differentiate between COVID-19 and other four viral chest diseases under uncertainty environment using the viruses primary symptoms and CT scans. The proposed model is based on a plithogenic set, which provides higher accurate evaluation results in an uncertain environment. The proposed model employs the best-worst method (BWM) and the technique in order of preference by similarity to ideal solution (TOPSIS). Besides, this study discusses how smart Internet of Things technology can assist medical staff in monitoring the spread of COVID-19. Experimental evaluation of the proposed model was conducted on five different chest diseases. Evaluation results demonstrate that the proposed model effectiveness in detecting the COVID-19 in all five cases achieving detection accuracy of up to 98%.

Abdel-Basst Mohamed, Mohamed Rehab, Elhoseny Mohamed

2020-Sep-04

Artificial Intelligence, BWM, COVID-19, CT imaging, Internet of Things, Plithogenic, TOPSIS, smart spaces, symptoms, viral chest diseases

Pathology Pathology

Artificial Intelligence-Assisted Loop Mediated Isothermal Amplification (AI-LAMP) for Rapid Detection of SARS-CoV-2.

In Viruses ; h5-index 58.0

Until vaccines and effective therapeutics become available, the practical solution to transit safely out of the current coronavirus disease 19 (CoVID-19) lockdown may include the implementation of an effective testing, tracing and tracking system. However, this requires a reliable and clinically validated diagnostic platform for the sensitive and specific identification of SARS-CoV-2. Here, we report on the development of a de novo, high-resolution and comparative genomics guided reverse-transcribed loop-mediated isothermal amplification (LAMP) assay. To further enhance the assay performance and to remove any subjectivity associated with operator interpretation of results, we engineered a novel hand-held smart diagnostic device. The robust diagnostic device was further furnished with automated image acquisition and processing algorithms and the collated data was processed through artificial intelligence (AI) pipelines to further reduce the assay run time and the subjectivity of the colorimetric LAMP detection. This advanced AI algorithm-implemented LAMP (ai-LAMP) assay, targeting the RNA-dependent RNA polymerase gene, showed high analytical sensitivity and specificity for SARS-CoV-2. A total of ~200 coronavirus disease (CoVID-19)-suspected NHS patient samples were tested using the platform and it was shown to be reliable, highly specific and significantly more sensitive than the current gold standard qRT-PCR. Therefore, this system could provide an efficient and cost-effective platform to detect SARS-CoV-2 in resource-limited laboratories.

Rohaim Mohammed A, Clayton Emily, Sahin Irem, Vilela Julianne, Khalifa Manar E, Al-Natour Mohammad Q, Bayoumi Mahmoud, Poirier Aurore C, Branavan Manoharanehru, Tharmakulasingam Mukunthan, Chaudhry Nouman S, Sodi Ravinder, Brown Amy, Burkhart Peter, Hacking Wendy, Botham Judy, Boyce Joe, Wilkinson Hayley, Williams Craig, Whittingham-Dowd Jayde, Shaw Elisabeth, Hodges Matt, Butler Lisa, Bates Michelle D, La Ragione Roberto, Balachandran Wamadeva, Fernando Anil, Munir Muhammad

2020-Sep-01

LAMP, SARS-CoV-2, artificial intelligence, diagnosis, point of care

Public Health Public Health

Using 164 Million Google Street View Images to Derive Built Environment Predictors of COVID-19 Cases.

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

The spread of COVID-19 is not evenly distributed. Neighborhood environments may structure risks and resources that produce COVID-19 disparities. Neighborhood built environments that allow greater flow of people into an area or impede social distancing practices may increase residents' risk for contracting the virus. We leveraged Google Street View (GSV) images and computer vision to detect built environment features (presence of a crosswalk, non-single family home, single-lane roads, dilapidated building and visible wires). We utilized Poisson regression models to determine associations of built environment characteristics with COVID-19 cases. Indicators of mixed land use (non-single family home), walkability (sidewalks), and physical disorder (dilapidated buildings and visible wires) were connected with higher COVID-19 cases. Indicators of lower urban development (single lane roads and green streets) were connected with fewer COVID-19 cases. Percent black and percent with less than a high school education were associated with more COVID-19 cases. Our findings suggest that built environment characteristics can help characterize community-level COVID-19 risk. Sociodemographic disparities also highlight differential COVID-19 risk across groups of people. Computer vision and big data image sources make national studies of built environment effects on COVID-19 risk possible, to inform local area decision-making.

Nguyen Quynh C, Huang Yuru, Kumar Abhinav, Duan Haoshu, Keralis Jessica M, Dwivedi Pallavi, Meng Hsien-Wen, Brunisholz Kimberly D, Jay Jonathan, Javanmardi Mehran, Tasdizen Tolga

2020-Sep-01

COVID-19, GIS, big data, built environment, computer vision, machine learning

General General

Machine learning reveals that prolonged exposure to air pollution is associated with SARS-CoV-2 mortality and infectivity in Italy.

In Environmental pollution (Barking, Essex : 1987)

Air pollution can increase the risk of respiratory diseases, enhancing the susceptibility to viral and bacterial infections. Some studies suggest that small air particles facilitate the spread of viruses and also of the new coronavirus, besides the direct person-to-person contagion. However, the effects of the exposure to particulate matter and other contaminants on SARS-CoV-2 has been poorly explored. Here we examined the possible reasons why the new coronavirus differently impacted on Italian regional and provincial populations. With the help of artificial intelligence, we studied the importance of air pollution for mortality and positivity rates of the SARS-CoV-2 outbreak in Italy. We discovered that among several environmental, health, and socio-economic factors, air pollution and fine particulate matter (PM2.5), as its main component, resulted as the most important predictors of SARS-CoV-2 effects. We also found that the emissions from industries, farms, and road traffic - in order of importance - might be responsible for more than 70% of the deaths associated with SARS-CoV-2 nationwide. Given the major contribution played by air pollution (much more important than other health and socio-economic factors, as we discovered), we projected that, with an increase of 5-10% in air pollution, similar future pathogens may inflate the epidemic toll of Italy by 21-32% additional cases, whose 19-28% more positives and 4-14% more deaths. Our findings, demonstrating that fine-particulate (PM2.5) pollutant level is the most important factor to predict SARS-CoV-2 effects that would worsen even with a slight decrease of air quality, highlight that the imperative of productivity before health and environmental protection is, indeed, a short-term/small-minded resolution.

Cazzolla Gatti Roberto, Velichevskaya Alena, Tateo Andrea, Amoroso Nicola, Monaco Alfonso

2020-Aug-21

Farms, Industry, Italy, Mortality, PM2.5, Pollution, SARS-CoV-2

General General

New technologies and Amyotrophic Lateral Sclerosis - Which step forward rushed by the COVID-19 pandemic?

In Journal of the neurological sciences

Amyotrophic Lateral Sclerosis (ALS) is a fast-progressive neurodegenerative disease leading to progressive physical immobility with usually normal or mild cognitive and/or behavioural involvement. Many patients are relatively young, instructed, sensitive to new technologies, and professionally active when developing the first symptoms. Older patients usually require more time, encouragement, reinforcement and a closer support but, nevertheless, selecting user-friendly devices, provided earlier in the course of the disease, and engaging motivated carers may overcome many technological barriers. ALS may be considered a model for neurodegenerative diseases to further develop and test new technologies. From multidisciplinary teleconsults to telemonitoring of the respiratory function, telemedicine has the potentiality to embrace other fields, including nutrition, physical mobility, and the interaction with the environment. Brain-computer interfaces and eye tracking expanded the field of augmentative and alternative communication in ALS but their potentialities go beyond communication, to cognition and robotics. Virtual reality and different forms of artificial intelligence present further interesting possibilities that deserve to be investigated. COVID-19 pandemic is an unprecedented opportunity to speed up the development and implementation of new technologies in clinical practice, improving the daily living of both ALS patients and carers. The present work reviews the current technologies for ALS patients already in place or being under evaluation with published publications, prompted by the COVID-19 pandemic.

Pinto Susana, Quintarelli Stefano, Silani Vincenzo

2020-Aug-05

Amyotrophic lateral sclerosis, Artificial intelligence, Brain-computer interfaces, COVID-19, Eye-tracking, Robotics, Telemedicine, Virtual reality

General General

Current Challenges of Digital Health Interventions in Pakistan: Mixed Methods Analysis.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Digital health is well-positioned in low and middle-income countries (LMICs) to revolutionize health care due, in part, to increasing mobile phone access and internet connectivity. This paper evaluates the underlying factors that can potentially facilitate or hinder the progress of digital health in Pakistan.

OBJECTIVE : The objective of this study is to identify the current digital health projects and studies being carried out in Pakistan, as well as the key stakeholders involved in these initiatives. We aim to follow a mixed-methods strategy and to evaluate these projects and studies through a strengths, weaknesses, opportunities, and threats (SWOT) analysis to identify the internal and external factors that can potentially facilitate or hinder the progress of digital health in Pakistan.

METHODS : This study aims to evaluate digital health projects carried out in the last 5 years in Pakistan with mixed methods. The qualitative and quantitative data obtained from field surveys were categorized according to the World Health Organization's (WHO) recommended building blocks for health systems research, and the data were analyzed using a SWOT analysis strategy.

RESULTS : Of the digital health projects carried out in the last 5 years in Pakistan, 51 are studied. Of these projects, 46% (23/51) used technology for conducting research, 30% (15/51) used technology for implementation, and 12% (6/51) used technology for app development. The health domains targeted were general health (23/51, 46%), immunization (13/51, 26%), and diagnostics (5/51, 10%). Smartphones and devices were used in 55% (28/51) of the interventions, and 59% (30/51) of projects included plans for scaling up. Artificial intelligence (AI) or machine learning (ML) was used in 31% (16/51) of projects, and 74% (38/51) of interventions were being evaluated. The barriers faced by developers during the implementation phase included the populations' inability to use the technology or mobile phones in 21% (11/51) of projects, costs in 16% (8/51) of projects, and privacy concerns in 12% (6/51) of projects.

CONCLUSIONS : We conclude that while digital health has a promising future in Pakistan, it is still in its infancy at the time of this study. However, due to the coronavirus disease 2019 (COVID-19) pandemic, there is an increase in demand for digital health and implementation of health outcomes following global social distancing protocols, especially in LMICs. Hence, there is a need for active involvement by public and private organizations to regulate, mobilize, and expand the digital health sector for the improvement of health care systems in countries.

Kazi Abdul Momin, Qazi Saad Ahmed, Ahsan Nazia, Khawaja Sadori, Sameen Fareeha, Saqib Muhammad, Khan Mughal Muhammad Ayub, Wajidali Zabin, Ali Sikander, Ahmed Rao Moueed, Kalimuddin Hussain, Rauf Yasir, Mahmood Fatima, Zafar Saad, Abbasi Tufail Ahmad, Khoumbati Khalil-Ur-Rahmen, Abbasi Munir A, Stergioulas Lampros K

2020-09-03

LMICs, Pakistan, SWOT, digital health, eHealth, mHealth, telehealth

General General

Real-World Implications of Rapidly Responsive COVID-19 Spread Model with Time Dependent Parameters Via Deep Learning: Algorithm Development and Validation.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : The coronavirus disease 2019 (COVID-19) pandemic has been a major shock to the whole world since March 2020. From the experience of the 1918 influenza pandemic, we know that decreases in infection rates of COVID-19 do not guarantee continuity of the trend.

OBJECTIVE : This study was conducted to develop a precise spread model of COVID-19 with time-dependent parameters via deep learning responding promptly to the dynamic situation of the outbreak to proactively minimize damage.

METHODS : In this study, we investigated a mathematical model with time-dependent parameters via deep learning based on forward-inverse problems. We used data from Korea Centers for Disease Control & Prevention (KCDC) and Center for Systems Science and Engineering (CSSE) at Johns Hopkins University for Korea and the other countries, respectively. Since the data consist of confirmed, recovered, and deceased cases, we selected the SIR (Susceptible - Infected - Recovered) model and found approximated solutions as well as model parameters. Specifically, we applied fully connected neural networks to the solutions and parameters, and designed suitable loss functions.

RESULTS : We developed an entirely new SIR model with time-dependent parameters via deep learning methods. Furthermore, we validated the model with the conventional Runge-Kutta of order 4 (RK4) model to confirm its convergent nature. In addition, we evaluated our model based on the real-world situation reported from KCDC, the Korean government, and news media. We also cross-validated our model by using data from CSSE for Italy, Sweden, and US.

CONCLUSIONS : The methodology and new model of this study could be employed for short term prediction of COVID-19, by which the government can be prepared for a new outbreak. In addition, from the perspective of measuring medical resources, our model has powerful strength because it assumes all the parameters as time-dependent, which reflects the exact status of viral spread.

CLINICALTRIAL :

Jung Se Young, Jo Hyeontae, Son Hwijae, Hwang Hyung Ju

2020-Sep-01

General General

A public-private partnership for the express development of antiviral leads: a perspective view.

In Expert opinion on drug discovery ; h5-index 34.0

INTRODUCTION : The COVID-19 pandemic raises the question of strategic readiness for emergent pathogens. The current case illustrates that the cost of inaction can be higher in the future. The perspective article proposes a dedicated, government-sponsored agency developing anti-viral leads against all potentially dangerous pathogen species.

AREAS COVERED : The author explores the methods of computational drug screening and in-silico synthesis and proposes a specialized government-sponsored agency focusing on leads and functioning in collaboration with a network of labs, pharma, biotech firms, and academia, in order to test each lead against multiple viral species. The agency will employ artificial intelligence and machine learning tools to cut the costs further. The algorithms are expected to receive continuous feedback from the network of partners conducting the tests.

EXPERT OPINION : The author proposes a bionic principle, emulating antibody response by producing a combinatorial diversity of high q uality generic antiviral leads, suitable for multiple potentially emerging species. The availability of multiple pre-tested agents and an even greater number of combinations would reduce the impact of the next outbreak. The methodologies developed in this effort are likely to find utility in the design of chronic disease therapeutics.

Mayburd Anatoly

2020-Sep-02

COVID-19, anti-virals, artificial intelligence, docking, emergent pathogens

Radiology Radiology

Visual and software-based quantitative chest CT assessment of COVID-19: correlation with clinical findings.

In Diagnostic and interventional radiology (Ankara, Turkey)

PURPOSE : The aim of this study was to evaluate visual and software-based quantitative assessment of parenchymal changes and normal lung parenchyma in patients with coronavirus disease 2019 (COVID-19) pneumonia. The secondary aim of the study was to compare the radiologic findings with clinical and laboratory data.

METHODS : Patients with COVID-19 who underwent chest computed tomography (CT) between March 11, 2020 and April 15, 2020 were retrospectively evaluated. Clinical and laboratory findings of patients with abnormal findings on chest CT and PCR-evidence of COVID-19 infection were recorded. Visual quantitative assessment score (VQAS) was performed according to the extent of lung opacities. Software-based quantitative assessment of the normal lung parenchyma percentage (SQNLP) was automatically quantified by a deep learning software. The presence of consolidation and crazy paving pattern (CPP) was also recorded. Statistical analyses were performed to evaluate the correlation between quantitative radiologic assessments, and clinical and laboratory findings, as well as to determine the predictive utility of radiologic findings for estimating severe pneumonia and admission to intensive care unit (ICU).

RESULTS : A total of 90 patients were enrolled. Both VQAS and SQNLP were significantly correlated with multiple clinical parameters. While VQAS >8.5 (sensitivity, 84.2%; specificity, 80.3%) and SQNLP <82.45% (sensitivity, 83.1%; specificity, 84.2%) were related to severe pneumonia, VQAS >9.5 (sensitivity, 93.3%; specificity, 86.5%) and SQNLP <81.1% (sensitivity, 86.5%; specificity, 86.7%) were predictive of ICU admission. Both consolidation and CPP were more commonly seen in patients with severe pneumonia than patients with nonsevere pneumonia (P = 0.197 for consolidation; P < 0.001 for CPP). Moreover, the presence of CPP showed high specificity (97.2%) for severe pneumonia.

CONCLUSION : Both SQNLP and VQAS were significantly related to the clinical findings, highlighting their clinical utility in predicting severe pneumonia, ICU admission, length of hospital stay, and management of the disease. On the other hand, presence of CPP has high specificity for severe COVID-19 pneumonia.

Durhan Gamze, Ardalı Düzgün Selin, Başaran Demirkazık Figen, Irmak İlim, İdilman İlkay, Gülsün Akpınar Meltem, Akpınar Erhan, Öcal Serpil, Telli Gülçin, Topeli Arzu, Arıyürek Orhan Macit

2020-Sep-02

General General

Coronavirus herd immunity optimizer (CHIO).

In Neural computing & applications

In this paper, a new nature-inspired human-based optimization algorithm is proposed which is called coronavirus herd immunity optimizer (CHIO). The inspiration of CHIO is originated from the herd immunity concept as a way to tackle coronavirus pandemic (COVID-19). The speed of spreading coronavirus infection depends on how the infected individuals directly contact with other society members. In order to protect other members of society from the disease, social distancing is suggested by health experts. Herd immunity is a state the population reaches when most of the population is immune which results in the prevention of disease transmission. These concepts are modeled in terms of optimization concepts. CHIO mimics the herd immunity strategy as well as the social distancing concepts. Three types of individual cases are utilized for herd immunity: susceptible, infected, and immuned. This is to determine how the newly generated solution updates its genes with social distancing strategies. CHIO is evaluated using 23 well-known benchmark functions. Initially, the sensitivity of CHIO to its parameters is studied. Thereafter, the comparative evaluation against seven state-of-the-art methods is conducted. The comparative analysis verifies that CHIO is able to yield very competitive results compared to those obtained by other well-established methods. For more validations, three real-world engineering optimization problems extracted from IEEE-CEC 2011 are used. Again, CHIO is proved to be efficient. In conclusion, CHIO is a very powerful optimization algorithm that can be used to tackle many optimization problems across a wide variety of optimization domains.

Al-Betar Mohammed Azmi, Alyasseri Zaid Abdi Alkareem, Awadallah Mohammed A, Abu Doush Iyad

2020-Aug-27

COVID-19, Coronavirus, Herd immunity, Metaheuristic, Nature inspired, Optimization

oncology Oncology

A Decision Aide for the Risk Stratification of GU Cancer Patients at Risk of SARS-CoV-2 Infection, COVID-19 Related Hospitalization, Intubation, and Mortality.

In Journal of clinical medicine

Treatment decisions for both early and advanced genitourinary (GU) malignancies take into account the risk of dying from the malignancy as well as the risk of death due to other causes such as other co-morbidities. COVID-19 is a new additional and immediate risk to a patient's morbidity and mortality and there is a need for an accurate assessment as to the potential impact on of this syndrome on GU cancer patients. The aim of this work was to develop a risk tool to identify GU cancer patients at risk of diagnosis, hospitalization, intubation, and mortality from COVID-19. A retrospective case showed a series of GU cancer patients screened for COVID-19 across the Mount Sinai Health System (MSHS). Four hundred eighty-four had a GU malignancy and 149 tested positive for SARS-CoV-2. Demographic and clinical variables of >38,000 patients were available in the institutional database and were utilized to develop decision aides to predict a positive SARS-CoV-2 test, as well as COVID-19-related hospitalization, intubation, and death. A risk tool was developed using a combination of machine learning methods and utilized BMI, temperature, heart rate, respiratory rate, blood pressure, and oxygen saturation. The risk tool for predicting a diagnosis of SARS-CoV-2 had an AUC of 0.83, predicting hospitalization for management of COVID-19 had an AUC of 0.95, predicting patients requiring intubation had an AUC of 0.97, and for predicting COVID-19-related death, the risk tool had an AUC of 0.79. The models had an acceptable calibration and provided a superior net benefit over other common strategies across the entire range of threshold probabilities.

Lundon Dara J, Kelly Brian D, Shukla Devki, Bolton Damien M, Wiklund Peter, Tewari Ash

2020-Aug-30

COVID-19, decision curve analysis, genito-urinary cancer, mortality, risk calculator, urologic oncology

Radiology Radiology

Coronavirus Disease 2019 (COVID-19) diagnostic technologies: A country-based retrospective analysis of screening and containment procedures during the first wave of the pandemic.

In Clinical imaging

Since first report of a novel coronavirus in December of 2019, the Coronavirus Disease 2019 (COVID-19) pandemic has crippled healthcare systems around the world. While many initial screening protocols centered around laboratory detection of the virus, early testing assays were thought to be poorly sensitive in comparison to chest computed tomography, especially in asymptomatic disease. Coupled with shortages of reverse transcription polymerase chain reaction (RT-PCR) testing kits in many parts of the world, these regions instead turned to the use of advanced imaging as a first-line screening modality. However, in contrast to previous Severe Acute Respiratory Syndrome and Middle East Respiratory Syndrome coronavirus epidemics, chest X-ray has not demonstrated optimal sensitivity to be of much utility in first-line screening protocols. Though current national and international guidelines recommend for the use of RT-PCR as the primary screening tool for suspected cases of COVID-19, institutional and regional protocols must consider local availability of resources when issuing universal recommendations. Successful containment and social mitigation strategies worldwide have been thus far predicated on unified governmental responses, though the underlying ideologies of these practices may not be widely applicable in many Western nations. As the strain on the radiology workforce continues to mount, early results indicate a promising role for the use of machine-learning algorithms as risk stratification schema in the months to come.

Fields Brandon K K, Demirjian Natalie L, Gholamrezanezhad Ali

2020-Nov

COVID-19, Chest CT, Coronavirus, Machine-learning, Pandemic, Pneumonia, RT-PCR, Radiology, SARS-CoV-2

Ophthalmology Ophthalmology

Toward automated severe pharyngitis detection with smartphone camera using deep learning networks.

In Computers in biology and medicine

PURPOSE : Severe pharyngitis is frequently associated with inflammations caused by streptococcal pharyngitis, which can cause immune-mediated and post-infectious complications. The recent global pandemic of coronavirus disease (COVID-19) encourages the use of telemedicine for patients with respiratory symptoms. This study, therefore, purposes automated detection of severe pharyngitis using a deep learning framework with self-taken throat images.

METHODS : A dataset composed of two classes of 131 throat images with pharyngitis and 208 normal throat images was collected. Before the training classifier, we constructed a cycle consistency generative adversarial network (CycleGAN) to augment the training dataset. The ResNet50, Inception-v3, and MobileNet-v2 architectures were trained with transfer learning and validated using a randomly selected test dataset. The performance of the models was evaluated based on the accuracy and area under the receiver operating characteristic curve (ROC-AUC).

RESULTS : The CycleGAN-based synthetic images reflected the pragmatic characteristic features of pharyngitis. Using the synthetic throat images, the deep learning model demonstrated a significant improvement in the accuracy of the pharyngitis diagnosis. ResNet50 with GAN-based augmentation showed the best ROC-AUC of 0.988 for pharyngitis detection in the test dataset. In the 4-fold cross-validation using the ResNet50, the highest detection accuracy and ROC-AUC achieved were 95.3% and 0.992, respectively.

CONCLUSION : The deep learning model for smartphone-based pharyngitis screening allows fast identification of severe pharyngitis with a potential of the timely diagnosis of pharyngitis. In the recent pandemic of COVID-19, this framework will help patients with upper respiratory symptoms to improve convenience in diagnosis and reduce transmission.

Yoo Tae Keun, Choi Joon Yul, Jang Younil, Oh Ein, Ryu Ik Hee

2020-Aug-20

Automated diagnosis, Deep learning, Pharyngitis, Smartphone, Telemedicine, Tonsillitis

Public Health Public Health

Assessing the Impact of the COVID-19 Pandemic in Spain: Large-Scale, Online, Self-Reported Population Survey.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Spain has been one of the countries most impacted by the COVID-19 pandemic. Since the first confirmed case was reported on January 31, 2020, there have been over 405,000 cases and 28,000 deaths in Spain. The economic and social impact is without precedent. Thus, it is important to quickly assess the situation and perception of the population. Large-scale online surveys have been shown to be an effective tool for this purpose.

OBJECTIVE : We aim to assess the situation and perception of the Spanish population in four key areas related to the COVID-19 pandemic: social contact behavior during confinement, personal economic impact, labor situation, and health status.

METHODS : We obtained a large sample using an online survey with 24 questions related to COVID-19 in the week of March 28-April 2, 2020, during the peak of the first wave of COVID-19 in Spain. The self-selection online survey method of nonprobability sampling was used to recruit 156,614 participants via social media posts that targeted the general adult population (age >18 years). Given such a large sample, the 95% CI was ±0.843 for all reported proportions.

RESULTS : Regarding social behavior during confinement, participants mainly left their homes to satisfy basic needs. We found several statistically significant differences in social behavior across genders and age groups. The population's willingness to comply with the confinement measures is evident. From the survey answers, we identified a significant adverse economic impact of the pandemic on those working in small businesses and a negative correlation between economic damage and willingness to stay in confinement. The survey revealed that close contacts play an important role in the transmission of the disease, and 28% of the participants lacked the necessary resources to properly isolate themselves. We also identified a significant lack of testing, with only 1% of the population tested and 6% of respondents unable to be tested despite their doctor's recommendation. We developed a generalized linear model to identify the variables that were correlated with a positive SARS-CoV-2 test result. Using this model, we estimated an average of 5% for SARS-CoV-2 prevalence in the Spanish population during the time of the study. A seroprevalence study carried out later by the Spanish Ministry of Health reported a similar level of disease prevalence (5%).

CONCLUSIONS : Large-scale online population surveys, distributed via social media and online messaging platforms, can be an effective, cheap, and fast tool to assess the impact and prevalence of an infectious disease in the context of a pandemic, particularly when there is a scarcity of official data and limited testing capacity.

Oliver Nuria, Barber Xavier, Roomp Kirsten, Roomp Kristof

2020-09-10

COVID-19, SARS-CoV-2, disease prevalence, impact, infectious disease, large-scale online surveys, outbreak, perception, public engagement, public health, public health authorities, spain, survey

General General

Identifying policy challenges of COVID-19 in hardly reliable data and judging the success of lockdown measures.

In Journal of population economics

Identifying structural breaks in the dynamics of COVID-19 contagion is crucial to promptly assess policies and evaluate the effectiveness of lockdown measures. However, official data record infections after a critical and unpredictable delay. Moreover, people react to the health risks of the virus and also anticipate lockdowns. All of this makes it complex to quickly and accurately detect changing patterns in the virus's infection dynamic. We propose a machine learning procedure to identify structural breaks in the time series of COVID-19 cases. We consider the case of Italy, an early-affected country that was unprepared for the situation, and detect the dates of structural breaks induced by three national lockdowns so as to evaluate their effects and identify some related policy issues. The strong but significantly delayed effect of the first lockdown suggests a relevant announcement effect. In contrast, the last lockdown had significantly less impact. The proposed methodology is robust as a real-time procedure for early detection of the structural breaks: the impact of the first two lockdowns could have been correctly identified just the day after they actually occurred.

Bonacini Luca, Gallo Giovanni, Patriarca Fabrizio

2020-Aug-26

COVID-19, Coronavirus, Lockdown, Machine learning

General General

Segmenting areas of potential contamination for adaptive robotic disinfection in built environments.

In Building and environment

Mass-gathering built environments such as hospitals, schools, and airports can become hot spots for pathogen transmission and exposure. Disinfection is critical for reducing infection risks and preventing outbreaks of infectious diseases. However, cleaning and disinfection are labor-intensive, time-consuming, and health-undermining, particularly during the pandemic of the coronavirus disease in 2019. To address the challenge, a novel framework is proposed in this study to enable robotic disinfection in built environments to reduce pathogen transmission and exposure. First, a simultaneous localization and mapping technique is exploited for robot navigation in built environments. Second, a deep-learning method is developed to segment and map areas of potential contamination in three dimensions based on the object affordance concept. Third, with short-wavelength ultraviolet light, the trajectories of robotic disinfection are generated to adapt to the geometries of areas of potential contamination to ensure complete and safe disinfection. Both simulations and physical experiments were conducted to validate the proposed methods, which demonstrated the feasibility of intelligent robotic disinfection and highlighted the applicability in mass-gathering built environments.

Hu Da, Zhong Hai, Li Shuai, Tan Jindong, He Qiang

2020-Oct-15

Built environment, COVID-19, Deep learning, Infection prevention, Robotic disinfection

General General

Automatically discriminating and localizing COVID-19 from community-acquired pneumonia on chest X-rays.

In Pattern recognition

The COVID-19 outbreak continues to threaten the health and life of people worldwide. It is an immediate priority to develop and test a computer-aided detection (CAD) scheme based on deep learning (DL) to automatically localize and differentiate COVID-19 from community-acquired pneumonia (CAP) on chest X-rays. Therefore, this study aims to develop and test an efficient and accurate deep learning scheme that assists radiologists in automatically recognizing and localizing COVID-19. A retrospective chest X-ray image dataset was collected from open image data and the Xiangya Hospital, which was divided into a training group and a testing group. The proposed CAD framework is composed of two steps with DLs: the Discrimination-DL and the Localization-DL. The first DL was developed to extract lung features from chest X-ray radiographs for COVID-19 discrimination and trained using 3548 chest X-ray radiographs. The second DL was trained with 406-pixel patches and applied to the recognized X-ray radiographs to localize and assign them into the left lung, right lung or bipulmonary. X-ray radiographs of CAP and healthy controls were enrolled to evaluate the robustness of the model. Compared to the radiologists' discrimination and localization results, the accuracy of COVID-19 discrimination using the Discrimination-DL yielded 98.71%, while the accuracy of localization using the Localization-DL was 93.03%. This work represents the feasibility of using a novel deep learning-based CAD scheme to efficiently and accurately distinguish COVID-19 from CAP and detect localization with high accuracy and agreement with radiologists.

Wang Zheng, Xiao Ying, Li Yong, Zhang Jie, Lu Fanggen, Hou Muzhou, Liu Xiaowei

2020-Aug-26

COVID-19, Chest X-ray (CXR), Community-acquired pneumonia (CAP), Computer-aided detection (CAD), Deep learning (DL)