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Internal Medicine Internal Medicine

Minimizing Selection and Classification Biases. Comment on "Clinical Characteristics and Prognostic Factors for Intensive Care Unit Admission of Patients With COVID-19: Retrospective Study Using Machine Learning and Natural Language Processing".

In Journal of medical Internet research ; h5-index 88.0

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Martos Pérez Francisco, Gomez Huelgas Ricardo, Martín Escalante María Dolores, Casas Rojo José Manuel

2021-May-13

General General

Is There an App for That?: Ethical Issues in the Digital Mental Health Response to COVID-19.

In AJOB neuroscience

Well before COVID-19, there was growing excitement about the potential of various digital technologies such as tele-health, smartphone apps, or AI chatbots to revolutionize mental healthcare. As the SARS-CoV-2 virus spread across the globe, clinicians warned of the mental illness epidemic within the coronavirus pandemic. Now, funding for digital mental health technologies is surging and many researchers are calling for widespread adoption to address the mental health sequelae of COVID-19. Reckoning with the ethical implications of these technologies is urgent because decisions made today will shape the future of mental health research and care for the foreseeable future. We contend that the most pressing ethical issues concern (1) the extent to which these technologies demonstrably improve mental health outcomes and (2) the likelihood that wide-scale adoption will exacerbate the existing health inequalities laid bare by the pandemic. We argue that the evidence for efficacy is weak and that the likelihood of increasing inequalities is high. First, we review recent trends in digital mental health. Next, we turn to the clinical literature to show that many technologies proposed as a response to COVID-19 are unlikely to improve outcomes. Then, we argue that even evidence-based technologies run the risk of increasing health disparities. We conclude by suggesting that policymakers should not allocate limited resources to the development of many digital mental health tools and should focus instead on evidence-based solutions to address mental health inequalities.

Skorburg Joshua August, Yam Josephine

2021-May-14

Bioethics, artificial intelligence, mental health, psychiatry

Radiology Radiology

Quantitative evaluation of COVID-19 pneumonia severity by CT pneumonia analysis algorithm using deep learning technology and blood test results.

In Japanese journal of radiology

PURPOSE : To evaluate whether early chest computed tomography (CT) lesions quantified by an artificial intelligence (AI)-based commercial software and blood test values at the initial presentation can differentiate the severity of COVID-19 pneumonia.

MATERIALS AND METHODS : This retrospective study included 100 SARS-CoV-2-positive patients with mild (n = 23), moderate (n = 37) or severe (n = 40) pneumonia classified according to the Japanese guidelines. Univariate Kruskal-Wallis and multivariate ordinal logistic analyses were used to examine whether CT parameters (opacity score, volume of opacity, % opacity, volume of high opacity, % high opacity and mean HU total on CT) as well as blood test parameters [procalcitonin, estimated glomerular filtration rate (eGFR), C-reactive protein, % lymphocyte, ferritin, aspartate aminotransferase, lactate dehydrogenase, alanine aminotransferase, creatine kinase, hemoglobin A1c, prothrombin time, activated partial prothrombin time (APTT), white blood cell count and creatinine] differed by disease severity.

RESULTS : All CT parameters and all blood test parameters except procalcitonin and APPT were significantly different among mild, moderate and severe groups. By multivariate analysis, mean HU total and eGFR were two independent factors associated with severity (p < 0.0001). Cutoff values for mean HU total and eGFR were, respectively, - 801 HU and 77 ml/min/1.73 m2 between mild and moderate pneumonia and - 704 HU and 53 ml/min/1.73 m2 between moderate and severe pneumonia.

CONCLUSION : The mean HU total of the whole lung, determined by the AI algorithm, and eGFR reflect the severity of COVID-19 pneumonia.

Okuma Tomohisa, Hamamoto Shinichi, Maebayashi Tetsunori, Taniguchi Akishige, Hirakawa Kyoko, Matsushita Shu, Matsushita Kazuki, Murata Katsuko, Manabe Takao, Miki Yukio

2021-May-14

COVID-19, Chest CT, Deep learning, Quantitative analysis

General General

Digital holographic deep learning of red blood cells for field-portable, rapid COVID-19 screening.

In Optics letters

Rapid screening of red blood cells for active infection of COVID-19 is presented using a compact and field-portable, 3D-printed shearing digital holographic microscope. Video holograms of thin blood smears are recorded, individual red blood cells are segmented for feature extraction, then a bi-directional long short-term memory network is used to classify between healthy and COVID positive red blood cells based on their spatiotemporal behavior. Individuals are then classified based on the simple majority of their cells' classifications. The proposed system may be beneficial for under-resourced healthcare systems. To the best of our knowledge, this is the first report of digital holographic microscopy for rapid screening of COVID-19.

O’Connor Timothy, Shen Jian-Bing, Liang Bruce T, Javidi Bahram

2021-May-15

Surgery Surgery

COVID-19 … What are drugs and strategies now?

In Acta bio-medica : Atenei Parmensis

From February 2019 the World faces the Covid19 pandemic. The data in our possession are still insufficient to effectively combat this pathology. The gold standard for diagnosis remains molecular testing, while clinical and instrumental and serological diagnostics are highly nonspecific leading to a slowdown in the battle against covid19.[3] Can Artificial Intelligence (AI) and Machine Learning (ML) help us? The use of large databases to cross-reference data to stratify the diagnostic scores, to quickly differentiate a critical Covid-19 patient from a non-critical one is the challenge of the future. All to achieve better management of resources in the field and a more effective therapeutic approach.[2].

Craca Michelangelo, Bignami Elena, Bellini Valentina, Saturno Francesco, Potì Francesco, Cortegiani Andrea, Vetrugno Luigi, Vetrugno Luigi

2021-May-12

General General

Fast prototyping of a local fuzzy search system for decision support and retraining of hospital staff during pandemic.

In Health information science and systems

Purpose : The COVID-19 pandemic showed an urgent need for decision support systems to help doctors at a time of stress and uncertainty. However, significant differences in hospital conditions, as well as skepticism of doctors about machine learning algorithms, limit their introduction into clinical practice. Our goal was to test and apply the principle of "patient-like-mine" decision support in rapidly changing conditions of a pandemic.

Methods : In the developed system we implemented a fuzzy search that allows a doctor to compare their medical case with similar cases recorded in their medical center since the beginning of the pandemic. Various distance metrics were tried for obtaining clinically relevant search results. With the use of R programming language, we designed the first version of the system in approximately a week. A set of features for the comparison of the cases was selected with the use of random forest algorithm implemented in Caret. Shiny package was chosen for the design of GUI.

Results : The deployed tool allowed doctors to quickly estimate the current conditions of their patients by means of studying the most similar previous cases stored in the local health information system. The extensive testing of the system during the first wave of COVID-19 showed that this approach helps not only to draw a conclusion about the optimal treatment tactics and to train medical staff in real-time but also to optimize patients' individual testing plans.

Conclusions : This project points to the possibility of rapid prototyping and effective usage of "patient-like-mine" search systems at the time of a pandemic caused by a poorly known pathogen.

Bakin Evgeny A, Stanevich Oksana V, Danilenko Daria M, Lioznov Dmitry A, Kulikov Alexander N

2021-Dec

COVID-19, Decision support algorithm, Fuzzy search, Patient-like-mine, Prototyping

Radiology Radiology

Quantitative assessment of lung involvement on chest CT at admission: Impact on hypoxia and outcome in COVID-19 patients.

In Clinical imaging

BACKGROUND : The aim of this study was to quantify COVID-19 pneumonia features using CT performed at time of admission to emergency department in order to predict patients' hypoxia during the hospitalization and outcome.

METHODS : Consecutive chest CT performed in the emergency department between March 1st and April 7th 2020 for COVID-19 pneumonia were analyzed. The three features of pneumonia (GGO, semi-consolidation and consolidation) and the percentage of well-aerated lung were quantified using a HU threshold based software. ROC curves identified the optimal cut-off values of CT parameters to predict hypoxia worsening and hospital discharge. Multiple Cox proportional hazards regression was used to analyze the capability of CT quantitative features, demographic and clinical variables to predict the time to hospital discharge.

RESULTS : Seventy-seven patients (median age 56-years-old, 51 men) with COVID-19 pneumonia at CT were enrolled. The quantitative features of COVID-19 pneumonia were not associated to age, sex and time-from-symptoms onset, whereas higher number of comorbidities was correlated to lower well-aerated parenchyma ratio (rho = -0.234, p = 0.04) and increased semi-consolidation ratio (rho = -0.303, p = 0.008). Well-aerated lung (≤57%), semi-consolidation (≥17%) and consolidation (≥9%) predicted worst hypoxemia during hospitalization, with moderate areas under curves (AUC 0.76, 0.75, 0.77, respectively). Multiple Cox regression identified younger age (p < 0.01), female sex (p < 0.001), longer time-from-symptoms onset (p = 0.049), semi-consolidation ≤17% (p < 0.01) and consolidation ≤13% (p = 0.03) as independent predictors of shorter time to hospital discharge.

CONCLUSION : Quantification of pneumonia features on admitting chest CT predicted hypoxia worsening during hospitalization and time to hospital discharge in COVID-19 patients.

Esposito Antonio, Palmisano Anna, Cao Roberta, Rancoita Paola, Landoni Giovanni, Grippaldi Daniele, Boccia Edda, Cosenza Michele, Messina Antonio, La Marca Salvatore, Palumbo Diego, Di Serio Clelia, Spessot Marzia, Tresoldi Moreno, Scarpellini Paolo, Ciceri Fabio, Zangrillo Alberto, De Cobelli Francesco

2021-Apr-29

Artificial intelligence, Covid-19, Outcome, Pneumonia, Quantitative CT

General General

Joint learning of 3D lesion segmentation and classification for explainable COVID-19 diagnosis.

In IEEE transactions on medical imaging ; h5-index 74.0

Given the outbreak of COVID-19 pandemic and the shortage of medical resource, extensive deep learning models have been proposed for automatic COVID-19 diagnosis, based on 3D computed tomography (CT) scans. However, the existing models independently process the 3D lesion segmentation and disease classification, ignoring the inherent correlation between these two tasks. In this paper, we propose a joint deep learning model of 3D lesion segmentation and classification for diagnosing COVID-19, called DeepSC-COVID, as the first attempt in this direction. Specifically, we establish a large-scale CT database containing 1,805 3D CT scans with fine-grained lesion annotations, and reveal 4 findings about lesion difference between COVID-19 and community acquired pneumonia (CAP). Inspired by our findings, DeepSC-COVID is designed with 3 subnets: a cross-task feature subnet for feature extraction, a 3D lesion subnet for lesion segmentation, and a classification subnet for disease diagnosis. Besides, the task-aware loss is proposed for learning the task interaction across the 3D lesion and classification subnets. Different from all existing models for COVID-19 diagnosis, our model is interpretable with fine-grained 3D lesion distribution. Finally, extensive experimental results show that the joint learning framework in our model significantly improves the performance of 3D lesion segmentation and disease classification in both efficiency and efficacy.

Wang Xiaofei, Jiang Lai, Li Liu, Xu Mai, Deng Xin, Dai Lisong, Xu Xiangyang, Li Tianyi, Guo Yichen, Wang Zulin, Dragotti Pier Luigi

2021-May-13

Radiology Radiology

An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department.

In NPJ digital medicine

During the coronavirus disease 2019 (COVID-19) pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making. We propose a data-driven approach for automatic prediction of deterioration risk using a deep neural network that learns from chest X-ray images and a gradient boosting model that learns from routine clinical variables. Our AI prognosis system, trained using data from 3661 patients, achieves an area under the receiver operating characteristic curve (AUC) of 0.786 (95% CI: 0.745-0.830) when predicting deterioration within 96 hours. The deep neural network extracts informative areas of chest X-ray images to assist clinicians in interpreting the predictions and performs comparably to two radiologists in a reader study. In order to verify performance in a real clinical setting, we silently deployed a preliminary version of the deep neural network at New York University Langone Health during the first wave of the pandemic, which produced accurate predictions in real-time. In summary, our findings demonstrate the potential of the proposed system for assisting front-line physicians in the triage of COVID-19 patients.

Shamout Farah E, Shen Yiqiu, Wu Nan, Kaku Aakash, Park Jungkyu, Makino Taro, Jastrzębski Stanisław, Witowski Jan, Wang Duo, Zhang Ben, Dogra Siddhant, Cao Meng, Razavian Narges, Kudlowitz David, Azour Lea, Moore William, Lui Yvonne W, Aphinyanaphongs Yindalon, Fernandez-Granda Carlos, Geras Krzysztof J

2021-May-12

General General

Modelling Singapore COVID-19 pandemic with a SEIR multiplex network model.

In Scientific reports ; h5-index 158.0

In this paper, we have implemented a large-scale agent-based model to study the outbreak of coronavirus infectious diseases (COVID-19) in Singapore, taking into account complex human interaction pattern. In particular, the concept of multiplex network is utilized to differentiate between social interactions that happen in households and workplaces. In addition, weak interactions among crowds, transient interactions within social gatherings, and dense human contact between foreign workers in dormitories are also taken into consideration. Such a categorization in terms of a multiplex of social network connections together with the Susceptible-Exposed-Infectious-Removed (SEIR) epidemic model have enabled a more precise study of the feasibility and efficacy of control measures such as social distancing, work from home, and lockdown, at different moments and stages of the pandemics. Using this model, we study an epidemic outbreak that occurs within densely populated residential areas in Singapore. Our simulations show that residents in densely populated areas could be infected easily, even though they constitute a very small fraction of the whole population. Once infection begins in these areas, disease spreading is uncontrollable if appropriate control measures are not implemented.

Chung N N, Chew L Y

2021-May-12

Radiology Radiology

COVID19-CT-dataset: an open-access chest CT image repository of 1000+ patients with confirmed COVID-19 diagnosis.

In BMC research notes

OBJECTIVES : The ongoing Coronavirus disease 2019 (COVID-19) pandemic has drastically impacted the global health and economy. Computed tomography (CT) is the prime imaging modality for diagnosis of lung infections in COVID-19 patients. Data-driven and Artificial intelligence (AI)-powered solutions for automatic processing of CT images predominantly rely on large-scale, heterogeneous datasets. Owing to privacy and data availability issues, open-access and publicly available COVID-19 CT datasets are difficult to obtain, thus limiting the development of AI-enabled automatic diagnostic solutions. To tackle this problem, large CT image datasets encompassing diverse patterns of lung infections are in high demand.

DATA DESCRIPTION : In the present study, we provide an open-source repository containing 1000+ CT images of COVID-19 lung infections established by a team of board-certified radiologists. CT images were acquired from two main general university hospitals in Mashhad, Iran from March 2020 until January 2021. COVID-19 infections were ratified with matching tests including Reverse transcription polymerase chain reaction (RT-PCR) and accompanying clinical symptoms. All data are 16-bit grayscale images composed of 512 × 512 pixels and are stored in DICOM standard. Patient privacy is preserved by removing all patient-specific information from image headers. Subsequently, all images corresponding to each patient are compressed and stored in RAR format.

Shakouri Shokouh, Bakhshali Mohammad Amin, Layegh Parvaneh, Kiani Behzad, Masoumi Farid, Ataei Nakhaei Saeedeh, Mostafavi Sayyed Mostafa

2021-May-12

Artificial intelligence, COVID-19, Chest CT image, Clinical imaging, Computed tomography, Coronavirus, Deep learning, Diagnosis, Lung infection, Radiology

General General

Segmenting lung lesions of COVID-19 from CT images via pyramid pooling improved Unet.

In Biomedical physics & engineering express

Segmenting lesion regions of Coronavirus Disease 2019 (COVID-19) from computed tomography (CT) images is a challenge owing to COVID-19 lesions characterized by high variation, low contrast between infection lesions and around normal tissues, and blurred boundaries of infections. Moreover, a shortage of available CT dataset hinders deep learning techniques applying to tackling COVID-19. To address these issues, we propose a deep learning-based approach known as PPM-Unet to segmenting COVID-19 lesions from CT images. Our method improves an Unet by adopting pyramid pooling modules instead of the conventional skip connection and then enhances the representation of the neural network by aiding the global attention mechanism. We first pre-train PPM-Unet on COVID-19 dataset of pseudo labels containing1600 samples producing a coarse model. Then we fine-tune the coarse PPM-Unet on the standard COVID-19 dataset consisting of 100 pairs of samples to achieve a fine PPM-Unet. Qualitative and quantitative results demonstrate that our method can accurately segment COVID-19 infection regions from CT images, and achieve higher performance than other state-of-the-art segmentation models in this study. It offers a promising tool to lay a foundation for quantitatively detecting COVID-19 lesions.

Ma Yinjin, Feng Peng, He Peng, Ren Yong, Guo Xiaodong, Wei Biao

2021-May-12

COVID-19, computed tomography, deep learning, image segmentation, medical image

General General

The diagnostic accuracy of Artificial Intelligence-Assisted CT imaging in COVID-19 disease: A systematic review and meta-analysis.

In Informatics in medicine unlocked

Artificial intelligence (AI) systems have become critical in support of decision-making. This systematic review summarizes all the data currently available on the AI-assisted CT-Scan prediction accuracy for COVID-19. The ISI Web of Science, Cochrane Library, PubMed, Scopus, CINAHL, Science Direct, PROSPERO, and EMBASE were systematically searched. We used the revised Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool to assess all included studies' quality and potential bias. A hierarchical receiver-operating characteristic summary (HSROC) curve and a summary receiver operating characteristic (SROC) curve have been implemented. The area under the curve (AUC) was computed to determine the diagnostic accuracy. Finally, 36 studies (a total of 39,246 image data) were selected for inclusion into the final meta-analysis. The pooled sensitivity for AI was 0.90 (95% CI, 0.90-0.91), specificity was 0.91 (95% CI, 0.90-0.92) and the AUC was 0.96 (95% CI, 0.91-0.98). For deep learning (DL) method, the pooled sensitivity was 0.90 (95% CI, 0.90-0.91), specificity was 0.88 (95% CI, 0.87-0.88) and the AUC was 0.96 (95% CI, 0.93-0.97). In case of machine learning (ML), the pooled sensitivity was 0.90 (95% CI, 0.90-0.91), specificity was 0.95 (95% CI, 0.94-0.95) and the AUC was 0.97 (95% CI, 0.96-0.99). AI in COVID-19 patients is useful in identifying symptoms of lung involvement. More prospective real-time trials are required to confirm AI's role for high and quick COVID-19 diagnosis due to the possible selection bias and retrospective existence of currently available studies.

Moezzi Meisam, Shirbandi Kiarash, Shahvandi Hassan Kiani, Arjmand Babak, Rahim Fakher

2021

Artificial intelligence, COVID-19, CT-Scan, Computed tomography, Coronavirus infections, Deep learning, Machine learning, Respiratory tract infections

Radiology Radiology

Neural network analysis of clinical variables predicts escalated care in COVID-19 patients: a retrospective study.

In PeerJ

This study sought to identify the most important clinical variables that can be used to determine which COVID-19 patients hospitalized in the general floor will need escalated care early on using neural networks (NNs). Analysis was performed on hospitalized COVID-19 patients between 7 February 2020 and 4 May 2020 in Stony Brook Hospital. Demographics, comorbidities, laboratory tests, vital signs and blood gases were collected. We compared those data obtained at the time in emergency department and the time of intensive care unit (ICU) upgrade of: (i) COVID-19 patients admitted to the general floor (N = 1203) vs. those directly admitted to ICU (N = 104), and (ii) patients not upgraded to ICU (N = 979) vs. those upgraded to the ICU (N = 224) from the general floor. A NN algorithm was used to predict ICU admission, with 80% training and 20% testing. Prediction performance used area under the curve (AUC) of the receiver operating characteristic analysis (ROC). We found that C-reactive protein, lactate dehydrogenase, creatinine, white-blood cell count, D-dimer and lymphocyte count showed temporal divergence between COVID-19 patients hospitalized in the general floor that were upgraded to ICU compared to those that were not. The NN predictive model essentially ranked the same laboratory variables to be important predictors of needing ICU care. The AUC for predicting ICU admission was 0.782 ± 0.013 for the test dataset. Adding vital sign and blood-gas data improved AUC (0.822 ± 0.018). This work could help frontline physicians to anticipate downstream ICU need to more effectively allocate healthcare resources.

Lu Joyce Q, Musheyev Benjamin, Peng Qi, Duong Tim Q

2021

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

General General

Predicting COVID-19 statistics using machine learning regression model: Li-MuLi-Poly.

In Multimedia systems

In this paper, linear regression (LR), multi-linear regression (MLR) and polynomial regression (PR) techniques are applied to propose a model Li-MuLi-Poly. The model predicts COVID-19 deaths happening in the United States of America. The experiment was carried out on machine learning model, minimum mean square error model, and maximum likelihood ratio model. The best-fitting model was selected according to the measures of mean square error, adjusted mean square error, mean square error, root mean square error (RMSE) and maximum likelihood ratio, and the statistical t-test was used to verify the results. Data sets are analyzed, cleaned up and debated before being applied to the proposed regression model. The correlation of the selected independent parameters was determined by the heat map and the Carl Pearson correlation matrix. It was found that the accuracy of the LR model best-fits the dataset when all the independent parameters are used in modeling, however, RMSE and mean absolute error (MAE) are high as compared to PR models. The PR models of a high degree are required to best-fit the dataset when not much independent parameter is considered in modeling. However, the PR models of low degree best-fits the dataset when independent parameters from all dimensions are considered in modeling.

Singh Hari, Bawa Seema

2021-May-06

Accuracy, COVID-19, Linear regression, Machine learning, Polynomial regression, t-Test

General General

COVID-19 Lesion Discrimination and Localization Network Based on Multi-Receptive Field Attention Module on CT Images.

In Optik

Since discovered in Hubei, China in December 2019, Corona Virus Disease 2019 named COVID-19 has lasted mor than one year, and the number of new confirmed cases and confirmed deaths is still at a high level. COVID-19 is an infectious disease caused by SARS-CoV-2. Although RT-PCR is considered the gold standard for detection of COVID-19, CT plays an important role in the diagnosis and evaluation of the therapeutic effect of COVID-19. Diagnosis and localization of COVID-19 on CT images using deep learning can provide quantitative auxiliary information for doctors. This article proposes a novel network with multi-receptive field attention module to diagnose COVID-19 on CT images. This attention module includes three parts, a pyramid convolution module (PCM), a multi-receptive field spatial attention block (SAB), and a multi-receptive field channel attention block (CAB). The PCM can improve the diagnostic ability of the network for lesions of different sizes and shapes. The role of SAB and CAB is to focus the features extracted from the network on the lesion area to improve the ability of COVID-19 discrimination and localization. We verify the effectiveness of the proposed method on two datasets. The accuracy rate of 97.12%, specificity of 96.89%, and sensitivity of 97.21% are achieved by the proposed network on DTDB dataset provided by the Beijing Ditan Hospital Capital Medical University. Compared with other state-of-the-art attention modules, the proposed method achieves better result. As for the public COVID-19 SARS-CoV-2 dataset, 95.16% for accuracy, 95.6% for F1-score and 99.01% for AUC are obtained. The proposed network can effectively assist doctors in the diagnosis of COVID-19 CT images.

Ma Xia, Zheng Bingbing, Zhu Yu, Yu Fuli, Zhang Rixin, Chen Budong

2021-May-07

COVID-19, attention, auxiliary diagnosis, deep learning

General General

SARS-CoV-2 gene content and COVID-19 mutation impact by comparing 44 Sarbecovirus genomes.

In Nature communications ; h5-index 260.0

Despite its clinical importance, the SARS-CoV-2 gene set remains unresolved, hindering dissection of COVID-19 biology. We use comparative genomics to provide a high-confidence protein-coding gene set, characterize evolutionary constraint, and prioritize functional mutations. We select 44 Sarbecovirus genomes at ideally-suited evolutionary distances, and quantify protein-coding evolutionary signatures and overlapping constraint. We find strong protein-coding signatures for ORFs 3a, 6, 7a, 7b, 8, 9b, and a novel alternate-frame gene, ORF3c, whereas ORFs 2b, 3d/3d-2, 3b, 9c, and 10 lack protein-coding signatures or convincing experimental evidence of protein-coding function. Furthermore, we show no other conserved protein-coding genes remain to be discovered. Mutation analysis suggests ORF8 contributes to within-individual fitness but not person-to-person transmission. Cross-strain and within-strain evolutionary pressures agree, except for fewer-than-expected within-strain mutations in nsp3 and S1, and more-than-expected in nucleocapsid, which shows a cluster of mutations in a predicted B-cell epitope, suggesting immune-avoidance selection. Evolutionary histories of residues disrupted by spike-protein substitutions D614G, N501Y, E484K, and K417N/T provide clues about their biology, and we catalog likely-functional co-inherited mutations. Previously reported RNA-modification sites show no enrichment for conservation. Here we report a high-confidence gene set and evolutionary-history annotations providing valuable resources and insights on SARS-CoV-2 biology, mutations, and evolution.

Jungreis Irwin, Sealfon Rachel, Kellis Manolis

2021-May-11

Ophthalmology Ophthalmology

Indian contribution toward biomedical research and development in COVID-19: A systematic review.

In Indian journal of pharmacology

COVID-19 pandemic led to an unprecedented collaborative effort among industry, academia, regulatory bodies, and governments with huge financial investments. Scientists and researchers from India also left no stone unturned to find therapeutic and preventive measures against COVID-19. Indian pharmaceutical companies are one of the leading manufacturers of vaccine in the world, are utilizing its capacity to its maximum, and are one among the forerunners in vaccine research against COVID-19 across the globe. In this systematic review, the information regarding contribution of Indian scientists toward COVID-19 research has been gathered from various news articles across Google platform apart from searching PubMed, WHO site, COVID-19 vaccine tracker, CTRI, clinicaltrials.gov, and websites of pharmaceutical companies. The article summarizes and highlights the various therapeutic and vaccine candidates, diagnostic kits, treatment agents, and technology being developed and tested by Indian researcher community against COVID-19.

Kaur Hardeep, Kaur Manpreet, Bhattacharyya Anusuya, Prajapat Manisha, Thota Prasad, Sarma Phulen, Kumar Subodh, Kaur Gurjeet, Sharma Saurabh, Prakash Ajay, Saifuddin P K, Medhi Bikash

AYUSH, Artificial intelligence, COVID-19, India, SARS-CoV2, clinical trials, drug designing, vaccine

Cardiology Cardiology

Prospective Case-Control Study of Cardiovascular Abnormalities 6 Months Following Mild COVID-19 in Healthcare Workers.

In JACC. Cardiovascular imaging

OBJECTIVES : The purpose of this study was to detect cardiovascular changes after mild severe acute respiratory syndrome coronavirus 2 infection.

BACKGROUND : Concern exists that mild coronavirus disease 2019 may cause myocardial and vascular disease.

METHODS : Participants were recruited from COVIDsortium, a 3-hospital prospective study of 731 health care workers who underwent first-wave weekly symptom, polymerase chain reaction, and serology assessment over 4 months, with seroconversion in 21.5% (n = 157). At 6 months post-infection, 74 seropositive and 75 age-, sex-, and ethnicity-matched seronegative control subjects were recruited for cardiovascular phenotyping (comprehensive phantom-calibrated cardiovascular magnetic resonance and blood biomarkers). Analysis was blinded, using objective artificial intelligence analytics where available.

RESULTS : A total of 149 subjects (mean age 37 years, range 18 to 63 years, 58% women) were recruited. Seropositive infections had been mild with case definition, noncase definition, and asymptomatic disease in 45 (61%), 18 (24%), and 11 (15%), respectively, with 1 person hospitalized (for 2 days). Between seropositive and seronegative groups, there were no differences in cardiac structure (left ventricular volumes, mass, atrial area), function (ejection fraction, global longitudinal shortening, aortic distensibility), tissue characterization (T1, T2, extracellular volume fraction mapping, late gadolinium enhancement) or biomarkers (troponin, N-terminal pro-B-type natriuretic peptide). With abnormal defined by the 75 seronegatives (2 SDs from mean, e.g., ejection fraction <54%, septal T1 >1,072 ms, septal T2 >52.4 ms), individuals had abnormalities including reduced ejection fraction (n = 2, minimum 50%), T1 elevation (n = 6), T2 elevation (n = 9), late gadolinium enhancement (n = 13, median 1%, max 5% of myocardium), biomarker elevation (borderline troponin elevation in 4; all N-terminal pro-B-type natriuretic peptide normal). These were distributed equally between seropositive and seronegative individuals.

CONCLUSIONS : Cardiovascular abnormalities are no more common in seropositive versus seronegative otherwise healthy, workforce representative individuals 6 months post-mild severe acute respiratory syndrome coronavirus 2 infection.

Joy George, Artico Jessica, Kurdi Hibba, Seraphim Andreas, Lau Clement, Thornton George D, Oliveira Marta Fontes, Adam Robert Daniel, Aziminia Nikoo, Menacho Katia, Chacko Liza, Brown James T, Patel Rishi K, Shiwani Hunain, Bhuva Anish, Augusto Joao B, Andiapen Mervyn, McKnight Aine, Noursadeghi Mahdad, Pierce Iain, Evain Timothée, Captur Gabriella, Davies Rhodri H, Greenwood John P, Fontana Marianna, Kellman Peter, Schelbert Erik B, Treibel Thomas A, Manisty Charlotte, Moon James C

2021-May-05

COVID-19, SARS-CoV-2, cardiovascular magnetic resonance, late gadolinium enhancement, myocardial edema, myocarditis, troponin

Surgery Surgery

Natural history, trajectory, and management of mechanically ventilated COVID-19 patients in the United Kingdom.

In Intensive care medicine ; h5-index 86.0

PURPOSE : The trajectory of mechanically ventilated patients with coronavirus disease 2019 (COVID-19) is essential for clinical decisions, yet the focus so far has been on admission characteristics without consideration of the dynamic course of the disease in the context of applied therapeutic interventions.

METHODS : We included adult patients undergoing invasive mechanical ventilation (IMV) within 48 h of intensive care unit (ICU) admission with complete clinical data until ICU death or discharge. We examined the importance of factors associated with disease progression over the first week, implementation and responsiveness to interventions used in acute respiratory distress syndrome (ARDS), and ICU outcome. We used machine learning (ML) and Explainable Artificial Intelligence (XAI) methods to characterise the evolution of clinical parameters and our ICU data visualisation tool is available as a web-based widget ( https://www.CovidUK.ICU ).

RESULTS : Data for 633 adults with COVID-19 who underwent IMV between 01 March 2020 and 31 August 2020 were analysed. Overall mortality was 43.3% and highest with non-resolution of hypoxaemia [60.4% vs17.6%; P < 0.001; median PaO2/FiO2 on the day of death was 12.3(8.9-18.4) kPa] and non-response to proning (69.5% vs.31.1%; P < 0.001). Two ML models using weeklong data demonstrated an increased predictive accuracy for mortality compared to admission data (74.5% and 76.3% vs 60%, respectively). XAI models highlighted the increasing importance, over the first week, of PaO2/FiO2 in predicting mortality. Prone positioning improved oxygenation only in 45% of patients. A higher peak pressure (OR 1.42[1.06-1.91]; P < 0.05), raised respiratory component (OR 1.71[ 1.17-2.5]; P < 0.01) and cardiovascular component (OR 1.36 [1.04-1.75]; P < 0.05) of the sequential organ failure assessment (SOFA) score and raised lactate (OR 1.33 [0.99-1.79]; P = 0.057) immediately prior to application of prone positioning were associated with lack of oxygenation response. Prone positioning was not applied to 76% of patients with moderate hypoxemia and 45% of those with severe hypoxemia and patients who died without receiving proning interventions had more missed opportunities for prone intervention [7 (3-15.5) versus 2 (0-6); P < 0.001]. Despite the severity of gas exchange deficit, most patients received lung-protective ventilation with tidal volumes less than 8 mL/kg and plateau pressures less than 30cmH2O. This was despite systematic errors in measurement of height and derived ideal body weight.

CONCLUSIONS : Refractory hypoxaemia remains a major association with mortality, yet evidence based ARDS interventions, in particular prone positioning, were not implemented and had delayed application with an associated reduced responsiveness. Real-time service evaluation techniques offer opportunities to assess the delivery of care and improve protocolised implementation of evidence-based ARDS interventions, which might be associated with improvements in survival.

Patel Brijesh V, Haar Shlomi, Handslip Rhodri, Auepanwiriyakul Chaiyawan, Lee Teresa Mei-Ling, Patel Sunil, Harston J Alex, Hosking-Jervis Feargus, Kelly Donna, Sanderson Barnaby, Borgatta Barbara, Tatham Kate, Welters Ingeborg, Camporota Luigi, Gordon Anthony C, Komorowski Matthieu, Antcliffe David, Prowle John R, Puthucheary Zudin, Faisal Aldo A

2021-May-11

ARDS, Artificial intelligence, COVID-19, Mechanical ventilation, Mortality risk, Prone position

Surgery Surgery

Answering the Challenge of COVID-19 Pandemic Through Innovation and Ingenuity.

In Advances in experimental medicine and biology

The novel coronavirus disease 2019 (COVID-19) pandemic has created a maelstrom of challenges affecting virtually every aspect of global healthcare system. Critical hospital capacity issues, depleted ventilator and personal protective equipment stockpiles, severely strained supply chains, profound economic slowdown, and the tremendous human cost all culminated in what is questionably one of the most profound challenges that humanity faced in decades, if not centuries. Effective global response to the current pandemic will require innovation and ingenuity. This chapter discusses various creative approaches and ideas that arose in response to COVID-19, as well as some of the most impactful future trends that emerged as a result. Among the many topics discussed herein are telemedicine, blockchain technology, artificial intelligence, stereolithography, and distance learning.

Kelley Kathryn Clare, Kamler Jonathan, Garg Manish, Stawicki Stanislaw P

2021

Artificial intelligence, Blockchain technology, COVID-19, Healthcare innovation, Pandemic, SARS-CoV-2, Technology, Telemedicine

Cardiology Cardiology

Real-Time Electronic Health Record Mortality Prediction During the COVID-19 Pandemic: A Prospective Cohort Study.

In Journal of the American Medical Informatics Association : JAMIA

OBJECTIVE : To rapidly develop, validate, and implement a novel real-time mortality score for the COVID-19 pandemic that improves upon SOFA for decision support for a Crisis Standards of Care team.

MATERIALS AND METHODS : We developed, verified, and deployed a stacked generalization model to predict mortality using data available in the EHR by combining five previously validated scores and additional novel variables reported to be associated with COVID-19-specific mortality. We verified the model with prospectively collected data from 12 hospitals in Colorado between March 2020 and July 2020. We compared the area under the receiver operator curve (AUROC) for the new model to the SOFA score and the Charlson Comorbidity Index.

RESULTS : The prospective cohort included 27,296 encounters, of which 1,358 (5.0%) were positive for SARS-CoV-2, 4,494 (16.5%) required intensive care unit care, 1,480 (5.4%) required mechanical ventilation, and 717 (2.6%) ended in death. The Charlson Comorbidity Index and SOFA scores predicted mortality with an AUROC of 0.72 and 0.90, respectively. Our novel score predicted mortality with AUROC 0.94. In the subset of patients with COVID-19, the stacked model predicted mortality with AUROC 0.90, whereas SOFA had AUROC of 0.85.

DISCUSSION : Stacked regression allows a flexible, updatable, live-implementable, ethically defensible predictive analytics tool for decision support that begins with validated models and includes only novel information that improves prediction.

CONCLUSION : We developed and validated an accurate in-hospital mortality prediction score in a live EHR for automatic and continuous calculation using a novel model that improved upon SOFA.

Sottile Peter D, Albers David, DeWitt Peter E, Russell Seth, Stroh J N, Kao David P, Adrian Bonnie, Levine Matthew E, Mooney Ryan, Larchick Lenny, Kutner Jean S, Wynia Matthew K, Glasheen Jeffrey J, Bennett Tellen D

2021-May-10

COVID-19, Crisis Triage, Machine Learning, Mortality Prediction

oncology Oncology

Contact Tracing in Healthcare Settings During the COVID-19 Pandemic Using Bluetooth Low Energy and Artificial Intelligence-A Viewpoint.

In Frontiers in artificial intelligence

The COVID-19 pandemic has inflicted great damage with effects that will likely linger for a long time. This crisis has highlighted the importance of contact tracing in healthcare settings because hospitalized patients are among the high risk for complications and death. Moreover, effective contact tracing schemes are not yet available in healthcare settings. A good contact tracing technology in healthcare settings should be equipped with six features: promptness, simplicity, high precision, integration, minimized privacy concerns, and social fairness. One potential solution that addresses all of these elements leverages an indoor real-time location system based on Bluetooth Low Energy and artificial intelligence.

Tang Guanglin, Westover Kenneth, Jiang Steve

2021

COVID-19, artificial intelligence, bluetooth, contact tracing, deep learning, healthcare, real-time location system

General General

Is There a Place for Responsible Artificial Intelligence in Pandemics? A Tale of Two Countries.

In Information systems frontiers : a journal of research and innovation

This research examines the considerations of responsible Artificial Intelligence in the deployment of AI-based COVID-19 digital proximity tracking and tracing applications in two countries; the State of Qatar and the United Kingdom. Based on the alignment level analysis with the Good AI Society's framework and sentiment analysis of official tweets, the diagnostic analysis resulted in contrastive findings for the two applications. While the application EHTERAZ (Arabic for precaution) in Qatar has fallen short in adhering to the responsible AI requirements, it has contributed significantly to controlling the pandemic. On the other hand, the UK's NHS COVID-19 application has exhibited limited success in fighting the virus despite relatively abiding by these requirements. This underlines the need for obtaining a practical and contextual view for a comprehensive discourse on responsible AI in healthcare. Thereby offering necessary guidance for striking a balance between responsible AI requirements and managing pressures towards fighting the pandemic.

El-Haddadeh Ramzi, Fadlalla Adam, Hindi Nitham M

2021-May-06

COVID-19 pandemic, Ethics, Responsible Artificial Intelligence, Sentiment analysis

Public Health Public Health

TClustVID: A novel machine learning classification model to investigate topics and sentiment in COVID-19 tweets.

In Knowledge-based systems

COVID-19, caused by SARS-CoV2 infection, varies greatly in its severity but presents with serious respiratory symptoms with vascular and other complications, particularly in older adults. The disease can be spread by both symptomatic and asymptomatic infected individuals. Uncertainty remains over key aspects of the virus infectiousness (particularly the newly emerging variants) and the disease has had severe economic impacts globally. For these reasons, COVID-19 is the subject of intense and widespread discussion on social media platforms including Facebook and Twitter. These public forums substantially influence public opinions and in some cases can exacerbate the widespread panic and misinformation spread during the crisis. Thus, this work aimed to design an intelligent clustering-based classification and topic extracting model named TClustVID that analyzes COVID-19-related public tweets to extract significant sentiments with high accuracy. We gathered COVID-19 Twitter datasets from the IEEE Dataport repository and employed a range of data preprocessing methods to clean the raw data, then applied tokenization and produced a word-to-index dictionary. Thereafter, different classifications were employed on these datasets which enabled the exploration of the performance of traditional classification and TClustVID. Our analysis found that TClustVID showed higher performance compared to traditional methodologies that are determined by clustering criteria. Finally, we extracted significant topics from the clusters, split them into positive, neutral and negative sentiments, and identified the most frequent topics using the proposed model. This approach is able to rapidly identify commonly prevailing aspects of public opinions and attitudes related to COVID-19 and infection prevention strategies spreading among different populations.

Satu Md Shahriare, Khan Md Imran, Mahmud Mufti, Uddin Shahadat, Summers Matthew A, Quinn Julian M W, Moni Mohammad Ali

2021-May-06

COVID-19, Classification, Machine learning, TClustVID, Topics modelling, Twitter data

General General

COVID-Classifier: an automated machine learning model to assist in the diagnosis of COVID-19 infection in chest X-ray images.

In Scientific reports ; h5-index 158.0

Chest-X ray (CXR) radiography can be used as a first-line triage process for non-COVID-19 patients with pneumonia. However, the similarity between features of CXR images of COVID-19 and pneumonia caused by other infections makes the differential diagnosis by radiologists challenging. We hypothesized that machine learning-based classifiers can reliably distinguish the CXR images of COVID-19 patients from other forms of pneumonia. We used a dimensionality reduction method to generate a set of optimal features of CXR images to build an efficient machine learning classifier that can distinguish COVID-19 cases from non-COVID-19 cases with high accuracy and sensitivity. By using global features of the whole CXR images, we successfully implemented our classifier using a relatively small dataset of CXR images. We propose that our COVID-Classifier can be used in conjunction with other tests for optimal allocation of hospital resources by rapid triage of non-COVID-19 cases.

Zargari Khuzani Abolfazl, Heidari Morteza, Shariati S Ali

2021-May-10

Public Health Public Health

Carceral-community epidemiology, structural racism, and COVID-19 disparities.

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

Black and Hispanic communities are disproportionately affected by both incarceration and COVID-19. The epidemiological relationship between carceral facilities and community health during the COVID-19 pandemic, however, remains largely unexamined. Using data from Cook County Jail, we examine temporal patterns in the relationship between jail cycling (i.e., arrest and processing of individuals through jails before release) and community cases of COVID-19 in Chicago ZIP codes. We use multivariate regression analyses and a machine-learning tool, elastic regression, with 1,706 demographic control variables. We find that for each arrested individual cycled through Cook County Jail in March 2020, five additional cases of COVID-19 in their ZIP code of residence are independently attributable to the jail as of August. A total 86% of this additional disease burden is borne by majority-Black and/or -Hispanic ZIPs, accounting for 17% of cumulative COVID-19 cases in these ZIPs, 6% in majority-White ZIPs, and 13% across all ZIPs. Jail cycling in March alone can independently account for 21% of racial COVID-19 disparities in Chicago as of August 2020. Relative to all demographic variables in our analysis, jail cycling is the strongest predictor of COVID-19 rates, considerably exceeding poverty, race, and population density, for example. Arrest and incarceration policies appear to be increasing COVID-19 incidence in communities. Our data suggest that jails function as infectious disease multipliers and epidemiological pumps that are especially affecting marginalized communities. Given disproportionate policing and incarceration of racialized residents nationally, the criminal punishment system may explain a large proportion of racial COVID-19 disparities noted across the United States.

Reinhart Eric, Chen Daniel L

2021-May-25

carceral-community epidemiology, inequality, mass incarceration, public health, racial disparities

Ophthalmology Ophthalmology

Variations in volume of emergency surgeries and emergency department access at a third level hospital in Milan, Lombardy, during the COVID-19 outbreak.

In BMC emergency medicine

BACKGROUND : During the recent outbreak of COVID-19 (coronavirus disease 2019), Lombardy was the most affected region in Italy, with 87,000 patients and 15,876 deaths up to May 26, 2020. Since February 22, 2020, well before the Government declared a state of emergency, there was a huge reduction in the number of emergency surgeries performed at hospitals in Lombardy. A general decrease in attendance at emergency departments (EDs) was also observed. The aim of our study is to report the experience of the ED of a third-level hospital in downtown Milan, Lombardy, and provide possible explanations for the observed phenomena.

METHODS : This retrospective, observational study assessed the volume of emergency surgeries and attendance at an ED during the course of the pandemic, i.e. immediately before, during and after a progressive community lockdown in response to the COVID-19 pandemic. These data were compared with data from the same time periods in 2019. The results are presented as means, standard error (SE), and 95% studentized confidence intervals (CI). The Wilcoxon rank signed test at a 0.05 significance level was used to assess differences in per-day ED access distributions.

RESULTS : Compared to 2019, a significant overall drop in emergency surgeries (60%, p < 0.002) and in ED admittance (66%, p ≅ 0) was observed in 2020. In particular, there were significant decreases in medical (40%), surgical (74%), specialist (ophthalmology, otolaryngology, traumatology, and urology) (92%), and psychiatric (60%) cases. ED admittance due to domestic violence (59%) and individuals who left the ED without being seen (76%) also decreased. Conversely, the number of deaths increased by 196%.

CONCLUSIONS : During the COVID-19 outbreak the volume of urgent surgeries and patients accessing our ED dropped. Currently, it is not known if mortality of people who did not seek care increased during the pandemic. Further studies are needed to understand if such reductions during the COVID-19 pandemic will result in a rebound of patients left untreated or in unwanted consequences for population health.

Castoldi Laura, Solbiati Monica, Costantino Giorgio, Casiraghi Elena

2021-May-10

COVID-19, Coronavirus, Emergency department attendance, Emergency department overcrowding, Emergency surgery

General General

Retro Drug Design: From Target Properties to Molecular Structures

bioRxiv Preprint

To generate drug molecules of desired properties with computational methods is the holy grail in pharmaceutical research. Here we describe an AI strategy, retro drug design, or RDD, to generate novel small molecule drugs from scratch to meet predefined requirements, including but not limited to biological activity against a drug target, and optimal range of physicochemical and ADMET properties. Traditional predictive models were first trained over experimental data for the target properties, using an atom typing based molecular descriptor system, ATP. Monte Carlo sampling algorithm was then utilized to find the solutions in the ATP space defined by the target properties, and the deep learning model of Seq2Seq was employed to decode molecular structures from the solutions. To test feasibility of the algorithm, we challenged RDD to generate novel drugs that can activate opioid receptor (MOR) and penetrate blood brain barrier (BBB). Starting from vectors of random numbers, RDD generated 180,000 chemical structures, of which 78% were chemically valid. About 42,000 (31%) of the valid structures fell into the property space defined by MOR activity and BBB permeability. Out of the 42,000 structures, only 267 chemicals were commercially available, indicating a high extent of novelty of the AI-generated compounds. We purchased and assayed 96 compounds, and 25 of which were found to be MOR agonists. These compounds also have excellent BBB scores. The results presented in this paper illustrate that RDD has potential to revolutionize the current drug discovery process and create novel structures with multiple desired properties, including biological functions and ADMET properties. Availability of an AI-enabled fast track in drug discovery is essential to cope with emergent public health threat, such as pandemic of COVID-19.

Wang, Y.; Michael, S.; Huang, R.; Zhao, J.; Recabo, K.; Bougie, D.; Shu, Q.; Shinn, P.; Sun, H.

2021-05-12

General General

Trade-offs between short-term mortality attributable to NO2 and O3 changes during the COVID-19 lockdown across major Spanish cities.

In Environmental pollution (Barking, Essex : 1987)

The emergence of the COVID-19 pandemic forced most countries to put in place lockdown measures to slow down the transmission of the virus. These lockdowns have led to temporal improvements in air quality. Here, we evaluate the changes in NO2 and O3 levels along with the associated impact upon premature mortality during the COVID-19 lockdown and deconfinement periods along the first epidemic wave across the provincial capital cities of Spain. We first quantify the change in pollutants solely due to the lockdown as the difference between business-as-usual (BAU) pollution levels, estimated with a machine learning-based meteorological normalization technique, and observed concentrations. Second, instead of using exposure-response functions between the pollutants and mortality reported in the literature, we fit conditional quasi-Poisson regression models to estimate city-specific associations between daily pollutant levels and non-accidental mortality during the period 2010-2018. Significant relative risk values are observed at lag 1 for NO2 (1.0047 [95% CI: 1.0014 to 1.0081]) and at lag 0 for O3 (1.0039 [1.0013 to 1.0065]). On average NO2 changed by -51% (intercity range -65.7 to -30.9%) and -36.4% (-53.7 to -11.6%), and O3 by -1.1% (-20.2 to 23.8%) and 0.6% (-12.4 to 23.0%), during the lockdown (57 days) and deconfinement (42 days) periods, respectively. We obtain a reduction in attributable mortality associated with NO2 changes of -119 (95% CI: -273 to -24) deaths over the lockdown, and of -53 (-114 to -10) deaths over the deconfinement. This was partially compensated by an increase in the attributable number of deaths, 14 (-72 to 99) during the lockdown, and 8 (-27 to 50) during the deconfinement, associated with the rise in O3 levels in the most populous cities during the analysed period, despite the overall small average reductions. Our study shows that the potential trade-offs between multiple air pollutants should be taken into account when evaluating the health impacts of environmental exposures.

Achebak Hicham, Petetin Hervé, Quijal-Zamorano Marcos, Bowdalo Dene, Pérez García-Pando Carlos, Ballester Joan

2021-May-04

COVID-19 lockdown, Mortality, NO(2), O(3), Spain

General General

COVID-19 diagnosis from CT scans and chest X-ray images using low-cost Raspberry Pi.

In PloS one ; h5-index 176.0

The diagnosis of COVID-19 is of vital demand. Several studies have been conducted to decide whether the chest X-ray and computed tomography (CT) scans of patients indicate COVID-19. While these efforts resulted in successful classification systems, the design of a portable and cost-effective COVID-19 diagnosis system has not been addressed yet. The memory requirements of the current state-of-the-art COVID-19 diagnosis systems are not suitable for embedded systems due to the required large memory size of these systems (e.g., hundreds of megabytes). Thus, the current work is motivated to design a similar system with minimal memory requirements. In this paper, we propose a diagnosis system using a Raspberry Pi Linux embedded system. First, local features are extracted using local binary pattern (LBP) algorithm. Second, the global features are extracted from the chest X-ray or CT scans using multi-channel fractional-order Legendre-Fourier moments (MFrLFMs). Finally, the most significant features (local and global) are selected. The proposed system steps are integrated to fit the low computational and memory capacities of the embedded system. The proposed method has the smallest computational and memory resources,less than the state-of-the-art methods by two to three orders of magnitude, among existing state-of-the-art deep learning (DL)-based methods.

Hosny Khalid M, Darwish Mohamed M, Li Kenli, Salah Ahmad

2021

Radiology Radiology

Discrepancies in the clinical and radiological profiles of COVID-19: A case-based discussion and review of literature.

In World journal of radiology

The current gold standard for the diagnosis of coronavirus disease-19 (COVID-19) is a positive reverse transcriptase polymerase chain reaction (RT-PCR) test, on the background of clinical suspicion. However, RT-PCR has its limitations; this includes issues of low sensitivity, sampling errors and appropriate timing of specimen collection. As pulmonary involvement is the most common manifestation of severe COVID-19, early and appropriate lung imaging is important to aid diagnosis. However, gross discrepancies can occur between the clinical and imaging findings in patients with COVID-19, which can mislead clinicians in their decision making. Although chest X-ray (CXR) has a low sensitivity for the diagnosis of COVID-19 associated lung disease, especially in the earlier stages, a positive CXR increases the pre-test probability of COVID-19. CXR scoring systems have shown to be useful, such as the COVID-19 opacification rating score which helps to predict the need of tracheal intubation. Furthermore, artificial intelligence-based algorithms have also shown promise in differentiating COVID-19 pneumonia on CXR from other lung diseases. Although costlier than CXR, unenhanced computed tomographic (CT) chest scans have a higher sensitivity, but lesser specificity compared to RT-PCR for the diagnosis of COVID-19 pneumonia. A semi-quantitative CT scoring system has been shown to predict short-term mortality. The routine use of CT pulmonary angiography as a first-line imaging modality in patients with suspected COVID-19 is not justifiable due to the risk of contrast nephropathy. Scoring systems similar to those pioneered in CXR and CT can be used to effectively plan and manage hospital resources such as ventilators. Lung ultrasound is useful in the assessment of critically ill COVID-19 patients in the hands of an experienced operator. Moreover, it is a convenient tool to monitor disease progression, as it is cheap, non-invasive, easily accessible and easy to sterilise. Newer lung imaging modalities such as magnetic resonance imaging (MRI) for safe imaging among children, adolescents and pregnant women are rapidly evolving. Imaging modalities are also essential for evaluating the extra-pulmonary manifestations of COVID-19: these include cranial imaging with CT or MRI; cardiac imaging with ultrasonography (US), CT and MRI; and abdominal imaging with US or CT. This review critically analyses the utility of each imaging modality to empower clinicians to use them appropriately in the management of patients with COVID-19 infection.

Kumar Hemant, Fernandez Cornelius James, Kolpattil Sangeetha, Munavvar Mohamed, Pappachan Joseph M

2021-Apr-28

COVID-19, Chest X-ray, Computed tomography, Lung imaging, Lung ultrasound, Pneumonia

General General

Prediction of COVID-19 with Computed Tomography Images using Hybrid Learning Techniques.

In Disease markers

Reverse Transcription Polymerase Chain Reaction (RT-PCR) used for diagnosing COVID-19 has been found to give low detection rate during early stages of infection. Radiological analysis of CT images has given higher prediction rate when compared to RT-PCR technique. In this paper, hybrid learning models are used to classify COVID-19 CT images, Community-Acquired Pneumonia (CAP) CT images, and normal CT images with high specificity and sensitivity. The proposed system in this paper has been compared with various machine learning classifiers and other deep learning classifiers for better data analysis. The outcome of this study is also compared with other studies which were carried out recently on COVID-19 classification for further analysis. The proposed model has been found to outperform with an accuracy of 96.69%, sensitivity of 96%, and specificity of 98%.

Perumal Varalakshmi, Narayanan Vasumathi, Rajasekar Sakthi Jaya Sundar

2021

Public Health Public Health

Automatic prediction of COVID- 19 from chest images using modified ResNet50.

In Multimedia tools and applications

Recently coronavirus 2019 (COVID-2019), discovered in Wuhan city of China in December 2019 announced as world pandemic by the World Health Organization (WHO). It has catastrophic impacts on daily lives, public health, and the global economy. The detection of coronavirus (COVID- 19) is now a critical task for medical specialists. Laboratory methods for detecting the virus such as Polymerase Chain Reaction, antigens, and antibodies have pros and cons represented in time required to obtain results, accuracy, cost and suitability of the test to phase of infection. The need for accurate, fast, and cheap auxiliary diagnostic tools has become a necessity as there are no accurate automated toolkits available. Other medical investigations such as chest X-ray and Computerized Tomography scans are imaging techniques that play an important role in the diagnosis of COVID- 19 virus. Application of advanced artificial intelligence techniques for processing radiological imaging can be helpful for the accurate detection of this virus. However, Due to the small dataset available for COVID- 19, transfer learning from pre-trained convolution neural networks, CNNs can be used as a promising solution for diagnosis of coronavirus. Transfer learning becomes an effective mechanism by transferring knowledge from generic object recognition tasks to domain-specific tasks. Hence, the main contribution of this paper is to exploit the pre-trained deep learning CNN architectures as a cornerstone to enhance and build up an automated tool for detection and diagnosis of COVID- 19 in chest X-Ray and Computerized Tomography images. The main idea is to make use of their convolutional neural network structure and its learned weights on large datasets such as ImageNet. Moreover, a modification to ResNet50 is proposed to classify the patients as COVID infected or not. This modification includes adding three new layers, named, 'Conv', 'Batch_Normaliz' and 'Activation_Relu' layers. These layers are injected in the ResNet50 architecture for accurate discrimination and robust feature extraction. Extensive experiments are carried out to assess the performance of the proposed model on COVID- 19 chest X-Ray and Computerized Tomography scan images. Experimental results approve that the proposed modification, injected layers, increases the diagnosis accuracy to 97.7% for Computerized Tomography dataset and 97.1% for X-Ray dataset which is superior compared to other approaches.

Elpeltagy Marwa, Sallam Hany

2021-May-04

COVID-19, ResNet50, Transfer learning

General General

Application of Artificial Neural Network for Internal Combustion Engines: A State of the Art Review.

In Archives of computational methods in engineering : state of the art reviews

The automotive industry is facing a crucial time. The transformation from internal combustion engines to new electrical technologies requires enormous investment, and hence the IC engines are likely to serve as a means of transportation for the coming decades. The search for sustainable green alternative fuel and operating parameter optimization is a current feasible solution and is a critical issue among the scientific community. Engine experiments are complicated, costly, and time-consuming, especially when the global economy is drastically down due to the COVID-19 pandemic and putting the limitation of social distancing. Industries are looking for proven computational solutions to address these issues. Recently, artificial neural network has been proven beneficial in several areas of engineering to reduce the time and experimentation cost. The IC engine is one of them. ANN has been used to predict and analyze different characteristics such as performance, combustion, and emissions of the IC engine to save time and energy. The complex nature of ANN may lead to computation time, energy, and space. Recent studies are centered on changing the network topology, deep learning, and design of ANN to get the highest performance. The present study summarizes the application of ANN to predict and optimize the complicated characteristics of various types of engines with different fuels. The study aims to investigate the network topologies adopted to design the model and thereafter statistical evaluation of the developed ANN models. A comparison of the ANN model with other prediction models is also presented.

Bhatt Aditya Narayan, Shrivastava Nitin

2021-May-03

Radiology Radiology

An integrated framework with machine learning and radiomics for accurate and rapid early diagnosis of COVID-19 from Chest X-ray.

In Expert systems with applications

The objective of the research article is to propose and validate a combination of machine learning and radiomics features to detect COVID-19 early and rapidly from chest X-ray (CXR) in presence of other viral/bacterial pneumonia and at different severity levels of diseases. It is vital to assess the performance of any diagnosis method on an independent data set and at very early stage of the disease when the disease severity of is very low. In such cases, most of the diagnosis methods fail. A total of 378 CXR images containing both normal lung and pneumonia (both COVID-19 and others lung conditions) were collected from publically available data set. 71 radiomics features for each lung segment were chosen from 100 extracted features based on Z-score heatmap and one way ANOVA test that can detect COVID-19. Three best performing classical machine learning algorithms during the training phase - 1) fine Gaussian support vector machine (SVM), 2) fine k-nearest neighbor (KNN) and 3) ensemble bagged model (EBM) trees were chosen for further evaluation on an independent test data set. The independent test data set consists of 115 COVID-19 CXR images collected from a local hospital and 100 CXR images collected from publically available data set containing normal lung and viral/bacterial pneumonia. Severity was scored between 0 to 4 by two experienced radiologists for each lung with pneumonia (both COVID-19 and non COVID-19) for the test data set. Ensemble Bagging Model Trees (EBM) with the selected radiomics features is the most suitable to distinguish between COVID-19 and other lung infections with an overall sensitivity of 87.8% and specificity of 97% (95.2% accuracy and 0.9228 area under curve) and is robust across severity levels. The method also can detect COVID-19 from CXR when two experienced radiologists were unable to detect any abnormality in the lung CXR (represented by severity score of 0). Once the CXR is acquired and lung is segmented, it takes less than two minutes for extracting radiomics features and providing diagnosis result. Since the proposed method does not require any manual intervention (e.g., sample collection etc.), it can be straightway integrated with standard X-ray reporting system to be used as an efficient, cost-effective and rapid early diagnosis device.

Tamal Mahbubunnabi, Alshammari Maha, Alabdullah Meernah, Hourani Rana, Alola Hossain Abu, Hegazi Tarek M

2021-Oct-15

COVID-19, Chest X-ray, Early diagnosis, Machine learning, Radiomics

General General

Determination of COVID-19 pneumonia based on generalized convolutional neural network model from chest X-ray images.

In Expert systems with applications

X-ray units have become one of the most advantageous candidates for triaging the new Coronavirus disease COVID-19 infected patients thanks to its relatively low radiation dose, ease of access, practical, reduced prices, and quick imaging process. This research intended to develop a reliable convolutional-neural-network (CNN) model for the classification of COVID-19 from chest X-ray views. Moreover, it is aimed to prevent bias issues due to the database. Transfer learning-based CNN model was developed by using a sum of 1,218 chest X-ray images (CXIs) consisting of 368 COVID-19 pneumonia and 850 other pneumonia cases by pre-trained architectures, including DenseNet-201, ResNet-18, and SqueezeNet. The chest X-ray images were acquired from publicly available databases, and each individual image was carefully selected to prevent any bias problem. A stratified 5-fold cross-validation approach was utilized with a ratio of 90% for training and 10% for the testing (unseen folds), in which 20% of training data was used as a validation set to prevent overfitting problems. The binary classification performances of the proposed CNN models were evaluated by the testing data. The activation mapping approach was implemented to improve the causality and visuality of the radiograph. The outcomes demonstrated that the proposed CNN model built on DenseNet-201 architecture outperformed amongst the others with the highest accuracy, precision, recall, and F1-scores of 94.96%, 89.74%, 94.59%, and 92.11%, respectively. The results indicated that the reliable diagnosis of COVID-19 pneumonia from CXIs based on the CNN model opens the door to accelerate triage, save critical time, and prioritize resources besides assisting the radiologists.

Alhudhaif Adi, Polat Kemal, Karaman Onur

2021-Oct-15

Chest X-ray images, Convolutional Neural Network (CNN), Corona Virus (COVID-19), Deep learning

General General

Automated detection of COVID-19 from CT scan using convolutional neural network.

In Biocybernetics and biomedical engineering

Under the prevailing circumstances of the global pandemic of COVID-19, early diagnosis and accurate detection of COVID-19 through tests/screening and, subsequently, isolation of the infected people would be a proactive measure. Artificial intelligence (AI) based solutions, using Convolutional Neural Network (CNN) and exploiting the Deep Learning model's diagnostic capabilities, have been studied in this paper. Transfer Learning approach, based on VGG16 and ResNet50 architectures, has been used to develop an algorithm to detect COVID-19 from CT scan images consisting of Healthy (Normal), COVID-19, and Pneumonia categories. This paper adopts data augmentation and fine-tuning techniques to improve and optimize the VGG16 and ResNet50 model. Further, stratified 5-fold cross-validation has been conducted to test the robustness and effectiveness of the model. The proposed model performs exceptionally well in case of binary classification (COVID-19 vs. Normal) with an average classification accuracy of more than 99% in both VGG16 and ResNet50 based models. In multiclass classification (COVID-19 vs. Normal vs. Pneumonia), the proposed model achieves an average classification accuracy of 86.74% and 88.52% using VGG16 and ResNet50 architectures as baseline, respectively. Experimental results show that the proposed model achieves superior performance and can be used for automated detection of COVID-19 from CT scans.

Mishra Narendra Kumar, Singh Pushpendra, Joshi Shiv Dutt

2021-Apr-30

COVID-19, CT scan images, Convolutional Neural Network, ResNet50, Transfer learning, VGG16

Public Health Public Health

Health information technology and digital innovation for national learning health and care systems.

In The Lancet. Digital health

Health information technology can support the development of national learning health and care systems, which can be defined as health and care systems that continuously use data-enabled infrastructure to support policy and planning, public health, and personalisation of care. The COVID-19 pandemic has offered an opportunity to assess how well equipped the UK is to leverage health information technology and apply the principles of a national learning health and care system in response to a major public health shock. With the experience acquired during the pandemic, each country within the UK should now re-evaluate their digital health and care strategies. After leaving the EU, UK countries now need to decide to what extent they wish to engage with European efforts to promote interoperability between electronic health records. Major priorities for strengthening health information technology in the UK include achieving the optimal balance between top-down and bottom-up implementation, improving usability and interoperability, developing capacity for handling, processing, and analysing data, addressing privacy and security concerns, and encouraging digital inclusivity. Current and future opportunities include integrating electronic health records across health and care providers, investing in health data science research, generating real-world data, developing artificial intelligence and robotics, and facilitating public-private partnerships. Many ethical challenges and unintended consequences of implementation of health information technology exist. To address these, there is a need to develop regulatory frameworks for the development, management, and procurement of artificial intelligence and health information technology systems, create public-private partnerships, and ethically and safely apply artificial intelligence in the National Health Service.

Sheikh Aziz, Anderson Michael, Albala Sarah, Casadei Barbara, Franklin Bryony Dean, Richards Mike, Taylor David, Tibble Holly, Mossialos Elias

2021-May-06

Internal Medicine Internal Medicine

A novel approach utilizing laser acupuncture teletherapy for management of elderly-onset rheumatoid arthritis: A randomized clinical trial.

In Journal of telemedicine and telecare ; h5-index 28.0

INTRODUCTION : Rheumatoid arthritis (RA) disease is a systemic progressive inflammatory autoimmune disorder. Elderly-onset RA can be assumed as a benign form of RA. Until recently, face-to-face therapeutic sessions between health professionals and patients are usually the method of its treatment. However, during pandemics, including coronavirus disease 2019 (COVID-19), teletherapeutic sessions can extensively increase the patient safety especially in elderly patients who are more vulnerable to these infections. Thus, the aim of this study was to evaluate a novel teletherapy approach for management of elderly patients suffering from RA by utilizing laser acupuncture.

METHODS : A teletherapy system was used for management of elderly patients suffering from RA. Sixty participants were allocated randomly into two groups and the ratio was 1:1. Patients in the first group were treated with laser acupuncture and telerehabilitation sessions, which consisted of aerobic exercise and virtual reality training. Patients in the second group received telerehabilitation sessions, which consisted of aerobic exercise and virtual reality training. Evaluation of patients was done by using the Health Assessment questionnaire (HAQ), the Rheumatoid Arthritis Quality of Life (RAQoL) questionnaire, and the analysis of interleukin-6 (IL-6), serum C-reactive protein (CRP), plasma adenosine triphosphate (ATP) concentration and plasma malondialdehyde (MDA).

RESULTS : A statistically significant difference was found in CRP, RAQoL, IL-6 and MDA between the pre- and post-treatments in the first group (p < 0.05) favouring the post-treatment group, while the HAQ showed a statistically significant difference between pre- and post-treatments (p < 0.05) in both groups. Statistically significant post-treatment differences were also observed between the two groups (p < 0.05) in RAQoL, CRP, ATP and MDA, favouring the first group.

DISCUSSION : Laser acupuncture teletherapy could be suggested as a reliable treatment method for elderly patients suffering from RA, as it can provide a safe and effective therapeutic approach. Teletherapy provided safer access to health professionals and patients while giving a high patient satisfaction value with a relatively lower cost (ClinicalTrials.gov Identifier: NCT04684693).

Adly Aya Sedky, Adly Afnan Sedky, Adly Mahmoud Sedky, Ali Mohammad F

2021-May-09

Teletherapy, elderly-onset rheumatoid arthritis, gerontechnology, laser acupuncture, randomized clinical trial, telerehabilitation

General General

Intelligent interactive technologies for mental health and well-being

ArXiv Preprint

Mental healthcare has seen numerous benefits from interactive technologies and artificial intelligence. Various interventions have successfully used intelligent technologies to automate the assessment and evaluation of psychological treatments and mental well-being and functioning. These technologies include different types of robots, video games, and conversational agents. The paper critically analyzes existing solutions with the outlooks for their future. In particular, we: i)give an overview of the technology for mental health, ii) critically analyze the technology against the proposed criteria, and iii) provide the design outlooks for these technologies.

Mladjan Jovanovic, Aleksandar Jevremovic, Milica Pejovic-Milovancevic

2021-05-11

General General

Classification of COVID-19 chest X-Ray and CT images using a type of dynamic CNN modification method.

In Computers in biology and medicine

Understanding and classifying Chest X-Ray (CXR) and computerised tomography (CT) images are of great significance for COVID-19 diagnosis. The existing research on the classification for COVID-19 cases faces the challenges of data imbalance, insufficient generalisability, the lack of comparative study, etc. To address these problems, this paper proposes a type of modified MobileNet to classify COVID-19 CXR images and a modified ResNet architecture for CT image classification. In particular, a modification method of convolutional neural networks (CNN) is designed to solve the gradient vanishing problem and improve the classification performance through dynamically combining features in different layers of a CNN. The modified MobileNet is applied to the classification of COVID-19, Tuberculosis, viral pneumonia (with the exception of COVID-19), bacterial pneumonia and normal controls using CXR images. Also, the proposed modified ResNet is used for the classification of COVID-19, non-COVID-19 infections and normal controls using CT images. The results show that the proposed methods achieve 99.6% test accuracy on the five-category CXR image dataset and 99.3% test accuracy on the CT image dataset. Six advanced CNN architectures and two specific COVID-19 detection models, i.e., COVID-Net and COVIDNet-CT are used in comparative studies. Two benchmark datasets and a CXR image dataset which combines eight different CXR image sources are employed to evaluate the performance of the above models. The results show that the proposed methods outperform the comparative models in classification accuracy, sensitivity, and precision, which demonstrate their potential in computer-aided diagnosis for healthcare applications.

Jia Guangyu, Lam Hak-Keung, Xu Yujia

2021-Apr-29

COVID-19 detection, Chest X-Ray and CT images, Deep learning, Modified CNN

Public Health Public Health

The barriers and enablers to downloading the COVIDSafe app - a topic modelling analysis.

In Australian and New Zealand journal of public health

OBJECTIVE : We report a survey in regional Queensland to understand the reasons for suboptimal uptake of the COVIDSafe app.

METHODS : A short five-minute electronic survey disseminated to healthcare professionals, mining groups and school communities in the Central Queensland region. Free text responses and their topics were modelled using natural language processing and a latent Dirichlet model.

RESULTS : We received a total of 723 responses; of these, 69% had downloaded the app and 31% had not. The respondents' reasons for not downloading the app were grouped under four topics: lack of perceived risk of COVID-19/lack of perceived need and privacy issues; phone-related issues; tracking and misuse of data; and trust, security and credibility. Among the 472 people who downloaded the app and provided text amenable to text mining, the two topics most commonly listed were: to assist with contact tracing; and to return to normal.

CONCLUSIONS : This survey of a regional population found that lack of perceived need, concerns around privacy and technical difficulties were the major barriers to users downloading the application. Implications for public health: Health promotion campaigns aimed at increasing the uptake of the COVIDSafe app should focus on promoting how the app will assist with contact tracing to help return to 'normal'. Additionally, health promotors should address the app's impacts on privacy, people's lack of perceived need for the app and technical barriers.

Smoll Nicolas R, Walker Jacina, Khandaker Gulam

2021-May-10

COVIDSafe, contact tracing, machine learning, natural language processing, survey

Radiology Radiology

On the Role of Artificial Intelligence in Medical Imaging of COVID-19.

In Patterns (New York, N.Y.)

Although a plethora of research articles on AI methods on COVID-19 medical imaging are published, their clinical value remains unclear. We conducted the largest systematic review of the literature addressing the utility of AI in imaging for COVID-19 patient care. By keyword searches on PubMed and preprint servers throughout 2020, we identified 463 manuscripts and performed a systematic meta-analysis to assess their technical merit and clinical relevance. Our analysis evidences a significant disparity between clinical and AI communities, both in the focus on imaging modalities (AI experts neglected CT and Ultrasound, favoring X-Ray) and performed tasks (71.9% of AI papers centered on diagnosis). The vast majority of manuscripts were found deficient regarding potential use in clinical practice, but 2.7% (N=12) publications were assigned a high maturity level and are summarized in greater detail. We provide an itemized discussion of the challenges in developing clinically relevant AI solutions with recommendations and remedies.

Born Jannis, Beymer David, Rajan Deepta, Coy Adam, Mukherjee Vandana V, Manica Matteo, Prasanna Prasanth, Ballah Deddeh, Guindy Michal, Shaham Dorith, Shah Pallav L, Karteris Emmanouil, Robertus Jan L, Gabrani Maria, Rosen-Zvi Michal

2021-Apr-30

ACR, (American College of Radiology), AI, (Artificial Intelligence), Artificial Intelligence, COVID-19, CT, (Computed Tomography), CXR, (Chest Radiographs), Chest CT, Chest Ultrasound, Chest X-ray, Coronavirus, DL, (Deep Learning), Deep Learning, Digital Healthcare, LUS, (Lung Ultrasound), Lung imaging, MI, (Medical Imaging), Machine Learning, Medical Imaging, Meta Review, PRISMA, PRISMA, (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), RT-PCR, (Reverse Transcriptase Polymerase Chain reaction), SARS-CoV-2, US, (Ultrasound)

Radiology Radiology

Clinical Factors and Quantitative CT Parameters Associated With ICU Admission in Patients of COVID-19 Pneumonia: A Multicenter Study.

In Frontiers in public health

The clinical spectrum of COVID-19 pneumonia is varied. Thus, it is important to identify risk factors at an early stage for predicting deterioration that require transferring the patients to ICU. A retrospective multicenter study was conducted on COVID-19 patients admitted to designated hospitals in China from Jan 17, 2020, to Feb 17, 2020. Clinical presentation, laboratory data, and quantitative CT parameters were also collected. The result showed that increasing risks of ICU admission were associated with age > 60 years (odds ratio [OR], 12.72; 95% confidence interval [CI], 2.42-24.61; P = 0.032), coexisting conditions (OR, 5.55; 95% CI, 1.59-19.38; P = 0.007) and CT derived total opacity percentage (TOP) (OR, 8.0; 95% CI, 1.45-39.29; P = 0.016). In conclusion, older age, coexisting conditions, larger TOP at the time of hospital admission are associated with ICU admission in patients with COVID-19 pneumonia. Early monitoring the progression of the disease and implementing appropriate therapies are warranted.

Yan Chengxi, Chang Ying, Yu Huan, Xu Jingxu, Huang Chencui, Yang Minglei, Wang Yiqiao, Wang Di, Yu Tian, Wei Shuqin, Li Zhenyu, Gong Feifei, Kou Mingqing, Gou Wenjing, Zhao Qili, Sun Penghui, Jia Xiuqin, Fan Zhaoyang, Xu Jiali, Li Sijie, Yang Qi

2021

COVID-19, computed tomography, deep learning, intensive care unit, pneumonia

Public Health Public Health

Alignment free sequence comparison methods and reservoir host prediction.

In Bioinformatics (Oxford, England)

MOTIVATION : The emergence and subsequent pandemic of the SARS-CoV-2 virus raised urgent questions about its origin and, particularly, its reservoir host. These types of questions are long-standing problems in the management of emerging infectious diseases and are linked to virus discovery programs and the prediction of viruses that are likely to become zoonotic. Conventional means to identify reservoir hosts have relied on surveillance, experimental studies and phylogenetics. More recently, machine learning approaches have been applied to generate tools to swiftly predict reservoir hosts from sequence data.

RESULTS : Here, we extend a recent work that combined sequence alignment and a mixture of alignment-free approaches using a gradient boosting machines (GBMs) machine learning model, which integrates genomic traits (GT) and phylogenetic neighbourhood (PN) signatures to predict reservoir hosts. We add a more uniform approach by applying Machine Learning with Digital Signal Processing (MLDSP)-based structural patterns (M-SP). The extended model was applied to an existing virus/reservoir host dataset and to the SARS-CoV-2 and related viruses and generated an improvement in prediction accuracy.

AVAILABILITY AND IMPLEMENTATION : The source code used in this work is freely available at https://github.com/bill1167/hostgbms.

SUPPLEMENTARY INFORMATION : Supplementary data are available at Bioinformatics online.

Lee Bill, Smith David K, Guan Yi

2021-May-08

Public Health Public Health

An intelligence design for detection and classification of COVID19 using fusion of classical and convolutional neural network and improved microscopic features selection approach.

In Microscopy research and technique

Coronavirus19 is caused due to infection in the respiratory system. It is the type of RNA virus that might infect animal and human species. In the severe stage, it causes pneumonia in human beings. In this research, hand-crafted and deep microscopic features are used to classify lung infection. The proposed work consists of two phases; in phase I, infected lung region is segmented using proposed U-Net deep learning model. The hand-crafted features are extracted such as histogram orientation gradient (HOG), noise to the harmonic ratio (NHr), and segmentation based fractal texture analysis (SFTA) from the segmented image, and optimum features are selected from each feature vector using entropy. In phase II, local binary patterns (LBPs), speeded up robust feature (Surf), and deep learning features are extracted using a pretrained network such as inceptionv3, ResNet101 from the input CT images, and select optimum features based on entropy. Finally, the optimum selected features using entropy are fused in two ways, (i) The hand-crafted features (HOG, NHr, SFTA, LBP, SURF) are horizontally concatenated/fused (ii) The hand-crafted features (HOG, NHr, SFTA, LBP, SURF) are combined/fused with deep features. The fused optimum features vector is passed to the ensemble models (Boosted tree, bagged tree, and RUSBoosted tree) in two ways for the COVID19 classification, (i) classification using fused hand-crafted features (ii) classification using fusion of hand-crafted features and deep features. The proposed methodology is tested /evaluated on three benchmark datasets. Two datasets employed for experiments and results show that hand-crafted & deep microscopic feature's fusion provide better results compared to only hand-crafted fused features.

Amin Javaria, Anjum Muhammad Almas, Sharif Muhammad, Saba Tanzila, Tariq Usman

2021-May-08

U-Net, ensemble methods, entropy, fusion, hand crafted features, healthcare, public health

General General

ModFOLD8: accurate global and local quality estimates for 3D protein models.

In Nucleic acids research ; h5-index 217.0

Methods for estimating the quality of 3D models of proteins are vital tools for driving the acceptance and utility of predicted tertiary structures by the wider bioscience community. Here we describe the significant major updates to ModFOLD, which has maintained its position as a leading server for the prediction of global and local quality of 3D protein models, over the past decade (>20 000 unique external users). ModFOLD8 is the latest version of the server, which combines the strengths of multiple pure-single and quasi-single model methods. Improvements have been made to the web server interface and there has been successive increases in prediction accuracy, which were achieved through integration of newly developed scoring methods and advanced deep learning-based residue contact predictions. Each version of the ModFOLD server has been independently blind tested in the biennial CASP experiments, as well as being continuously evaluated via the CAMEO project. In CASP13 and CASP14, the ModFOLD7 and ModFOLD8 variants ranked among the top 10 quality estimation methods according to almost every official analysis. Prior to CASP14, ModFOLD8 was also applied for the evaluation of SARS-CoV-2 protein models as part of CASP Commons 2020 initiative. The ModFOLD8 server is freely available at: https://www.reading.ac.uk/bioinf/ModFOLD/.

McGuffin Liam J, Aldowsari Fahd M F, Alharbi Shuaa M A, Adiyaman Recep

2021-May-08

General General

COVIDOUTCOME-estimating COVID severity based on mutation signatures in the SARS-CoV-2 genome.

In Database : the journal of biological databases and curation

Numerous studies demonstrate frequent mutations in the genome of SARS-CoV-2. Our goal was to statistically link mutations to severe disease outcome. We used an automated machine learning approach where 1594 viral genomes with available clinical follow-up data were used as the training set (797 'severe' and 797 'mild'). The best algorithm, based on random forest classification combined with the LASSO feature selection algorithm, was employed to the training set to link mutation signatures and outcome. The performance of the final model was estimated by repeated, stratified, 10-fold cross validation (CV) and then adjusted for multiple testing with Bootstrap Bias Corrected CV. We identified 26 protein and Untranslated Region (UTR) mutations significantly linked to severe outcome. The best classification algorithm uses a mutation signature of 22 mutations as well as the patient's age as the input and shows high classification efficiency with an area under the curve (AUC) of 0.94 [confidence interval (CI): [0.912, 0.962]] and a prediction accuracy of 87% (CI: [0.830, 0.903]). Finally, we established an online platform (https://covidoutcome.com/) that is capable to use a viral sequence and the patient's age as the input and provides a percentage estimation of disease severity. We demonstrate a statistical association between mutation signatures of SARS-CoV-2 and severe outcome of COVID-19. The established analysis platform enables a real-time analysis of new viral genomes.

Nagy Ádám, Ligeti Balázs, Szebeni János, Pongor Sándor, Gyrffy Balázs

2021-May-08

General General

Development and validation of a machine learning model to predict mortality risk in patients with COVID-19.

In BMJ health & care informatics

New York City quickly became an epicentre of the COVID-19 pandemic. An ability to triage patients was needed due to a sudden and massive increase in patients during the COVID-19 pandemic as healthcare providers incurred an exponential increase in workload,which created a strain on the staff and limited resources. Further, methods to better understand and characterise the predictors of morbidity and mortality was needed. METHODS: We developed a prediction model to predict patients at risk for mortality using only laboratory, vital and demographic information readily available in the electronic health record on more than 3395 hospital admissions with COVID-19. Multiple methods were applied, and final model was selected based on performance. A variable importance algorithm was used for interpretability, and understanding of performance and predictors was applied to the best model. We built a model with an area under the receiver operating characteristic curve of 83-97 to identify predictors and patients with high risk of mortality due to COVID-19. Oximetry, respirations, blood urea nitrogen, lymphocyte per cent, calcium, troponin and neutrophil percentage were important features, and key ranges were identified that contributed to a 50% increase in patients' mortality prediction score. With an increasing negative predictive value starting 0.90 after the second day of admission suggests we might be able to more confidently identify likely survivors DISCUSSION: This study serves as a use case of a machine learning methods with visualisations to aide clinicians with a better understanding of the model and predictors of mortality. CONCLUSION: As we continue to understand COVID-19, computer assisted algorithms might be able to improve the care of patients.

Stachel Anna, Daniel Kwesi, Ding Dan, Francois Fritz, Phillips Michael, Lighter Jennifer

2021-May

information science, medical informatics, patient care

Surgery Surgery

Thoracic Point-of-Care Ultrasound: A SARS-CoV-2 Data Repository for Future Artificial Intelligence and Machine Learning.

In Surgical innovation

Current experience suggests that artificial intelligence (AI) and machine learning (ML) may be useful in the management of hospitalized patients, including those with COVID-19. In light of the challenges faced with diagnostic and prognostic indicators in SARS-CoV-2 infection, our center has developed an international clinical protocol to collect standardized thoracic point of care ultrasound data in these patients for later AI/ML modeling. We surmise that in the future AI/ML may assist in the management of SARS-CoV-2 patients potentially leading to improved outcomes, and to that end, a corpus of curated ultrasound images and linked patient clinical metadata is an invaluable research resource.

Mohamed Ali Abdel-Moneim, El-Alali Emran, Weltz Adam S, Rehrig Scott T

2021-May-07

biomedical engineering, radiologist, surgical education

Internal Medicine Internal Medicine

Prediction of Disease Progression of COVID-19 Based upon Machine Learning.

In International journal of general medicine

Background : Since December 2019, COVID-19 has spread throughout the world. Clinical outcomes of COVID-19 patients vary among infected individuals. Therefore, it is vital to identify patients at high risk of disease progression.

Methods : In this retrospective, multicenter cohort study, COVID-19 patients from Huoshenshan Hospital and Taikang Tongji Hospital (Wuhan, China) were included. Clinical features showing significant differences between the severe and nonsevere groups were screened out by univariate analysis. Then, these features were used to generate classifier models to predict whether a COVID-19 case would be severe or nonsevere based on machine learning. Two test sets of data from the two hospitals were gathered to evaluate the predictive performance of the models.

Results : A total of 455 patients were included, and 21 features showing significant differences between the severe and nonsevere groups were selected for the training and validation set. The optimal subset, with eleven features in the k-nearest neighbor model, obtained the highest area under the curve (AUC) value among the four models in the validation set. D-dimer, CRP, and age were the three most important features in the optimal-feature subsets. The highest AUC value was obtained using a support vector-machine model for a test set from Huoshenshan Hospital. Software for predicting disease progression based on machine learning was developed.

Conclusion : The predictive models were successfully established based on machine learning, and achieved satisfactory predictive performance of disease progression with optimal-feature subsets.

Xu Fumin, Chen Xiao, Yin Xinru, Qiu Qiu, Xiao Jingjing, Qiao Liang, He Mi, Tang Liang, Li Xiawei, Zhang Qiao, Lv Yanling, Xiao Shili, Zhao Rong, Guo Yan, Chen Mingsheng, Chen Dongfeng, Wen Liangzhi, Wang Bin, Nian Yongjian, Liu Kaijun

2021

COVID-19, disease progression, machine-learning models

General General

Convolutional neural networks for the diagnosis and prognosis of the coronavirus disease pandemic.

In Visual computing for industry, biomedicine, and art

A neural network is one of the current trends in deep learning, which is increasingly gaining attention owing to its contribution in transforming the different facets of human life. It also paves a way to approach the current crisis caused by the coronavirus disease (COVID-19) from all scientific directions. Convolutional neural network (CNN), a type of neural network, is extensively applied in the medical field, and is particularly useful in the current COVID-19 pandemic. In this article, we present the application of CNNs for the diagnosis and prognosis of COVID-19 using X-ray and computed tomography (CT) images of COVID-19 patients. The CNN models discussed in this review were mainly developed for the detection, classification, and segmentation of COVID-19 images. The base models used for detection and classification were AlexNet, Visual Geometry Group Network with 16 layers, residual network, DensNet, GoogLeNet, MobileNet, Inception, and extreme Inception. U-Net and voxel-based broad learning network were used for segmentation. Even with limited datasets, these methods proved to be beneficial for efficiently identifying the occurrence of COVID-19. To further validate these observations, we conducted an experimental study using a simple CNN framework for the binary classification of COVID-19 CT images. We achieved an accuracy of 93% with an F1-score of 0.93. Thus, with the availability of improved medical image datasets, it is evident that CNNs are very useful for the efficient diagnosis and prognosis of COVID-19.

Kugunavar Sneha, Prabhakar C J

2021-May-05

COVID-19, Convolutional neural network, Deep learning, Medical image analysis, Neural network

General General

Personalized analytics and wearable biosensor platform for early detection of COVID-19 decompensation (DeCODe: Detection of COVID-19 Decompensation): protocol for development of COVID-19 Decompensation Index.

In JMIR research protocols ; h5-index 26.0

BACKGROUND : During the COVID-19 pandemic, novel digital health technologies have the potential to improve our understanding of SARS Co-V2/ COVID-19, improve care delivery and produce better health outcomes. The National Institutes of Health called on digital health leaders to contribute to a high-quality data repository that will support the work of researchers to make discoveries not possible through small, limited data sets.

OBJECTIVE : To this end, we seek to develop a COVID-19 digital biomarker that could provide early detection of a patient's physiologic worsening or decompensation. We propose developing and validating a COVID-19 Decompensation Index (CDI) in a two-phased project that builds off existing wearable biosensor-derived analytics generated by physIQ's end-to-end cloud platform for continuous monitoring of physiology with wearable biosensors. This effort will achieve two primary objectives: 1) collect adequate data to enable the development of the CDI; and 2) collect rich deidentified clinical data correlative with outcomes and symptomology related to COVID-19 disease progression. Secondary objectives include evaluation of feasibility and usability of pinpointIQ™, the digital platform through which data is gathered, analyzed, and displayed.

METHODS : This study is a prospective, non-randomized, open-label, two-phase design. Phase I will involve data collection for the NIH digital data hub as well as data to support the preliminary development of the CDI. Phase II will involve data collection for the hub and contribute to continued refinement and validation of the CDI. While this study will focus on development of a CDI, the digital platform will also be evaluated for feasibility and usability while clinicians deliver care to continuously monitored patients enrolled in the study.

RESULTS : Our target COVID-19 Decompensation Index (CDI) will be a binary classifier trained to distinguish between participants decompensating and not decompensating. The primary performance metric for CDI will be ROC AUC with a minimum performance criterion of AUC ≥ 0.75 (significance α = 0.05 and power 1 - β = 0.80). Determination of sex/gender, race or ethnic characteristics that impact differences in the CDI performance, as well as lead time-time to predict decompensation and the relationship to ultimate severity of disease based on the World Health Organization COVID-19 Ordinal Scale will be explored.

CONCLUSIONS : Using machine learning techniques on a large data set of COVID-19 positive patients could produce valuable insights into the physiology of COVID-19 as well as a digital biomarker for COVID-19 decompensation. We plan, with this study, to develop a tool that can uniquely reflect the physiologic data of a diverse population and contribute to a trove of high-quality data that will help researchers better understand COVID-19.

CLINICALTRIAL : Trial Registration: ClinicalTrials.gov NCT NCT04575532.

Larimer Karen, Wegerich Stephan, Splan Joel, Chestek David, Prendergast Heather, Vanden Hoek Terry

2021-May-04

Surgery Surgery

"P3": an adaptive modeling tool for post-COVID-19 restart of surgical services.

In JAMIA open

Objective : To develop a predictive analytics tool that would help evaluate different scenarios and multiple variables for clearance of surgical patient backlog during the COVID-19 pandemic.

Materials and Methods : Using data from 27 866 cases (May 1 2018-May 1 2020) stored in the Johns Hopkins All Children's data warehouse and inputs from 30 operations-based variables, we built mathematical models for (1) time to clear the case backlog (2), utilization of personal protective equipment (PPE), and (3) assessment of overtime needs.

Results : The tool enabled us to predict desired variables, including number of days to clear the patient backlog, PPE needed, staff/overtime needed, and cost for different backlog reduction scenarios.

Conclusions : Predictive analytics, machine learning, and multiple variable inputs coupled with nimble scenario-creation and a user-friendly visualization helped us to determine the most effective deployment of operating room personnel. Operating rooms worldwide can use this tool to overcome patient backlog safely.

Joshi Divya, Jalali Ali, Whipple Todd, Rehman Mohamed, Ahumada Luis M

2021-Apr

COVID-19, decision support, optimization, predictive analytics, surgical backlog

General General

A novel augmented deep transfer learning for classification of COVID-19 and other thoracic diseases from X-rays.

In Neural computing & applications

Deep learning has provided numerous breakthroughs in natural imaging tasks. However, its successful application to medical images is severely handicapped with the limited amount of annotated training data. Transfer learning is commonly adopted for the medical imaging tasks. However, a large covariant shift between the source domain of natural images and target domain of medical images results in poor transfer learning. Moreover, scarcity of annotated data for the medical imaging tasks causes further problems for effective transfer learning. To address these problems, we develop an augmented ensemble transfer learning technique that leads to significant performance gain over the conventional transfer learning. Our technique uses an ensemble of deep learning models, where the architecture of each network is modified with extra layers to account for dimensionality change between the images of source and target data domains. Moreover, the model is hierarchically tuned to the target domain with augmented training data. Along with the network ensemble, we also utilize an ensemble of dictionaries that are based on features extracted from the augmented models. The dictionary ensemble provides an additional performance boost to our method. We first establish the effectiveness of our technique with the challenging ChestXray-14 radiography data set. Our experimental results show more than 50% reduction in the error rate with our method as compared to the baseline transfer learning technique. We then apply our technique to a recent COVID-19 data set for binary and multi-class classification tasks. Our technique achieves 99.49% accuracy for the binary classification, and 99.24% for multi-class classification.

Altaf Fouzia, Islam Syed M S, Janjua Naeem Khalid

2021-Apr-29

COVID-19, Chest radiography, Computer-aided diagnosis, Deep learning, Dictionary learning, Thoracic disease classification, Transfer learning

Cardiology Cardiology

Artificial Intelligence Applications for COVID-19 in Intensive Care and Emergency Settings: A Systematic Review.

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

Background: Little is known about the role of artificial intelligence (AI) as a decisive technology in the clinical management of COVID-19 patients. We aimed to systematically review and critically appraise the current evidence on AI applications for COVID-19 in intensive care and emergency settings. Methods: We systematically searched PubMed, Embase, Scopus, CINAHL, IEEE Xplore, and ACM Digital Library databases from inception to 1 October 2020, without language restrictions. We included peer-reviewed original studies that applied AI for COVID-19 patients, healthcare workers, or health systems in intensive care, emergency, or prehospital settings. We assessed predictive modelling studies and critically appraised the methodology and key findings of all other studies. Results: Of fourteen eligible studies, eleven developed prognostic or diagnostic AI predictive models, all of which were assessed to be at high risk of bias. Common pitfalls included inadequate sample sizes, poor handling of missing data, failure to account for censored participants, and weak validation of models. Conclusions: Current AI applications for COVID-19 are not ready for deployment in acute care settings, given their limited scope and poor quality. Our findings underscore the need for improvements to facilitate safe and effective clinical adoption of AI applications, for and beyond the COVID-19 pandemic.

Chee Marcel Lucas, Ong Marcus Eng Hock, Siddiqui Fahad Javaid, Zhang Zhongheng, Lim Shir Lynn, Ho Andrew Fu Wah, Liu Nan

2021-Apr-29

COVID-19, artificial intelligence, critical care, emergency department, intensive care, machine learning

General General

Detecting Topic and Sentiment Trends in Physician Rating Websites: Analysis of Online Reviews Using 3-Wave Datasets.

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

(1) Background: Physician rating websites (PRWs) are a rich resource of information where individuals learn other people response to various health problems. The current study aims to investigate and analyze the people top concerns and sentiment dynamics expressed in physician online reviews (PORs). (2) Methods: Text data were collected from four U.S.-based PRWs during the three time periods of 2018, 2019 and 2020. Based on the dynamic topic modeling, hot topics related to different aspects of healthcare were identified. Following the hybrid approach of aspect-based sentiment analysis, the social network of prevailing topics was also analyzed whether people expressed positive, neutral or negative sentiments in PORs. (3) Results: The study identified 30 dominant topics across three different stages which lead toward four key findings. First, topics discussed in Stage III were quite different from the earlier two stages due to the COVID-19 outbreak. Second, based on the keyword co-occurrence analysis, the most prevalent keywords in all three stages were related to the treatment, questions asked by patients, communication problem, patients' feelings toward the hospital environment, disease symptoms, time spend with patients and different issues related to the COVID-19 (i.e., pneumonia, death, spread and cases). Third, topics related to the provider service quality, hospital servicescape and treatment cost were the most dominant topics in Stages I and II, while the quality of online information regarding COVID-19 and government countermeasures were the most dominant topics in Stage III. Fourth, when zooming into the topic-based sentiments analysis, hot topics in Stage I were mostly positive (joy be the dominant emotion), then negative (disgust be the dominant emotion) in Stage II. Furthermore, sentiments in the initial period of Stage III (COVID-19) were negative (anger be the dominant emotion), then transformed into positive (trust be the dominant emotion) later. The findings also revealed that the proposed method outperformed the conventional machine learning models in analyzing topic and sentiment dynamics expressed in PRWs. (4) Conclusions: Methodologically, this research demonstrates the ability and importance of computational techniques for analyzing large corpora of text and complementing conventional social science approaches.

Shah Adnan Muhammad, Naqvi Rizwan Ali, Jeong Ok-Ran

2021-Apr-29

COVID-19, discrete emotions, online reviews, sentiment analysis, text mining, topic modeling

General General

In the Danger Zone: U-Net Driven Quantile Regression can Predict High-risk SARS-CoV-2 Regions via Pollutant Particulate Matter and Satellite Imagery

ArXiv Preprint

Since the outbreak of COVID-19 policy makers have been relying upon non-pharmacological interventions to control the outbreak. With air pollution as a potential transmission vector there is need to include it in intervention strategies. We propose a U-net driven quantile regression model to predict $PM_{2.5}$ air pollution based on easily obtainable satellite imagery. We demonstrate that our approach can reconstruct $PM_{2.5}$ concentrations on ground-truth data and predict reasonable $PM_{2.5}$ values with their spatial distribution, even for locations where pollution data is unavailable. Such predictions of $PM_{2.5}$ characteristics could crucially advise public policy strategies geared to reduce the transmission of and lethality of COVID-19.

Jacquelyn Shelton, Przemyslaw Polewski, Wei Yao

2021-05-06

Radiology Radiology

A Novel Method for COVID-19 Diagnosis Using Artificial Intelligence in Chest X-ray Images.

In Healthcare (Basel, Switzerland)

The Coronavirus disease 2019 (COVID-19) is an infectious disease spreading rapidly and uncontrollably throughout the world. The critical challenge is the rapid detection of Coronavirus infected people. The available techniques being utilized are body-temperature measurement, along with anterior nasal swab analysis. However, taking nasal swabs and lab testing are complex, intrusive, and require many resources. Furthermore, the lack of test kits to meet the exceeding cases is also a major limitation. The current challenge is to develop some technology to non-intrusively detect the suspected Coronavirus patients through Artificial Intelligence (AI) techniques such as deep learning (DL). Another challenge to conduct the research on this area is the difficulty of obtaining the dataset due to a limited number of patients giving their consent to participate in the research study. Looking at the efficacy of AI in healthcare systems, it is a great challenge for the researchers to develop an AI algorithm that can help health professionals and government officials automatically identify and isolate people with Coronavirus symptoms. Hence, this paper proposes a novel method CoVIRNet (COVID Inception-ResNet model), which utilizes the chest X-rays to diagnose the COVID-19 patients automatically. The proposed algorithm has different inception residual blocks that cater to information by using different depths feature maps at different scales, with the various layers. The features are concatenated at each proposed classification block, using the average-pooling layer, and concatenated features are passed to the fully connected layer. The efficient proposed deep-learning blocks used different regularization techniques to minimize the overfitting due to the small COVID-19 dataset. The multiscale features are extracted at different levels of the proposed deep-learning model and then embedded into various machine-learning models to validate the combination of deep-learning and machine-learning models. The proposed CoVIR-Net model achieved 95.7% accuracy, and the CoVIR-Net feature extractor with random-forest classifier produced 97.29% accuracy, which is the highest, as compared to existing state-of-the-art deep-learning methods. The proposed model would be an automatic solution for the assessment and classification of COVID-19. We predict that the proposed method will demonstrate an outstanding performance as compared to the state-of-the-art techniques being used currently.

Almalki Yassir Edrees, Qayyum Abdul, Irfan Muhammad, Haider Noman, Glowacz Adam, Alshehri Fahad Mohammed, Alduraibi Sharifa K, Alshamrani Khalaf, Alkhalik Basha Mohammad Abd, Alduraibi Alaa, Saeed M K, Rahman Saifur

2021-Apr-29

chest X-ray images, data analytics, feature extraction, healthcare, image processing, pandemic

Radiology Radiology

Quantification of pulmonary involvement in COVID-19 pneumonia by means of a cascade oftwo U-nets: training and assessment on multipledatasets using different annotation criteria

ArXiv Preprint

The automatic assignment of a severity score to the CT scans of patients affected by COVID-19 pneumonia could reduce the workload in radiology departments. This study aims at exploiting Artificial intelligence (AI) for the identification, segmentation and quantification of COVID-19 pulmonary lesions. We investigated the effects of using multiple datasets, heterogeneously populated and annotated according to different criteria. We developed an automated analysis pipeline, the LungQuant system, based on a cascade of two U-nets. The first one (U-net_1) is devoted to the identification of the lung parenchyma, the second one (U-net_2) acts on a bounding box enclosing the segmented lungs to identify the areas affected by COVID-19 lesions. Different public datasets were used to train the U-nets and to evaluate their segmentation performances, which have been quantified in terms of the Dice index. The accuracy in predicting the CT-Severity Score (CT-SS) of the LungQuant system has been also evaluated. Both Dice and accuracy showed a dependency on the quality of annotations of the available data samples. On an independent and publicly available benchmark dataset, the Dice values measured between the masks predicted by LungQuant system and the reference ones were 0.95$\pm$0.01 and 0.66$\pm$0.13 for the segmentation of lungs and COVID-19 lesions, respectively. The accuracy of 90% in the identification of the CT-SS on this benchmark dataset was achieved. We analysed the impact of using data samples with different annotation criteria in training an AI-based quantification system for pulmonary involvement in COVID-19 pneumonia. In terms of the Dice index, the U-net segmentation quality strongly depends on the quality of the lesion annotations. Nevertheless, the CT-SS can be accurately predicted on independent validation sets, demonstrating the satisfactory generalization ability of the LungQuant.

Francesca Lizzi, Abramo Agosti, Francesca Brero, Raffaella Fiamma Cabini, Maria Evelina Fantacci, Silvia Figini, Alessandro Lascialfari, Francesco Laruina, Piernicola Oliva, Stefano Piffer, Ian Postuma, Lisa Rinaldi, Cinzia Talamonti, Alessandra Retico

2021-05-06

General General

Correction: A Patient Journey Map to Improve the Home Isolation Experience of Persons With Mild COVID-19: Design Research for Service Touchpoints of Artificial Intelligence in eHealth.

In JMIR medical informatics ; h5-index 23.0

[This corrects the article DOI: 10.2196/23238.].

He Qian, Du Fei, Simonse Lianne W L

2021-May-04

General General

News Sentiment Informed Time-series Analyzing AI (SITALA) to curb the spread of COVID-19 in Houston.

In Expert systems with applications

Coronavirus disease (COVID-19) has evolved into a pandemic with many unknowns. Houston, located in the Harris County of Texas, is becoming the next hotspot of this pandemic. With a severe decline in international and inter-state travel, a model at the county level is needed as opposed to the state or country level. Existing approaches have a few drawbacks. Firstly, the data used is the number of COVID-19 positive cases instead of positivity. The former is a function of the number of tests carried out while the number of tests normalizes the latter. Positivity gives a better picture of the spread of this pandemic as, with time, more tests are being administered. Positivity under 5% has been desired for the reopening of businesses to almost 100% capacity. Secondly, the data used by models like SEIRD (Susceptible, Exposed, Infectious, Recovered, and Deceased) lacks information about the sentiment of people concerning coronavirus. Thirdly, models that make use of social media posts might have too much noise and misinformation. On the other hand, news sentiment can capture long-term effects of hidden variables like public policy, opinions of local doctors, and disobedience of state-wide mandates. The present study introduces a new artificial intelligence (i.e., AI) model, viz., Sentiment Informed Time-series Analyzing AI (SITALA), trained on COVID-19 test positivity data and news sentiment from over 2750 news articles for Harris county. The news sentiment was obtained using IBM Watson Discovery News. SITALA is inspired by Google-Wavenet architecture and makes use of TensorFlow. The mean absolute error for the training dataset of 66 consecutive days is 2.76, and that for the test dataset of 22 consecutive days is 9.6. A cone of uncertainty is provided within which future COVID-19 test positivity has been shown to fall with high accuracy. The model predictions fare better than a published Bayesian-based SEIRD model. The model forecasts that in order to curb the spread of coronavirus in Houston, a sustained negative news sentiment (e.g., death count for COVID-19 will grow at an alarming rate in Houston if mask orders are not followed) will be desirable. Public policymakers may use SITALA to set the tone of the local policies and mandates.

Desai Prathamesh S

2021-Apr-29

Artificial Intelligence, COVID-19 Model, Deep Learning, News Sentiment, Pandemic Forecast, Public Policy

Radiology Radiology

Projecting COVID-19 disease severity in cancer patients using purposefully-designed machine learning.

In BMC infectious diseases ; h5-index 58.0

BACKGROUND : Accurately predicting outcomes for cancer patients with COVID-19 has been clinically challenging. Numerous clinical variables have been retrospectively associated with disease severity, but the predictive value of these variables, and how multiple variables interact to increase risk, remains unclear.

METHODS : We used machine learning algorithms to predict COVID-19 severity in 348 cancer patients at Memorial Sloan Kettering Cancer Center in New York City. Using only clinical variables collected on or before a patient's COVID-19 positive date (time zero), we sought to classify patients into one of three possible future outcomes: Severe-early (the patient required high levels of oxygen support within 3 days of being tested positive for COVID-19), Severe-late (the patient required high levels of oxygen after 3 days), and Non-severe (the patient never required oxygen support).

RESULTS : Our algorithm classified patients into these classes with an area under the receiver operating characteristic curve (AUROC) ranging from 70 to 85%, significantly outperforming prior methods and univariate analyses. Critically, classification accuracy is highest when using a potpourri of clinical variables - including basic patient information, pre-existing diagnoses, laboratory and radiological work, and underlying cancer type - suggesting that COVID-19 in cancer patients comes with numerous, combinatorial risk factors.

CONCLUSIONS : Overall, we provide a computational tool that can identify high-risk patients early in their disease progression, which could aid in clinical decision-making and selecting treatment options.

Navlakha Saket, Morjaria Sejal, Perez-Johnston Rocio, Zhang Allen, Taur Ying

2021-May-04

COVID-19, Cancer, Clinical machine learning, Infectious diseases, Predictive modeling

General General

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

In BMC bioinformatics

BACKGROUND : Leveraging previously identified viral interactions with human host proteins, we apply a machine learning-based approach to connect SARS-CoV-2 viral proteins to relevant host biological functions, diseases, and pathways in a large-scale knowledge graph derived from the biomedical literature. Our goal is to explore how SARS-CoV-2 could interfere with various host cell functions, and to identify drug targets amongst the host genes that could potentially be modulated against COVID-19 by repurposing existing drugs. The machine learning model employed here involves gene embeddings that leverage causal gene expression signatures curated from literature. In contrast to other network-based approaches for drug repurposing, our approach explicitly takes the direction of effects into account, distinguishing between activation and inhibition.

RESULTS : We have constructed 70 networks connecting SARS-CoV-2 viral proteins to various biological functions, diseases, and pathways reflecting viral biology, clinical observations, and co-morbidities in the context of COVID-19. Results are presented in the form of interactive network visualizations through a web interface, the Coronavirus Network Explorer (CNE), that allows exploration of underlying experimental evidence. We find that existing drugs targeting genes in those networks are strongly enriched in the set of drugs that are already in clinical trials against COVID-19.

CONCLUSIONS : The approach presented here can identify biologically plausible hypotheses for COVID-19 pathogenesis, explicitly connected to the immunological, virological and pathological observations seen in SARS-CoV-2 infected patients. The discovery of repurposable drugs is driven by prior knowledge of relevant functional endpoints that reflect known viral biology or clinical observations, therefore suggesting potential mechanisms of action. We believe that the CNE offers relevant insights that go beyond more conventional network approaches, and can be a valuable tool for drug repurposing. The CNE is available at https://digitalinsights.qiagen.com/coronavirus-network-explorer .

Krämer Andreas, Billaud Jean-Noël, Tugendreich Stuart, Shiffman Dan, Jones Martin, Green Jeff

2021-May-03

COVID-19, Drug repurposing, Knowledge graph, Network biology

Public Health Public Health

Using machine learning to develop a novel COVID-19 Vulnerability Index (C19VI).

In The Science of the total environment

COVID-19 is now one of the most leading causes of death in the United States (US). Systemic health, social and economic disparities have put the minorities and economically poor communities at a higher risk than others. There is an immediate requirement to develop a reliable measure of county-level vulnerabilities that can capture the heterogeneity of vulnerable communities. This study reports a COVID-19 Vulnerability Index (C19VI) for identifying and mapping vulnerable counties. We proposed a Random Forest machine learning-based vulnerability model using CDC's sociodemographic and COVID-19-specific themes. An innovative 'COVID-19 Impact Assessment' algorithm was also developed for evaluating severity of the pandemic and to train the vulnerability model. Developed C19VI was statistically validated and compared with the CDC COVID-19 Community Vulnerability Index (CCVI). Finally, using C19VI and the census data, we explored racial inequalities and economic disparities in COVID-19 health outcomes. Our index indicates that 575 counties (45 million people) fall into the 'very high' vulnerability class, 765 counties (66 million people) in the 'high' vulnerability class, and 1435 counties (204 million people) in the 'moderate' or 'low' vulnerability class. Only 367 counties (20 million people) were found as 'very low' vulnerable areas. Furthermore, C19VI reveals that 524 counties with a racial minority population higher than 13% and 420 counties with poverty higher than 20% are in the 'very high' or 'high' vulnerability classes. The C19VI aims at helping public health officials and disaster management agencies to develop effective mitigation strategies especially for the disproportionately impacted communities.

Tiwari Anuj, Dadhania Arya V, Ragunathrao Vijay Avin Balaji, Oliveira Edson R A

2021-Jun-15

COVID-19, Disproportionate COVID-19, Machine learning, Racial minority, Vulnerability modeling

General General

Perspective: Wearable Internet of Medical Things for Remote Tracking of Symptoms, Prediction of Health Anomalies, Implementation of Preventative Measures, and Control of Virus Spread During the Era of COVID-19.

In Frontiers in robotics and AI

The COVID-19 pandemic has highly impacted the communities globally by reprioritizing the means through which various societal sectors operate. Among these sectors, healthcare providers and medical workers have been impacted prominently due to the massive increase in demand for medical services under unprecedented circumstances. Hence, any tool that can help the compliance with social guidelines for COVID-19 spread prevention will have a positive impact on managing and controlling the virus outbreak and reducing the excessive burden on the healthcare system. This perspective article disseminates the perspectives of the authors regarding the use of novel biosensors and intelligent algorithms embodied in wearable IoMT frameworks for tackling this issue. We discuss how with the use of smart IoMT wearables certain biomarkers can be tracked for detection of COVID-19 in exposed individuals. We enumerate several machine learning algorithms which can be used to process a wide range of collected biomarkers for detecting (a) multiple symptoms of SARS-CoV-2 infection and (b) the dynamical likelihood of contracting the virus through interpersonal interaction. Eventually, we enunciate how a systematic use of smart wearable IoMT devices in various social sectors can intelligently help controlling the spread of COVID-19 in communities as they enter the reopening phase. We explain how this framework can benefit individuals and their medical correspondents by introducing Systems for Symptom Decoding (SSD), and how the use of this technology can be generalized on a societal level for the control of spread by introducing Systems for Spread Tracing (SST).

Mehrdad Sarmad, Wang Yao, Atashzar S Farokh

2021

AI for health, COVID-19, IoMT, smart connected health, smart wearables, spread control, symptom tracking, telemedicine

General General

Classification of COVID-19 Chest CT Images Based on Ensemble Deep Learning.

In Journal of healthcare engineering

Novel coronavirus pneumonia (NCP) has become a global pandemic disease, and computed tomography-based (CT) image analysis and recognition are one of the important tools for clinical diagnosis. In order to assist medical personnel to achieve an efficient and fast diagnosis of patients with new coronavirus pneumonia, this paper proposes an assisted diagnosis algorithm based on ensemble deep learning. The method combines the Stacked Generalization ensemble learning with the VGG16 deep learning to form a cascade classifier, and the information constituting the cascade classifier comes from multiple subsets of the training set, each of which is used to collect deviant information about the generalization behavior of the data set, such that this deviant information fills the cascade classifier. The algorithm was experimentally validated for classifying patients with novel coronavirus pneumonia, patients with common pneumonia (CP), and normal controls, and the algorithm achieved a prediction accuracy of 93.57%, sensitivity of 94.21%, specificity of 93.93%, precision of 89.40%, and F1-score of 91.74% for the three categories. The results show that the method proposed in this paper has good classification performance and can significantly improve the performance of deep neural networks for multicategory prediction tasks.

Li Xiaoshuo, Tan Wenjun, Liu Pan, Zhou Qinghua, Yang Jinzhu

2021

Public Health Public Health

Leveraging Informatics and Technology to Support Public Health Response: Framework and Illustrations using COVID-19.

In Online journal of public health informatics

Objective : To develop a conceptual model and novel, comprehensive framework that encompass the myriad ways informatics and technology can support public health response to a pandemic.

Method : The conceptual model and framework categorize informatics solutions that could be used by stakeholders (e.g., government, academic institutions, healthcare providers and payers, life science companies, employers, citizens) to address public health challenges across the prepare, respond, and recover phases of a pandemic, building on existing models for public health operations and response.

Results : Mapping existing solutions, technology assets, and ideas to the framework helped identify public health informatics solution requirements and gaps in responding to COVID-19 in areas such as applied science, epidemiology, communications, and business continuity. Two examples of technologies used in COVID-19 illustrate novel applications of informatics encompassed by the framework. First, we examine a hub from The Weather Channel, which provides COVID-19 data via interactive maps, trend graphs, and details on case data to individuals and businesses. Second, we examine IBM Watson Assistant for Citizens, an AI-powered virtual agent implemented by healthcare providers and payers, government agencies, and employers to provide information about COVID-19 via digital and telephone-based interaction.

Discussion : Early results from these novel informatics solutions have been positive, showing high levels of engagement and added value across stakeholders.

Conclusion : The framework supports development, application, and evaluation of informatics approaches and technologies in support of public health preparedness, response, and recovery during a pandemic. Effective solutions are critical to success in recovery from COVID-19 and future pandemics.

Snowdon Jane L, Kassler William, Karunakaram Hema, Dixon Brian E, Rhee Kyu

2021

artificial intelligence, clinical informatics, coronavirus, information technology, pandemics, public health informatics

General General

A COVID-19 time series forecasting model based on MLP ANN.

In Procedia computer science

With the accelerated spread of COVID-19 worldwide and its potentially fatal effects on human health, the development of a tool that effectively describes and predicts the number of infected cases and deaths over time becomes relevant. This makes it possible for administrative sectors and the population itself to become aware and act more precisely. In this work, a machine learning model based on the multilayer Perceptron artificial neural network structure was used, which effectively predicts the behavior of the series mentioned in up to six days. The model, which is trained with data from 30 countries together in a 20-day context, is assessed using global and local MSE and MAE measures. For the construction of training and test sets, four time series (number of: accumulated infected cases, new cases, accumulated deaths and new deaths) from each country are used, which are started on the day of the first confirmed infection case. In order to soften the sudden transitions between samples, a moving average filter with a window size 3 and a normalization by maximum value were used. It is intended to make the model's predictions available online, collaborating with the fight against the pandemic.

Borghi Pedro Henrique, Zakordonets Oleksandr, Teixeira João Paulo

2021

COVID-19 Brazil forecast, COVID-19 Italy forecast, COVID-19 worldwide forecast

General General

The Role of Artificial Intelligence in Fighting the COVID-19 Pandemic.

In Information systems frontiers : a journal of research and innovation

The first few months of 2020 have profoundly changed the way we live our lives and carry out our daily activities. Although the widespread use of futuristic robotaxis and self-driving commercial vehicles has not yet become a reality, the COVID-19 pandemic has dramatically accelerated the adoption of Artificial Intelligence (AI) in different fields. We have witnessed the equivalent of two years of digital transformation compressed into just a few months. Whether it is in tracing epidemiological peaks or in transacting contactless payments, the impact of these developments has been almost immediate, and a window has opened up on what is to come. Here we analyze and discuss how AI can support us in facing the ongoing pandemic. Despite the numerous and undeniable contributions of AI, clinical trials and human skills are still required. Even if different strategies have been developed in different states worldwide, the fight against the pandemic seems to have found everywhere a valuable ally in AI, a global and open-source tool capable of providing assistance in this health emergency. A careful AI application would enable us to operate within this complex scenario involving healthcare, society and research.

Piccialli Francesco, di Cola Vincenzo Schiano, Giampaolo Fabio, Cuomo Salvatore

2021-Apr-26

Artificial intelligence, COVID-19, Deep learning, Healthcare, Machine learning, Review, SARS-CoV-2, Survey

General General

A Novel Ensemble-based Classifier for Detecting the COVID-19 Disease for Infected Patients.

In Information systems frontiers : a journal of research and innovation

The recently discovered coronavirus, SARS-CoV-2, which was detected in Wuhan, China, has spread worldwide and is still being studied at the end of 2019. Detection of COVID-19 at an early stage is essential to provide adequate healthcare to affected patients and protect the uninfected community. This paper aims to design and develop a novel ensemble-based classifier to predict COVID-19 cases at a very early stage so that appropriate action can be taken by patients, doctors, health organizations, and the government. In this paper, a synthetic dataset of COVID-19 is generated by a dataset generation algorithm. A novel ensemble-based classifier of machine learning is employed on the COVID-19 dataset to predict the disease. A convex hull-based approach is also applied to the data to improve the proposed novel, ensemble-based classifier's accuracy and speed. The model is designed and developed through the python programming language and compares with the most popular classifier, i.e., Decision Tree, ID3, and support vector machine. The results indicate that the proposed novel classifier provides a more significant precision, kappa static, root means a square error, recall, F-measure, and accuracy.

Singh Prabh Deep, Kaur Rajbir, Singh Kiran Deep, Dhiman Gaurav

2021-Apr-25

Artificial intelligence, COVID-19, Corona virus, Ensemble classifier, Machine learning, Quality of service

General General

Optimization of Thermal and Structural Design in Lithium-Ion Batteries to Obtain Energy Efficient Battery Thermal Management System (BTMS): A Critical Review.

In Archives of computational methods in engineering : state of the art reviews

Covid-19 has given one positive perspective to look at our planet earth in terms of reducing the air and noise pollution thus improving the environmental conditions globally. This positive outcome of pandemic has given the indication that the future of energy belong to green energy and one of the emerging source of green energy is Lithium-ion batteries (LIBs). LIBs are the backbone of the electric vehicles but there are some major issues faced by the them like poor thermal performance, thermal runaway, fire hazards and faster rate of discharge under low and high temperature environment,. Therefore to overcome these problems most of the researchers have come up with new methods of controlling and maintaining the overall thermal performance of the LIBs. The present review paper mainly is focused on optimization of thermal and structural design parameters of the LIBs under different BTMSs. The optimized BTMS generally demonstrated in this paper are maximum temperature of battery cell, battery pack or battery module, temperature uniformity, maximum or average temperature difference, inlet temperature of coolant, flow velocity, and pressure drop. Whereas the major structural design optimization parameters highlighted in this paper are type of flow channel, number of channels, length of channel, diameter of channel, cell to cell spacing, inlet and outlet plenum angle and arrangement of channels. These optimized parameters investigated under different BTMS heads such as air, PCM (phase change material), mini-channel, heat pipe, and water cooling are reported profoundly in this review article. The data are categorized and the results of the recent studies are summarized for each method. Critical review on use of various optimization algorithms (like ant colony, genetic, particle swarm, response surface, NSGA-II, etc.) for design parameter optimization are presented and categorized for different BTMS to boost their objectives. The single objective optimization techniques helps in obtaining the optimal value of important design parameters related to the thermal performance of battery cooling systems. Finally, multi-objective optimization technique is also discussed to get an idea of how to get the trade-off between the various conflicting parameters of interest such as energy, cost, pressure drop, size, arrangement, etc. which is related to minimization and thermal efficiency/performance of the battery system related to maximization. This review will be very helpful for researchers working with an objective of improving the thermal performance and life span of the LIBs.

Fayaz H, Afzal Asif, Samee A D Mohammed, Soudagar Manzoore Elahi M, Akram Naveed, Mujtaba M A, Jilte R D, Islam Md Tariqul, Ağbulut Ümit, Saleel C Ahamed

2021-Apr-26

General General

Predicting compassion fatigue among psychological hotline counselors using machine learning techniques.

In Current psychology (New Brunswick, N.J.)

** : During the outbreak of coronavirus disease 2019, psychological hotline counselors frequently address help-seekers' traumatic experiences from time to time, which possibly causes counselors' compassion fatigue. The present study aimed to explore the predictors of compassion fatigue among a high-risk population of psychological hotline counselors. Seven hundred and twelve psychological hotline counselors were recruited from the Mental Health Service Platform at Central China Normal University, Ministry of Education, then were asked to complete the questionnaires measuring compassion fatigue, trait empathy, social support, trait mindfulness, counselor's self-efficacy, humor, life meaning, and post-traumatic growth. A chi-square test was utilized to filter for the top-20 predictive variables. Machine learning techniques, including logistic regression, decision tree, random forest, k-nearest neighbor, support vector machine, and naïve Bayes were employed to predict compassion fatigue. The results showed that the most important predictors of compassion fatigue were meaning in life, counselors' self-efficacy, mindfulness, and empathy. Except for the decision tree, the rest machine learning techniques obtained good performance. Naïve Bayes presented the highest area under the receiver operating characteristic curve of 0.803. Random forest achieved the least classification error of 23.64, followed by Naïve Bayes with a classification error of 23.85. These findings support the potential application of machine learning techniques in the prediction of compassion fatigue.

Supplementary Information : The online version contains supplementary material available at 10.1007/s12144-021-01776-7.

Zhang Lin, Zhang Tao, Ren Zhihong, Jiang Guangrong

2021-Apr-26

COVID-19, Compassion fatigue, Hotline psychological counselor, Machine learning

General General

Medical image-based detection of COVID-19 using Deep Convolution Neural Networks.

In Multimedia systems

The demand for automatic detection of Novel Coronavirus or COVID-19 is increasing across the globe. The exponential rise in cases burdens healthcare facilities, and a vast amount of multimedia healthcare data is being explored to find a solution. This study presents a practical solution to detect COVID-19 from chest X-rays while distinguishing those from normal and impacted by Viral Pneumonia via Deep Convolution Neural Networks (CNN). In this study, three pre-trained CNN models (EfficientNetB0, VGG16, and InceptionV3) are evaluated through transfer learning. The rationale for selecting these specific models is their balance of accuracy and efficiency with fewer parameters suitable for mobile applications. The dataset used for the study is publicly available and compiled from different sources. This study uses deep learning techniques and performance metrics (accuracy, recall, specificity, precision, and F1 scores). The results show that the proposed approach produced a high-quality model, with an overall accuracy of 92.93%, COVID-19, a sensitivity of 94.79%. The work indicates a definite possibility to implement computer vision design to enable effective detection and screening measures.

Gaur Loveleen, Bhatia Ujwal, Jhanjhi N Z, Muhammad Ghulam, Masud Mehedi

2021-Apr-28

COVID-19, Chest X-rays, Computer vision, Deep CNN, Deep learning, Transfer learning

General General

Increased ozone pollution alongside reduced nitrogen dioxide concentrations during Vienna's first COVID-19 lockdown: Significance for air quality management.

In Environmental pollution (Barking, Essex : 1987)

BACKGROUND : Lockdowns amid the COVID-19 pandemic have offered a real-world opportunity to better understand air quality responses to previously unseen anthropogenic emission reductions.

METHODS AND MAIN OBJECTIVE : This work examines the impact of Vienna's first lockdown on ground-level concentrations of nitrogen dioxide (NO2), ozone (O3) and total oxidant (Ox). The analysis runs over January to September 2020 and considers business as usual scenarios created with machine learning models to provide a baseline for robustly diagnosing lockdown-related air quality changes. Models were also developed to normalise the air pollutant time series, enabling facilitated intervention assessment.

CORE FINDINGS : NO2 concentrations were on average -20.1% [13.7-30.4%] lower during the lockdown. However, this benefit was offset by amplified O3 pollution of +8.5% [3.7-11.0%] in the same period. The consistency in the direction of change indicates that the NO2 reductions and O3 increases were ubiquitous over Vienna. Ox concentrations increased slightly by +4.3% [1.8-6.4%], suggesting that a significant part of the drops in NO2 was compensated by gains in O3. Accordingly, 82% of lockdown days with lowered NO2 were accompanied by 81% of days with amplified O3. The recovery shapes of the pollutant concentrations were depicted and discussed. The business as usual-related outcomes were broadly consistent with the patterns outlined by the normalised time series. These findings allowed to argue further that the detected changes in air quality were of anthropogenic and not of meteorological reason. Pollutant changes on the machine learning baseline revealed that the impact of the lockdown on urban air quality were lower than the raw measurements show. Besides, measured traffic drops in major Austrian roads were more significant for light-duty than for heavy-duty vehicles. It was also noted that the use of mobility reports based on cell phone movement as activity data can overestimate the reduction of emissions for the road transport sector, particularly for heavy-duty vehicles. As heavy-duty vehicles can make up a large fraction of the fleet emissions of nitrogen oxides, the change in the volume of these vehicles on the roads may be the main driver to explain the change in NO2 concentrations.

INTERPRETATION AND IMPLICATIONS : A probable future with emissions of volatile organic compounds (VOCs) dropping slower than emissions of nitrogen oxides could risk worsened urban O3 pollution under a VOC-limited photochemical regime. More holistic policies will be needed to achieve improved air quality levels across different regions and criteria pollutants.

Brancher Marlon

2021-Apr-15

Air quality data, Atmospheric composition, COVID-19 lockdown, Machine learning, Meteorology

Public Health Public Health

Artificial Intelligence-enabled analysis of social media data to understand public perceptions of COVID-19 contact tracing apps.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : The emergence of SARS-CoV-2 in late 2019 and its subsequent global spread continues to be a global health crisis. Many governments see contact tracing of citizens through apps installed on mobile phones as a key mechanism to contain the spread of SARS-Cov-2.

OBJECTIVE : We here report on an analysis of the suitability of Artificial Intelligence (AI)-enabled social media analysis of Facebook and Twitter to understand public perceptions of COVID-19 contact tracing apps in the United Kingdom.

METHODS : We extracted over 10,000 relevant social media posts and analysed these, over an eight month period, from 1st of March to 31st of October 2020. We used an initial filter with COVID-19 related keywords, which were pre-defined as part of an open Twitter-based COVID-19 dataset. We then applied a second filter using contract tracing app keywords and a geographical filter. A hybrid rule-based ensemble model was developed and utilised for the study, combining state-of-the-art lexicon rule-based and Deep Learning-based approaches.

RESULTS : Overall, we observed 76% positive and 12% negative sentiments, with the majority of negative sentiments being in the North of England. These sentiments varied over time, likely being influenced by ongoing public debates around implementing app-based contact tracing using a centralised model where data would be shared with the health service, versus de-centralised contact-tracing technology.

CONCLUSIONS : Variations in sentiments corroborate with ongoing debates surrounding the information governance of health related information. AI-enabled social media analysis of public attitudes in healthcare can help to facilitate the implementation of effective public health campaigns.

Cresswell Kathrin, Tahir Ahsen, Sheikh Zakariya, Hussain Zain, Domínguez Hernández Andrés, Harrison Ewen, Williams Robin, Sheikh Aziz, Hussain Amir

2021-Apr-17

Public Health Public Health

Experimental and natural evidence of SARS-CoV-2 infection-induced activation of type I interferon responses.

In iScience

Type I interferons (IFNs) are our first line of defence against virus infection. Recent studies have suggested the ability of SARS-CoV-2 proteins to inhibit IFN responses. Emerging data also suggest that timing and extent of IFN production is associated with manifestation of COVID-19 severity. In spite of progress in understanding how SARS-CoV-2 activates antiviral responses, mechanistic studies into wildtype SARS-CoV-2-mediated induction and inhibition of human type I IFN responses are scarce. Here we demonstrate that SARS-CoV-2 infection induces a type I IFN response in vitro and in moderate cases of COVID-19. In vitro stimulation of type I IFN expression and signaling in human airway epithelial cells is associated with activation of canonical transcriptions factors, and SARS-CoV-2 is unable to inhibit exogenous induction of these responses. Furthermore, we show that physiological levels of IFNα detected in patients with moderate COVID-19 is sufficient to suppress SARS-CoV-2 replication in human airway cells.

Banerjee Arinjay, El-Sayes Nader, Budylowski Patrick, Jacob Rajesh Abraham, Richard Daniel, Maan Hassaan, Aguiar Jennifer A, Demian Wael L, Baid Kaushal, D’Agostino Michael R, Ang Jann Catherine, Murdza Tetyana, Tremblay Benjamin J-M, Afkhami Sam, Karimzadeh Mehran, Irving Aaron T, Yip Lily, Ostrowski Mario, Hirota Jeremy A, Kozak Robert, Capellini Terence D, Miller Matthew S, Wang Bo, Mubareka Samira, McGeer Allison J, McArthur Andrew G, Doxey Andrew C, Mossman Karen

2021-Apr-26

Radiology Radiology

CT-Based COVID-19 triage: Deep multitask learning improves joint identification and severity quantification.

In Medical image analysis

The current COVID-19 pandemic overloads healthcare systems, including radiology departments. Though several deep learning approaches were developed to assist in CT analysis, nobody considered study triage directly as a computer science problem. We describe two basic setups: Identification of COVID-19 to prioritize studies of potentially infected patients to isolate them as early as possible; Severity quantification to highlight patients with severe COVID-19, thus direct them to a hospital or provide emergency medical care. We formalize these tasks as binary classification and estimation of affected lung percentage. Though similar problems were well-studied separately, we show that existing methods could provide reasonable quality only for one of these setups. We employ a multitask approach to consolidate both triage approaches and propose a convolutional neural network to leverage all available labels within a single model. In contrast with the related multitask approaches, we show the benefit from applying the classification layers to the most spatially detailed feature map at the upper part of U-Net instead of the less detailed latent representation at the bottom. We train our model on approximately 1500 publicly available CT studies and test it on the holdout dataset that consists of 123 chest CT studies of patients drawn from the same healthcare system, specifically 32 COVID-19 and 30 bacterial pneumonia cases, 30 cases with cancerous nodules, and 31 healthy controls. The proposed multitask model outperforms the other approaches and achieves ROC AUC scores of 0.87±0.01 vs. bacterial pneumonia, 0.93±0.01 vs. cancerous nodules, and 0.97±0.01 vs. healthy controls in Identification of COVID-19, and achieves 0.97±0.01 Spearman Correlation in Severity quantification. We have released our code and shared the annotated lesions masks for 32 CT images of patients with COVID-19 from the test dataset.

Goncharov Mikhail, Pisov Maxim, Shevtsov Alexey, Shirokikh Boris, Kurmukov Anvar, Blokhin Ivan, Chernina Valeria, Solovev Alexander, Gombolevskiy Victor, Morozov Sergey, Belyaev Mikhail

2021-Apr-01

COVID-19, Chest computed tomography, Convolutional neural network, Triage

Radiology Radiology

A comprehensive review of imaging findings in COVID-19 - status in early 2021.

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

Medical imaging methods are assuming a greater role in the workup of patients with COVID-19, mainly in relation to the primary manifestation of pulmonary disease and the tissue distribution of the angiotensin-converting-enzyme 2 (ACE 2) receptor. However, the field is so new that no consensus view has emerged guiding clinical decisions to employ imaging procedures such as radiography, computer tomography (CT), positron emission tomography (PET), and magnetic resonance imaging, and in what measure the risk of exposure of staff to possible infection could be justified by the knowledge gained. The insensitivity of current RT-PCR methods for positive diagnosis is part of the rationale for resorting to imaging procedures. While CT is more sensitive than genetic testing in hospitalized patients, positive findings of ground glass opacities depend on the disease stage. There is sparse reporting on PET/CT with [18F]-FDG in COVID-19, but available results are congruent with the earlier literature on viral pneumonias. There is a high incidence of cerebral findings in COVID-19, and likewise evidence of gastrointestinal involvement. Artificial intelligence, notably machine learning is emerging as an effective method for diagnostic image analysis, with performance in the discriminative diagnosis of diagnosis of COVID-19 pneumonia comparable to that of human practitioners.

Afshar-Oromieh Ali, Prosch Helmut, Schaefer-Prokop Cornelia, Bohn Karl Peter, Alberts Ian, Mingels Clemens, Thurnher Majda, Cumming Paul, Shi Kuangyu, Peters Alan, Geleff Silvana, Lan Xiaoli, Wang Feng, Huber Adrian, Gräni Christoph, Heverhagen Johannes T, Rominger Axel, Fontanellaz Matthias, Schöder Heiko, Christe Andreas, Mougiakakou Stavroula, Ebner Lukas

2021-May-01

COVID-19, Corona virus, Imaging, SARS-CoV-2

General General

Immunoinformatics approach for a novel multi-epitope vaccine construct against spike protein of human coronaviruses

bioRxiv Preprint

Spike (S) proteins are an attractive target as it mediates the binding of the SARS-CoV-2 to the host through ACE-2 receptors. We hypothesize that the screening of S protein sequences of all the HCoVs would result in the identification of potential multi-epitope vaccine candidates capable of conferring immunity against various HCoVs. In the present study, several machine learning-based in-silico tools were employed to design a broad-spectrum multi-epitope vaccine candidate against S protein of human coronaviruses. To the best of our knowledge, it is one of the first study, where multiple B-cell epitopes and T-cell epitopes (CTL and HTL) were predicted from the S protein sequences of all seven known HCoVs and linked together with an adjuvant to construct a potential broad-spectrum vaccine candidate. Secondary and tertiary structures were predicted, validated and the refined 3D-model was docked with an immune receptor. The vaccine candidate was evaluated for antigenicity, allergenicity, solubility, and its ability to achieve high-level expression in bacterial hosts. Finally, the immune simulation was carried out to evaluate the immune response after three vaccine doses. The designed vaccine is antigenic (with or without the adjuvant), non-allergenic, binds well with TLR-3 receptor and might elicit a diverse and strong immune response.

kumar, A.; Rathi, E.; Kini, S. G.

2021-05-02

General General

Comparative study of machine learning methods for COVID-19 transmission forecasting.

In Journal of biomedical informatics ; h5-index 55.0

Within the recent pandemic, scientists and clinicians are engaged in seeking new technology to stop or slow down the COVID-19 pandemic. The benefit of machine learning, as an essential aspect of artificial intelligence, on past epidemics offers a new line to tackle the novel Coronavirus outbreak. Accurate short-term forecasting of COVID-19 spread plays an essential role in improving the management of the overcrowding problem in hospitals and enables appropriate optimization of the available resources (i.e., materials and staff).This paper presents a comparative study of machine learning methods for COVID-19 transmission forecasting. We investigated the performances of deep learning methods, including the hybrid convolutional neural networks-Long short-term memory (LSTM-CNN), the hybrid gated recurrent unit-convolutional neural networks (GAN-GRU), GAN, CNN, LSTM, and Restricted Boltzmann Machine (RBM), as well as baseline machine learning methods, namely logistic regression (LR) and support vector regression (SVR). The employment of hybrid models (i.e., LSTM-CNN and GAN-GRU) is expected to eventually improve the forecasting accuracy of COVID-19 future trends. The performance of the investigated deep learning and machine learning models was tested using confirmed and recovered COVID-19 cases time-series data from seven impacted countries: Brazil, France, India, Mexico, Russia, Saudi Arabia, and the US. The results reveal that hybrid deep learning models can efficiently forecast COVID-19 cases. Also, results confirmed the superior performance of deep learning models compared to the two considered baseline machine learning models. Furthermore, results showed that LSTM-CNN achieved improved performances with an averaged mean absolute percentage error of 3.718%, among others.

Dairi Abdelkader, Harrou Fouzi, Zeroual Abdelhafid, Hittawe Mohamad Mazen, Sun Ying

2021-Apr-26

COVID-19, GAN-GRU., Hybrid deep learning, LSTM-CNN, short-term forecasting

General General

[Research progress in lung parenchyma segmentation based on computed tomography].

In Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi

Lung diseases such as lung cancer and COVID-19 seriously endanger human health and life safety, so early screening and diagnosis are particularly important. computed tomography (CT) technology is one of the important ways to screen lung diseases, among which lung parenchyma segmentation based on CT images is the key step in screening lung diseases, and high-quality lung parenchyma segmentation can effectively improve the level of early diagnosis and treatment of lung diseases. Automatic, fast and accurate segmentation of lung parenchyma based on CT images can effectively compensate for the shortcomings of low efficiency and strong subjectivity of manual segmentation, and has become one of the research hotspots in this field. In this paper, the research progress in lung parenchyma segmentation is reviewed based on the related literatures published at domestic and abroad in recent years. The traditional machine learning methods and deep learning methods are compared and analyzed, and the research progress of improving the network structure of deep learning model is emphatically introduced. Some unsolved problems in lung parenchyma segmentation were discussed, and the development prospect was prospected, providing reference for researchers in related fields.

Xiao Hanguang, Ran Zhiqiang, Huang Jinfeng, Ren Huijiao, Liu Chang, Zhang Banglin, Zhang Bolong, Dang Jun

2021-Apr-25

computed tomography, deep learning, lung parenchyma segmentation

General General

Deep CNN models for predicting COVID-19 in CT and x-ray images.

In Journal of medical imaging (Bellingham, Wash.)

Purpose: Coronavirus disease 2019 (COVID-19) is a new infection that has spread worldwide and with no automatic model to reliably detect its presence from images. We aim to investigate the potential of deep transfer learning to predict COVID-19 infection using chest computed tomography (CT) and x-ray images. Approach: Regions of interest (ROI) corresponding to ground-glass opacities (GGO), consolidations, and pleural effusions were labeled in 100 axial lung CT images from 60 COVID-19-infected subjects. These segmented regions were then employed as an additional input to six deep convolutional neural network (CNN) architectures (AlexNet, DenseNet, GoogleNet, NASNet-Mobile, ResNet18, and DarkNet), pretrained on natural images, to differentiate between COVID-19 and normal CT images. We also explored the model's ability to classify x-ray images as COVID-19, non-COVID-19 pneumonia, or normal. Performance on test images was measured with global accuracy and area under the receiver operating characteristic curve (AUC). Results: When using raw CT images as input to the tested models, the highest accuracy of 82% and AUC of 88.16% is achieved. Incorporating the three ROIs as an additional model inputs further boosts performance to an accuracy of 82.30% and an AUC of 90.10% (DarkNet). For x-ray images, we obtained an outstanding AUC of 97% for classifying COVID-19 versus normal versus other. Combing chest CT and x-ray images, DarkNet architecture achieves the highest accuracy of 99.09% and AUC of 99.89% in classifying COVID-19 from non-COVID-19. Our results confirm the ability of deep CNNs with transfer learning to predict COVID-19 in both chest CT and x-ray images. Conclusions: The proposed method could help radiologists increase the accuracy of their diagnosis and increase efficiency in COVID-19 management.

Chaddad Ahmad, Hassan Lama, Desrosiers Christian

2021-Jan

Coronavirus disease 2019, convolutional neural network, radiomics, transfer learning

General General

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

bioRxiv Preprint

Origin of the COVID-19 virus (SARS-CoV-2) has been intensely debated in the scientific community since the first infected cases were detected in December 2019. The disease has caused a global pandemic, leading to deaths of thousands of people across the world and thus finding origin of this novel coronavirus is important in responding and controlling the pandemic. Recent research results suggest that bats or pangolins might be the hosts for SARS-CoV-2 based on comparative studies using its genomic sequences. This paper investigates the SARS-CoV-2 origin by using artificial intelligence (AI) and raw genomic sequences of the virus. More than 300 genome sequences of COVID-19 infected cases collected from different countries are explored and analysed using unsupervised clustering methods. The results obtained from various AI-enabled experiments using clustering algorithms demonstrate that all examined SARS-CoV-2 genomes belong to a cluster that also contains bat and pangolin coronavirus genomes. This provides evidence strongly supporting scientific hypotheses that bats and pangolins are probable hosts for SARS-CoV-2. At the whole genome analysis level, our findings also indicate that bats are more likely the hosts for the COVID-19 virus than pangolins.

Nguyen, T. T.; Abdelrazek, M.; Nguyen, D. T.; Aryal, S.; Nguyen, D. T.; Reddy, S.; Nguyen, Q. V. H.; Khatami, A.; Hsu, E. B.; Yang, S.

2021-04-30

General General

Utilizing Artificial Intelligence to Manage COVID-19 Scientific Evidence Torrent with Risklick AI: A Critical Tool for Pharmacology and Therapy Development.

In Pharmacology

INTRODUCTION : The SARS-CoV-2 pandemic has led to one of the most critical and boundless waves of publications in the history of modern science. The necessity to find and pursue relevant information and quantify its quality is broadly acknowledged. Modern information retrieval techniques combined with artificial intelligence (AI) appear as one of the key strategies for COVID-19 living evidence management. Nevertheless, most AI projects that retrieve COVID-19 literature still require manual tasks.

METHODS : In this context, we pre-sent a novel, automated search platform, called Risklick AI, which aims to automatically gather COVID-19 scientific evidence and enables scientists, policy makers, and healthcare professionals to find the most relevant information tailored to their question of interest in real time.

RESULTS : Here, we compare the capacity of Risklick AI to find COVID-19-related clinical trials and scientific publications in comparison with clinicaltrials.gov and PubMed in the field of pharmacology and clinical intervention.

DISCUSSION : The results demonstrate that Risklick AI is able to find COVID-19 references more effectively, both in terms of precision and recall, compared to the baseline platforms. Hence, Risklick AI could become a useful alternative assistant to scientists fighting the COVID-19 pandemic.

Haas Quentin, Alvarez David Vicente, Borissov Nikolay, Ferdowsi Sohrab, von Meyenn Leonhard, Trelle Sven, Teodoro Douglas, Amini Poorya

2021-Apr-28

Artificial intelligence, COVID-19, Risklick, Search platform

General General

Introducing frequency representation into convolution neural networks for medical image segmentation via twin-Kernel Fourier convolution.

In Computer methods and programs in biomedicine

BACKGROUND AND OBJECTIVE : For medical image segmentation, deep learning-based methods have achieved state-of-the-art performance. However, the powerful spectral representation in the field of image processing is rarely considered in these models.

METHODS : In this work, we propose to introduce frequency representation into convolution neural networks (CNNs) and design a novel model, tKFC-Net, to combine powerful feature representation in both frequency and spatial domains. Through the Fast Fourier Transform (FFT) operation, frequency representation is employed on pooling, upsampling, and convolution without any adjustments to the network architecture. Furthermore, we replace original convolution with twin-Kernel Fourier Convolution (t-KFC), a new designed convolution layer, to specify the convolution kernels for particular functions and extract features from different frequency components.

RESULTS : We experimentally show that our method has an edge over other models in the task of medical image segmentation. Evaluated on four datasets-skin lesion segmentation (ISIC 2018), retinal blood vessel segmentation (DRIVE), lung segmentation (COVID-19-CT-Seg), and brain tumor segmentation (BraTS 2019), the proposed model achieves outstanding results: the metric F1-Score is 0.878 for ISIC 2018, 0.8185 for DRIVE, 0.9830 for COVID-19-CT-Seg, and 0.8457 for BraTS 2019.

CONCLUSION : The introduction of spectral representation retains spectral features which result in more accurate segmentation. The proposed method is orthogonal to other topology improvement methods and very convenient to be combined.

Tang Xianlun, Peng Jiangping, Zhong Bing, Li Jie, Yan Zhenfu

2021-Apr-14

Convolution neural networks, Frequency representation, Medical image segmentation, U-Net

General General

A seq2seq model to forecast the COVID-19 cases, deaths and reproductive R numbers in US counties.

In medRxiv : the preprint server for health sciences

The global pandemic of coronavirus disease 2019 (COVID-19) has killed almost two million people worldwide and over 400 thousand in the United States (US). As the pandemic evolves, informed policy-making and strategic resource allocation relies on accurate forecasts. To predict the spread of the virus within US counties, we curated an array of county-level demographic and COVID-19-relevant health risk factors. In combination with the county-level case and death numbers curated by John Hopkins university, we developed a forecasting model using deep learning (DL). We implemented an autoencoder-based Seq2Seq model with gated recurrent units (GRUs) in the deep recurrent layers. We trained the model to predict future incident cases, deaths and the reproductive number, R . For most counties, it makes accurate predictions of new incident cases, deaths and R values, up to 30 days in the future. Our framework can also be used to predict other targets that are useful indices for policymaking, for example hospitalization or the occupancy of intensive care units. Our DL framework is publicly available on GitHub and can be adapted for other indices of the COVID-19 spread. We hope that our forecasts and model can help local governments in the continued fight against COVID-19.

Zhang-James Yanli, Hess Jonathan, Salekin Asif, Wang Dongliang, Chen Samuel, Winkelstein Peter, Morley Christopher P, Faraone Stephen V

2021-Apr-20

General General

Smart Simon Bot with Public Sentiment Analysis for Novel Covid-19 Tweets Stratification.

In SN computer science

In present modern era, the outbreak of COVID-19 pandemic has created informational crisis. The public sentiments collected from different reflexions (hashtags, comments, tweets, posts of twitter) are measured accordingly, ensuring different policy decisions and messaging are incorporated. The implementation demonstrates intuition in to the advancement of fear sentiment eventually as COVID-19 approaches maximum levels in the world, by making use of detailed textual analysis with the help of required text data visualization. In addition, technical outline of machine learning stratification approaches are provided in the frame of text analytics, and comparing their efficiency in stratifying coronavirus tweets of different lengths. Using Naïve Bayes method, 91% accuracy is achieved for short tweets and using logistic regression classification method, 74% accuracy is achieved for short tweets.

Ramya B N, Shetty Shyleshwari M, Amaresh A M, Rakshitha R

2021

COVID-19, Sentiment analysis, Tweet

General General

Artificial intelligence in the diagnosis of COVID-19: challenges and perspectives.

In International journal of biological sciences

Artificial intelligence (AI) is being used to aid in various aspects of the COVID-19 crisis, including epidemiology, molecular research and drug development, medical diagnosis and treatment, and socioeconomics. The association of AI and COVID-19 can accelerate to rapidly diagnose positive patients. To learn the dynamics of a pandemic with relevance to AI, we search the literature using the different academic databases (PubMed, PubMed Central, Scopus, Google Scholar) and preprint servers (bioRxiv, medRxiv, arXiv). In the present review, we address the clinical applications of machine learning and deep learning, including clinical characteristics, electronic medical records, medical images (CT, X-ray, ultrasound images, etc.) in the COVID-19 diagnosis. The current challenges and future perspectives provided in this review can be used to direct an ideal deployment of AI technology in a pandemic.

Huang Shigao, Yang Jie, Fong Simon, Zhao Qi

2021

Artificial intelligence, COVID-19, deep learning, diagnosis, machine learning

General General

A computational approach to aid clinicians in selecting anti-viral drugs for COVID-19 trials.

In Scientific reports ; h5-index 158.0

The year 2020 witnessed a heavy death toll due to COVID-19, calling for a global emergency. The continuous ongoing research and clinical trials paved the way for vaccines. But, the vaccine efficacy in the long run is still questionable due to the mutating coronavirus, which makes drug re-positioning a reasonable alternative. COVID-19 has hence fast-paced drug re-positioning for the treatment of COVID-19 and its symptoms. This work builds computational models using matrix completion techniques to predict drug-virus association for drug re-positioning. The aim is to assist clinicians with a tool for selecting prospective antiviral treatments. Since the virus is known to mutate fast, the tool is likely to help clinicians in selecting the right set of antivirals for the mutated isolate. The main contribution of this work is a manually curated database publicly shared, comprising of existing associations between viruses and their corresponding antivirals. The database gathers similarity information using the chemical structure of drugs and the genomic structure of viruses. Along with this database, we make available a set of state-of-the-art computational drug re-positioning tools based on matrix completion. The tools are first analysed on a standard set of experimental protocols for drug target interactions. The best performing ones are applied for the task of re-positioning antivirals for COVID-19. These tools select six drugs out of which four are currently under various stages of trial, namely Remdesivir (as a cure), Ribavarin (in combination with others for cure), Umifenovir (as a prophylactic and cure) and Sofosbuvir (as a cure). Another unanimous prediction is Tenofovir alafenamide, which is a novel Tenofovir prodrug developed in order to improve renal safety when compared to its original counterpart (older version) Tenofovir disoproxil. Both are under trail, the former as a cure and the latter as a prophylactic. These results establish that the computational methods are in sync with the state-of-practice. We also demonstrate how the drugs to be used against the virus would vary as SARS-Cov-2 mutates over time by predicting the drugs for the mutated strains, suggesting the importance of such a tool in drug prediction. We believe this work would open up possibilities for applying machine learning models to clinical research for drug-virus association prediction and other similar biological problems.

Mongia Aanchal, Saha Sanjay Kr, Chouzenoux Emilie, Majumdar Angshul

2021-Apr-27

General General

A Deep Learning Radiomics Model to Identify Poor Outcome in COVID-19 Patients with Underlying Health Conditions: A Multicenter Study.

In IEEE journal of biomedical and health informatics

OBJECTIVE : Coronavirus disease 2019 (COVID-19) has caused considerable morbidity and mortality, especially in patients with underlying health conditions. A precise prognostic tool to identify poor outcomes among such cases is desperately needed.

METHODS : Total 400 COVID-19 patients with underlying health conditions were retrospectively recruited from 4 centers, including 54 dead cases (labeled as poor outcomes) and 346 patients discharged or hospitalized for at least 7 days since initial CT scan. Patients were allocated to a training set (n = 271), a test set (n = 68), and an external test set (n = 61). We proposed an initial CT-derived hybrid model by combining a 3D-ResNet10 based deep learning model and a quantitative 3D radiomics model to predict the probability of COVID-19 patients reaching poor outcome. The model performance was assessed by area under the receiver operating characteristic curve (AUC), survival analysis, and subgroup analysis.

RESULTS : The hybrid model achieved AUCs of 0.876 (95% confidence interval: 0.752-0.999) and 0.864 (0.766-0.962) in test and external test sets, outperforming other models. The survival analysis verified the hybrid model as a significant risk factor for mortality (hazard ratio, 2.049 [1.462-2.871], P < 0.001) that could well stratify patients into high-risk and low-risk of reaching poor outcomes (P < 0.001).

CONCLUSION : The hybrid model that combined deep learning and radiomics could accurately identify poor outcomes in COVID-19 patients with underlying health conditions from initial CT scans. The great risk stratification ability could help alert risk of death and allow for timely surveillance plans.

Wang Siwen, Dong Di, Li Liang, Li Hailin, Bai Yan, Hu Yahua, Huang Yuanyi, Yu Xiangrong, Liu Sibin, Qiu Xiaoming, Lu Ligong, Wang Meiyun, Zha Yunfei, Tian Jie

2021-Apr-27

General General

Targeting SARS-CoV-2 Spike Protein/ACE2 Protein-Protein Interactions: a Computational Study.

In Molecular informatics

The spike glycoprotein (S) of the SARS-CoV-2 virus surface plays a key role in receptor binding and virus entry. The S protein uses the angiotensin converting enzyme (ACE2) for entry into the host cell and binding to ACE2 occurs at the receptor binding domain (RBD) of the S protein. Therefore, the protein-protein interactions (PPIs) between the SARS-CoV-2 RBD and human ACE2, could be attractive therapeutic targets for drug discovery approaches designed to inhibit the entry of SARS-CoV-2 into the host cells. Herein, with the support of machine learning approaches, we report structure-based virtual screening as an effective strategy to discover PPIs inhibitors from ZINC database. The proposed computational protocol led to the identification of a promising scaffold which was selected for subsequent binding mode analysis and that can represent a useful starting point for the development of new treatments of the SARS-CoV-2 infection.

Pirolli Davide, Righino Benedetta, De Rosa Maria Cristina

2021-Apr-27

COVID-19, PPI focused library, QSAR, Virtual screening, docking

Public Health Public Health

FaceGuard: A Wearable System To Avoid Face Touching.

In Frontiers in robotics and AI

Most people touch their faces unconsciously, for instance to scratch an itch or to rest one's chin in their hands. To reduce the spread of the novel coronavirus (COVID-19), public health officials recommend against touching one's face, as the virus is transmitted through mucous membranes in the mouth, nose and eyes. Students, office workers, medical personnel and people on trains were found to touch their faces between 9 and 23 times per hour. This paper introduces FaceGuard, a system that utilizes deep learning to predict hand movements that result in touching the face, and provides sensory feedback to stop the user from touching the face. The system utilizes an inertial measurement unit (IMU) to obtain features that characterize hand movement involving face touching. Time-series data can be efficiently classified using 1D-Convolutional Neural Network (CNN) with minimal feature engineering; 1D-CNN filters automatically extract temporal features in IMU data. Thus, a 1D-CNN based prediction model is developed and trained with data from 4,800 trials recorded from 40 participants. Training data are collected for hand movements involving face touching during various everyday activities such as sitting, standing, or walking. Results showed that while the average time needed to touch the face is 1,200 ms, a prediction accuracy of more than 92% is achieved with less than 550 ms of IMU data. As for the sensory response, the paper presents a psychophysical experiment to compare the response time for three sensory feedback modalities, namely visual, auditory, and vibrotactile. Results demonstrate that the response time is significantly smaller for vibrotactile feedback (427.3 ms) compared to visual (561.70 ms) and auditory (520.97 ms). Furthermore, the success rate (to avoid face touching) is also statistically higher for vibrotactile and auditory feedback compared to visual feedback. These results demonstrate the feasibility of predicting a hand movement and providing timely sensory feedback within less than a second in order to avoid face touching.

Michelin Allan Michael, Korres Georgios, Ba’ara Sara, Assadi Hadi, Alsuradi Haneen, Sayegh Rony R, Argyros Antonis, Eid Mohamad

2021

IMU-based hand tracking, face touching avoidance, sensory feedback, vibrotactile stimulation, wearable technologies for health care

General General

Determinants of Tourism Stocks During the COVID-19: Evidence From the Deep Learning Models.

In Frontiers in public health

This paper examines the determinants of tourism stock returns in China from October 25, 2018, to October 21, 2020, including the COVID-19 era. We propose four deep learning prediction models based on the Back Propagation Neural Network (BPNN): Quantum Swarm Intelligence Algorithms (QSIA), Quantum Step Fruit-Fly Optimization Algorithm (QSFOA), Quantum Particle Swarm Optimization Algorithm (QPSO) and Quantum Genetic Algorithm (QGA). Firstly, the rough dataset is used to reduce the dimension of the indices. Secondly, the number of neurons in the multilayer of BPNN is optimized by QSIA, QSFOA, QPSO, and QGA, respectively. Finally, the deep learning models are then used to establish prediction models with the best number of neurons under these three algorithms for the non-linear real stock returns. The results indicate that the QSFOA-BPNN model has the highest prediction accuracy among all models, and it is defined as the most effective feasible method. This evidence is robust to different sub-periods.

Pan Wen-Tsao, Huang Qiu-Yu, Yang Zi-Yin, Zhu Fei-Yan, Pang Yu-Ning, Zhuang Mei-Er

2021

COVID-19 era, backpropagation neural network, deep learning, quantum genetic algorithm, quantum particle swarm optimization algorithm, quantum step fruit fly optimization algorithm

Public Health Public Health

Covid-19 Dynamic Monitoring and Real-Time Spatio-Temporal Forecasting.

In Frontiers in public health

Background: Periodically, humanity is often faced with new and emerging viruses that can be a significant global threat. It has already been over a century post-the Spanish Flu pandemic, and we are witnessing a new type of coronavirus, the SARS-CoV-2, which is responsible for Covid-19. It emerged from the city of Wuhan (China) in December 2019, and within a few months, the virus propagated itself globally now resulting more than 50 million cases with over 1 million deaths. The high infection rates coupled with dynamic population movement demands for tools, especially within a Brazilian context, that will support health managers to develop policies for controlling and combating the new virus. Methods: In this work, we propose a tool for real-time spatio-temporal analysis using a machine learning approach. The COVID-SGIS system brings together routinely collected health data on Covid-19 distributed across public health systems in Brazil, as well as taking to under consideration the geographic and time-dependent features of Covid-19 so as to make spatio-temporal predictions. The data are sub-divided by federative unit and municipality. In our case study, we made spatio-temporal predictions of the distribution of cases and deaths in Brazil and in each federative unit. Four regression methods were investigated: linear regression, support vector machines (polynomial kernels and RBF), multilayer perceptrons, and random forests. We use the percentage RMSE and the correlation coefficient as quality metrics. Results: For qualitative evaluation, we made spatio-temporal predictions for the period from 25 to 27 May 2020. Considering qualitatively and quantitatively the case of the State of Pernambuco and Brazil as a whole, linear regression presented the best prediction results (thematic maps with good data distribution, correlation coefficient >0.99 and RMSE (%) <4% for Pernambuco and around 5% for Brazil) with low training time: [0.00; 0.04 ms], CI 95%. Conclusion: Spatio-temporal analysis provided a broader assessment of those in the regions where the accumulated confirmed cases of Covid-19 were concentrated. It was possible to differentiate in the thematic maps the regions with the highest concentration of cases from the regions with low concentration and regions in the transition range. This approach is fundamental to support health managers and epidemiologists to elaborate policies and plans to control the Covid-19 pandemics.

da Silva Cecilia Cordeiro, de Lima Clarisse Lins, da Silva Ana Clara Gomes, Silva Eduardo Luiz, Marques Gabriel Souza, de Araújo Lucas Job Brito, Albuquerque Júnior Luiz Antônio, de Souza Samuel Barbosa Jatobá, de Santana Maíra Araújo, Gomes Juliana Carneiro, Barbosa Valter Augusto de Freitas, Musah Anwar, Kostkova Patty, Dos Santos Wellington Pinheiro, da Silva Filho Abel Guilhermino

2021

COVID-19, Covid-19 pandemics forecasting, SARS-CoV-2, digital epidemiology, spatio-temporal analysis, spatio-temporal forecasting

General General

Forecasting COVID-19 cases: A comparative analysis between recurrent and convolutional neural networks.

In Results in physics

Though many countries have already launched COVID-19 mass vaccination programs to control the disease outbreak quickly, numerous countries around worldwide are grappling with unprecedented surges of new COVID-19 cases due to a more contagious and deadly variant of coronavirus. As the number of new cases is skyrocketing, pandemic fatigue and public apathy towards different intervention strategies pose new challenges to government officials to combat the pandemic. Henceforth, it is indispensable for the government officials to understand the future dynamics of COVID-19 flawlessly to develop strategic preparedness and resilient response planning. In light of the above circumstances, probable future outbreak scenarios in Brazil, Russia, and the United kingdom have been sketched in this study with the help of four deep learning models: long short term memory (LSTM), gated recurrent unit (GRU), convolutional neural network (CNN) and multivariate convolutional neural network (MCNN). In our analysis, the CNN algorithm has outperformed other deep learning models in terms of validation accuracy and forecasting consistency. It is unearthed in our study that CNN can provide robust long-term forecasting results in time-series analysis due to its capability of essential features learning, distortion invariance, and temporal dependence learning. However, the prediction accuracy of the LSTM algorithm has been found to be poor as it tries to discover seasonality and periodic intervals from any time-series dataset, which were absent in our studied countries. Our study has highlighted the promising validation of using convolutional neural networks instead of recurrent neural networks when forecasting with very few features and less amount of historical data.

Nabi Khondoker Nazmoon, Tahmid Md Toki, Rafi Abdur, Kader Muhammad Ehsanul, Haider Md Asif

2021-May

Convolutional neural network (CNN), Deep learning, Long short term memory (LSTM), Time series analysis

oncology Oncology

Making a complex dental care tailored to the person: population health in focus of predictive, preventive and personalised (3P) medical approach.

In The EPMA journal

An evident underestimation of the targeted prevention of dental diseases is strongly supported by alarming epidemiologic statistics globally. For example, epidemiologists demonstrated 100% prevalence of dental caries in the Russian population followed by clinical manifestation of periodontal diseases. Inadequately provided oral health services in populations are caused by multi-factorial deficits including but not limited to low socio-economic status of affected individuals, lack of insurance in sub-populations, insufficient density of dedicated medical units. Another important aspect is the "participatory" medicine based on the active participation of population in maintaining oral health: healthcare will remain insufficient as long as the patient is not motivated and does not feel responsible for their oral health. To this end, nearly half of chronically diseased people do not comply with adequate medical services suffering from severely progressing pathologies. Noteworthy, the prominent risk factors and comorbidities linked to the severe disease course and poor outcomes in COVID-19-infected individuals, such as elderly, diabetes mellitus, hypertension and cardiovascular disease, are frequently associated with significantly altered oral microbiome profiles, systemic inflammatory processes and poor oral health. Suggested pathomechanisms consider potential preferences in the interaction between the viral particles and the host microbiota including oral cavity, the respiratory and gastrointestinal tracts. Since an aspiration of periodontopathic bacteria induces the expression of angiotensin-converting enzyme 2, the receptor for SARS-CoV-2, and production of inflammatory cytokines in the lower respiratory tract, poor oral hygiene and periodontal disease have been proposed as leading to COVID-19 aggravation. Consequently, the issue-dedicated expert recommendations are focused on the optimal oral hygiene as being crucial for improved individual outcomes and reduced morbidity under the COVID-19 pandemic condition. Current study demonstrated that age, gender, socio-economic status, quality of environment and life-style, oral hygiene quality, regularity of dental services requested, level of motivation and responsibility for own health status and corresponding behavioural patterns are the key parameters for the patient stratification considering person-tailored approach in a complex dental care in the population. Consequently, innovative screening programmes and adapted treatment schemes are crucial for the complex person-tailored dental care to improve individual outcomes and healthcare provided to the population.

Tachalov V V, Orekhova L Y, Kudryavtseva T V, Loboda E S, Pachkoriia M G, Berezkina I V, Golubnitschaja O

2021-Apr-19

Age, Aggravation, Bacterial load, Bacterial superinfections, Big data, Bio-banking, COVID-19, Collateral pathologies, Comorbidities, Compliance, Dental diseases, Disease severity, Dry mouth syndrome, Elderly, Gut-lung axis, Health policy, Healthcare, Hygiene, Individualised patient profiling, Inflammation, Influenza, Lower respiratory tract, Machine learning, Microbiome, Morbidity, Motivation, Oral cavity, Pathomechanism, Patient stratification, Periodontitis, Periodontopathic microflora, Predictive preventive personalised medicine (PPPM / 3PM), Probiotics, Psychological aspects, Risk factors, SARS-CoV-2, Socio-economic status, Tailored care, Treatment algorithm, Viral infection

Internal Medicine Internal Medicine

Ensemble-based Bag of Features for Automated Classification of Normal and COVID-19 CXR Images.

In Biomedical signal processing and control

The medical and scientific communities are currently trying to treat infected patients and develop vaccines for preventing a future outbreak. In healthcare, machine learning is proven to be an efficient technology for helping to combat the COVID-19. Hospitals are now overwhelmed with the increased infections of COVID-19 cases and given patients' confidentiality and rights. It becomes hard to assemble quality medical image datasets in a timely manner. For COVID-19 diagnosis, several traditional computer-aided detection systems based on classification techniques were proposed. The bag-of-features (BoF) model has shown a promising potential in this domain. Thus, this work developed an ensemble-based BoF classification system for the COVID-19 detection. In this model, we proposed ensemble at the classification step of the BoF. The proposed system was evaluated and compared to different classification systems for different number of visual words to evaluate their effect on the classification efficiency. The results proved the superiority of the proposed ensemble-based BoF for the classification of normal and COVID19 chest X-ray (CXR) images compared to other classifiers.

Ashour Amira S, Eissa Merihan M, Wahba Maram A, Elsawy Radwa A, Elgnainy Hamada Fathy, Tolba Mohamed Saeed, Mohamed Waleed S

2021-Apr-20

Bag of features, COVID-19, K-means, chest X-ray images, classification, ensemble classifiers, invariant feature transform, speeded up robust features detector

General General

COVID-19 mortality analysis from soft-data multivariate curve regression and machine learning.

In Stochastic environmental research and risk assessment : research journal

** : A multiple objective space-time forecasting approach is presented involving cyclical curve log-regression, and multivariate time series spatial residual correlation analysis. Specifically, the mean quadratic loss function is minimized in the framework of trigonometric regression. While, in our subsequent spatial residual correlation analysis, maximization of the likelihood allows us to compute the posterior mode in a Bayesian multivariate time series soft-data framework. The presented approach is applied to the analysis of COVID-19 mortality in the first wave affecting the Spanish Communities, since March 8, 2020 until May 13, 2020. An empirical comparative study with Machine Learning (ML) regression, based on random k-fold cross-validation, and bootstrapping confidence interval and probability density estimation, is carried out. This empirical analysis also investigates the performance of ML regression models in a hard- and soft-data frameworks. The results could be extrapolated to other counts, countries, and posterior COVID-19 waves.

Supplementary Information : The online version contains supplementary material available at 10.1007/s00477-021-02021-0.

Torres-Signes Antoni, Frías María P, Ruiz-Medina María D

2021-Apr-19

COVID-19 analysis, Curve regression, Hard-data, Machine learning, Multivariate time series, Soft-data

General General

A Proposed Sentiment Analysis Deep Learning Algorithm for Analyzing COVID-19 Tweets.

In Information systems frontiers : a journal of research and innovation

With the rise in cases of COVID-19, a bizarre situation of pressure was mounted on each country to make arrangements to control the population and utilize the available resources appropriately. The swiftly rising of positive cases globally created panic, anxiety and depression among people. The effect of this deadly disease was found to be directly proportional to the physical and mental health of the population. As of 28 October 2020, more than 40 million people are tested positive and more than 1 million deaths have been recorded. The most dominant tool that disturbed human life during this time is social media. The tweets regarding COVID-19, whether it was a number of positive cases or deaths, induced a wave of fear and anxiety among people living in different parts of the world. Nobody can deny the truth that social media is everywhere and everybody is connected with it directly or indirectly. This offers an opportunity for researchers and data scientists to access the data for academic and research use. The social media data contains many data that relate to real-life events like COVID-19. In this paper, an analysis of Twitter data has been done through the R programming language. We have collected the Twitter data based on hashtag keywords, including COVID-19, coronavirus, deaths, new case, recovered. In this study, we have designed an algorithm called Hybrid Heterogeneous Support Vector Machine (H-SVM) and performed the sentiment classification and classified them positive, negative and neutral sentiment scores. We have also compared the performance of the proposed algorithm on certain parameters like precision, recall, F1 score and accuracy with Recurrent Neural Network (RNN) and Support Vector Machine (SVM).

Kaur Harleen, Ahsaan Shafqat Ul, Alankar Bhavya, Chang Victor

2021-Apr-20

COVID-19, Heterogeneous Euclidean overlap metric (H-EOM), Hybrid heterogeneous support vector machine (H-SVM), Recurrent neural network (RCN), Sentiment analysis, Twitter

General General

Sufmacs: a machine learning-based robust image segmentation framework for covid-19 radiological image interpretation.

In Expert systems with applications

The absence of dedicated vaccines or drugs makes the COVID-19 a global pandemic, and early diagnosis can be an effective prevention mechanism. RT-PCR test is considered as one of the gold standards worldwide to confirm the presence of COVID-19 infection reliably. Radiological images can also be used for the same purpose to some extent. Easy and no contact acquisition of the radiological images makes it a suitable alternative and this work can help to locate and interpret some prominent features for the screening purpose. One major challenge of this domain is the absence of appropriately annotated ground truth data. Motivated from this, a novel unsupervised machine learning-based method called SUFMACS (SUperpixel based Fuzzy Memetic Advanced Cuckoo Search) is proposed to efficiently interpret and segment the COVID-19 radiological images. This approach adapts the superpixel approach to reduce a large amount of spatial information. The original cuckoo search approach is modified and the Luus-Jaakola heuristic method is incorporated with McCulloch's approach. This modified cuckoo search approach is used to optimize the fuzzy modified objective function. This objective function exploits the advantages of the superpixel. Both CT scan and X-ray images are investigated in detail. Both qualitative and quantitative outcomes are quite promising and prove the efficiency and the real-life applicability of the proposed approach.

Chakraborty Shouvik, Mali Kalyani

2021-Apr-20

COVID-19, SUFMACS, clustering, image segmentation, machine learning, radiological image interpretation

Surgery Surgery

A COVID-19 pandemic AI-based system with deep learning forecasting and automatic statistical data acquisition: Development and Implementation Study.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : More than 79.2 million confirmed cases and 1.7 million deaths were caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which was named coronavirus disease (COVID-19) by the World Health Organization. Control of the COVID-19 epidemic has become a crucial issue around the globe, but there are limited studies that investigate the global trend of COVID-19 together with each country's policy measures.

OBJECTIVE : We aimed to develop an online AI system to analyze the dynamic trend of the COVID-19 pandemic, facilitate forecasting and predictive modeling, and produce a heatmap visualization of policy measures in 171 countries.

METHODS : COVID-19 pandemic AI system (CPAIS) integrated two datasets: the datasets of Oxford COVID-19 Government Response Tracker from the Blavatnik School of Government that is maintained by Oxford University; and the datasets of the COVID-19 Data Repository that is established by the Johns Hopkins University Center for Systems Science and Engineering. This study utilized four statistical and deep learning techniques for forecasting, including autoregressive Integrated Moving Average (ARIMA); Feedforward Neural Network (FNN); Multilayer Perceptron Neural Networks (MLPs); and Long Short-term Memory (LSTM). With regard to one-year records (i.e., whole time series data), records of the last 14 days served as the validation set to evaluate the performance of forecast, whereas earlier records served as the training set.

RESULTS : A total of 171 countries that featured in both the databases were included in the online system (https://covid19.mldoctor.com.tw). CPAIS was developed to explore variations, trends, and forecasts related to the COVID-19 pandemic across several counties. For instance, the number of confirmed monthly cases in the United States reached a local peak in July and another peak of 6,368,591 in December 2020. The dynamic heatmap with policy measures depicts changes in COVID-19 measures for each country. Nineteen measures were embedded within the three sections presented on the website, and only 4 of the 19 measures were continuous measures related to financial support or investment. Deep learning models were used to enable COVID-19 forecasting, and the performance of ARIMA, FNN, and MLP were not stable because their forecast accuracy was only better than LSTM for few countries. LSTM demonstrated the best forecast accuracy for Canada, as the RMSE, MAE, and MAPE were 2272.551, 1501.248, and 0.2723075, respectively. ARIMA and FNN demonstrated better performance for South Korea (RMSE = 317.53169 and 181.29894, MAPE = 0.4641688 and 0.2708482, respectively).

CONCLUSIONS : CPAIS collects and summarizes information about the COVID-19 pandemic and offers data visualization and deep learning-based prediction. It might be a useful reference for predicting a serious outbreak or epidemic. Moreover, the system undergoes daily updates and includes the latest information on vaccination, which may change the dynamics of the pandemic.

CLINICALTRIAL :

Yu Cheng-Sheng, Chang Shy-Shin, Chang Tzu-Hao, Wu Jenny L, Lin Yu-Jiun, Chien Hsiung-Fei, Chen Ray-Jade

2021-Apr-23

General General

Functional Connectome Prediction of Anxiety Related to the COVID-19 Pandemic.

In The American journal of psychiatry

OBJECTIVE : Increased anxiety in response to the COVID-19 pandemic has been widely noted. The purpose of this study was to test whether the prepandemic functional connectome predicted individual anxiety induced by the pandemic.

METHODS : Anxiety scores from healthy undergraduate students were collected during the severe and remission periods of the pandemic (first survey, February 22-28, 2020, N=589; second survey, April 24 to May 1, 2020, N=486). Brain imaging data and baseline (daily) anxiety ratings were acquired before the pandemic. The predictive performance of the functional connectome on individual anxiety was examined using machine learning and was validated in two external undergraduate student samples (N=149 and N=474). The clinical relevance of the findings was further explored by applying the connectome-based neuromarkers of pandemic-related anxiety to distinguish between individuals with specific mental disorders and matched healthy control subjects (generalized anxiety disorder, N=43; major depression, N=536; schizophrenia, N=72).

RESULTS : Anxiety scores increased from the prepandemic baseline to the severe stage of the pandemic and remained high in the remission stage. The prepandemic functional connectome predicted pandemic-related anxiety and generalized to the external sample but showed poor performance for predicting daily anxiety. The connectome-based neuromarkers of pandemic-related anxiety further distinguished between participants with generalized anxiety and healthy control subjects but were not useful for diagnostic classification in major depression and schizophrenia.

CONCLUSIONS : These findings demonstrate the feasibility of using the functional connectome to predict individual anxiety induced by major stressful events (e.g., the current global health crisis), which advances our understanding of the neurobiological basis of anxiety susceptibility and may have implications for developing targeted psychological and clinical interventions that promote the reduction of stress and anxiety.

He Li, Wei Dongtao, Yang Fan, Zhang Jie, Cheng Wei, Feng Jianfeng, Yang Wenjing, Zhuang Kaixiang, Chen Qunlin, Ren Zhiting, Li Yu, Wang Xiaoqin, Mao Yu, Chen Zhiyi, Liao Mei, Cui Huiru, Li Chunbo, He Qinghua, Lei Xu, Feng Tingyong, Chen Hong, Xie Peng, Rolls Edmund T, Su Linyan, Li Lingjiang, Qiu Jiang

2021-Apr-26

Anxiety, Coronavirus/COVID-19, Functional Connectome, Neuroimaging, Resting-State fMRI

General General

Optimal Hyperparameter Selection of Deep Learning Models for COVID-19 Chest X-ray Classification.

In Intelligence-based medicine

The first and most critical response to curbing the spread of the novel coronavirus disease (COVID-19) is to deploy effective techniques to test potentially infected patients, isolate them and commence immediate treatment. However, several test kits currently in use are slow and in a shortage of supply. This paper presents techniques for diagnosing COVID-19 from chest X-ray (CXR) and address problems associated with training deep models with less voluminous datasets and class imbalance as obtained in most available CXR datasets on COVID-19. We introduced the discriminative fine-tuning approach, which dynamically assigns different learning rates to each layer of the network. The learning rate is set using the cyclical learning rate policy that changes per iteration. This flexibility ensured rapid convergence and avoided being stuck in saddle point plateau. In addition, we addressed the high computational demand of deep models by implementing our algorithm using the memory- and computational-efficient mixed-precision training. Despite the availability of scanty datasets, our model achieved high performance and generalisation. A Validation accuracy of 96.83%, sensitivity and specificity of 96.26% and 95.54% were obtained, respectively. When tested on an entirely new dataset, the model achieves 97% accuracy without further training. Lastly, we presented a visual interpretation of the model's output to prove that the model can aid radiologists in rapidly screening for the symptoms of COVID-19.

Adedigba Adeyinka P, Adeshina Steve A, Aina Oluwatomisin E, Aibinu Abiodun M

2021-Apr-21

COVID-19, Chest X-ray, Computer-Aided Diagnosis, Cyclical learning rate, Deep Convolutional Neural Network (CNN), Discriminative Fine-tuning, Hyperparameter Optimization, Memory and computation efficient, Mixed-precision training, Overfitting

General General

Machine Learning and Deep Learning Approaches to Analyze and Detect COVID-19: A Review.

In SN computer science

COVID-19 also referred to as Corona Virus disease is a communicable disease that is caused by a coronavirus. Significant number of people who are tainted with this infection will have to brave and encounter moderate to severe respiratory sickness. Aged persons, sick, convalescing people and all those having underlying health complications like diabetes, chronic breathing diseases and cardiovascular diseases are bound to contract this sickness if not taken proper care of. At the current scenario, there are neither definite treatments nor inoculations against COVID-19. Nevertheless, there are numerous continuing clinical trials assessing the impending treatments and vaccines. Sensing the threatening impacts of Covid-19, researchers of computer science have started using various techniques and approaches of Machine Learning and Deep Learning to detect the presence of the disease using X-rays and CT images. The biggest stumbling block here is that there are only a few datasets available. There is also less number of experts for marking the information explicit to this new strain of infection in people. Artificial Intelligence centred tools can be designed and developed quickly for adapting the existing AI models and for leveraging the ability to modify and associating them with the preliminary clinical understanding to address the new group of COVID-19 and the novel challenges associated with it. In this paper, we look into a few techniques of Machine Learning and Deep Learning that have been employed to analyse Corona Virus Data.

Aishwarya T, Ravi Kumar V

2021

CT images, CheXNet, Convolutional neural network, Corona virus, Covid-19, Covid-net, Deep learning, IRCNN, Machine learning

General General

Prediction of COVID-19 Trend in India and Its Four Worst-Affected States Using Modified SEIRD and LSTM Models.

In SN computer science

Since the beginning of COVID-19 (corona virus disease 2019), the Indian government implemented several policies and restrictions to curtail its spread. The timely decisions taken by the government helped in decelerating the spread of COVID-19 to a large extent. Despite these decisions, the pandemic continues to spread. Future predictions about the spread can be helpful for future policy-making, i.e., to plan and control the COVID-19 spread. Further, it is observed throughout the world that asymptomatic corona cases play a major role in the spread of the disease. This motivated us to include such cases for accurate trend prediction. India was chosen for the study as the population and population density is very high for India, resulting in the spread of the disease at high speed. In this paper, the modified SEIRD (susceptible-exposed-infected-recovered-deceased) model is proposed for predicting the trend and peak of COVID-19 in India and its four worst-affected states. The modified SEIRD model is based on the SEIRD model, which also uses an asymptomatic exposed population that is asymptomatic but infectious for the predictions. Further, a deep learning-based long short-term memory (LSTM) model is also used for trend prediction in this paper. Predictions of LSTM are compared with the predictions obtained from the proposed modified SEIRD model for the next 30 days. The epidemiological data up to 6th September 2020 have been used for carrying out predictions in this paper. Different lockdowns imposed by the Indian government have also been used in modeling and analyzing the proposed modified SEIRD model.

Bedi Punam, Dhiman Shivani, Gole Pushkar, Gupta Neha, Jindal Vinita

2021

COVID-19, Coronavirus, Lockdown, Long short-term memory (LSTM), Modified SEIRD (susceptible–exposed–infected–recovered–deceased), Pandemic

General General

Evaluating the performance of personal, social, health-related, biomarker and genetic data for predicting an individuals future health using machine learning: A longitudinal analysis

ArXiv Preprint

As we gain access to a greater depth and range of health-related information about individuals, three questions arise: (1) Can we build better models to predict individual-level risk of ill health? (2) How much data do we need to effectively predict ill health? (3) Are new methods required to process the added complexity that new forms of data bring? The aim of the study is to apply a machine learning approach to identify the relative contribution of personal, social, health-related, biomarker and genetic data as predictors of future health in individuals. Using longitudinal data from 6830 individuals in the UK from Understanding Society (2010-12 to 2015-17), the study compares the predictive performance of five types of measures: personal (e.g. age, sex), social (e.g. occupation, education), health-related (e.g. body weight, grip strength), biomarker (e.g. cholesterol, hormones) and genetic single nucleotide polymorphisms (SNPs). The predicted outcome variable was limiting long-term illness one and five years from baseline. Two machine learning approaches were used to build predictive models: deep learning via neural networks and XGBoost (gradient boosting decision trees). Model fit was compared to traditional logistic regression models. Results found that health-related measures had the strongest prediction of future health status, with genetic data performing poorly. Machine learning models only offered marginal improvements in model accuracy when compared to logistic regression models, but also performed well on other metrics e.g. neural networks were best on AUC and XGBoost on precision. The study suggests that increasing complexity of data and methods does not necessarily translate to improved understanding of the determinants of health or performance of predictive models of ill health.

Mark Green

2021-04-26

General General

Modelling and predicting the spread of COVID-19 cases depending on restriction policy based on mined recommendation rules.

In Mathematical biosciences and engineering : MBE

This paper is an extended and supplemented version of the paper "Recommendation Rules Mining for Reducing the Spread of COVID-19 Cases", presented by the authors at the 3rd International Conference on Informatics & Data-Driven Medicine in November 2020. The paper examines the impact of government restrictive measures on the spread and effects of COVID-19. The work is devoted to the improvement of recommendation rules based on novel ensemble of machine learning methods such as regression tree and clustering. The dynamics of migration between countries in clusters, and their relationship with the number of confirmed cases and the percentage of deaths caused by COVID-19, were studied on the example of Poland, Italy and Germany. It is shown that there is a clear relationship between the cluster number and the number of new cases of diseases and death. It has also been shown that different countries' policies to prevent the disease, in particular the timing of restrictive measures, correlate with the dynamics of the spread of COVID-19 and the consequences of the disease. For example, the results show a clear proactive tactic of restrictive measures by example of Germany, and catching up on the spread of the disease by example of Italy. A regression tree and guidelines about influence of features on the spreading of COVID-19 and mortality due to this infection have been constructed. The paper predicts the number of deaths due to COVID-19 on a 21-day interval using the obtained guidelines on the example of Sweden. Such forecasting was carried out for two potential government action options: with existing precautionary actions and the same precautionary actions, if they had been taken 20 days earlier (following the example of Germany). The RMSE of the mortality forecast does not exceed 4.2, which shows a good prognostic ability of the developed model. At the same time, the simulation based on the strategy of anticipatory introduction of restrictions gives 2-6% lower values of the forecast of the number of new cases. Thus, the results of this study provide an opportunity to assess the impact of decisions about restrictive measures and predict, simulate the consequences of restrictions policy.

Yakovyna Vitaliy, Shakhovska Natalya

2021-Mar-24

** COVID-19 , classification , clustering , machine learning , prognostic data **

oncology Oncology

DenseCapsNet: Detection of COVID-19 from X-ray images using a capsule neural network.

In Computers in biology and medicine

At present, the global pandemic as it relates to novel coronavirus pneumonia is still a very difficult situation. Due to the recent outbreak of novel coronavirus pneumonia, novel chest X-ray (CXR) images that can be used for deep learning analysis are very rare. To solve this problem, we propose a deep learning framework that integrates a convolutional neural network and a capsule network. DenseCapsNet, a new deep learning framework, is formed by the fusion of a dense convolutional network (DenseNet) and the capsule neural network (CapsNet), leveraging their respective advantages and reducing the dependence of convolutional neural networks on a large amount of data. Using 750 CXR images of lungs of healthy patients as well as those of patients with other pneumonia and novel coronavirus pneumonia, the method can obtain an accuracy of 90.7% and an F1 score of 90.9%, and the sensitivity for detecting COVID-19 can reach 96%. These results show that the deep fusion neural network DenseCapsNet has good performance in novel coronavirus pneumonia CXR radiography detection.

Quan Hao, Xu Xiaosong, Zheng Tingting, Li Zhi, Zhao Mingfang, Cui Xiaoyu

2021-Apr-15

COVID-19, Capsule neural network, Chest X-ray, Classification, Deep learning

General General

CARJ 2021: Year in Review.

In Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes

The past year has been one of unprecedented challenge for the modern world and especially the medical profession. This review explores some of the most impactful topics published in the CARJ during the COVID-19 pandemic including physician wellbeing and burnout, patient safety, and technological innovations including dual energy CT, quantitative imaging and ultra-high frequency ultrasound. The impact of the COVID-19 pandemic on trainee education is discussed and evidence-based tips for providing value-added care are reviewed. Patient privacy considerations relevant to the development of artificial intelligence applications for medical imaging are explored. These publications in the CARJ demonstrate that although this year has brought adversity, it has also been a harbinger for new and exciting areas of focus in our field.

Ward Caitlin J, van der Pol Christian B, Patlas Michael N

2021-Apr-22

artificial intelligence, burnout, imaging genomics, patient safety, professional, review

General General

Building An OMOP Common Data Model-Compliant Annotated Corpus for COVID-19 Clinical Trials.

In Journal of biomedical informatics ; h5-index 55.0

Clinical trials are essential for generating reliable medical evidence, but often suffer from expensive and delayed patient recruitment because the unstructured eligibility criteria description prevents automatic query generation for eligibility screening. In response to the COVID-19 pandemic, many trials have been created but their information is not computable. We included 700 COVID-19 trials available at the point of study and developed a semi-automatic approach to generate an annotated corpus for COVID-19 clinical trial eligibility criteria called COVIC. A hierarchical annotation schema based on the OMOP Common Data Model was developed to accommodate four levels of annotation granularity: i.e., study cohort, eligibility criteria, named entity and standard concept. In COVIC, 39 trials with more than one study cohorts were identified and labelled with an identifier for each cohort. 1,943 criteria for non-clinical characteristics such as "informed consent", "exclusivity of participation" were annotated. 9767 criteria were represented by 18,161 entities in 8 domains, 7,743 attributes of 7 attribute types and 16,443 relationships of 11 relationship types. 17,171 entities were mapped to standard medical concepts and 1,009 attributes were normalized into computable representations. COVIC can serve as a corpus indexed by semantic tags for COVID-19 trial search and analytics, and a benchmark for machine learning based criteria extraction.

Sun Yingcheng, Butler Alex, Stewart Latoya A, Liu Hao, Yuan Chi, Southard Christopher T, Hyun Kim Jae, Weng Chunhua

2021-Apr-19

COVID-19, Clinical Trial, Eligibility Criteria, Machine Readable Dataset, Structured Text Corpus

General General

Digital Transformation in Personalized Medicine with Artificial Intelligence and the Internet of Medical Things.

In Omics : a journal of integrative biology

Digital transformation is impacting every facet of science and society, not least because there is a growing need for digital services and products with the COVID-19 pandemic. But the need for digital transformation in diagnostics and personalized medicine field cuts deeper. In the past, personalized/precision medicine initiatives have been unable to capture the patients' experiences and clinical outcomes in real-time and in real-world settings. The availability of wearable smart sensors, wireless connectivity, artificial intelligence, and the Internet of Medical Things is changing the personalized/precision medicine research and implementation landscape. Digital transformation in poised to accelerate personalized/precision medicine and systems science in multiple fronts such as deep real-time phenotyping with patient-reported outcomes, high-throughput association studies between omics and highly granular phenotypic variation, digital clinical trials, among others. The present expert review offers an analysis of these systems science frontiers with a view to future applications at the intersection of digital health and personalized medicine, or put in other words, signaling the rise of "digital personalized medicine."

Lin Biaoyang, Wu Shengjun

2021-Apr-21

Internet of Medical Things, artificial intelligence, deep phenotyping, digital health, machine learning, personalized medicine, theranostics

General General

Twitter Speaks: An Analysis of Australian Twitter Users' Topics and Sentiments About COVID-19 Vaccination Using Machine Learning.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : The novel coronavirus disease (COVID-19) is one of the greatest threats to human beings in terms of healthcare, economy and the society in recent history. Up to this moment, there are no signs of remission and there is no proven effective cure. The vaccine is the primary biomedical preventive measure against the novel coronavirus. However, the public bias or sentiments, as reflected on social media, may have significant impact on the progress to achieve the herd immunity needed principally.

OBJECTIVE : This study aims to use machine learning methods to extract public topics and sentiments on the COVID-19 vaccination on Twitter.

METHODS : We collected 31,100 English tweets containing COVID-19 vaccine-related keywords between January and October 2020 from Australian Twitter users. Specifically, we analyzed the tweets by visualizing the high-frequency word clouds and correlations between word tokens. We built the Latent Dirichlet Allocation (LDA) topic model to identify the commonly discussed topics from massive tweets. We also performed sentiment analysis to understand the overall sentiments and emotions on COVID-19 vaccination in the Australian society.

RESULTS : Our analysis identified three LDA topics including "Attitudes towards COVID-19 and its vaccination", "Advocating infection control measures against COVID-19", and "Misconceptions and complaints about COVID-19 control". In all tweets, nearly two-thirds of the sentiments were positive, and around one-third were negative in the public opinion about the COVID-19 vaccine. Among the eight basic emotions, "trust" and "anticipation" were the two prominent positive emotions, while "fear" was the top negative emotion in the tweets.

CONCLUSIONS : Our new findings indicate that some Australian Twitter users supported infection control measures against COVID-19, and would refute misinformation. However, the others who underestimated the risks and severity of COVID-19 would probably rationalize their position on the COVID-19 vaccinations with certain conspiracy theories. It is also noticed that the level of positive sentiment in the public may not be enough to further a vaccination coverage which would be sufficient to achieve vaccination-induced herd immunity. Governments should explore the public opinion and sentiments towards COVID-19 and its vaccination, and implement an effective vaccination promotion scheme besides supporting the development and clinical administration of COVID-19 vaccines.

CLINICALTRIAL :

Kwok Stephen Wai Hang, Vadde Sai Kumar, Wang Guanjin

2021-Apr-16

General General

COVID-19 in the Age of Artificial Intelligence: A Comprehensive Review.

In Interdisciplinary sciences, computational life sciences

The recent COVID-19 pandemic, which broke at the end of the year 2019 in Wuhan, China, has infected more than 98.52 million people by today (January 23, 2021) with over 2.11 million deaths across the globe. To combat the growing pandemic on urgent basis, there is need to design effective solutions using new techniques that could exploit recent technology, such as machine learning, deep learning, big data, artificial intelligence, Internet of Things, for identification and tracking of COVID-19 cases in near real time. These technologies have offered inexpensive and rapid solution for proper screening, analyzing, prediction and tracking of COVID-19 positive cases. In this paper, a detailed review of the role of AI as a decisive tool for prognosis, analyze, and tracking the COVID-19 cases is performed. We searched various databases including Google Scholar, IEEE Library, Scopus and Web of Science using a combination of different keywords consisting of COVID-19 and AI. We have identified various applications, where AI can help healthcare practitioners in the process of identification and monitoring of COVID-19 cases. A compact summary of the corona virus cases are first highlighted, followed by the application of AI. Finally, we conclude the paper by highlighting new research directions and discuss the research challenges. Even though scientists and researchers have gathered and exchanged sufficient knowledge over last couple of months, but this structured review also examined technological perspectives while encompassing the medical aspect to help the healthcare practitioners, policymakers, decision makers, policymakers, AI scientists and virologists to quell this infectious COVID-19 pandemic outbreak.

Rasheed Jawad, Jamil Akhtar, Hameed Alaa Ali, Al-Turjman Fadi, Rasheed Ahmad

2021-Apr-22

COVID-19, Deep learning, Disease prediction, Drug discovery, Infectious diseases, Machine learning, SARS-CoV-2

General General

RANDGAN: Randomized generative adversarial network for detection of COVID-19 in chest X-ray.

In Scientific reports ; h5-index 158.0

COVID-19 spread across the globe at an immense rate and has left healthcare systems incapacitated to diagnose and test patients at the needed rate. Studies have shown promising results for detection of COVID-19 from viral bacterial pneumonia in chest X-rays. Automation of COVID-19 testing using medical images can speed up the testing process of patients where health care systems lack sufficient numbers of the reverse-transcription polymerase chain reaction tests. Supervised deep learning models such as convolutional neural networks need enough labeled data for all classes to correctly learn the task of detection. Gathering labeled data is a cumbersome task and requires time and resources which could further strain health care systems and radiologists at the early stages of a pandemic such as COVID-19. In this study, we propose a randomized generative adversarial network (RANDGAN) that detects images of an unknown class (COVID-19) from known and labelled classes (Normal and Viral Pneumonia) without the need for labels and training data from the unknown class of images (COVID-19). We used the largest publicly available COVID-19 chest X-ray dataset, COVIDx, which is comprised of Normal, Pneumonia, and COVID-19 images from multiple public databases. In this work, we use transfer learning to segment the lungs in the COVIDx dataset. Next, we show why segmentation of the region of interest (lungs) is vital to correctly learn the task of classification, specifically in datasets that contain images from different resources as it is the case for the COVIDx dataset. Finally, we show improved results in detection of COVID-19 cases using our generative model (RANDGAN) compared to conventional generative adversarial networks for anomaly detection in medical images, improving the area under the ROC curve from 0.71 to 0.77.

Motamed Saman, Rogalla Patrik, Khalvati Farzad

2021-Apr-21

General General

Pandemic analysis of infection and death correlated with genomic Orf10 mutation in SARS-CoV-2 victims.

In Journal of the Chinese Medical Association : JCMA

BACKGROUND : Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues the pandemic spread of the coronavirus disease 2019 (COVID-19), over 60 million people confirmed infected and at least 1.8 million dead. One of the most known features of this RNA virus is its easiness to be mutated. In late 2020, almost no region of this SARS-CoV-2 genome can be found completely conserved within the original Wu-Han coronavirus. Any information of the SARS-CoV-2 variants emerged through as time being will be evaluated for diagnosis, treatment, and prevention of COVID-19.

METHODS : We extracted more than two million data of SARS-CoV-2 infected patients from the open COVID-19 dashboard. The sequences of the 38-aminoi acid putative Orf10 protein within infected patients were gather output through from National Center for Biotechnology Information (NCBI) and the mutation rates in each position were analyzed and presented in each month of 2020. The mutation rates of A8 and V30 within orf10 are displayed in selected counties: America, India, German and Japan.

RESULTS : The numbers of COVID-19 patients are correlated to the death numbers, but not with the death rates (stable and lower than 3%). The amino acid positions locating at A8(F/G/L), I13 and V30(L) within the Orf10 sequence stay the highest mutation rate; N5, N25 and N36 rank at the lowest one. A8F expressed highly dominant in Japan (over 80%) and German (around 40%) coming to the end of 2020, but no significant finding in other countries.

CONCLUSION : The results demonstrate via mutation analysis of Orf10 can be further combined with advanced tools such as molecular simulation, artificial intelligence (AI) and biosensors that can practically revealed for protein interactions and thus to imply the authentic orf10 function of SARS-CoV-2 in the future.

Yang De-Ming, Lin Fan-Chi, Tsai Pin-Hsing, Chien Yueh, Wang Mong-Lien, Yang Yi-Ping, Chang Tai-Jay

2021-Apr-19

General General

Medical Specialty Recommendations by an Artificial Intelligence Chatbot on a Smartphone: Development and Deployment.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : The current coronavirus disease 2019 (COVID-19) pandemic limits daily activities, even contact between patients and primary care providers. This makes it more difficult to provide adequate primary care services, which include connecting patients to an appropriate medical specialist. A smartphone-compatible artificial intelligence (AI) chatbot that classifies patients' symptoms and recommends the appropriate medical specialty could provide a valuable solution.

OBJECTIVE : In order to establish a contactless method of recommending the appropriate medical specialty, this study aims to construct a deep learning-based natural language processing (NLP) pipeline and to develop an AI chatbot that can be used on a smartphone.

METHODS : We collected 118,008 sentences containing information on symptoms with labels (medical specialty), conducted data cleansing, and finally constructed a pipeline of 51,134 sentences for this study. Several deep learning models, including four different Long Short-Term Memory (LSTM) models with or without attention and with or without a pretrained FastText embedding layer as well as Bidirectional Encoder Representations from Transformers (BERT) for NLP, were trained and validated using a randomly selected test dataset. The performance of the models was evaluated by the precision, recall, F1 score and area under the receiver operating characteristic curve (AUC). An AI chatbot was also designed to make it easy for patients to use this recommendation system. We used an open-source framework called Alpha to develop our AI chatbot. This takes the form of a web application with a frontend chat interface capable of conversing in text and a backend cloud-based server application to handle data collection, process the data with a deep learning model, and offer the medical specialty recommendation in a responsive web which is compatible with both desktops and smartphones.

RESULTS : The BERT model yielded the best performance, with an AUC of 0.964 and F1 score of 0.768, followed by LSTM with embedding vectors, with an AUC of 0.965 and F1 score of 0.739. Considering the limitations of computing resources and the wide availability of smartphones, the LSTM model with embedding vectors trained on our dataset was adopted for our AI chatbot service. We also deployed an Alpha version of the AI chatbot to be executed on both desktops and smartphones.

CONCLUSIONS : With the increasing need for telemedicine during the current COVID-19 pandemic, an AI chatbot based on a deep learning-based NLP model that can recommend a medical specialty to patients using their smartphones would be exceedingly useful. The chatbot allows patients to quickly and contactlessly identify the proper medical specialist based on their symptoms, and so may support both patients and primary care providers.

CLINICALTRIAL :

Lee Hyeonhoon, Kang Jaehyun, Yeo Jonghyeon

2021-Apr-17

General General

COVID-19 Automatic Diagnosis with Radiographic Imaging: Explainable AttentionTransfer Deep Neural Networks.

In IEEE journal of biomedical and health informatics

Researchers seek help from machine learning methods to alleviate the enormous burden of reading radiological images for clinicians under the COVID- 19 pandemic. However, clinicians often feel reluctant to trust AI-based models because of its black-box characteristic and lack of proper explainability. This paper proposes an explainable attention transfer classification model based on the knowledge distillation network structure to automatically differentiate COVID-19, community acquired pneumonia (CAP) from healthy lungs with radiographic imaging. The proposed network structure can be divided into teacher network and student network based on the attention transfer direction. Firstly, the teacher network extracts global features and concentrates on the infection regions to generate attention maps. We propose a deformable attention module (DAM) to strengthen infection regions response and suppress noise in irrelevant regions with expanded reception field. Moreover, combining essential information in original input, attention knowledge transfers from teacher network to student network via an image fusion module. Trained with teacher network jointly, the student branch with weighted dense connectivity can focus on irregularly shaped lesion regions to learn discriminative features and improve network performance. Comprehensive experiments have been conducted on public chest X-ray and CT imaging datasets. The proposed architecture achieves state-of-art performance and improves the AI-based model's explainable ability by attention map, severity assessment, and prediction confidence.

Shi Wenqi, Tong Li, Zhu Yuanda, Wang May Dongmei

2021-Apr-21

Radiology Radiology

CT-based radiomics for predicting the rapid progression of coronavirus disease 2019 (COVID-19) pneumonia lesions.

In The British journal of radiology

OBJECTIVES : To develop and validate a radiomic model to predict the rapid progression (defined as volume growth of pneumonia lesions > 50% within seven days) in patients with coronavirus disease 2019 (COVID-19).

METHODS : Patients with laboratory-confirmed COVID-19 who underwent longitudinal chest CT between January 01 and February 18, 2020 were included. A total of 1316 radiomic features were extracted from the lung parenchyma window for each CT. The least absolute shrinkage and selection operator (LASSO), Relief, Las Vegas Wrapper (LVW), L1-norm-Support Vector Machine (L1-norm-SVM), and recursive feature elimination (RFE) were applied to select the features that associated with rapid progression. Four machine learning classifiers were used for modeling, including Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), and Decision Tree (DT). Accordingly, 20 radiomic models were developed on the basis of 296 CT scans and validated in 74 CT scans. Model performance was determined by the receiver operating characteristic curve.

RESULTS : A total of 107 patients (median age, 49.0 years, interquartile range, 35-54) were evaluated. The patients underwent a total of 370 chest CT scans with a median interval of 4 days (interquartile range, 3-5 days). The combination methods of L1-norm SVM and SVM with 17 radiomic features yielded the highest performance in predicting the likelihood of rapid progression of pneumonia lesions on next CT scan, with an AUC of 0.857 (95% CI: 0.766-0.947), sensitivity of 87.5%, and specificity of 70.7%.

CONCLUSIONS : Our radiomic model based on longitudinal chest CT data could predict the rapid progression of pneumonia lesions, which may facilitate the CT follow-up intervals and reduce the radiation.

ADVANCES IN KNOWLEDGE : Radiomic features extracted from the current chest CT have potential in predicting the likelihood of rapid progression of pneumonia lesions on the next chest CT, which would improve clinical decision-making regarding timely treatment.

Zhang Bin, Ni-Jia-Ti Ma-Yi-di-Li, Yan Ruike, An Nan, Chen Lv, Liu Shuyi, Chen Luyan, Chen Qiuying, Li Minmin, Chen Zhuozhi, You Jingjing, Dong Yuhao, Xiong Zhiyuan, Zhang Shuixing

2021-Apr-21

General General

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

In bioRxiv : the preprint server for biology

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

Chen Jiahui, Gao Kaifu, Wang Rui, Wei Guo-Wei

2021-Apr-12

Public Health Public Health

Loneliness and Neurocognitive Aging.

In Advances in geriatric medicine and research

Loneliness imposes significant risks to physical, mental and brain health in older adulthood. With the social distancing regimes implemented during the COVID-19 pandemic, there is even greater urgency to understand the human health costs of social isolation. In this viewpoint we describe how the experience of loneliness may alter the structure and function of the human brain, and how these discoveries may guide public health policy to reduce the burden of loneliness in later life.

Spreng R Nathan, Bzdok Danilo

2021

Alzheimer’s disease, MRI, aging, default mode network, dementia, loneliness, social cognition, social isolation

General General

Smart access development for classifying lung disease with chest x-ray images using deep learning.

In Materials today. Proceedings

Recently the world has come across a pandemic disease known as covid-19. The presence of symptoms of covid-19 and pneumonia may be alike to other types of lung illnesses. So, because of this, it is difficult for the affected person or medical experts to identify the condition. Chest x-ray provides general orientation which can be an initial investigative study in the analysis of lung diseases. Information from retenogram studies help the finding of covid-19 and pneumonia affecting the lungs. We use a Convolution Neural Network (CNN) in Tensor Flow and Keras based covid-19, pneumonia classification. The best fit model of CNN is then deployed in the Django framework for providing a better user interface and predicting the output.

Kumaraguru Tarunika, Abirami P, Darshan K M, Angeline Kirubha S P, Latha S, Muthu P

2021-Apr-16

Covid-19, Deep learning, Django framework, Keras, Pneumonia, TensorFlow

General General

Prediction of covid-19 growth and trend using machine learning approach.

In Materials today. Proceedings

The Covid-19 Corona Virus, also known as SARS-CoV-2, has wreaked havoc around the world, and the condition is only getting worse.It is a pandemic disease spreading from person-to-person every day. Therefore, it is important to keep track the number of patients being affected. The current system gives the computerized data in a collective way which is very difficult to analyze and predict the growth of disease in a particular area and in the world. Machine learning algorithms can be used to successfully map the disease and its progression to solve this problem. Machine Learning, a branch of computer science, is critical in correctly distinguishing patients with the condition by analyzing their chest X-ray photographs. Supervised Machine learning models with associated algorithms (like LR, SVR and Time series algorithms) to analyze data for regression and classification helps in training the model to predict the number of total number of global confirmed cases who will be prone to the disease in the upcoming days. In this proposed work, the overall dataset of the world is being collected, preprocessed and the number of confirmed cases up to a particular date are extracted which is given as the training set to the model. The model is being trained by supervised machine learning algorithms to predict the growth of cases in the upcoming days. The experimental setup with the above mentioned algorithms shows that Time series Holt's model outperforms Linear Regression and Support Vector Regression algorithms.

Gothai E, Thamilselvan R, Rajalaxmi R R, Sadana R M, Ragavi A, Sakthivel R

2021-Apr-15

Accuracy, Prediction, Regression, Supervised Learning

General General

Detection of COVID-19 from CT scan images: A spiking neural network-based approach.

In Neural computing & applications

The outbreak of a global pandemic called coronavirus has created unprecedented circumstances resulting into a large number of deaths and risk of community spreading throughout the world. Desperate times have called for desperate measures to detect the disease at an early stage via various medically proven methods like chest computed tomography (CT) scan, chest X-Ray, etc., in order to prevent the virus from spreading across the community. Developing deep learning models for analysing these kinds of radiological images is a well-known methodology in the domain of computer based medical image analysis. However, doing the same by mimicking the biological models and leveraging the newly developed neuromorphic computing chips might be more economical. These chips have been shown to be more powerful and are more efficient than conventional central and graphics processing units. Additionally, these chips facilitate the implementation of spiking neural networks (SNNs) in real-world scenarios. To this end, in this work, we have tried to simulate the SNNs using various deep learning libraries. We have applied them for the classification of chest CT scan images into COVID and non-COVID classes. Our approach has achieved very high F1 score of 0.99 for the potential-based model and outperforms many state-of-the-art models. The working code associated with our present work can be found here.

Garain Avishek, Basu Arpan, Giampaolo Fabio, Velasquez Juan D, Sarkar Ram

2021-Apr-16

COVID-19, CT scan, Deep learning, Medical image, Spiking neural network

General General

Data mining of coronavirus: SARS-CoV-2, SARS-CoV and MERS-CoV.

In BMC research notes

OBJECTIVE : In this study we compare the amino acid and codon sequence of SARS-CoV-2, SARS-CoV and MERS-CoV using different statistics programs to understand their characteristics. Specifically, we are interested in how differences in the amino acid and codon sequence can lead to different incubation periods and outbreak periods. Our initial question was to compare SARS-CoV-2 to different viruses in the coronavirus family using BLAST program of NCBI and machine learning algorithms.

RESULTS : The result of experiments using BLAST, Apriori and Decision Tree has shown that SARS-CoV-2 had high similarity with SARS-CoV while having comparably low similarity with MERS-CoV. We decided to compare the codons of SARS-CoV-2 and MERS-CoV to see the difference. Though the viruses are very alike according to BLAST and Apriori experiments, SVM proved that they can be effectively classified using non-linear kernels. Decision Tree experiment proved several remarkable properties of SARS-CoV-2 amino acid sequence that cannot be found in MERS-CoV amino acid sequence. The consequential purpose of this paper is to minimize the damage on humanity from SARS-CoV-2. Hence, further studies can be focused on the comparison of SARS-CoV-2 virus with other viruses that also can be transmitted during latent periods.

Huh Jung Eun, Han Seunghee, Yoon Taeseon

2021-Apr-20

Apriori, BLAST, Coronavirus, Decision Tree, MERS-CoV, SARS-CoV, SARS-CoV-2, SVM

General General

COVID-19 and Big Data: Multi-faceted Analysis for Spatio-temporal Understanding of the Pandemic with Social Media Conversations

ArXiv Preprint

COVID-19 has been devastating the world since the end of 2019 and has continued to play a significant role in major national and worldwide events, and consequently, the news. In its wake, it has left no life unaffected. Having earned the world's attention, social media platforms have served as a vehicle for the global conversation about COVID-19. In particular, many people have used these sites in order to express their feelings, experiences, and observations about the pandemic. We provide a multi-faceted analysis of critical properties exhibited by these conversations on social media regarding the novel coronavirus pandemic. We present a framework for analysis, mining, and tracking the critical content and characteristics of social media conversations around the pandemic. Focusing on Twitter and Reddit, we have gathered a large-scale dataset on COVID-19 social media conversations. Our analyses cover tracking potential reports on virus acquisition, symptoms, conversation topics, and language complexity measures through time and by region across the United States. We also present a BERT-based model for recognizing instances of hateful tweets in COVID-19 conversations, which achieves a lower error-rate than the state-of-the-art performance. Our results provide empirical validation for the effectiveness of our proposed framework and further demonstrate that social media data can be efficiently leveraged to provide public health experts with inexpensive but thorough insight over the course of an outbreak.

Shayan Fazeli, Davina Zamanzadeh, Anaelia Ovalle, Thu Nguyen, Gilbert Gee, Majid Sarrafzadeh

2021-04-22

General General

Digital Health during COVID-19: Informatics Dialogue with the World Health Organization.

In Yearbook of medical informatics ; h5-index 24.0

BACKGROUND : On December 16, 2020 representatives of the International Medical Informatics Association (IMIA), a Non-Governmental Organization in official relations with the World Health Organization (WHO), along with its International Academy for Health Sciences Informatics (IAHSI), held an open dialogue with WHO Director General (WHO DG) Tedros Adhanom Ghebreyesus about the opportunities and challenges of digital health during the COVID-19 global pandemic.

OBJECTIVES : The aim of this paper is to report the outcomes of the dialogue and discussions with more than 200 participants representing different civil society organizations (CSOs).

METHODS : The dialogue was held in form of a webinar. After an initial address of the WHO DG, short presentations by the panelists, and live discussions between panelists, the WHO DG and WHO representatives took place. The audience was able to post questions in written. These written discussions were saved with participants' consent and summarized in this paper.

RESULTS : The main themes that were brought up by the audience for discussion were: (a) opportunities and challenges in general; (b) ethics and artificial intelligence; (c) digital divide; (d) education. Proposed actions included the development of a roadmap based on the lessons learned from the COVID-19 pandemic.

CONCLUSIONS : Decision making by policy makers needs to be evidence-based and health informatics research should be used to support decisions surrounding digital health, and we further propose next steps in the collaboration between IMIA and WHO such as future engagement in the World Health Assembly.

Koch Sabine, Hersh William R, Bellazzi Riccardo, Leong Tze Yun, Yedaly Moctar, Al-Shorbaji Najeeb

2021-Apr-21

Cardiology Cardiology

Development of a severity of disease score and classification model by machine learning for hospitalized COVID-19 patients.

In PloS one ; h5-index 176.0

BACKGROUND : Efficient and early triage of hospitalized Covid-19 patients to detect those with higher risk of severe disease is essential for appropriate case management.

METHODS : We trained, validated, and externally tested a machine-learning model to early identify patients who will die or require mechanical ventilation during hospitalization from clinical and laboratory features obtained at admission. A development cohort with 918 Covid-19 patients was used for training and internal validation, and 352 patients from another hospital were used for external testing. Performance of the model was evaluated by calculating the area under the receiver-operating-characteristic curve (AUC), sensitivity and specificity.

RESULTS : A total of 363 of 918 (39.5%) and 128 of 352 (36.4%) Covid-19 patients from the development and external testing cohort, respectively, required mechanical ventilation or died during hospitalization. In the development cohort, the model obtained an AUC of 0.85 (95% confidence interval [CI], 0.82 to 0.87) for predicting severity of disease progression. Variables ranked according to their contribution to the model were the peripheral blood oxygen saturation (SpO2)/fraction of inspired oxygen (FiO2) ratio, age, estimated glomerular filtration rate, procalcitonin, C-reactive protein, updated Charlson comorbidity index and lymphocytes. In the external testing cohort, the model performed an AUC of 0.83 (95% CI, 0.81 to 0.85). This model is deployed in an open source calculator, in which Covid-19 patients at admission are individually stratified as being at high or non-high risk for severe disease progression.

CONCLUSIONS : This machine-learning model, applied at hospital admission, predicts risk of severe disease progression in Covid-19 patients.

Marcos Miguel, Belhassen-García Moncef, Sánchez-Puente Antonio, Sampedro-Gomez Jesús, Azibeiro Raúl, Dorado-Díaz Pedro-Ignacio, Marcano-Millán Edgar, García-Vidal Carolina, Moreiro-Barroso María-Teresa, Cubino-Bóveda Noelia, Pérez-García María-Luisa, Rodríguez-Alonso Beatriz, Encinas-Sánchez Daniel, Peña-Balbuena Sonia, Sobejano-Fuertes Eduardo, Inés Sandra, Carbonell Cristina, López-Parra Miriam, Andrade-Meira Fernanda, López-Bernús Amparo, Lorenzo Catalina, Carpio Adela, Polo-San-Ricardo David, Sánchez-Hernández Miguel-Vicente, Borrás Rafael, Sagredo-Meneses Víctor, Sanchez Pedro-Luis, Soriano Alex, Martín-Oterino José-Ángel

2021

General General

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

In PLoS pathogens ; h5-index 92.0

The COVID-19 pandemic has demonstrated the serious potential for novel zoonotic coronaviruses to emerge and cause major outbreaks. The immediate animal origin of the causative virus, SARS-CoV-2, remains unknown, a notoriously challenging task for emerging disease investigations. Coevolution with hosts leads to specific evolutionary signatures within viral genomes that can inform likely animal origins. We obtained a set of 650 spike protein and 511 whole genome nucleotide sequences from 222 and 185 viruses belonging to the family Coronaviridae, respectively. We then trained random forest models independently on genome composition biases of spike protein and whole genome sequences, including dinucleotide and codon usage biases in order to predict animal host (of nine possible categories, including human). In hold-one-out cross-validation, predictive accuracy on unseen coronaviruses consistently reached ~73%, indicating evolutionary signal in spike proteins to be just as informative as whole genome sequences. However, different composition biases were informative in each case. Applying optimised random forest models to classify human sequences of MERS-CoV and SARS-CoV revealed evolutionary signatures consistent with their recognised intermediate hosts (camelids, carnivores), while human sequences of SARS-CoV-2 were predicted as having bat hosts (suborder Yinpterochiroptera), supporting bats as the suspected origins of the current pandemic. In addition to phylogeny, variation in genome composition can act as an informative approach to predict emerging virus traits as soon as sequences are available. More widely, this work demonstrates the potential in combining genetic resources with machine learning algorithms to address long-standing challenges in emerging infectious diseases.

Brierley Liam, Fowler Anna

2021-Apr-20

General General

Learning the Mental Health Impact of COVID-19 in the United States With Explainable Artificial Intelligence: Observational Study.

In JMIR mental health

BACKGROUND : The COVID-19 pandemic has affected the health, economic, and social fabric of many nations worldwide. Identification of individual-level susceptibility factors may help people in identifying and managing their emotional, psychological, and social well-being.

OBJECTIVE : This study is focused on learning a ranked list of factors that could indicate a predisposition to a mental disorder during the COVID-19 pandemic.

METHODS : In this study, we have used a survey of 17,764 adults in the United States from different age groups, genders, and socioeconomic statuses. Through initial statistical analysis and Bayesian network inference, we have identified key factors affecting mental health during the COVID-19 pandemic. Integrating Bayesian networks with classical machine learning approaches led to effective modeling of the level of mental health prevalence.

RESULTS : Overall, females were more stressed than males, and people in the age group 18-29 years were more vulnerable to anxiety than other age groups. Using the Bayesian network model, we found that people with a chronic mental illness were more prone to mental disorders during the COVID-19 pandemic. The new realities of working from home; homeschooling; and lack of communication with family, friends, and neighbors induces mental pressure. Financial assistance from social security helps in reducing mental stress during the COVID-19-generated economic crises. Finally, using supervised machine learning models, we predicted the most mentally vulnerable people with ~80% accuracy.

CONCLUSIONS : Multiple factors such as social isolation, digital communication, and working and schooling from home were identified as factors of mental illness during the COVID-19 pandemic. Regular in-person communication with friends and family, a healthy social life, and social security were key factors, and taking care of people with a history of mental disease appears to be even more important during this time.

Jha Indra Prakash, Awasthi Raghav, Kumar Ajit, Kumar Vibhor, Sethi Tavpritesh

2021-Apr-20

Bayesian network, COVID-19, artificial intelligence, disorder, explainable artificial intelligence, machine learning, mental health, susceptibility, well-being

General General

Artificial intelligence (AI) for medical imaging to combat coronavirus disease (COVID-19): a detailed review with direction for future research.

In Artificial intelligence review

Since early 2020, the whole world has been facing the deadly and highly contagious disease named coronavirus disease (COVID-19) and the World Health Organization declared the pandemic on 11 March 2020. Over 23 million positive cases of COVID-19 have been reported till late August 2020. Medical images such as chest X-rays and Computed Tomography scans are becoming one of the main leading clinical diagnosis tools in fighting against COVID-19, underpinned by Artificial Intelligence based techniques, resulting in rapid decision-making in saving lives. This article provides an extensive review of AI-based methods to assist medical practitioners with comprehensive knowledge of the efficient AI-based methods for efficient COVID-19 diagnosis. Nearly all the reported methods so far along with their pros and cons as well as recommendations for improvements are discussed, including image acquisition, segmentation, classification, and follow-up diagnosis phases developed between 2019 and 2020. AI and machine learning technologies have boosted the accuracy of Covid-19 diagnosis, and most of the widely used deep learning methods have been implemented and worked well with a small amount of data for COVID-19 diagnosis. This review presents a detailed mythological analysis for the evaluation of AI-based methods used in the process of detecting COVID-19 from medical images. However, due to the quick outbreak of Covid-19, there are not many ground-truth datasets available for the communities. It is necessary to combine clinical experts' observations and information from images to have a reliable and efficient COVID-19 diagnosis. This paper suggests that future research may focus on multi-modality based models as well as how to select the best model architecture where AI can introduce more intelligence to medical systems to capture the characteristics of diseases by learning from multi-modality data to obtain reliable results for COVID-19 diagnosis for timely treatment .

Soomro Toufique A, Zheng Lihong, Afifi Ahmed J, Ali Ahmed, Yin Ming, Gao Junbin

2021-Apr-15

Artificial intelligence(AI), Classification, Coronavirus (COVID-19), Deep learning, Medical imaging, Segmentation

General General

Analyzing hCov Genome Sequences: Predicting Virulence and Mutation

bioRxiv Preprint

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

Sawmya, S.; Saha, A.; Tasnim, S.; Toufikuzzaman, M.; Anjum, N.; Rafid, A. H. M.; Rahman, M. S.; Rahman, M. S.

2021-04-20

General General

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

bioRxiv Preprint

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

Rynkiewicz, P.; Babbitt, G. A.; Cui, F.; Hudson, A. O.; Lynch, M. L.

2021-04-20

General General

Boosting Masked Face Recognition with Multi-Task ArcFace

ArXiv Preprint

In this paper, we address the problem of face recognition with masks. Given the global health crisis caused by COVID-19, mouth and nose-covering masks have become an essential everyday-clothing-accessory. This sanitary measure has put the state-of-the-art face recognition models on the ropes since they have not been designed to work with masked faces. In addition, the need has arisen for applications capable of detecting whether the subjects are wearing masks to control the spread of the virus. To overcome these problems a full training pipeline is presented based on the ArcFace work, with several modifications for the backbone and the loss function. From the original face-recognition dataset, a masked version is generated using data augmentation, and both datasets are combined during the training process. The selected network, based on ResNet-50, is modified to also output the probability of mask usage without adding any computational cost. Furthermore, the ArcFace loss is combined with the mask-usage classification loss, resulting in a new function named Multi-Task ArcFace (MTArcFace). Experimental results show that the proposed approach highly boosts the original model accuracy when dealing with masked faces, while preserving almost the same accuracy on the original non-masked datasets. Furthermore, it achieves an average accuracy of 99.78% in mask-usage classification.

David Montero, Marcos Nieto, Peter Leskovsky, Naiara Aginako

2021-04-20

General General

Masked Face Recognition using ResNet-50

ArXiv Preprint

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

Bishwas Mandal, Adaeze Okeukwu, Yihong Theis

2021-04-19

Public Health Public Health

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

In Medical image analysis

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

Signoroni Alberto, Savardi Mattia, Benini Sergio, Adami Nicola, Leonardi Riccardo, Gibellini Paolo, Vaccher Filippo, Ravanelli Marco, Borghesi Andrea, Maroldi Roberto, Farina Davide

2021-Mar-31

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

Pathology Pathology

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

In Scientific reports ; h5-index 158.0

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

Tran Nam K, Howard Taylor, Walsh Ryan, Pepper John, Loegering Julia, Phinney Brett, Salemi Michelle R, Rashidi Hooman H

2021-Apr-15

General General

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

In BJPsych open

The coronavirus disease 2019 (COVID-19) outbreak may have affected the mental health of patients at high risk of suicide. In this study we explored the wish to die and other suicide risk factors using smartphone-based ecological momentary assessment (EMA) in patients with a history of suicidal thoughts and behaviour. Contrary to our expectations we found a decrease in the wish to die during lockdown. This is consistent with previous studies showing that suicide rates decrease during periods of social emergency. Smartphone-based EMA can allow us to remotely assess patients and overcome the physical barriers imposed by lockdown.

Cobo Aurora, Porras-Segovia Alejandro, Pérez-Rodríguez María Mercedes, Artés-Rodríguez Antonio, Barrigón Maria Luisa, Courtet Philippe, Baca-García Enrique

2021-Apr-16

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

General General

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

ArXiv Preprint

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

Shiva Azimi, Rohan Wadhawan, Tapan K. Gandhi

2021-04-16

Public Health Public Health

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

In PloS one ; h5-index 176.0

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

Eastman Brydon, Meaney Cameron, Przedborski Michelle, Kohandel Mohammad

2021

General General

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

In medRxiv : the preprint server for health sciences

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

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

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

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

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

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

2021-Apr-05

General General

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

In Global health journal (Amsterdam, Netherlands)

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

Zhang Yanjie, Tambo Ernest, Djuikoue Ingrid C, Tazemda Gildas K, Fotsing Michael F, Zhou Xiao-Nong

2021-Mar

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

General General

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

In Journal of medical Internet research ; h5-index 88.0

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

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

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

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

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

CLINICALTRIAL :

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

2021-Apr-11

General General

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

ArXiv Preprint

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

Sidharth R, Abhiraj Tiwari, Parthivi Choubey, Saisha Kashyap, Sahil Khose, Kumud Lakara, Nishesh Singh, Ujjwal Verma

2021-04-15

Radiology Radiology

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

In Journal of medical Internet research ; h5-index 88.0

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

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

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

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

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

CLINICALTRIAL :

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

2021-Apr-03

Public Health Public Health

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

In Journal of medical Internet research ; h5-index 88.0

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

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

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

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

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

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

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

2021-Apr-13

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

Radiology Radiology

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

In Chinese journal of academic radiology

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

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

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

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

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

2021-Apr-08

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

General General

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

In Biomedical signal processing and control

The coronavirus (COVID-19) is currently the most common contagious disease which is prevalent all over the world. The main challenge of this disease is the primary diagnosis to prevent secondary infections and its spread from one person to another. Therefore, it is essential to use an automatic diagnosis system along with clinical procedures for the rapid diagnosis of COVID-19 to prevent its spread. Artificial intelligence techniques using computed tomography (CT) images of the lungs and chest radiography have the potential to obtain high diagnostic performance for Covid-19 diagnosis. In this study, a fusion of convolutional neural network (CNN), support vector machine (SVM), and Sobel filter is proposed to detect COVID-19 using X-ray images. A new X-ray image dataset was collected and subjected to high pass filter using a Sobel filter to obtain the edges of the images. Then these images are fed to CNN deep learning model followed by SVM classifier with ten-fold cross validation strategy. This method is designed so that it can learn with not many data. Our results show that the proposed CNN-SVM with Sobel filter (CNN-SVM + Sobel) achieved the highest classification accuracy, sensitivity and specificity of 99.02%, 100% and 95.23%, respectively in automated detection of COVID-19. It showed that using Sobel filter can improve the performance of CNN. Unlike most of the other researches, this method does not use a pre-trained network. We have also validated our developed model using six public databases and obtained the highest performance. Hence, our developed model is ready for clinical application.

Sharifrazi Danial, Alizadehsani Roohallah, Roshanzamir Mohamad, Joloudari Javad Hassannataj, Shoeibi Afshin, Jafari Mahboobeh, Hussain Sadiq, Sani Zahra Alizadeh, Hasanzadeh Fereshteh, Khozeimeh Fahime, Khosravi Abbas, Nahavandi Saeid, Panahiazar Maryam, Zare Assef, Islam Sheikh Mohammed Shariful, Acharya U Rajendra

2021-Apr-08

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

General General

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

In Information sciences

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

Ouyang Liwei, Yuan Yong, Cao Yumeng, Wang Fei-Yue

2021-Apr-08

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

General General

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

In NPJ digital medicine

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

Esteva Andre, Kale Anuprit, Paulus Romain, Hashimoto Kazuma, Yin Wenpeng, Radev Dragomir, Socher Richard

2021-Apr-12

Radiology Radiology

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

In European journal of radiology ; h5-index 47.0

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

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

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

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

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

2021-Feb-05

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

Surgery Surgery

Abdominal Organ Transplantation: Noteworthy Literature in 2020.

In Seminars in cardiothoracic and vascular anesthesia ; h5-index 16.0

In 2020, we identified and screened over 490 peer-reviewed publications on pancreatic transplantation, over 500 on intestinal transplantation, and over 5000 on kidney transplantation. The liver transplantation section specially focused on clinical trials and systematic reviews published in 2020 and featured selected articles. This review highlights noteworthy literature pertinent to anesthesiologists and critical care physicians caring for patients undergoing abdominal organ transplantation. We explore a wide range of topics, including COVID-19 and organ transplantation, risk factors and outcomes, pain management, artificial intelligence, robotic donor surgery, and machine perfusion.

Wang Ryan F, Fagelman Erica J, Smith Natalie K, Sakai Tetsuro

2021-Apr-13

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

General General

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

In Journal of computational social science

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

Caliskan Cantay

2021-Apr-05

Awareness, COVID-19, Emotion classification, Twitter

General General

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

In Informatics in medicine unlocked

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

Ghany Kareem Kamal A, Zawbaa Hossam M, Sabri Heba M

2021-Apr-06

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

General General

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

In Informatics in medicine unlocked

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

Alballa Norah, Al-Turaiki Isra

2021-Apr-03

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

General General

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

In Frontiers in robotics and AI

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

Ahmed Syed Nizamuddin

2021

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

General General

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

In International journal of disaster risk reduction : IJDRR

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

Reddin Karen, Bang Henry, Miles Lee

2021-Apr-05

Emergency Exercise, Epidemic, Lessons learnt, Pandemic, Simulation

General General

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

In Computational and structural biotechnology journal

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

Wang Hongzhi, Miao Zhiying, Zhang Chaobao, Wei Xiaona, Li Xiangqi

2021-Apr-07

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

General General

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

In Evolutionary intelligence

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

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

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

2021-Apr-03

Coronavirus, Covid-19 simulations, Daily count

General General

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

In Vacunas

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

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

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

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

2021-Apr-05

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

General General

Forecasting of the COVID-19 pandemic situation of Korea.

In Genomics & informatics

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

Goo Taewan, Apio Catherine, Heo Gyujin, Lee Doeun, Lee Jong Hyeok, Lim Jisun, Han Kyulhee, Park Taesung

2021-Mar

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

General General

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

In Computers in biology and medicine

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

Kadioglu Onat, Saeed Mohamed, Greten Henry Johannes, Efferth Thomas

2021-Mar-30

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

Public Health Public Health

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

In Journal of medical Internet research ; h5-index 88.0

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

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

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

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

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

CLINICALTRIAL :

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

2021-Mar-23

Cardiology Cardiology

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

In Cardiology in the young

Despite enormous strides in our field with respect to patient care, there has been surprisingly limited dialogue on how to train and educate the next generation of congenital cardiologists. This paper reviews the current status of training and evolving developments in medical education pertinent to congenital cardiology. The adoption of competency-based medical education has been lauded as a robust framework for contemporary medical education over the last two decades. However, inconsistencies in frameworks across different jurisdictions remain, and bridging gaps between competency frameworks and clinical practice has proved challenging. Entrustable professional activities have been proposed as a solution but integration of such activities into busy clinical cardiology practices will present its own challenges. Consequently, this pivot toward a more structured approach to medical education necessitates the widespread availability of appropriately trained medical educationalists; a development that will better inform curriculum development, instructional design, and assessment. Differentiation between superficial and deep learning, the vital role of rich formative feedback and coaching, should guide our trainees to become self-regulated learners, capable of critical reasoning yet retaining an awareness of uncertainty and ambiguity. Furthermore, disruptive innovations such as 'technology enhanced learning' may be leveraged to improve education, especially for trainees from low- and middle-income countries. Each of these initiatives will require resources, widespread advocacy and raised awareness, and publication of supporting data, and so it is especially gratifying that Cardiology in The Young has fostered a progressive approach, agreeing to publish one or two articles in each journal issue in this domain.

McMahon Colin J, Tretter Justin T, Redington Andrew N, Bu’Lock Frances, Zühlke Liesl, Heying Ruth, Mattos Sandra, Kumar R Krishna, Jacobs Jeffrey P, Windram Jonathan D

2021-Apr-12

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

General General

Knowledge graphs and their applications in drug discovery.

In Expert opinion on drug discovery ; h5-index 34.0

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

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

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

MacLean Finlay

2021-Apr-12

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

General General

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

bioRxiv Preprint

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

Chen, J.; Gao, K.; Wang, R.; Wei, G.-W.

2021-04-12

Public Health Public Health

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

In Transboundary and emerging diseases ; h5-index 40.0

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

Bhimala Kantha Rao, Patra Gopal Krishna, Mopuri Rajashekar, Mutheneni Srinivasa Rao

2021-Apr-10

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

Radiology Radiology

Get Protected! Recommendations for Staff in IR.

In Cardiovascular and interventional radiology

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

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

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

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

Bartal Gabriel, Vano Eliseo, Paulo Graciano

2021-Apr-09

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

General General

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

In Journal of medical Internet research ; h5-index 88.0

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

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

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

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

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

CLINICALTRIAL :

Patrício André, Costa Rafael S, Henriques Rui

2021-Mar-18

General General

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

In IEEE journal of biomedical and health informatics

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

Owais Muhammad, Lee Young Won, Mahmood Tahir, Haider Adnan, Sultan Haseeb, Park Kang Ryoung

2021-Apr-09

Internal Medicine Internal Medicine

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

In Internal medicine journal

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

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

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

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

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

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

2021-Apr-09

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

General General

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

In PeerJ. Computer science

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

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

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

Mohammadpoor Mojtaba, Sheikhi Karizaki Mehran, Sheikhi Karizaki Mina

2021

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

oncology Oncology

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

In Frontiers in cell and developmental biology

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

Krassowski Michal, Pellegrina Diogo, Mee Miles W, Fradet-Turcotte Amelie, Bhat Mamatha, Reimand Jüri

2021

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

General General

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

In Frontiers in psychology ; h5-index 92.0

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

Trenerry Brigid, Chng Samuel, Wang Yang, Suhaila Zainal Shah, Lim Sun Sun, Lu Han Yu, Oh Peng Ho

2021

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

General General

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

In European review for medical and pharmacological sciences

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

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

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

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

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

2021-Mar

oncology Oncology

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

In Frontiers in robotics and AI

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

Akbari Mojtaba, Carriere Jay, Meyer Tyler, Sloboda Ron, Husain Siraj, Usmani Nawaid, Tavakoli Mahdi

2021

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

General General

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

In Biomedical signal processing and control

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

Montalbo Francis Jesmar P

2021-Jul

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

Dermatology Dermatology

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

ArXiv Preprint

We develop and rigorously evaluate a deep learning based system that can accurately classify skin conditions while detecting rare conditions for which there is not enough data available for training a confident classifier. We frame this task as an out-of-distribution (OOD) detection problem. Our novel approach, hierarchical outlier detection (HOD) assigns multiple abstention classes for each training outlier class and jointly performs a coarse classification of inliers vs. outliers, along with fine-grained classification of the individual classes. We demonstrate the effectiveness of the HOD loss in conjunction with modern representation learning approaches (BiT, SimCLR, MICLe) and explore different ensembling strategies for further improving the results. We perform an extensive subgroup analysis over conditions of varying risk levels and different skin types to investigate how the OOD detection performance changes over each subgroup and demonstrate the gains of our framework in comparison to baselines. Finally, we introduce a cost metric to approximate downstream clinical impact. We use this cost metric to compare the proposed method against a baseline system, thereby making a stronger case for the overall system effectiveness in a real-world deployment scenario.

Abhijit Guha Roy, Jie Ren, Shekoofeh Azizi, Aaron Loh, Vivek Natarajan, Basil Mustafa, Nick Pawlowski, Jan Freyberg, Yuan Liu, Zach Beaver, Nam Vo, Peggy Bui, Samantha Winter, Patricia MacWilliams, Greg S. Corrado, Umesh Telang, Yun Liu, Taylan Cemgil, Alan Karthikesalingam, Balaji Lakshminarayanan, Jim Winkens

2021-04-08

Radiology Radiology

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

In Biomedical signal processing and control

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

Rahimzadeh Mohammad, Attar Abolfazl, Sakhaei Seyed Mohammad

2021-Mar-31

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

General General

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

In International journal of imaging systems and technology

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

Khan Murtaza Ali

2021-Mar-01

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

General General

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

In International journal of imaging systems and technology

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

El-Dosuky Mohamed A, Soliman Mona, Hassanien Aboul Ella

2021-Feb-24

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

General General

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

In International journal of imaging systems and technology

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

Tiwari Shamik, Jain Anurag

2021-Mar-02

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

Radiology Radiology

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

In International journal of imaging systems and technology

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

Dhaka Vijaypal Singh, Rani Geeta, Oza Meet Ganpatlal, Sharma Tarushi, Misra Ankit

2021-Feb-03

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

Cardiology Cardiology

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

In International journal of imaging systems and technology

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

Jahmunah Vicnesh, Sudarshan Vidya K, Oh Shu Lih, Gururajan Raj, Gururajan Rashmi, Zhou Xujuan, Tao Xiaohui, Faust Oliver, Ciaccio Edward J, Ng Kwan Hoong, Acharya U Rajendra

2021-Feb-09

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

Radiology Radiology

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

In International journal of imaging systems and technology

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

Polat Hasan, Özerdem Mehmet Siraç, Ekici Faysal, Akpolat Veysi

2021-Feb-16

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

oncology Oncology

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

In Journal of Raman spectroscopy : JRS

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

Yin Gang, Li Lintao, Lu Shun, Yin Yu, Su Yuanzhang, Zeng Yilan, Luo Mei, Ma Maohua, Zhou Hongyan, Orlandini Lucia, Yao Dezhong, Liu Gang, Lang Jinyi

2021-Feb-19

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

General General

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

In International journal of energy research

Accurately quantifying the social distancing (SD) practice of a population is essential for governments and health agencies to better plan and adapt restrictions during a pandemic crisis. In such a scenario, the reduction of social mobility also has a significant impact on electricity consumption, since people are encouraged to stay at home and many commercial and industrial activities are reduced or even halted. This paper proposes a methodology to qualify the SD of a medium-sized city, located in the northwest of the state of Rio Grande do Sul (RS), Brazil, using data of electricity consumption measured by the municipality's energy utility. The methodology consists of combining a data set, and an average consumption profile of Sundays is obtained using data from 4-months, it is then defined as a high SD profile due to the typical lower social activities on Sundays. An supervised and an unsupervised artificial neural network (ANN) are trained with this profile and used to analyze electricity consumption of this city during the COVID-19 pandemic. Low, moderate, and high SD ranges are also created, and the daily population behavior is evaluated by the ANNs. The results are strongly correlated and discussed with government restrictions imposed during the analyzed period and indicate that the ANNs can correctly classify the intensity of SD practiced by people. The unsupervised ANN is used more easily and in different scenarios, so it can be indicated for use by public administration for purposes of assess the effectiveness of SD policies based on the guidelines established during the COVID-19 pandemic.

Sausen Airam T Z R, de Campos Maurício, Sausen Paulo S, Binelo Manuel O, Binelo Marcia F B, da Silva João M L V, Dos Santos Moises

2021-Jan-26

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

General General

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

ArXiv Preprint

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

Tingyi Wanyan, Jing Zhang, Ying Ding, Ariful Azad, Zhangyang Wang, Benjamin S Glicksberg

2021-04-07

General General

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

In American journal of epidemiology ; h5-index 65.0

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

Kostoulas Polychronis, Eusebi Paolo, Hartnack Sonja

2021-Mar-31

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

Internal Medicine Internal Medicine

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

In Journal of medical Internet research ; h5-index 88.0

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

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

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

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

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

CLINICALTRIAL :

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

2021-Mar-18

General General

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

In Computational and structural biotechnology journal

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

Esmail Sally, Danter Wayne R

2021

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

General General

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

In Evolutionary intelligence

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

Das Sourav, Kolya Anup Kumar

2021-Mar-30

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

Radiology Radiology

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

In Journal of inflammation research

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

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

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

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

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

2021

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

General General

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

In The Indian journal of radiology & imaging

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

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

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

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

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

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

2021-Jan

Artificial intelligence, COVID pneumonia, chest radiographs, rapid triaging

General General

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

In The Indian journal of radiology & imaging

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

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

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

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

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

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

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

Kohli Anirudh, Jha Tanya, Pazhayattil Amal Babu

2021-Jan

Covid-19, HRCT chest, oxygen requirement

Internal Medicine Internal Medicine

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

In The Indian journal of radiology & imaging

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

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

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

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

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

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

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

2021-Jan

Artificial intelligence, COVID 19, CXR, deep learning

Radiology Radiology

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

In The Indian journal of radiology & imaging

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

Ahuja Gauri, Verma Mitusha, Patkar Deepak

2021-Jan

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

Radiology Radiology

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

In The Indian journal of radiology & imaging

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

Pankhania Mayur

2021-Jan

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

Public Health Public Health

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

In Journal of medical Internet research ; h5-index 88.0

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

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

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

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

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

CLINICALTRIAL :

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

2021-Apr-01

Internal Medicine Internal Medicine

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

In Journal of medical Internet research ; h5-index 88.0

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

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

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

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

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

CLINICALTRIAL : ClinicalTrials.gov, identifier: NCT04368923.

Adly Aya Sedky, Adly Mahmoud Sedky, Adly Afnan Sedky

2021-Apr-01

General General

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

In Archives of Razi Institute

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

Amanpour S

2021-Mar

Vaccine, cancer, covid 19

General General

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

In The Journal of applied psychology

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

Min Hanyi, Peng Yisheng, Shoss Mindy, Yang Baojiang

2021-Feb

General General

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

In Multimedia systems

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

Iwendi Celestine, Mahboob Kainaat, Khalid Zarnab, Javed Abdul Rehman, Rizwan Muhammad, Ghosh Uttam

2021-Mar-28

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

Surgery Surgery

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

In Journal of the American College of Emergency Physicians open

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

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

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

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

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

2021-Apr

COVID, machine learning, predictive model, risk of admission

General General

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

In Patterns (New York, N.Y.)

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

Du Hangyu, Chen Feng, Liu Hongfu, Hong Pengyu

2021-Mar-29

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

General General

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

In PeerJ. Computer science

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

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

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

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

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

2021

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

General General

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

In PeerJ. Computer science

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

Elzeki Omar M, Shams Mahmoud, Sarhan Shahenda, Abd Elfattah Mohamed, Hassanien Aboul Ella

2021

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

General General

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

In PeerJ. Computer science

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

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

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

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

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

2020

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

General General

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

In PeerJ. Computer science

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

Ragab Dina A, Attallah Omneya

2020

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

General General

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

In PeerJ. Computer science

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

El-Bana Shimaa, Al-Kabbany Ahmad, Sharkas Maha

2020

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

Radiology Radiology

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

In Medical journal of the Islamic Republic of Iran

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

Ghomi Zahra, Mirshahi Reza, Khameneh Bagheri Arash, Fattahpour Ali, Mohammadiun Saeed, Alavi Gharahbagh Abdorreza, Djavadifar Abtin, Arabalibeik Hossein, Sadiq Rehan, Hewage Kasun

2020

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

General General

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

In Annals of global health

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

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

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

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

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

Pigoga J L, Omer Y O, Wallis L A

2021-Mar-26

General General

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

In Results in physics

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

Ndondo A M, Kasereka S K, Bisuta S F, Kyamakya K, Doungmo E F G, Ngoie R-B M

2021-Mar-27

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

General General

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

bioRxiv Preprint

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

Kang, J. E.

2021-04-04

General General

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

ArXiv Preprint

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

Carlos Rojas-Azabache, Karen Vilca-Janampa, Renzo Guerrero-Huayta, Dennis Núñez-Fernández

2021-04-04

General General

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

In Omics : a journal of integrative biology

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

Özdemir Vural

2021-Apr-01

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

Surgery Surgery

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

In PloS one ; h5-index 176.0

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

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

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

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

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

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

2021

General General

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

In Journal of medical Internet research ; h5-index 88.0

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

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

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

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

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

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

2021-Mar-08

General General

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

In Clinical pharmacology and therapeutics

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

Alemayehu Demissie, Hemmings Robert, Natarajan Kannan, Roychoudhury Satrajit

2021-Apr-01

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

General General

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

In medRxiv : the preprint server for health sciences

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

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

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

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

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

2021-Mar-26

General General

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

In International journal of endocrinology ; h5-index 44.0

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

Lu Shan, Xing Zhiheng, Zhao Shiyu, Meng Xianglu, Yang Juhong, Ding Wenlong, Wang Jigang, Huang Chencui, Xu Jingxu, Chang Baocheng, Shen Jun

2021

Public Health Public Health

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

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

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

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

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

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

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

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

2021-Mar-31

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

General General

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

bioRxiv Preprint

Despite the recent availability of vaccines against the acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the search for inhibitory therapeutic agents has assumed importance especially in the context of emerging new viral variants. In this paper, we describe the discovery of a novel non-covalent small-molecule inhibitor, MCULE-5948770040, that binds to and inhibits the SARS-Cov-2 main protease (Mpro) by employing a scalable high throughput virtual screening (HTVS) framework and a targeted compound library of over 6.5 million molecules that could be readily ordered and purchased. Our HTVS framework leverages the U.S. supercomputing infrastructure achieving nearly 91% resource utilization and nearly 126 million docking calculations per hour. Downstream biochemical assays validate this Mpro inhibitor with an inhibition constant (Ki) of 2.9 uM [95% CI 2.2, 4.0]. Further, using room-temperature X-ray crystallography, we show that MCULE-5948770040 binds to a cleft in the primary binding site of Mpro forming stable hydrogen bond and hydrophobic interactions. We then used multiple s-timescale molecular dynamics (MD) simulations, and machine learning (ML) techniques to elucidate how the bound ligand alters the conformational states accessed by Mpro, involving motions both proximal and distal to the binding site. Together, our results demonstrate how MCULE-5948770040 inhibits Mpro and offers a springboard for further therapeutic design.

Clyde, A.; Galanie, S.; Kneller, D. W.; Ma, H.; Babuji, Y.; Blaiszik, B.; Brace, A.; Brettin, T.; Chard, K.; Chard, R.; Coates, L.; Foster, I.; Hauner, D.; Kertesz, V.; Kumar, N.; Lee, H.; Li, Z.; Merzky, A.; Schmidt, J. G.; Tan, L.; Titov, M.; Trifan, A.; Turilli, M.; Van Dam, H.; Chennubhotla, S. C.; Jha, S.; Kovalevsky, A.; Ramanathan, A.; Head, M.; Stevens, R.

2021-04-02

General General

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

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

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

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

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

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

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

2021-Mar-30

Pathology Pathology

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

In IEEE journal of biomedical and health informatics

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

Panetta Karen, Sanghavi Foram, Agaian Sos, Madan Neel

2021-Mar-31

General General

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

In Database : the journal of biological databases and curation

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

Kropiwnicki Eryk, Evangelista John E, Stein Daniel J, Clarke Daniel J B, Lachmann Alexander, Kuleshov Maxim V, Jeon Minji, Jagodnik Kathleen M, Ma’ayan Avi

2021-Mar-31

General General

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

In Geophysical research letters

The worldwide research initiatives on Corona Virus disease 2019 lockdown effect on air quality agree on pollution reduction, but the most reliable method to pollution reduction quantification is still in debate. In this paper, machine learning models based on a Gradient Boosting Machine algorithm are built to assess the outbreak impact on air quality in Quito, Ecuador. First, the precision of the prediction was evaluated by cross-validation on the four years prelockdown, showing a high accuracy to estimate the real pollution levels. Then, the changes in pollution are quantified. During the full lockdown, air pollution decreased by -53 ± 2%, -45 ± 11%, -30 ± 13%, and -15 ± 9% for NO2, SO2, CO, and PM2.5, respectively. The traffic-busy districts were the most impacted areas of the city. After the transition to the partial relaxation, the concentrations have nearly returned to the levels as before the pandemic. The quantification of pollution drop is supported by an assessment of the prediction confidence.

Rybarczyk Yves, Zalakeviciute Rasa

2021-Feb-28

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

General General

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

In Scientific reports ; h5-index 158.0

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

Gangloff Cedric, Rafi Sonia, Bouzillé Guillaume, Soulat Louis, Cuggia Marc

2021-Mar-30

General General

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

bioRxiv Preprint

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

Cho, E.; Rosa, M.; Anjum, R.; Mehmood, S.; Soban, M.; Mujtaba, M.; Bux, K.; Dantu, S. C.; Pandini, A.; Yin, J.; Ma, H.; Ramanathan, A.; Islam, B.; Mey, A.; BHOWMIK, D.; Haider, S.

2021-04-01

General General

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

In IEEE reviews in biomedical engineering

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

Jiang Wei, Majumder Sumit, Subramaniam Sophini, Li Xiaohe, Khedri Ridha, Monday Tapas, Abolghasemian Mansour, Satia Imran, Deen M Jamal

2021-Mar-30

General General

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

In Journal of medical virology

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

Rehman Hafiz Abdul, Ramzan Farheen, Basharat Zarrin, Shakeel Muhammad, Khan Muhammad Usman Ghani, Khan Ishtiaq Ahmad

2021-Mar-30

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

General General

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

In Environmental science and pollution research international

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

Wang Qiang, Zhang Chen

2021-Mar-29

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

Radiology Radiology

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

In NPJ digital medicine

Data privacy mechanisms are essential for rapidly scaling medical training databases to capture the heterogeneity of patient data distributions toward robust and generalizable machine learning systems. In the current COVID-19 pandemic, a major focus of artificial intelligence (AI) is interpreting chest CT, which can be readily used in the assessment and management of the disease. This paper demonstrates the feasibility of a federated learning method for detecting COVID-19 related CT abnormalities with external validation on patients from a multinational study. We recruited 132 patients from seven multinational different centers, with three internal hospitals from Hong Kong for training and testing, and four external, independent datasets from Mainland China and Germany, for validating model generalizability. We also conducted case studies on longitudinal scans for automated estimation of lesion burden for hospitalized COVID-19 patients. We explore the federated learning algorithms to develop a privacy-preserving AI model for COVID-19 medical image diagnosis with good generalization capability on unseen multinational datasets. Federated learning could provide an effective mechanism during pandemics to rapidly develop clinically useful AI across institutions and countries overcoming the burden of central aggregation of large amounts of sensitive data.

Dou Qi, So Tiffany Y, Jiang Meirui, Liu Quande, Vardhanabhuti Varut, Kaissis Georgios, Li Zeju, Si Weixin, Lee Heather H C, Yu Kevin, Feng Zuxin, Dong Li, Burian Egon, Jungmann Friederike, Braren Rickmer, Makowski Marcus, Kainz Bernhard, Rueckert Daniel, Glocker Ben, Yu Simon C H, Heng Pheng Ann

2021-Mar-29

General General

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

In Thorax ; h5-index 75.0

Non-pharmaceutical interventions (NPIs) have been widely implemented to mitigate the spread of COVID-19. We assessed the effect of NPIs on hospitalisations for pneumonia, influenza, COPD and asthma. This retrospective, ecological study compared the weekly incidence of hospitalisation for four respiratory conditions before (January 2016-January 2020) and during (February-July 2020) the implementation of NPI against COVID-19. Hospitalisations for all four respiratory conditions decreased substantially during the intervention period. The cumulative incidence of admissions for COPD and asthma was 58% and 48% of the mean incidence during the 4 preceding years, respectively.

Huh Kyungmin, Kim Young-Eun, Ji Wonjun, Kim Dong Wook, Lee Eun-Joo, Kim Jong-Hun, Kang Ji-Man, Jung Jaehun

2021-Mar-29

COVID-19, respiratory infection

General General

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

In Journal of biomedical informatics ; h5-index 55.0

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

Lybarger Kevin, Ostendorf Mari, Thompson Matthew, Yetisgen Meliha

2021-Mar-26

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

General General

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

In Current medicinal chemistry ; h5-index 49.0

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

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

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

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

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

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

2021-Mar-28

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

General General

Mobility Functional Areas and COVID-19 Spread

ArXiv Preprint

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

Stefano Maria Iacus, Carlos Santamaria, Francesco Sermi, Spyridon Spyratos, Dario Tarchi, Michele Vespe

2021-03-31

Radiology Radiology

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

In Radiology. Cardiothoracic imaging

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

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

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

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

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

2020-Oct

Radiology Radiology

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

In Radiology. Cardiothoracic imaging

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

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

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

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

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

2020-Apr

Radiology Radiology

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

In Radiology. Cardiothoracic imaging

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

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

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

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

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

2020-Feb

General General

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

In Frontiers in psychiatry

Introduction: Little is known about the impact of restrictive measures during the COVID-19 pandemic on self-image and engagement in exercise and other coping strategies alongside the use of image and performance-enhancing drugs (IPEDs) to boost performance and appearance. Objectives: To assess the role of anxiety about appearance and self-compassion on the practice of physical exercise and use of IPEDs during lockdown. Methods: An international online questionnaire was carried out using the Exercise Addiction Inventory (EAI), the Appearance Anxiety Inventory (AAI), and the Self-Compassion Scale (SCS) in addition to questions on the use of IPEDs. Results: The sample consisted of 3,161 (65% female) adults from Italy (41.1%), Spain (15.7%), the United Kingdom (UK) (12.0%), Lithuania (11.6%), Portugal (10.5%), Japan (5.5%), and Hungary (3.5%). The mean age was 35.05 years (SD = 12.10). Overall, 4.3% of the participants were found to engage in excessive or problematic exercise with peaks registered in the UK (11.0%) and Spain (5.4%). The sample reported the use of a wide range of drugs and medicines to boost image and performance (28%) and maintained use during the lockdown, mostly in Hungary (56.6%), Japan (46.8%), and the UK (33.8%), with 6.4% who started to use a new drug. Significant appearance anxiety levels were found across the sample, with 18.1% in Italy, 16.9% in Japan, and 16.7% in Portugal. Logistic regression models revealed a strong association between physical exercise and IPED use. Anxiety about appearance also significantly increased the probability of using IPEDs. However, self-compassion did not significantly predict such behavior. Anxiety about appearance and self-compassion were non-significant predictors associated with engaging in physical exercise. Discussion and Conclusion: This study identified risks of problematic exercising and appearance anxiety among the general population during the COVID-19 lockdown period across all the participating countries with significant gender differences. Such behaviors were positively associated with the unsupervised use of IPEDs, although no interaction between physical exercise and appearance anxiety was observed. Further considerations are needed to explore the impact of socially restrictive measures among vulnerable groups, and the implementation of more targeted responses.

Dores Artemisa R, Carvalho Irene P, Burkauskas Julius, Simonato Pierluigi, De Luca Ilaria, Mooney Roisin, Ioannidis Konstantinos, Gómez-Martínez M Ángeles, Demetrovics Zsolt, Ábel Krisztina Edina, Szabo Attila, Fujiwara Hironobu, Shibata Mami, Ventola Alejandra Rebeca Melero, Arroyo-Anlló Eva Maria, Santos-Labrador Ricardo M, Griskova-Bulanova Inga, Pranckeviciene Aiste, Kobayashi Kei, Martinotti Giovanni, Fineberg Naomi A, Barbosa Fernando, Corazza Ornella

2021

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

General General

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

In Personal and ubiquitous computing

[This corrects the article DOI: 10.1007/s00779-021-01541-4.].

Poongodi M, Hamdi Mounir, Malviya Mohit, Sharma Ashutosh, Dhiman Gaurav, Vimal S

2021-Mar-23

General General

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

In Applied soft computing

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

Feki Ines, Ammar Sourour, Kessentini Yousri, Muhammad Khan

2021-Jul

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

General General

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

In Information processing & management

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

Ayoub Jackie, Yang X Jessie, Zhou Feng

2021-Jul

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

General General

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

In Scientometrics

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

Zhang Yi, Cai Xiaojing, Fry Caroline V, Wu Mengjia, Wagner Caroline S

2021-Mar-20

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

General General

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

In Environmental research ; h5-index 67.0

The advent of COVID-19 has kept the whole world on their toes. Countries are maximizing their efforts to combat the virus and to minimize the infection. Since infectious microorganisms may be transmitted by variety of routes, respiratory and facial protection is required for those that are usually transmitted via droplets/aerosols. Therefore this pandemic has caused a sudden increase in the demand for personal protective equipment (PPE) such as gloves, masks, and many other important items since, the evidence of individual-to-individual transmission (through respiratory droplets/coughing) and secondary infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). But the disposal of these personal protective measures remains a huge question mark towards the environmental impact. Huge waste generation demands proper segregation according to waste types, collection, and recycling to minimize the risk of infection spread through aerosols and attempts to implement measures to monitor infections. Hence, this review focuses on the impact of environment due to improper disposal of these personal protective measures and to investigate the safe disposal methods for these protective measures by using the safe, secure and innovative biological methods such as the use of Artificial Intelligence (AI) and Ultraviolet (UV) lights for killing such deadly viruses.

Iyer Mahalaxmi, Tiwari Sushmita, Renu Kaviyarasi, Pasha Md Younus, Pandit Shraddha, Singh Bhupender, Raj Neethu, Saikrishna Krothapalli, Kwak Hee Jeong, Balasubramanian Venkatesh, Jang Soo Bin, Dileep Kumar G, Anand Uttpal, Narayanasamy Arul, Kinoshita Masako, Subramaniam Mohana Devi, Kumar Nachimuthu Senthil, Roy Ayan, Gopalakrishnan Abilash Valsala, Parthasarathi Ramakrishnan, Cho Ssang-Goo, Vellingiri Balachandar

2021-Mar-25

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

General General

Deep Learning and Machine Vision for Food Processing: A Survey

ArXiv Preprint

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

Lili Zhu, Petros Spachos, Erica Pensini, Konstantinos Plataniotis

2021-03-30

General General

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

In Computers in biology and medicine

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

Serte Sertan, Demirel Hasan

2021-Mar-10

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

Public Health Public Health

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

In PLoS computational biology

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

Watson Gregory L, Xiong Di, Zhang Lu, Zoller Joseph A, Shamshoian John, Sundin Phillip, Bufford Teresa, Rimoin Anne W, Suchard Marc A, Ramirez Christina M

2021-Mar-29

Public Health Public Health

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

In PLoS computational biology

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

Torneri Andrea, Libin Peter, Scalia Tomba Gianpaolo, Faes Christel, Wood James G, Hens Niel

2021-Mar-29

General General

Digital Mental Health Challenges and the Horizon Ahead for Solutions.

In JMIR mental health

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

Balcombe Luke, De Leo Diego

2021-Mar-29

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

Public Health Public Health

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

In JMIR medical informatics ; h5-index 23.0

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

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

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

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

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

CLINICALTRIAL :

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

2021-Mar-21

General General

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

In Ergonomics

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

de Winter J C F, Hancock P A

2021-Mar-29

Public Health Public Health

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

In The Lancet. Microbe

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

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

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

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

Funding : National Medical Research Council, Biomedical Research Council, and A*STAR, Singapore.

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

2021-Mar-23

Radiology Radiology

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

In Radiology. Cardiothoracic imaging

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

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

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

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

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

2021-Feb

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

General General

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

bioRxiv Preprint

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

Bartoszewicz, J. M.; Genske, U.; Renard, B. Y.

2021-03-29

General General

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

In Journal of biomedical informatics ; h5-index 55.0

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

Shahid Osama, Nasajpour Mohammad, Pouriyeh Seyedamin, Parizi Reza M, Han Meng, Valero Maria, Li Fangyu, Aledhari Mohammed, Sheng Quan Z

2021-Mar-23

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

General General

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

In IEEE journal of biomedical and health informatics

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

Fridadar Maayan, Amer Rula, Gozes Ophir, Nassar Jannette, Greenspan Hayit

2021-Mar-26

General General

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

In IEEE reviews in biomedical engineering

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

John Christopher Clement, Ponnusamy Vijayakumar, Krishnan Chandrasekaran Sriharipriya, R Nandakumar

2021-Mar-26

General General

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

In IEEE transactions on big data

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

Wanyan Tingyi, Vaid Akhil, De Freitas Jessica K, Somani Sulaiman, Miotto Riccardo, Nadkarni Girish N, Azad Ariful, Ding Ying, Glicksberg Benjamin S

2021-Mar

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

Radiology Radiology

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

In Scientific reports ; h5-index 158.0

A better understanding of temporal relationships between chest CT and labs may provide a reference for disease severity over the disease course. Generalized curves of lung opacity volume and density over time can be used as standardized references from well before symptoms develop to over a month after recovery, when residual lung opacities remain. 739 patients with COVID-19 underwent CT and RT-PCR in an outbreak setting between January 21st and April 12th, 2020. 29 of 739 patients had serial exams (121 CTs and 279 laboratory measurements) over 50 ± 16 days, with an average of 4.2 sequential CTs each. Sequential volumes of total lung, overall opacity and opacity subtypes (ground glass opacity [GGO] and consolidation) were extracted using deep learning and manual segmentation. Generalized temporal curves of CT and laboratory measurements were correlated. Lung opacities appeared 3.4 ± 2.2 days prior to symptom onset. Opacity peaked 1 day after symptom onset. GGO onset was earlier and resolved later than consolidation. Lactate dehydrogenase, and C-reactive protein peaked earlier than procalcitonin and leukopenia. The temporal relationships of quantitative CT features and clinical labs have distinctive patterns and peaks in relation to symptom onset, which may inform early clinical course in patients with mild COVID-19 pneumonia, or may shed light upon chronic lung effects or mechanisms of medical countermeasures in clinical trials.

Kassin Michael T, Varble Nicole, Blain Maxime, Xu Sheng, Turkbey Evrim B, Harmon Stephanie, Yang Dong, Xu Ziyue, Roth Holger, Xu Daguang, Flores Mona, Amalou Amel, Sun Kaiyun, Kadri Sameer, Patella Francesca, Cariati Maurizio, Scarabelli Alice, Stellato Elvira, Ierardi Anna Maria, Carrafiello Gianpaolo, An Peng, Turkbey Baris, Wood Bradford J

2021-Mar-25

General General

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

bioRxiv Preprint

Despite the recent availability of vaccines against the acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the search for inhibitory therapeutic agents has assumed importance especially in the context of emerging new viral variants. In this paper, we describe the discovery of a novel non-covalent small-molecule inhibitor, MCULE-5948770040, that binds to and inhibits the SARS-Cov-2 main protease (Mpro) by employing a scalable high throughput virtual screening (HTVS) framework and a targeted compound library of over 6.5 million molecules that could be readily ordered and purchased. Our HTVS framework leverages the U.S. supercomputing infrastructure achieving nearly 91% resource utilization and nearly 126 million docking calculations per hour. Downstream biochemical assays validate this Mpro inhibitor with an inhibition constant (Ki) of 2.9 uM [95% CI 2.2, 4.0]. Further, using room-temperature X-ray crystallography, we show that MCULE-5948770040 binds to a cleft in the primary binding site of Mpro forming stable hydrogen bond and hydrophobic interactions. We then used multiple s-timescale molecular dynamics (MD) simulations, and machine learning (ML) techniques to elucidate how the bound ligand alters the conformational states accessed by Mpro, involving motions both proximal and distal to the binding site. Together, our results demonstrate how MCULE-5948770040 inhibits Mpro and offers a springboard for further therapeutic design.

Clyde, A.; Galanie, S.; Kneller, D. W.; Ma, H.; Babuji, Y.; Blaiszik, B.; Brace, A.; Brettin, T.; Chard, K.; Chard, R.; Coates, L.; Foster, I.; Hauner, D.; Kertesz, V.; Kumar, N.; Lee, H.; Li, Z.; Merzky, A.; Schmidt, J. G.; Tan, L.; Titov, M.; Trifan, A.; Turilli, M.; Van Dam, H.; Chennubhotla, S. C.; Jha, S.; Kovalevsky, A.; Ramanathan, A.; Head, M.; Stevens, R.

2021-03-27

General General

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

In PloS one ; h5-index 176.0

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

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

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

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

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

2021

Dermatology Dermatology

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

In JMIR medical informatics ; h5-index 23.0

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

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

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

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

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

CLINICALTRIAL : N/a.

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

2021-Mar-21

Radiology Radiology

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

In Journal of medical Internet research ; h5-index 88.0

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

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

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

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

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

CLINICALTRIAL :

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

2021-Mar-24

General General

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

In Journal of healthcare engineering

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

Kaur Manjit, Kumar Vijay, Yadav Vaishali, Singh Dilbag, Kumar Naresh, Das Nripendra Narayan

2021

General General

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

In Frontiers in immunology ; h5-index 100.0

Although widely prevalent, Lyme disease is still under-diagnosed and misunderstood. Here we followed 73 acute Lyme disease patients and uninfected controls over a period of a year. At each visit, RNA-sequencing was applied to profile patients' peripheral blood mononuclear cells in addition to extensive clinical phenotyping. Based on the projection of the RNA-seq data into lower dimensions, we observe that the cases are separated from controls, and almost all cases never return to cluster with the controls over time. Enrichment analysis of the differentially expressed genes between clusters identifies up-regulation of immune response genes. This observation is also supported by deconvolution analysis to identify the changes in cell type composition due to Lyme disease infection. Importantly, we developed several machine learning classifiers that attempt to perform various Lyme disease classifications. We show that Lyme patients can be distinguished from the controls as well as from COVID-19 patients, but classification was not successful in distinguishing those patients with early Lyme disease cases that would advance to develop post-treatment persistent symptoms.

Clarke Daniel J B, Rebman Alison W, Bailey Allison, Wojciechowicz Megan L, Jenkins Sherry L, Evangelista John E, Danieletto Matteo, Fan Jinshui, Eshoo Mark W, Mosel Michael R, Robinson William, Ramadoss Nitya, Bobe Jason, Soloski Mark J, Aucott John N, Ma’ayan Avi

2021

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

Internal Medicine Internal Medicine

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

In BMC pulmonary medicine ; h5-index 38.0

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

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

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

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

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

2021-Mar-24

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

Radiology Radiology

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

In Medicine

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

Xiong Fei, Wang Ye, You Tao, Li Han Han, Fu Ting Ting, Tan Huibin, Huang Weicai, Jiang Yuanliang

2021-Mar-26

General General

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

bioRxiv Preprint

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

Zheng, S.; Aldahdooh, J.; Shadbahr, T.; Wang, Y.; Aldahdooh, D.; Bao, J.; Wang, W.; Tang, J.

2021-03-26

Public Health Public Health

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

In JMIR public health and surveillance

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

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

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

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

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

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

2021-Mar-24

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

General General

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

In medRxiv : the preprint server for health sciences

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

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

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

2021-Mar-13

General General

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

In Applied soft computing

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

Göreke Volkan, Sarı Vekil, Kockanat Serdar

2021-Jul

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

General General

A deep learning-based medication behavior monitoring system.

In Mathematical biosciences and engineering : MBE

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

Roh Hyeji, Shin Seulgi, Han Jinseo, Lim Sangsoon

2021-Jan-28

** IoT , deep learning , healthcare , medication, monitoring **

General General

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

In IEEE transactions on ultrasonics, ferroelectrics, and frequency control

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

Awasthi Navchetan, Dayal Aveen, Cenkeramaddi Linga Reddy, Yalavarthy Phaneendra K

2021-Mar-23

Public Health Public Health

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

In Psychology & health

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

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

MAIN OUTCOME MEASURES : Physician-patient trust.

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

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

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

2021-Mar-23

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

General General

CvDeep-COVID-19 Detection Model.

In SN computer science

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

Ingle Vaishali Arjun, Ambad Prashant Mahadev

2021

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

General General

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

In Information systems frontiers : a journal of research and innovation

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

Elakkiya R, Vijayakumar Pandi, Karuppiah Marimuthu

2021-Mar-17

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

General General

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

In Neurocomputing

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

Xu Yujia, Lam Hak-Keung, Jia Guangyu

2021-Mar-18

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

General General

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

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

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

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

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

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

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

2021-Mar-22

COVID-19, France, Trauma, Traumacenter

Public Health Public Health

Prediction of Sepsis in COVID-19 Using Laboratory Indicators.

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

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

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

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

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

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

2020

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

Radiology Radiology

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

In Journal of healthcare engineering

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

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

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

Ghaderzadeh Mustafa, Asadi Farkhondeh

2021

Radiology Radiology

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

In Journal of healthcare engineering

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

Li Tianyi, Wei Wei, Cheng Lidan, Zhao Shengjie, Xu Chuanjun, Zhang Xia, Zeng Yi, Gu Jihua

2021

General General

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

In Applied soft computing

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

Saygılı Ahmet

2021-Jul

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

Radiology Radiology

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

In International journal of medical sciences

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

Hou Wei, Zhao Zirun, Chen Anne, Li Haifang, Duong Tim Q

2021

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

General General

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

In Expert systems with applications

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

Nigam Bhawna, Nigam Ayan, Jain Rahul, Dodia Shubham, Arora Nidhi, B Annappa

2021-Mar-16

COVID-19, Coronavirus, Deep Learning, Pandemic

General General

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

In Expert systems with applications

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

Jin Qiangguo, Cui Hui, Sun Changming, Meng Zhaopeng, Wei Leyi, Su Ran

2021-Mar-13

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

General General

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

In Computers & industrial engineering

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

Dhayne Houssein, Kilany Rima, Haque Rafiqul, Taher Yehia

2021-Mar-15

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

General General

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

In The Spanish journal of psychology

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

Romero-Rivas Carlos, Rodriguez-Cuadrado Sara

2021-Mar-22

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

Public Health Public Health

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

In Nature communications ; h5-index 260.0

The COVID-19 pandemic caused many governments to impose policies restricting social interactions. A controlled and persistent release of lockdown measures covers many potential strategies and is subject to extensive scenario analyses. Here, we use an individual-based model (STRIDE) to simulate interactions between 11 million inhabitants of Belgium at different levels including extended household settings, i.e., "household bubbles". The burden of COVID-19 is impacted by both the intensity and frequency of physical contacts, and therefore, household bubbles have the potential to reduce hospital admissions by 90%. In addition, we find that it is crucial to complete contact tracing 4 days after symptom onset. Assumptions on the susceptibility of children affect the impact of school reopening, though we find that business and leisure-related social mixing patterns have more impact on COVID-19 associated disease burden. An optimal deployment of the mitigation policies under study require timely compliance to physical distancing, testing and self-isolation.

Willem Lander, Abrams Steven, Libin Pieter J K, Coletti Pietro, Kuylen Elise, Petrof Oana, Møgelmose Signe, Wambua James, Herzog Sereina A, Faes Christel, Beutels Philippe, Hens Niel

2021-Mar-09

Public Health Public Health

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

In Journal of medical Internet research ; h5-index 88.0

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

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

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

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

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

CLINICALTRIAL :

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

2021-Mar-11

General General

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

In JMIR formative research

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

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

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

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

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

CLINICALTRIAL :

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

2021-Mar-15

General General

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

In Journal of medical Internet research ; h5-index 88.0

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

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

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

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

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

CLINICALTRIAL :

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

2021-Mar-21

Radiology Radiology

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

In BMC cardiovascular disorders

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

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

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

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

2021-Mar-09

Cardiovascular disease, Influenza vaccination, Machine learning

General General

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

In Journal of medical engineering & technology

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

Garg Poojita, Joshi Deepak

2021-Mar-22

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

General General

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

In Intelligence-based medicine

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

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

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

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

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

2021-Mar-17

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

General General

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

In Frontiers in big data

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

McDermott Conor, Łącki Maciej, Sainsbury Ben, Henry Jessica, Filippov Mihail, Rossa Carlos

2021

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

Public Health Public Health

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

In Frontiers in artificial intelligence

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

Khan Md Hasinur Rahaman, Hossain Ahmed

2020

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

General General

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

bioRxiv Preprint

The SARS-CoV-2 pandemic has raised the concern for identifying hosts of the virus since the early-stage outbreak. To address this problem, we proposed a deep learning method, DeepHoF, based on extracting the viral genomic features automatically, to predict host likelihood scores on five host types, including plant, germ, invertebrate, non-human vertebrate and human, for novel viruses. DeepHoF made up for the lack of an accurate tool applicable to any novel virus and overcame the limitation of the sequence similarity-based methods, reaching a satisfactory AUC of 0.987 on the five-classification. Additionally, to fill the gap in the efficient inference of host species for SARS-CoV-2 using existed tools, we conducted a deep analysis on the host likelihood profile calculated by DeepHoF. Using the isolates sequenced in the earliest stage of COVID-19, we inferred minks, bats, dogs and cats were potential hosts of SARS-CoV-2, while minks might be one of the most noteworthy hosts. Several genes of SARS-CoV-2 demonstrated their significance in determining the host range. Furthermore, the large-scale genome analysis, based on DeepHoF's computation for the later world-wide pandemic in 2020, disclosed the uniformity of host range among SARS-CoV-2 samples and the strong association of SARS-CoV-2 between humans and minks.

Guo, Q.; Li, M.; Wang, C.; Guo, J.; Jiang, X.; Tan, J.; Wu, S.; Wang, P.; Xiao, T.; Zhou, M.; Fang, Z.; Xiao, Y.; Zhu, H.

2021-03-22

General General

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

In Journal of infection and public health

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

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

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

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

2021-Jan-12

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

General General

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

In Molecular genetics and genomics : MGG

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

Shafi Gowhar, Desai Shruti, Srinivasan Krithika, Ramesh Aarthi, Chaturvedi Rupesh, Uttarwar Mohan

2021-Mar-20

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

Ophthalmology Ophthalmology

Artificial Intelligence: the unstoppable revolution in ophthalmology.

In Survey of ophthalmology ; h5-index 35.0

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

Benet David, Pellicer-Valero Oscar J

2021-Mar-16

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

Radiology Radiology

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

In Academic radiology

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

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

RESULTS : We included 50 CT examinations (mean age, 57.24 years), of whom 24 (48%) had an ICU stay. Extent of COVID-19 like opacities on chest CT showed correlations (all p < 0.001 if not otherwise stated) with occurrence of ICU stay (R = 0.74), length of ICU stay (R = 0.81), lethal outcome (R = 0.56) and length of hospital stay (R = 0.33, p < 0.05). The opacities extent was correlated with laboratory parameters: neutrophil count (NEU) (R = 0.60), lactate dehydrogenase (LDH) (R = 0.60), troponin (TNTHS) (R = 0.55) and c-reactive protein (CR