Doctor Penguin

General

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

In PloS one ; h5-index 176.0
In this work we present a three-stage Machine Learning strategy to country-level risk classification based on countries that are reporting COVID-19 information. A K% binning discretisation (K = 25) is used to create four risk groups of countries based on the risk of transmission (coronavirus cases per million population), risk of mortality (coronavirus deaths per million population), and risk of inability to test (coronavirus tests per million population). The four risk groups produced by K% binning are labelled as 'low', 'medium-low', 'medium-high', and 'high'. Coronavirus-related data are then removed and the attributes for prediction of the three types of risk are given as the geopolitical and demographic data describing each country. Thus, the calculation of class label is based on coronavirus data but the input attributes are country-level information regardless of coronavirus data. The three four-class classification problems are then explored and benchmarked through leave-one-country-out cross validation to find the strongest model, producing a Stack of Gradient Boosting and Decision Tree algorithms for risk of transmission, a Stack of Support Vector Machine and Extra Trees for risk of mortality, and a Gradient Boosting algorithm for the risk of inability to test. It is noted that high risk for inability to test is often coupled with low risks for transmission and mortality, therefore the risk of inability to test should be interpreted first, before consideration is given to the predicted transmission and mortality risks. Finally, the approach is applied to more recent risk levels to data from September 2020 and weaker results are noted due to the growth of international collaboration detracting useful knowledge from country-level attributes which suggests that similar machine learning approaches are more useful prior to situations later unfolding.
Bird Jordan J, Barnes Chloe M, Premebida Cristiano, Ekárt Anikó, Faria Diego R

2020

Pathology

Capsule endoscopy - Recent developments and future directions.

In Expert review of gastroenterology & hepatology

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

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

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

Zammit Chetcuti Stefania, Sidhu Reena

2020-Oct-28

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

General

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

In Journal of global health

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

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

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

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

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

2020-Dec

General

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

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

2020-Oct-22

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

General

Artificial intelligence in medicine and the disclosure of risks.

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

2020-Oct-22

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

General

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

In PloS one ; h5-index 176.0
Many different countries have been under lockdown or extreme social distancing measures to control the spread of COVID-19. The potentially far-reaching side effects of these measures have not yet been fully understood. In this study we analyse the results of a multi-country survey conducted in Italy (N = 3,504), Spain (N = 3,524) and the United Kingdom (N = 3,523), with two separate analyses. In the first analysis, we examine the elicitation of citizens' concerns over the downplaying of the economic consequences of the lockdown during the COVID-19 pandemic. We control for Social Desirability Bias through a list experiment included in the survey. In the second analysis, we examine the data from the same survey to predict the level of stress, anxiety and depression associated with being economically vulnerable and having been affected by a negative economic shock. To accomplish this, we have used a prediction algorithm based on machine learning techniques. To quantify the size of this affected population, we compare its magnitude with the number of people affected by COVID-19 using measures of susceptibility, vulnerability and behavioural change collected in the same questionnaire. We find that the concern for the economy and for "the way out" of the lockdown is diffuse and there is evidence of minor underreporting. Additionally, we estimate that around 42.8% of the populations in the three countries are at high risk of stress, anxiety, and depression, based on their level of economic vulnerability and their exposure to a negative economic shock.
Codagnone Cristiano, Bogliacino Francesco, Gómez Camilo, Charris Rafael, Montealegre Felipe, Liva Giovanni, Lupiáñez-Villanueva Francisco, Folkvord Frans, Veltri Giuseppe A

2020

Internal Medicine

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

In Journal of medical Internet research ; h5-index 88.0

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

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

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

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

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

CLINICALTRIAL :

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

2020-Oct-25

General

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

In Journal of medical Internet research ; h5-index 88.0

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

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

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

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

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

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

2020-Oct-26

General

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

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

2020-Oct-27

General

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

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

2020-Oct-21

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

General

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

In The journal of physical chemistry letters ; h5-index 129.0
Chemical similarity-based approaches employed to repurpose or develop new treatments for emerging diseases, such as COVID-19, correlates molecular structure-based descriptors of drugs with those of a physiological counterpart or clinical phenotype. We propose novel descriptors based on a COSMO-RS (short for conductor-like screening model for real solvents) σ-profiles for enhanced drug screening enabled by machine learning (ML). The descriptors' performance is hereby illustrated for nucleotide analogue drugs that inhibit the ribonucleic acid-dependent ribonucleic acid polymerase, key to viral transcription and genome replication. The COSMO-RS-based descriptors account for both chemical reactivity and structure, and are more effective for ML-based screening than fingerprints based on molecular structure and simple physical/chemical properties. The descriptors are evaluated using principal component analysis, an unsupervised ML technique. Our results correlate with the active monophosphate forms of the leading drug remdesivir and the prospective drug EIDD-2801 with nucleotides, followed by other promising drugs, and are superior to those from molecular structure-based descriptors and molecular docking. The COSMO-RS-based descriptors could help accelerate drug discovery for the treatment of emerging diseases.
Gusarov Sergey, Stoyanov Stanislav R

2020-Oct-26

General

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

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

2020-Oct-20

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

General

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

In Informatics in medicine unlocked

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

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

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

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

AlJame Maryam, Ahmad Imtiaz, Imtiaz Ayyub, Mohammed Ameer

2020

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

General

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

In BioMed research international ; h5-index 102.0
Coronaviruses have been reported previously due to their association with the severe acute respiratory syndrome (SARS). After SARS, these viruses were known to be causing Middle East respiratory syndrome (MERS) and caused 35% evanescence amid victims pursuing remedial care. Nowadays, beta coronaviruses, members of Coronaviridae, family order Nidovirales, have become subjects of great importance due to their latest pandemic originating from Wuhan, China. The virus named as human-SARS-like coronavirus-2 contains four structural as well as sixteen nonstructural proteins encoded by single-stranded ribonucleic acid of positive polarity. As there is no vaccine available to treat the infection caused by these viruses, there is a dire need for taking necessary steps against this virus. Herein, we have targeted two nonstructural proteins of SARS-CoV-2, namely, methyltransferase (nsp16) and helicase (nsp13), respectively, due to their substantial activity in viral pathogenesis. A total of 2035 compounds were analyzed for their pharmacokinetics and pharmacological properties. The screened 108 compounds were docked against both targeted proteins and were compared with previously reported known compounds. Compounds with high binding affinity were analyzed for their reactivity through DFT analysis, and binding was analyzed using molecular dynamics simulations. Through the analyses performed in this study, it is concluded that EryvarinM, Silydianin, Osajin, and Raddeanine can be considered potential inhibitors for MTase, while TomentodiplaconeB, Osajin, Sesquiterpene Glycoside, Rhamnetin, and Silydianin for helicase after these compounds are validated thoroughly using in vitro and in vivo protocols.
Kousar Kafila, Majeed Arshia, Yasmin Farkhanda, Hussain Waqar, Rasool Nouman

2020

General

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

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

2020

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

General

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

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

2020

COVID-19, Classical machine learning, Deep transfer learning

General

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

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

2020-10-27

General

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

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

2021-Jan

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

Radiology

On the diminishing return of labeling clinical reports

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

2020-10-27

Public Health

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

In Journal of medical Internet research ; h5-index 88.0

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

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

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

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

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

CLINICALTRIAL :

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

2020-Oct-22

General

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

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

2020-Oct-13

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

Internal Medicine

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

In Diabetes research and clinical practice ; h5-index 50.0

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

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

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

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

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

2020-Oct-20

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

General

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

In PloS one ; h5-index 176.0

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

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

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

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

Roy Satyaki, Ghosh Preetam

2020

General

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

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

2020-Oct-23

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

Radiology

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

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

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

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

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

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

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

2020-Oct-23

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

Radiology

Integrative analysis for COVID-19 patient outcome prediction.

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

2020-Oct-13

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

General

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

In Journal of medical Internet research ; h5-index 88.0

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

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

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

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

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

CLINICALTRIAL :

Izquierdo Jose Luis, Ancochea Julio, Soriano Joan B

2020-Oct-20

General

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

In Brain informatics

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

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

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

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

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

2020-Oct-22

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

General

Modelling of COVID-19 Morbidity in Russia.

In Studies in health technology and informatics ; h5-index 23.0
The outbreak of COVID-19 has led to a crucial change in ordinary healthcare approaches. In comparison with emergencies re-allocation of resources for a long period of time is required and the peak utilization of the resources is also hard to predict. Furthermore, the epidemic models do not provide reliable information of the development of the pandemic's development, so it creates a high load on the healthcare systems with unforeseen duration. To predict morbidity of the novel COVID-19, we used records covering the time period from 01-03-2020 to 25-05-2020 and include sophisticated information of the morbidity in Russia. Total of 45238 patients were analyzed. The predictive model was developed as a combination of Holt and Holt-Winter models with Gradient boosting Regression. As we can see from the table 2, the models demonstrated a very good performance on the test data set. The forecast is quite reliable, however, due to the many uncertainties, only a real-world data can prove the correctness of the forecast.
Kopanitsa Georgy, Metsker Oleg, Yakovlev Alexey, Fedorenko Alexey, Zvartau Nadezhda

2020-Sep-04

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

General

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

In Studies in health technology and informatics ; h5-index 23.0
The current pandemic can likely have several waves and will require a major effort to save lives and provide optimal treatment. The efficient clinical resource planning and efficient treatment require identification of risk groups and specific clinical features of the patients. In this study we develop analyze mortality for COVID19 patients in Russia. We identify comorbidities and risk factors for different groups of patients including cardiovascular diseases and therapy. In the study we used a Russian national COVID registry, that provides sophisticated information about all the COVID-19 patients in Russia. To analyze Features importance for the mortality we have calculated Shapley values for the "mortality" class and ANN hidden layer coefficients for patient lifetime. We calculated the distribution of days spent in hospital before death to show how many days a patient occupies a bed depending on the age and the severity of the disease to allow optimal resource planning and enable age-based risk assessment. Predictors of the days spent in hospital were calculated using Pearson correlation coefficient. Decisions trees were developed to classify the patients into the groups and reveal the lethality factors.
Metsker Oleg, Kopanitsa Georgy, Yakovlev Alexey, Veronika Karlina, Zvartau Nadezhda

2020-Sep-04

COVID-19, Russia, mortality, risk factors

General

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

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

2020-10-23

Public Health

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

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

2020-Oct-19

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

General

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

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

2020-10-23

Radiology

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

In Aging ; h5-index 49.0
COVID-19 shared many symptoms with seasonal flu, and community-acquired pneumonia (CAP) Since the responses to COVID-19 are dramatically different, this multicenter study aimed to develop and validate a multivariate model to accurately discriminate COVID-19 from influenza and CAP. Three independent cohorts from two hospitals (50 in discovery and internal validation sets, and 55 in the external validation cohorts) were included, and 12 variables such as symptoms, blood tests, first reverse transcription-polymerase chain reaction (RT-PCR) results, and chest CT images were collected. An integrated multi-feature model (RT-PCR, CT features, and blood lymphocyte percentage) established with random forest algorism showed the diagnostic accuracy of 92.0% (95% CI: 73.9 - 99.1) in the training set, and 96. 6% (95% CI: 79.6 - 99.9) in the internal validation cohort. The model also performed well in the external validation cohort with an area under the receiver operating characteristic curve of 0.93 (95% CI: 0.79 - 1.00), an F1 score of 0.80, and a Matthews correlation coefficient (MCC) of 0.76. In conclusion, the developed multivariate model based on machine learning techniques could be an efficient tool for COVID-19 screening in nonendemic regions with a high rate of influenza and CAP in the post-COVID-19 era.
Guo Xing, Li Yanrong, Li Hua, Li Xueqin, Chang Xu, Bai Xuemei, Song Zhanghong, Li Junfeng, Li Kefeng

2020-Oct-21

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

General

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

In NPJ digital medicine
Digital contact tracing approaches based on Bluetooth low energy (BLE) have the potential to efficiently contain and delay outbreaks of infectious diseases such as the ongoing SARS-CoV-2 pandemic. In this work we propose a machine learning based approach to reliably detect subjects that have spent enough time in close proximity to be at risk of being infected. Our study is an important proof of concept that will aid the battery of epidemiological policies aiming to slow down the rapid spread of COVID-19.
Sattler Felix, Ma Jackie, Wagner Patrick, Neumann David, Wenzel Markus, Schäfer Ralf, Samek Wojciech, Müller Klaus-Robert, Wiegand Thomas

2020

Computer science, Risk factors, Viral infection

General

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

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

2020

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

Pathology

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

In Proceedings of the National Academy of Sciences of the United States of America
Coronavirus disease 2019 (COVID-19), the global pandemic caused by SARS-CoV-2, has resulted thus far in greater than 933,000 deaths worldwide; yet disease pathogenesis remains unclear. Clinical and immunological features of patients with COVID-19 have highlighted a potential role for changes in immune activity in regulating disease severity. However, little is known about the responses in human lung tissue, the primary site of infection. Here we show that pathways related to neutrophil activation and pulmonary fibrosis are among the major up-regulated transcriptional signatures in lung tissue obtained from patients who died of COVID-19 in Wuhan, China. Strikingly, the viral burden was low in all samples, which suggests that the patient deaths may be related to the host response rather than an active fulminant infection. Examination of the colonic transcriptome of these patients suggested that SARS-CoV-2 impacted host responses even at a site with no obvious pathogenesis. Further proteomics analysis validated our transcriptome findings and identified several key proteins, such as the SARS-CoV-2 entry-associated protease cathepsins B and L and the inflammatory response modulator S100A8/A9, that are highly expressed in fatal cases, revealing potential drug targets for COVID-19.
Wu Meng, Chen Yaobing, Xia Han, Wang Changli, Tan Chin Yee, Cai Xunhui, Liu Yufeng, Ji Fenghu, Xiong Peng, Liu Ran, Guan Yuanlin, Duan Yaqi, Kuang Dong, Xu Sanpeng, Cai Hanghang, Xia Qin, Yang Dehua, Wang Ming-Wei, Chiu Isaac M, Cheng Chao, Ahern Philip P, Liu Liang, Wang Guoping, Surana Neeraj K, Xia Tian, Kasper Dennis L

2020-Oct-20

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

Radiology

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

In BMC medical imaging

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

METHODS : From February 10 to March 10, 2020, 203 mild COVID-19 patients in Fangcang Shelter Hospital were retrospectively included (training: n = 141; testing: n = 62), and clinical characteristics were collected. Lung abnormalities on chest CT images were segmented with a deep learning algorithm. CT quantitative features and radiomic features were automatically extracted. Clinical characteristics and CT quantitative features were compared between RT-PCR-negative and RT-PCR-positive groups. Univariate logistic regression and Spearman correlation analyses identified the strongest features associated with RT-PCR negativity, and a multivariate logistic regression model was established. The diagnostic performance was evaluated for both cohorts.

RESULTS : The RT-PCR-negative group had a longer time interval from symptom onset to CT exams than the RT-PCR-positive group (median 23 vs. 16 days, p < 0.001). There was no significant difference in the other clinical characteristics or CT quantitative features. In addition to the time interval from symptom onset to CT exams, nine CT radiomic features were selected for the model. ROC curve analysis revealed AUCs of 0.811 and 0.812 for differentiating the RT-PCR-negative group, with sensitivity/specificity of 0.765/0.625 and 0.784/0.600 in the training and testing datasets, respectively.

CONCLUSION : The model combining CT radiomic features and clinical data helped predict RT-PCR negativity during clinical treatment, indicating the proper time for RT-PCR retesting.

Cai Quan, Du Si-Yao, Gao Si, Huang Guo-Liang, Zhang Zheng, Li Shu, Wang Xin, Li Pei-Ling, Lv Peng, Hou Gang, Zhang Li-Na

2020-Oct-20

COVID-19, Computed tomography, Quantitative, RT-PCR, Radiomics

General

Comparison of ARIMA, ETS, NNAR and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy

ArXiv Preprint
Coronavirus disease (COVID-19) is a severe ongoing novel pandemic that has emerged in Wuhan, China, in December 2019. As of October 13, the outbreak has spread rapidly across the world, affecting over 38 million people, and causing over 1 million deaths. In this article, I analysed several time series forecasting methods to predict the spread of COVID-19 second wave in Italy, over the period after October 13, 2020. I used an autoregressive model (ARIMA), an exponential smoothing state space model (ETS), a neural network autoregression model (NNAR), and the following hybrid combinations of them: ARIMA-ETS, ARIMA-NNAR, ETS-NNAR, and ARIMA-ETS-NNAR. About the data, I forecasted the number of patients hospitalized with mild symptoms, and in intensive care units (ICU). The data refer to the period February 21, 2020-October 13, 2020 and are extracted from the website of the Italian Ministry of Health (www.salute.gov.it). The results show that i) the hybrid models, except for ARIMA-ETS, are better at capturing the linear and non-linear epidemic patterns, by outperforming the respective single models; and ii) the number of COVID-19-related hospitalized with mild symptoms and in ICU will rapidly increase in the next weeks, by reaching the peak in about 50-60 days, i.e. in mid-December 2020, at least. To tackle the upcoming COVID-19 second wave, on one hand, it is necessary to hire healthcare workers and implement sufficient hospital facilities, protective equipment, and ordinary and intensive care beds; and on the other hand, it may be useful to enhance social distancing by improving public transport and adopting the double-shifts schooling system, for example.
Gaetano Perone

2020-10-22

General

Application of tele-podiatry in diabetic foot management: A series of illustrative cases.

In Diabetes & metabolic syndrome

BACKGROUND AND AIMS : Telemedicine had been proposed as a tool to manage diabetes, but its role in management of diabetic foot ulcer is still evolving. The COVID-19 pandemic and related social restrictions have necessitated the use of telemedicine in the management of diabetic foot disease (tele-podiatry), particularly of patients classified as low-risk.

MATERIALS AND METHODS : We present a report of three cases of varied diabetic foot problems assessed during the present pandemic using different forms of telemedicine for triaging, management of low-risk cases and for follow-up.

RESULTS : Tele-podiatry was effective in the management of low-risk subjects with diabetic foot ulcer, and also useful in referral of high-risk subjects for hospital/clinic visit, facilitating proper management. It also helped in the follow-up of the cases.

CONCLUSION : Telemedicine is a good screening tool for diagnosing and managing low-risk subjects with diabetic foot problems, and also enables a triaging system for deciding on hospital visits and hospitalization. Telemedicine offers several benefits in the management of diabetic foot disease, although it also has some limitations. Based on our experience during the pandemic, we recommend its judicious use in the triaging of patients of diabetic foot disease and management of low-risk cases. Future innovation in technology and artificial intelligence may help in better tele-podiatry care in the time to come.

Kavitha Karakkattu V, Deshpande Shailesh R, Pandit Anil P, Unnikrishnan Ambika G

2020-Oct-11

Diabetes mellitus, Diabetic foot triaging, Pandemic, Tele-podiatry, Telemedicine

General

Development, evaluation, and validation of machine learning models for COVID-19 detection based on routine blood tests.

In Clinical chemistry and laboratory medicine ; h5-index 46.0
Objectives The rRT-PCR test, the current gold standard for the detection of coronavirus disease (COVID-19), presents with known shortcomings, such as long turnaround time, potential shortage of reagents, false-negative rates around 15-20%, and expensive equipment. The hematochemical values of routine blood exams could represent a faster and less expensive alternative. Methods Three different training data set of hematochemical values from 1,624 patients (52% COVID-19 positive), admitted at San Raphael Hospital (OSR) from February to May 2020, were used for developing machine learning (ML) models: the complete OSR dataset (72 features: complete blood count (CBC), biochemical, coagulation, hemogasanalysis and CO-Oxymetry values, age, sex and specific symptoms at triage) and two sub-datasets (COVID-specific and CBC dataset, 32 and 21 features respectively). 58 cases (50% COVID-19 positive) from another hospital, and 54 negative patients collected in 2018 at OSR, were used for internal-external and external validation. Results We developed five ML models: for the complete OSR dataset, the area under the receiver operating characteristic curve (AUC) for the algorithms ranged from 0.83 to 0.90; for the COVID-specific dataset from 0.83 to 0.87; and for the CBC dataset from 0.74 to 0.86. The validations also achieved good results: respectively, AUC from 0.75 to 0.78; and specificity from 0.92 to 0.96. Conclusions ML can be applied to blood tests as both an adjunct and alternative method to rRT-PCR for the fast and cost-effective identification of COVID-19-positive patients. This is especially useful in developing countries, or in countries facing an increase in contagions.
Cabitza Federico, Campagner Andrea, Ferrari Davide, Di Resta Chiara, Ceriotti Daniele, Sabetta Eleonora, Colombini Alessandra, De Vecchi Elena, Banfi Giuseppe, Locatelli Massimo, Carobene Anna

2020-Oct-20

COVID-19, SARS-CoV-2, blood laboratory tests, complete blood count, gradient boosted decision tree, machine learning

General

How the COVID-19 pandemic favored the adoption of digital technologies in healthcare: a systematic review of early scientific literature.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : The COVID-19 pandemic is favoring the digital transition in many industries and in the society as a whole. Healthcare responded to the first phase of the pandemic through the rapid adoption of digital solutions and advanced technology tools.

OBJECTIVE : The aim of this study is to describe which digital solutions have been reported in the early scientific literature to mitigate the impact of COVID-19 on individuals and health systems.

METHODS : We conducted a systematic review of COVID-19 early literature (from January 1, 2020 to April 30, 2020) searching MEDLINE and medRxiv with terms considered adequate to find relevant literature on the use of digital technologies in response to the pandemic. We extracted study characteristics such as paper title, journal, publication date, and categorized the retrieved papers by type of technology, and patient needs addressed. We built a scoring rubric by cross-classifying the patient needs with the type of technology. We also extracted information and classified each technology reported by the selected articles according to healthcare system targets, grade of innovation, and scalability to other geographical areas.

RESULTS : The search identified 269 articles, of which 124 full-text articles were assessed and included in the review after screening. Of selected articles, most of them addressed the use of digital technologies for diagnosis, surveillance and prevention. We report that digital solutions and innovative technologies have mainly been proposed for the diagnosis of COVID-19. In particular, within the reviewed articles we identified numerous suggestions on the use of artificial-intelligence-powered tools for the diagnosis and screening of COVID-19. Digital technologies are useful also for prevention and surveillance measures, for example through contact-tracing apps or monitoring of internet searches and social media usage. Fewer scientific contributions address the use of digital technologies for lifestyle empowerment or patient engagement.

CONCLUSIONS : In the field of diagnosis, digital solutions that integrate with the traditional methods, such as AI-based diagnostic algorithms based both on imaging and/or clinical data, seem promising. As for surveillance, digital apps have already proven their effectiveness, but problems related to privacy and usability remain. For other patient needs, several solutions have been proposed using, for example, telemedicine or telehealth tools. These have long been available, but perhaps this historical moment could actually favor their definitive large-scale adoption. It is worth taking advantage of the push given by the crisis and important to keep track of the digital solutions proposed today to implement tomorrow's best practices and models of care, and to adopt at least some of the solutions proposed in the scientific literature, especially in those national health systems which in recent years proved to be particularly resistant to the digital transition.

CLINICALTRIAL :

Golinelli Davide, Boetto Erik, Carullo Gherardo, Nuzzolese Andrea Giovanni, Landini Maria Paola, Fantini Maria Pia

2020-Sep-15

General

MCCS: a novel recognition pattern-based method for fast track discovery of anti-SARS-CoV-2 drugs.

In Briefings in bioinformatics
Given the scale and rapid spread of the coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, or 2019-nCoV), there is an urgent need to identify therapeutics that are effective against COVID-19 before vaccines are available. Since the current rate of SARS-CoV-2 knowledge acquisition via traditional research methods is not sufficient to match the rapid spread of the virus, novel strategies of drug discovery for SARS-CoV-2 infection are required. Structure-based virtual screening for example relies primarily on docking scores and does not take the importance of key residues into consideration, which may lead to a significantly higher incidence rate of false-positive results. Our novel in silico approach, which overcomes these limitations, can be utilized to quickly evaluate FDA-approved drugs for repurposing and combination, as well as designing new chemical agents with therapeutic potential for COVID-19. As a result, anti-HIV or antiviral drugs (lopinavir, tenofovir disoproxil, fosamprenavir and ganciclovir), antiflu drugs (peramivir and zanamivir) and an anti-HCV drug (sofosbuvir) are predicted to bind to 3CLPro in SARS-CoV-2 with therapeutic potential for COVID-19 infection by our new protocol. In addition, we also propose three antidiabetic drugs (acarbose, glyburide and tolazamide) for the potential treatment of COVID-19. Finally, we apply our new virus chemogenomics knowledgebase platform with the integrated machine-learning computing algorithms to identify the potential drug combinations (e.g. remdesivir+chloroquine), which are congruent with ongoing clinical trials. In addition, another 10 compounds from CAS COVID-19 antiviral candidate compounds dataset are also suggested by Molecular Complex Characterizing System with potential treatment for COVID-19. Our work provides a novel strategy for the repurposing and combinations of drugs in the market and for prediction of chemical candidates with anti-COVID-19 potential.
Feng Zhiwei, Chen Maozi, Xue Ying, Liang Tianjian, Chen Hui, Zhou Yuehan, Nolin Thomas D, Smith Randall B, Xie Xiang-Qun

2020-Oct-20

COVID-19, MCCS, drug combination, drug repurposing, residue energy contribution

General

Advanced Machine Learning-Based Analytics on COVID-19 Data Using Generative Adversarial Networks.

In Materials today. Proceedings
The domain of medical diagnosis and predictive analytics is one of the key domains of research with enormous dimensions whereby the diseases of different types can be predicted. Nowadays, there is a huge panic of impact and rapid mutation of the COVID-19 virus impression. The world is getting affected by this virus to a huge extent and there is no vaccine developed so far. India is also having more than 10,000 patients with than 300 deceased. The global human community is having around 20 lacs of Coronavirus patients. The Generative Adversarial Network (GAN) is the contemporary high-performance approach in which the use of advanced neural networks is done for the cavernous analytics of the images and multimedia data. In this research work, the analytics of key points from medical images of the COVID-19 dataset is to be presented using which the diagnosis and predictions can be done for the patients. The GANs are used for the generation, transformation as well as presentation of the dataset and key points using advanced deep learning models which can analyze the patterns in the medical images including X-Ray, CT Scan, and many others. Using such approaches with the integration of GANs, the overall predictive analytics can be made high performance aware as compared to the classical neural networks with multiple layers. In this research manuscript, the inscription of work is projected on the benchmark datasets with the advanced scripting so that the predictive mining and knowledge discovery can be done effectively with more accuracy.
Vijay Kumar Janga, Harshavardhan A, Bhukya Hanumanthu, Krishna Prasad A V

2020-Oct-14

COVID-19 Data Analytics, GAN, Generative Adversarial Network, Generative Adversarial Network in Medical Diagnosis

General

Numerical simulation of the novel coronavirus spreading.

In Expert systems with applications
The COVID-19 virus outbreak has affected most of the world in 2020. This paper deals with artificial intelligence (AI) methods that can address the problem of predicting scale, dynamics and sensitivity of the outbreak to preventive actions undertaken with a view to combatting the epidemic. In our study, we developed a cellular automata (CA) model for simulating the COVID-19 disease spreading. The enhanced infectious disease dynamics S E I R (Susceptible, Exposed, Infectious, and Recovered) model was applied to estimate the epidemic trends in Poland, France, and Spain. We introduced new parameters into the simulation framework which reflect the statistically confirmed dependencies such as age-dependent death probability, a different definition of the contact rate and enhanced parameters reflecting population mobility. To estimate key epidemiological measures and to predict possible dynamics of the disease, we juxtaposed crucial CA framework parameters to the reported COVID-19 values, e.g. length of infection, mortality rates and the reproduction number. Moreover, we used real population density and age structures of the studied epidemic populations. The model presented allows for the examination of the effectiveness of preventive actions and their impact on the spreading rate and the duration of the disease. It also shows the influence of structure and behavior of the populations studied on key epidemic parameters, such as mortality and infection rates. Although our results are critically dependent on the assumptions underpinning our model and there is considerable uncertainty associated with the outbreaks at such an early epidemic stage, the obtained simulation results seem to be in general agreement with the observed behavior of the real COVID-19 disease, and our numerical framework can be effectively used to analyze the dynamics and efficacy of epidemic containment methods.
Medrek M, Pastuszak Z

2021-Mar-15

Cellular automata, Epidemic spread model, Mathematical model, Novel coronavirus, SEIR model

Public Health

Evaluating the plausible application of advanced machine learnings in exploring determinant factors of present pandemic: A case for continent specific COVID-19 analysis.

In The Science of the total environment
Coronavirus disease, a novel severe acute respiratory syndrome (SARS COVID-19), has become a global health concern due to its unpredictable nature and lack of adequate medicines. Machine Learning (ML) models could be effective in identifying the most critical factors which are responsible for the overall fatalities caused by COVID-19. The functional capabilities of ML models in epidemiological research, especially for COVID-19, are not substantially explored. To bridge this gap, this study has adopted two advanced ML models, viz. Random Forest (RF) and Gradient Boosted Machine (GBM), to perform the regression modelling and provide subsequent interpretation. Five successive steps were followed to carry out the analysis: (1) identification of relevant key explanatory variables; (2) application of data dimensionality reduction for eliminating redundant information; (3) utilizing ML models for measuring relative influence (RI) of the explanatory variables; (4) evaluating interconnections between and among the key explanatory variables and COVID-19 case and death counts; (5) time series analysis for examining the rate of incidences of COVID-19 cases and deaths. Among the explanatory variables considered in this study, air pollution, migration, economy, and demographic factor were found to be the most significant controlling factors. Since a very limited research is available to discuss the superiority of ML models for identifying the key determinants of COVID-19, this study could be a reference for future public health research. Additionally, all the models and data used in this study are open source and freely available, thereby, reproducibility and scientific replication will be achievable easily.
Chakraborti Suman, Maiti Arabinda, Pramanik Suvamoy, Sannigrahi Srikanta, Pilla Francesco, Banerjee Anushna, Das Dipendra Nath

2020-Oct-06

Air pollution, COVID-19, Machine learning, Pandemic, Relative importance, Socioeconomic

General

High Tech, High Risk: Tech Ethics Lessons for the COVID-19 Pandemic Response.

In Patterns (New York, N.Y.)
The COVID-19 pandemic has, in a matter of a few short months, drastically reshaped society around the world. Because of the growing perception of machine learning as a technology capable of addressing large problems at scale, machine learning applications have been seen as desirable interventions in mitigating the risks of the pandemic disease. However, machine learning, like many tools of technocratic governance, is deeply implicated in the social production and distribution of risk and the role of machine learning in the production of risk must be considered as engineers and other technologists develop tools for the current crisis. This paper describes the coupling of machine learning and the social production of risk, generally, and in pandemic responses specifically. It goes on to describe the role of risk management in the effort to institutionalize ethics in the technology industry and how such efforts can benefit from a deeper understanding of the social production of risk through machine learning.
Moss Emanuel, Metcalf Jacob

2020-Oct-09

General

Designing low-cost, accurate cervical screening strategies that take into account COVID-19: a role for self-sampled HPV typing2.

In Infectious agents and cancer

Background : We propose an economical cervical screening research and implementation strategy designed to take into account the typically slow natural history of cervical cancer and the severe but hopefully temporary impact of COVID-19. The commentary introduces the practical validation of some critical components of the strategy, described in three manuscripts detailing recent project results in Asia and Africa.The main phases of a cervical screening program are 1) primary screening of women in the general population, 2) triage testing of the small minority of women that screen positive to determine need for treatment, and 3) treatment of triage-positive women thought to be at highest risk of precancer or even cancer. In each phase, attention must now be paid to safety in relation to SARS-CoV-2 transmission. The new imperatives of the COVID-19 pandemic support self-sampled HPV testing as the primary cervical screening method. Most women can be reassured for several years by a negative test performed on a self-sample collected at home, without need of clinic visit and speculum examination. The advent of relatively inexpensive, rapid and accurate HPV DNA testing makes it possible to return screening results from self-sampling very soon after specimen collection, minimizing loss to follow-up. Partial HPV typing provides important risk stratification useful for triage of HPV-positive women. A second "triage" test is often useful to guide management. In lower-resource settings, visual inspection with acetic acid (VIA) is still proposed but it is inaccurate and poorly reproducible, misclassifying the risk stratification gained by primary HPV testing. A deep-learning based approach to recognizing cervical precancer, adaptable to a smartphone camera, is being validated to improve VIA performance. The advent and approval of thermal ablation permits quick, affordable and safe, immediate treatment at the triage clinic of the majority of HPV-positive, triage-positive women.

Conclusions : Overall, only a small percentage of women in cervical screening programs need to attend the hospital clinic for a surgical procedure, particularly when screening is targeted to the optimal age range for detection of precancer rather than older ages with decreased visual screening performance and higher risks of hard-to-treat outcomes including invasive cancer.

Ajenifuja Kayode Olusegun, Belinson Jerome, Goldstein Andrew, Desai Kanan T, de Sanjose Silvia, Schiffman Mark

2020

COVID-19, Cervical screening, HPV, Self-sampling, Triage

General

Assessing countries' performances against COVID-19 via WSIDEA and machine learning algorithms.

In Applied soft computing
The COVID-19 pandemic, which first spread to the People of Republic of China and then to other countries in a short time, affected the whole world by infecting millions of people and have been increasing its impact day by day. Hundreds of researchers in many countries are in search of a solution to end up this pandemic. This study aims to contribute to the literature by performing detailed analyses via a new three-staged framework constructed based on data envelopment analysis and machine learning algorithms to assess the performances of 142 countries against the COVID-19 outbreak. Particularly, clustering analyses were made using k-means and hierarchic clustering methods. Subsequently, efficiency analysis of countries were performed by a novel model, the weighted stochastic imprecise data envelopment analysis. Finally, parameters were analyzed with decision tree and random forest algorithms. Results have been analyzed in detail, and the classification of countries are determined by providing the most influential parameters. The analysis showed that the optimum number of clusters for 142 countries is three. In addition, while 20 countries out of 142 countries were fully effective, 36% of them were found to be effective at a rate of 90%. Finally, it has been observed that the data such as GDP, smoking rates, and the rate of diabetes patients do not affect the effectiveness level of the countries.
Aydin Nezir, Yurdakul Gökhan

2020-Dec

COVID-19, Clustering, Machine learning, Weighted stochastic imprecise data envelopment analysis

General

Comparing the accuracy of several network-based COVID-19 prediction algorithms.

In International journal of forecasting
Researchers from various scientific disciplines have attempted to forecast the spread of the Coronavirus Disease 2019 (COVID-19). The proposed epidemic prediction methods range from basic curve fitting methods and traffic interaction models to machine-learning approaches. If we combine all these approaches, we obtain the Network Inference-based Prediction Algorithm (NIPA). In this paper, we analyse a diverse set of COVID-19 forecast algorithms, including several modifications of NIPA. Among the diverse set of algorithms that we evaluated, original NIPA performs best on forecasting the spread of COVID-19 in Hubei, China and in the Netherlands. In particular, we show that network-based forecasting is superior to any other forecasting algorithm.
Achterberg Massimo A, Prasse Bastian, Ma Long, Trajanovski Stojan, Kitsak Maksim, Van Mieghem Piet

2020-Oct-09

Bayesian methods, Epidemiology, Forecast accuracy, Machine learning methods, Network inference, SIR model, Time series methods

Cardiology

Usefulness of machine learning in COVID-19 for the detection and prognosis of cardiovascular complications.

In Reviews in cardiovascular medicine
Since January 2020, coronavirus disease 2019 (COVID-19) has rapidly become a global concern, and its cardiovascular manifestations have highlighted the need for fast, sensitive and specific tools for early identification and risk stratification. Machine learning is a software solution with the ability to analyze large amounts of data and make predictions without prior programming. When faced with new problems with unique challenges as evident in the COVID-19 pandemic, machine learning can offer solutions that are not apparent on the surface by sifting quickly through massive quantities of data and making associations that may have been missed. Artificial intelligence is a broad term that encompasses different tools, including various types of machine learning and deep learning. Here, we review several cardiovascular applications of machine learning and artificial intelligence and their potential applications to cardiovascular diagnosis, prognosis, and therapy in COVID-19 infection.
Zimmerman Allison, Kalra Dinesh

2020-Sep-30

COVID-19, artificial intelligence, cardiovascular, machine learning

Pathology

Characterizing Deep Gaussian Processes via Nonlinear Recurrence Systems

ArXiv Preprint
Recent advances in Deep Gaussian Processes (DGPs) show the potential to have more expressive representation than that of traditional Gaussian Processes (GPs). However, there exists a pathology of deep Gaussian processes that their learning capacities reduce significantly when the number of layers increases. In this paper, we present a new analysis in DGPs by studying its corresponding nonlinear dynamic systems to explain the issue. Existing work reports the pathology for the squared exponential kernel function. We extend our investigation to four types of common stationary kernel functions. The recurrence relations between layers are analytically derived, providing a tighter bound and the rate of convergence of the dynamic systems. We demonstrate our finding with a number of experimental results.
Anh Tong, Jaesik Choi

2020-10-19

General

Explainable Automated Fact-Checking for Public Health Claims

ArXiv Preprint
Fact-checking is the task of verifying the veracity of claims by assessing their assertions against credible evidence. The vast majority of fact-checking studies focus exclusively on political claims. Very little research explores fact-checking for other topics, specifically subject matters for which expertise is required. We present the first study of explainable fact-checking for claims which require specific expertise. For our case study we choose the setting of public health. To support this case study we construct a new dataset PUBHEALTH of 11.8K claims accompanied by journalist crafted, gold standard explanations (i.e., judgments) to support the fact-check labels for claims. We explore two tasks: veracity prediction and explanation generation. We also define and evaluate, with humans and computationally, three coherence properties of explanation quality. Our results indicate that, by training on in-domain data, gains can be made in explainable, automated fact-checking for claims which require specific expertise.
Neema Kotonya, Francesca Toni

2020-10-19

General

Efficient Estimation and Evaluation of Prediction Rules in Semi-Supervised Settings under Stratified Sampling

ArXiv Preprint
In many contemporary applications, large amounts of unlabeled data are readily available while labeled examples are limited. There has been substantial interest in semi-supervised learning (SSL) which aims to leverage unlabeled data to improve estimation or prediction. However, current SSL literature focuses primarily on settings where labeled data is selected randomly from the population of interest. Non-random sampling, while posing additional analytical challenges, is highly applicable to many real world problems. Moreover, no SSL methods currently exist for estimating the prediction performance of a fitted model under non-random sampling. In this paper, we propose a two-step SSL procedure for evaluating a prediction rule derived from a working binary regression model based on the Brier score and overall misclassification rate under stratified sampling. In step I, we impute the missing labels via weighted regression with nonlinear basis functions to account for nonrandom sampling and to improve efficiency. In step II, we augment the initial imputations to ensure the consistency of the resulting estimators regardless of the specification of the prediction model or the imputation model. The final estimator is then obtained with the augmented imputations. We provide asymptotic theory and numerical studies illustrating that our proposals outperform their supervised counterparts in terms of efficiency gain. Our methods are motivated by electronic health records (EHR) research and validated with a real data analysis of an EHR-based study of diabetic neuropathy.
Jessica Gronsbell, Molei Liu, Lu Tian, Tianxi Cai

2020-10-19

General

Knowledge Graph-based Question Answering with Electronic Health Records

ArXiv Preprint
Question Answering (QA) on Electronic Health Records (EHR), namely EHR QA, can work as a crucial milestone towards developing an intelligent agent in healthcare. EHR data are typically stored in a relational database, which can also be converted to a Directed Acyclic Graph (DAG), allowing two approaches for EHR QA: Table-based QA and Knowledge Graph-based QA. We hypothesize that the graph-based approach is more suitable for EHR QA as graphs can represent relations between entities and values more naturally compared to tables, which essentially require JOIN operations. To validate our hypothesis, we first construct EHR QA datasets based on MIMIC-III, where the same question-answer pairs are represented in SQL (table-based) and SPARQL (graph-based), respectively. We then test a state-of-the-art EHR QA model on both datasets where the model demonstrated superior QA performance on the SPARQL version. Finally, we open-source both MIMICSQL* and MIMIC-SPARQL* to encourage further EHR QA research in both direction
Junwoo Park, Youngwoo Cho, Haneol Lee, Jaegul Choo, Edward Choi

2020-10-19

General

A Reinforcement Learning Approach to Health Aware Control Strategy

Mediterranean Conference on Control and Automation (MED). IEEE, 2019, Jul 2019, Akko, Israel
Health-aware control (HAC) has emerged as one of the domains where control synthesis is sought based upon the failure prognostics of system/component or the Remaining Useful Life (RUL) predictions of critical components. The fact that mathematical dynamic (transition) models of RUL are rarely available, makes it difficult for RUL information to be incorporated into the control paradigm. A novel framework for health aware control is presented in this paper where reinforcement learning based approach is used to learn an optimal control policy in face of component degradation by integrating global system transition data (generated by an analytical model that mimics the real system) and RUL predictions. The RUL predictions generated at each step, is tracked to a desired value of RUL. The latter is integrated within a cost function which is maximized to learn the optimal control. The proposed method is studied using simulation of a DC motor and shaft wear.
Mayank Shekhar Jha, Philippe Weber, Didier Theilliol, Jean-Christophe Ponsart, Didier Maquin

2020-10-19

General

IoT Platform for COVID-19 Prevention and Control: A Survey

ArXiv Preprint
As a result of the worldwide transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), coronavirus disease 2019 (COVID-19) has evolved into an unprecedented pandemic. Currently, with unavailable pharmaceutical treatments and vaccines, this novel coronavirus results in a great impact on public health, human society, and global economy, which is likely to last for many years. One of the lessons learned from the COVID-19 pandemic is that a long-term system with non-pharmaceutical interventions for preventing and controlling new infectious diseases is desirable to be implemented. Internet of things (IoT) platform is preferred to be utilized to achieve this goal, due to its ubiquitous sensing ability and seamless connectivity. IoT technology is changing our lives through smart healthcare, smart home, and smart city, which aims to build a more convenient and intelligent community. This paper presents how the IoT could be incorporated into the epidemic prevention and control system. Specifically, we demonstrate a potential fog-cloud combined IoT platform that can be used in the systematic and intelligent COVID-19 prevention and control, which involves five interventions including COVID-19 Symptom Diagnosis, Quarantine Monitoring, Contact Tracing \& Social Distancing, COVID-19 Outbreak Forecasting, and SARS-CoV-2 Mutation Tracking. We investigate and review the state-of-the-art literatures of these five interventions to present the capabilities of IoT in countering against the current COVID-19 pandemic or future infectious disease epidemics.
Yudi Dong, Yu-Dong Yao

2020-10-15

General

M3Lung-Sys: A Deep Learning System for Multi-Class Lung Pneumonia Screening from CT Imaging.

In IEEE journal of biomedical and health informatics
To counter the outbreak of COVID-19, the accurate diagnosis of suspected cases plays a crucial role in timely quarantine, medical treatment, and preventing the spread of the pandemic. Considering the limited training cases and resources (e.g, time and budget), we propose a Multi-task Multi-slice Deep Learning System ( M³Lung-Sys) for multi-class lung pneumonia screening from CT imaging, which only consists of two 2D CNN networks, i.e., slice- and patient-level classification networks. The former aims to seek the feature representations from abundant CT slices instead of limited CT volumes, and for the overall pneumonia screening, the latter one could recover the temporal information by feature refinement and aggregation between different slices. In addition to distinguish COVID-19 from Healthy, H1N1, and CAP cases, our M³Lung-Sys also be able to locate the areas of relevant lesions, without any pixel-level annotation. To further demonstrate the effectiveness of our model, we conduct extensive experiments on a chest CT imaging dataset with a total of 734 patients (251 healthy people, 245 COVID-19 patients, 105 H1N1 patients, and 133 CAP patients). The quantitative results with plenty of metrics indicate the superiority of our proposed model on both slice- and patient-level classification tasks. More importantly, the generated lesion location maps make our system interpretable and more valuable to clinicians.
Qian Xuelin, Fu Huazhu, Shi Weiya, Chen Tao, Fu Yanwei, Shan Fei, Xue Xiangyang

2020-Oct-13

Radiology

Clinical and laboratory data, radiological structured report findings and quantitative evaluation of lung involvement on baseline chest CT in COVID-19 patients to predict prognosis.

In La Radiologia medica

OBJECTIVE : To evaluate by means of regression models the relationships between baseline clinical and laboratory data and lung involvement on baseline chest CT and to quantify the thoracic disease using an artificial intelligence tool and a visual scoring system to predict prognosis in patients with COVID-19 pneumonia.

MATERIALS AND METHODS : This study included 103 (41 women and 62 men; 68.8 years of mean age-range, 29-93 years) with suspicious COVID-19 viral infection evaluated by reverse transcription real-time fluorescence polymerase chain reaction (RT-PCR) test. All patients underwent CT examinations at the time of admission in addition to clinical and laboratory findings recording. All chest CT examinations were reviewed using a structured report. Moreover, using an artificial intelligence tool we performed an automatic segmentation on CT images based on Hounsfield unit to calculate residual healthy lung parenchyma, ground-glass opacities (GGO), consolidations and emphysema volumes for both right and left lungs. Two expert radiologists, in consensus, attributed at the CT pulmonary disease involvement a severity score using a scale of 5 levels; the score was attributed for GGO and consolidation for each lung, and then, an overall radiological severity visual score was obtained summing the single score. Univariate and multivariate regression analysis was performed.

RESULTS : Symptoms and comorbidities did not show differences statistically significant in terms of patient outcome. Instead, SpO2 was significantly lower in patients hospitalized in critical conditions or died while age, HS CRP, leukocyte count, neutrophils, LDH, d-dimer, troponin, creatinine and azotemia, ALT, AST and bilirubin values were significantly higher. GGO and consolidations were the main CT patterns (a variable combination of GGO and consolidations was found in 87.8% of patients). CT COVID-19 disease was prevalently bilateral (77.6%) with peripheral distribution (74.5%) and multiple lobes localizations (52.0%). Consolidation, emphysema and residual healthy lung parenchyma volumes showed statistically significant differences in the three groups of patients based on outcome (patients discharged at home, patients hospitalized in stable conditions and patient hospitalized in critical conditions or died) while GGO volume did not affect the patient's outcome. Moreover, the overall radiological severity visual score (cutoff ≥ 8) was a predictor of patient outcome. The highest value of R-squared (R² = 0.93) was obtained by the model that combines clinical/laboratory findings at CT volumes. The highest accuracy was obtained by clinical/laboratory and CT findings model with a sensitivity, specificity and accuracy, respectively, of 88%, 78% and 81% to predict discharged/stable patients versus critical/died patients.

CONCLUSION : In conclusion, both CT visual score and computerized software-based quantification of the consolidation, emphysema and residual healthy lung parenchyma on chest CT images were independent predictors of outcome in patients with COVID-19 pneumonia.

Salvatore Cappabianca, Roberta Fusco, Angela de Lisio, Cesare Paura, Alfredo Clemente, Giuliano Gagliardi, Giulio Lombardi, Giuliana Giacobbe, Maria Russo Gaetano, Paola Belfiore Maria, Fabrizio Urraro, Roberta Grassi, Beatrice Feragalli, Vittorio Miele

2020-Oct-12

COVID-19, Chest CT, Outcome, Regression model

Radiology

CT Quantification and Machine-learning Models for Assessment of Disease Severity and Prognosis of COVID-19 Patients.

In Academic radiology

OBJECTIVE : This study was to investigate the CT quantification of COVID-19 pneumonia and its impacts on the assessment of disease severity and the prediction of clinical outcomes in the management of COVID-19 patients.

MATERIALS METHODS : Ninety-nine COVID-19 patients who were confirmed by positive nucleic acid test (NAT) of RT-PCR and hospitalized from January 19, 2020 to February 19, 2020 were collected for this retrospective study. All patients underwent arterial blood gas test, routine blood test, chest CT examination, and physical examination on admission. In addition, follow-up clinical data including the disease severity, clinical treatment, and clinical outcomes were collected for each patient. Lung volume, lesion volume, nonlesion lung volume (NLLV) (lung volume - lesion volume), and fraction of nonlesion lung volume (%NLLV) (nonlesion lung volume / lung volume) were quantified in CT images by using two U-Net models trained for segmentation of lung and COVID-19 lesions in CT images. Furthermore, we calculated 20 histogram textures for lesions volume and NLLV, respectively. To investigate the validity of CT quantification in the management of COVID-19, we built random forest (RF) models for the purpose of classification and regression to assess the disease severity (Moderate, Severe, and Critical) and to predict the need and length of ICU stay, the duration of oxygen inhalation, hospitalization, sputum NAT-positive, and patient prognosis. The performance of RF classifiers was evaluated using the area under the receiver operating characteristic curves (AUC) and that of RF regressors using the root-mean-square error.

RESULTS : Patients were classified into three groups of disease severity: moderate (n = 25), severe (n = 47) and critical (n = 27), according to the clinical staging. Of which, a total of 32 patients, 1 (1/25) moderate, 6 (6/47) severe, and 25 critical (25/27), respectively, were admitted to ICU. The median values of ICU stay were 0, 0, and 12 days, the duration of oxygen inhalation 10, 15, and 28 days, the hospitalization 12, 16, and 28 days, and the sputum NAT-positive 8, 9, and 13 days, in three severity groups, respectively. The clinical outcomes were complete recovery (n = 3), partial recovery with residual pulmonary damage (n = 80), prolonged recovery (n = 15), and death (n = 1). The %NLLV in three severity groups were 92.18 ± 9.89%, 82.94 ± 16.49%, and 66.19 ± 24.15% with p value <0.05 among each two groups. The AUCs of RF classifiers using hybrid models were 0.927 and 0.929 in classification of moderate vs (severe + critical), and severe vs critical, respectively, which were significantly higher than either radiomics models or clinical models (p < 0.05). The root-mean-square errors of RF regressors were 0.88 weeks for prediction of duration of hospitalization (mean: 2.60 ± 1.01 weeks), 0.92 weeks for duration of oxygen inhalation (mean: 2.44 ± 1.08 weeks), 0.90 weeks for duration of sputum NAT-positive (mean: 1.59 ± 0.98 weeks), and 0.69 weeks for stay of ICU (mean: 1.32 ± 0.67 weeks), respectively. The AUCs for prediction of ICU treatment and prognosis (partial recovery vs prolonged recovery) were 0.945 and 0.960, respectively.

CONCLUSION : CT quantification and machine-learning models show great potentials for assisting decision-making in the management of COVID-19 patients by assessing disease severity and predicting clinical outcomes.

Cai Wenli, Liu Tianyu, Xue Xing, Luo Guibo, Wang Xiaoli, Shen Yihong, Fang Qiang, Sheng Jifang, Chen Feng, Liang Tingbo

2020-Sep-21

COVID-19, Computed tomography, Machine-learning, Novel coronavirus pneumonia, Quantitative image analysis

Radiology

Development of a quantitative segmentation model to assess the effect of comorbidity on patients with COVID-19.

In European journal of medical research

BACKGROUND : The coronavirus disease 2019 (COVID-19) has brought a global disaster. Quantitative lesions may provide the radiological evidence of the severity of pneumonia and further to assess the effect of comorbidity on patients with COVID-19.

METHODS : 294 patients with COVID-19 were enrolled from February, 24, 2020 to June, 1, 2020 from six centers. Multi-task Unet network was used to segment the whole lung and lesions from chest CT images. This deep learning method was pre-trained in 650 CT images (550 in primary dataset and 100 in test dataset) with COVID-19 or community-acquired pneumonia and Dice coefficients in test dataset were calculated. 50 CT scans of 50 patients (15 with comorbidity and 35 without comorbidity) were random selected to mark lesions manually. The results will be compared with the automatic segmentation model. Eight quantitative parameters were calculated based on the segmentation results to evaluate the effect of comorbidity on patients with COVID-19.

RESULTS : Quantitative segmentation model was proved to be effective and accurate with all Dice coefficients more than 0.85 and all accuracies more than 0.95. Of the 294 patients, 52 (17.7%) patients were reported having at least one comorbidity; 14 (4.8%) having more than one comorbidity. Patients with any comorbidity were older (P < 0.001), had longer incubation period (P < 0.001), were more likely to have abnormal laboratory findings (P < 0.05), and be in severity status (P < 0.001). More lesions (including larger volume of lesion, consolidation, and ground-glass opacity) were shown in patients with any comorbidity than patients without comorbidity (all P < 0.001). More lesions were found on CT images in patients with more comorbidities. The median volumes of lesion, consolidation, and ground-glass opacity in diabetes mellitus group were largest among the groups with single comorbidity that had the incidence rate of top three.

CONCLUSIONS : Multi-task Unet network can make quantitative CT analysis of lesions to assess the effect of comorbidity on patients with COVID-19, further to provide the radiological evidence of the severity of pneumonia. More lesions (including GGO and consolidation) were found in CT images of cases with comorbidity. The more comorbidities patients have, the more lesions CT images show.

Zhang Cui, Yang Guangzhao, Cai Chunxian, Xu Zhihua, Wu Hai, Guo Youmin, Xie Zongyu, Shi Hengfeng, Cheng Guohua, Wang Jian

2020-Oct-12

COVID-19, Comorbidity, Deep learning, X-ray computed tomography

General

"Individualized learning in a course with a tight schedule".

In Procedia computer science
The article presents a solution supporting individualised learning in courses with a tight schedule. Such courses pose additional organisational challenges and require appropriate tools. The presented solution is based on an Intelligent Tutoring System immersed in repository of e-learning content, which enables selection of content immediately before its provision to the student instead of at the beginning of a course. Thanks to this, the system, having identified the student's needs, is able to make available the most suitable repository content at a given stage of education. The flexibility of the system is guaranteed by modularisation of content and its logical division using the UCTS taxonomy. The content has been described by means of concepts arranged according to the specificity of the domain to which the resources belong in order to ensure that the ITS is able to select relevant content. The proposed solution was used to set up an Applications of Fuzzy Logic course, which was part of an Artificial Intelligence class. The course was conducted within a very limited time frame resulting from the COVID-19 epidemic.
Marciniak Jacek, Szczepański Marcin

2020

Intelligent Tutoring Systems, content repositories, individualized learning

General

MH-COVIDNet: Diagnosis of COVID-19 using Deep Neural Networks and Meta-heuristic-based Feature Selection on X-ray Images.

In Biomedical signal processing and control
COVID-19 is a disease that causes symptoms in the lungs and causes deaths around the world. Studies are ongoing for the diagnosis and treatment of this disease, which is defined as a pandemic. Early diagnosis of this disease is important for human life. This process is progressing rapidly with diagnostic studies based on deep learning. Therefore, to contribute to this field, a deep learning-based approach that can be used for early diagnosis of the disease is proposed in our study. In this approach, a data set consisting of 3 classes of COVID19, normal and pneumonia lung X-ray images was created, with each class containing 364 images. Pre-processing was performed using the image contrast enhancement algorithm on the prepared data set and a new data set was obtained. Feature extraction was completed from this data set with deep learning models such as AlexNet, VGG19, GoogleNet, and ResNet. For the selection of the best potential features, two metaheuristic algorithms of binary particle swarm optimization and binary gray wolf optimization were used. After combining the features obtained in the feature selection of the enhancement data set, they were classified using SVM. The overall accuracy of the proposed approach was obtained as 99.38%. The results obtained by verification with two different metaheuristic algorithms proved that the approach we propose can help experts during COVID-19 diagnostic studies.
Canayaz Murat

2020-Oct-06

BGWO, BPSO, COVID-19, deep learning models, pneumonia

General

A novel comparative study for detection of Covid-19 on CT lung images using texture analysis, machine learning, and deep learning methods.

In Multimedia tools and applications
The Covid-19 virus outbreak that emerged in China at the end of 2019 caused a huge and devastating effect worldwide. In patients with severe symptoms of the disease, pneumonia develops due to Covid-19 virus. This causes intense involvement and damage in lungs. Although the emergence of the disease occurred a short time ago, many literature studies have been carried out in which these effects of the disease on the lungs were revealed by the help of lung CT imaging. In this study, 1.396 lung CT images in total (386 Covid-19 and 1.010 Non-Covid-19) were subjected to automatic classification. In this study, Convolutional Neural Network (CNN), one of the deep learning methods, was used which suggested automatic classification of CT images of lungs for early diagnosis of Covid-19 disease. In addition, k-Nearest Neighbors (k-NN) and Support Vector Machine (SVM) was used to compare the classification successes of deep learning with machine learning. Within the scope of the study, a 23-layer CNN architecture was designed and used as a classifier. Also, training and testing processes were performed for Alexnet and Mobilenetv2 CNN architectures as well. The classification results were also calculated for the case of increasing the number of images used in training for the first 23-layer CNN architecture by 5, 10, and 20 times using data augmentation methods. To reveal the effect of the change in the number of images in the training and test clusters on the results, two different training and testing processes, 2-fold and 10-fold cross-validation, were performed and the results of the study were calculated. As a result, thanks to these detailed calculations performed within the scope of the study, a comprehensive comparison of the success of the texture analysis method, machine learning, and deep learning methods in Covid-19 classification from CT images was made. The highest mean sensitivity, specificity, accuracy, F-1 score, and AUC values obtained as a result of the study were 0,9197, 0,9891, 0,9473, 0,9058, 0,9888; respectively for 2-fold cross-validation, and they were 0,9404, 0,9901, 0,9599, 0,9284, 0,9903; respectively for 10-fold cross-validation.
Yasar Huseyin, Ceylan Murat

2020-Oct-06

Convolutional neural networks (CNN), Covid-19, Deep learning, Lung CT classification, Machine learning, Texture analysis methods

General

Isfahan and Covid-19: Deep Spatiotemporal Representation.

In Chaos, solitons, and fractals
The coronavirus COVID-19 is affecting 213 countries and territories around the world. Iran was one of the first affected countries by this virus. Isfahan, as the third most populated province of Iran, experienced a noticeable epidemic. The prediction of epidemic size, peak value, and peak time can help policymakers in correct decisions. In this study, deep learning is selected as a powerful tool for forecasting this epidemic in Isfahan. A combination of effective Social Determinant of Health (SDH) and the occurrences of COVID-19 data are used as spatiotemporal input by using time-series information from different locations. Different models are utilized, and the best performance is found to be for a tailored type of long short-term memory (LSTM). This new method incorporates mutual effect of all classes (confirmed/ death / recovered) in predication process. The future trajectory of the outbreak in Isfahan is forecasted with the proposed model. The paper demonstrates the positive effect of adding SDHs in pandemic prediction. Furthermore, the effectiveness of different SDHs is discussed, and the most effective terms are introduced. The method expresses high ability in both short- and long- term forecasting of the outbreak. The model proves that in predicting one class (like the number of confirmed cases), the effect of other accompanying numbers (like death and recovered cases) cannot be ignored. In conclusion, the superiorities of this model (particularity the long term predication ability) turn it into a reliable tool for helping the health decision makers.
Kafieh Rahele, Saeedizadeh Narges, Arian Roya, Amini Zahra, Serej Nasim Dadashi, Vaezi Atefeh, Javanmard Shaghayegh Haghjooy

2020-Oct-05

COVID-19, Isfahan, deep learning, predication

General

Applications of Artificial Intelligence in Battling Against Covid-19: A Literature Review.

In Chaos, solitons, and fractals
Colloquially known as coronavirus, the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2), that causes CoronaVirus Disease 2019 (COVID-19), has become a matter of grave concern for every country around the world. The rapid growth of the pandemic has wreaked havoc and prompted the need for immediate reactions to curb the effects. To manage the problems, many research in a variety of area of science have started studying the issue. Artificial Intelligence is among the area of science that has found great applications in tackling the problem in many aspects. Here, we perform an overview on the applications of AI in a variety of fields including diagnosis of the disease via different types of tests and symptoms, monitoring patients, identifying severity of a patient, processing covid-19 related imaging tests, epidemiology, pharmaceutical studies, etc. The aim of this paper is to perform a comprehensive survey on the applications of AI in battling against the difficulties the outbreak has caused. Thus we cover every way that AI approaches have been employed and to cover all the research until the writing of this paper. We try organize the works in a way that overall picture is comprehensible. Such a picture, although full of details, is very helpful in understand where AI sits in current pandemonium. We also tried to conclude the paper with ideas on how the problems can be tackled in a better way and provide some suggestions for future works.
Tayarani-N Mohammad-H

2020-Oct-03

Artificial Intelligence, Artificial Neural Networks, Convolutional Neural Networks, Coronavirus, Covid-19, Deep Learning, Deep Neural Networks, Drug discovery, Epidemiology, Evolutionary Algorithms, Machine Learning, SARS-CoV-2, Vaccine Development

General

A time series-based statistical approach for outbreak spread forecasting: Application of COVID-19 in Greece.

In Expert systems with applications
The aim of this paper is the generation of a time-series based statistical data-driven procedure in order to track an outbreak. At first are used univariate time series models in order to predict the evolution of the reported cases. Moreover, are considered combinations of the models in order to provide more accurate and robust results. Additionally, statistical probability distributions are considered in order to generate future scenarios. Final step is the build and use of an epidemiological model (tSIR) and the calculation of an epidemiological ratio (R₀) for estimating the termination of the outbreak. The time series models include Exponential Smoothing and ARIMA approaches from the classical models, also Feed-Forward Artificial Neural Networks and Multivariate Adaptive Regression Splines from the machine learning toolbox. Combinations include simple mean, Newbolt-Granger and Bates-Granger approaches. Finally, the tSIR model and the R₀ ratio are used for estimating the spread and the reversion of the pandemic. The suggested procedure is used to track the COVID-19 epidemic in Greece. This epidemic has appeared in China in December 2019 and has been widespread since then to all over the world. Greece is the center of this empirical study as is considered an early successful paradigm of resistance against the virus.
Katris Christos

2021-Mar-15

Classical time series models, Forecast combinations, Machine learning approaches, Time series forecasting, tSIR epidemiological model

General

A light CNN for detecting COVID-19 from CT scans of the chest.

In Pattern recognition letters
Computer Tomography (CT) imaging of the chest is a valid diagnosis tool to detect COVID-19 promptly and to control the spread of the disease. In this work we propose a light Convolutional Neural Network (CNN) design, based on the model of the SqueezeNet, for the efficient discrimination of COVID-19 CT images with respect to other community-acquired pneumonia and/or healthy CT images. The architecture allows to an accuracy of 85.03% with an improvement of about 3.2% in the first dataset arrangement and of about 2.1% in the second dataset arrangement. The obtained gain, though of low entity, can be really important in medical diagnosis and, in particular, for Covid-19 scenario. Also the average classification time on a high-end workstation, 1.25 s, is very competitive with respect to that of more complex CNN designs, 13.41 s, witch require pre-processing. The proposed CNN can be executed on medium-end laptop without GPU acceleration in 7.81 s: this is impossible for methods requiring GPU acceleration. The performance of the method can be further improved with efficient pre-processing strategies for witch GPU acceleration is not necessary.
Polsinelli Matteo, Cinque Luigi, Placidi Giuseppe

2020-Dec

CNN, COVID-19, Deep Learning, Pattern Recognition

Radiology

Radiology in the News: A Content Analysis of Radiology-Related Information Retrieved From Google Alerts.

In Current problems in diagnostic radiology

INTRODUCTION : Radiology topics receive substantial online media attention, with prior studies focusing on social media platform coverage. We used Google Alerts, a content change detection and notification service, to prospectively analyze new radiology-related content appearing on the internet.

MATERIALS AND METHODS : An automated notification was created on Google Alerts for the search term "radiology," sending the user emails with up to 3 new links daily. All links from November 2019 through April 2020 were assessed by 2 of 3 independent raters using a coding system to classify the content source and primary topic of discussion. The top 5 primary topics were retrospectively evaluated to identify prevalent subcategories. Content viewing restrictions were documented.

RESULTS : 526 links were accessed. The majority (68%) of links were created by non-radiology lay press, followed by radiology-related lay press (28%), university-based publications (2%), and professional society websites (2%). The primary topic of these links most frequently related to market trends (28%), promotional material (20%), COVID-19 (13%), artificial intelligence (8%), and new technology or equipment (5%). 15% of links discussed a topic sourced from another article, such as a peer-reviewed journal, though only 2 linked directly to the journal itself. 8% of links had content viewing restrictions.

CONCLUSION : New radiology content was largely disseminated via non-radiology news sources; radiologists should therefore ensure their research and viewpoints are presented in these outlets. Google Alerts may be a useful tool to stay abreast of the most current public radiology subject matters, especially during these times of social isolation and rapidly evolving clinical practice.

Munawar Kamran, Sugi Mark D, Prabhu Vinay

2020-Oct-08

General

Enhancing the Identification of Cyberbullying through Participant Roles

ArXiv Preprint
Cyberbullying is a prevalent social problem that inflicts detrimental consequences to the health and safety of victims such as psychological distress, anti-social behaviour, and suicide. The automation of cyberbullying detection is a recent but widely researched problem, with current research having a strong focus on a binary classification of bullying versus non-bullying. This paper proposes a novel approach to enhancing cyberbullying detection through role modeling. We utilise a dataset from ASKfm to perform multi-class classification to detect participant roles (e.g. victim, harasser). Our preliminary results demonstrate promising performance including 0.83 and 0.76 of F1-score for cyberbullying and role classification respectively, outperforming baselines.
Gathika Ratnayaka, Thushari Atapattu, Mahen Herath, Georgia Zhang, Katrina Falkner

2020-10-13

General

Monitoring the Impact of Air Quality on the COVID-19 Fatalities in Delhi, India: Using Machine Learning Techniques.

In Disaster medicine and public health preparedness

OBJECTIVE : The focus of this study is to monitor the effect of lockdown on the various air pollutants due to COVID-19 pandemic and identify the ones that affect COVID-19 fatalities so that measures to control the pollution could be enforced.

METHODS : Various machine learning techniques: Decision Trees, Linear Regression and Random Forest have been applied to correlate air pollutants and COVID-19 fatalities in Delhi. Furthermore, a comparison between the concentration of various air pollutants and the air quality index during lockdown period and last two years 2018 and 2019 has been presented.

RESULTS : From the experimental work, it has been observed that the pollutants Ozone and Toluene have increased during the lockdown period. It has also been deduced that the pollutants that may impact the mortalities due to COVID-19 are Ozone, NH3, NO2, and PM10.

CONCLUSIONS : The novel corona virus has led to environmental restoration due to lockdown. However, there is a need to impose measures to control Ozone pollution as there has been a significant increase in its concentration and it also impacts the COVID-19 mortality rate.

Sethi Jasleen Kaur, Mittal Mamta

2020-Oct-12

Air Pollutants, COVID-19, Decision Trees, Linear Regression, Machine Learning, Random Forest

General

COVID-19 Imaging Data Privacy by Federated Learning Design: A Theoretical Framework

ArXiv Preprint
To address COVID-19 healthcare challenges, we need frequent sharing of health data, knowledge and resources at a global scale. However, in this digital age, data privacy is a big concern that requires the secure embedding of privacy assurance into the design of all technological solutions that use health data. In this paper, we introduce differential privacy by design (dPbD) framework and discuss its embedding into the federated machine learning system. To limit the scope of our paper, we focus on the problem scenario of COVID-19 imaging data privacy for disease diagnosis by computer vision and deep learning approaches. We discuss the evaluation of the proposed design of federated machine learning systems and discuss how differential privacy by design (dPbD) framework can enhance data privacy in federated learning systems with scalability and robustness. We argue that scalable differentially private federated learning design is a promising solution for building a secure, private and collaborative machine learning model such as required to combat COVID19 challenge.
Anwaar Ulhaq, Oliver Burmeister

2020-10-13

General

A combined approach of MALDI-TOF Mass Spectrometry and multivariate analysis as a potential tool for the detection of SARS-CoV-2 virus in nasopharyngeal swabs.

In Journal of virological methods
Coronavirus disease 2019, known as COVID-19, is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The early, sensitive and specific detection of SARS-CoV-2 virus is widely recognized as the critical point in responding to the ongoing outbreak. Currently, the diagnosis is based on molecular real time RT-PCR techniques, although their implementation is being threatened due to the extraordinary demand for supplies worldwide. That is why the development of alternative and / or complementary tests becomes so relevant. Here, we exploit the potential of mass spectrometry technology combined with machine learning algorithms, for the detection of COVID-19 positive and negative protein profiles directly from nasopharyngeal swabs samples. According to the preliminary results obtained, accuracy = 67.66%, sensitivity = 61.76%, specificity = 71.72%, and although these parameters still need to be improved to be used as a screening technique, mass spectrometry-based methods coupled with multivariate analysis showed that it is an interesting tool that deserves to be explored as a complementary diagnostic approach due to the low cost and fast performance. However, further steps, such as the analysis of a large number of samples, should be taken in consideration to determine the applicability of the method developed.
Rocca María Florencia, Zintgraff Jonathan Cristian, Dattero María Elena, Santos Leonardo Silva, Ledesma Martín, Vay Carlos, Prieto Mónica, Benedetti Estefanía, Avaro Martín, Russo Mara, Nachtigall Fabiane Manke, Baumeister Elsa

2020-Oct-09

COVID-19, MALDI-TOF, Mass spectrometry, SARS-CoV-2, machine learning

General

Severity and Consolidation Quantification of COVID-19 from CT Images Using Deep Learning Based on Hybrid Weak Labels.

In IEEE journal of biomedical and health informatics
Early and accurate diagnosis of Coronavirus disease (COVID-19) is essential for patient isolation and contact tracing so that the spread of infection can be limited. Computed tomography (CT) can provide important information in COVID-19, especially for patients with moderate to severe disease as well as those with worsening cardiopulmonary status. As an automatic tool, deep learning methods can be utilized to perform semantic segmentation of affected lung regions, which is important to establish disease severity and prognosis prediction. Both the extent and type of pulmonary opacities help assess disease severity. However, manually pixel-level multi-class labelling is time-consuming, subjective, and non-quantitative. In this work, we proposed a hybrid weak label-based deep learning method that utilize both the manually annotated pulmonary opacities from COVID-19 pneumonia and the patient-level disease-type information available from the clinical report. A UNet was firstly trained with semantic labels to segment the total infected region. It was used to initialize another UNet, which was trained to segment the consolidations with patient-level information using the Expectation-Maximization (EM) algorithm. To demonstrate the performance of the proposed method, multi-institutional CT datasets from Iran, Italy, South Korea, and the United States were utilized. Results show that our proposed method can predict the infected regions as well as the consolidation regions with a good correlation to human annotation.
Wu Dufan, Gong Kuang, Arru Chiara, Homayounieh Fatemeh, Bizzo Bernardo, Buch Varun, Ren Hui, Kim Kyungsang, Neumark Nir, Tak Won Young, Kang Min Kyu, Carriero Alessandro, Saba Luca, Dayan Ittai, Masjedi Mahsa, Babaei Rosa, Kalra Mannudeep K, Li Quanzheng

2020-Oct-12

Public Health

A national fight against COVID-19: lessons and experiences from China.

In Australian and New Zealand journal of public health

OBJECTIVE : This paper aims to review the public health measures and actions taken during the fight against COVID-19 in China, to generate a model for prevention and control public health emergency by summarising the lessons and experiences gained.

METHODS : This paper adopts a widely accepted qualitative research and coding method to form an analysis on word materials.

RESULTS : Although Chinese CDC didn't work effectively in the early stages on risk identification and warning, China was able to respond quickly and successfully to this medical emergency after the initial shock of the awareness of a novel epidemic with a swift implementation of national-scale health emergency management.

CONCLUSIONS : The success in fighting against COVID-19 in China can be attributed to: 1) adaptable governance to changing situations; 2) culture of moral compliance with rules; 3) trusted collaboration between government and people; 4) an advanced technical framework ABCD+5G (A-Artificial intelligence; B-Block chain; C-Cloud computing; D-Big data). Implications for public health: This paper constructs a conceptual model for pandemic management based on the lessons and experiences of fighting COVID-19 in China. It provides insights for pandemic control and public emergency management in similar context.

Wang Lixia, Yan Beibei, Boasson Vigdis

2020-Oct-12

ABCD+5G, COVID-19, emergency management, pandemic, public health emergencies

General

Zero-Shot Learning and its Applications from Autonomous Vehicles to COVID-19 Diagnosis: A Review.

In Intelligence-based medicine
The challenge of learning a new concept, object, or a new medical disease recognition without receiving any examples beforehand is called Zero-Shot Learning (ZSL). One of the major issues in deep learning based methodologies such as in Medical Imaging and other real-world applications is the requirement of large annotated datasets prepared by clinicians or experts to train the model. ZSL is known for having minimal human intervention by relying only on previously known or trained concepts plus currently existing auxiliary information. This is ever-growing research for the cases where we have very limited or no annotated datasets available and the detection / recognition system has human-like characteristics in learning new concepts. This makes the ZSL applicable in many real-world scenarios, from unknown object detection in autonomous vehicles to medical imaging and unforeseen diseases such as COVID-19 Chest X-Ray (CXR) based diagnosis. In this review paper, we introduce a novel and broaden solution called Few / one-shot learning, and present the definition of the ZSL problem as an extreme case of the few-shot learning. We review over fundamentals and the challenging steps of Zero-Shot Learning, including state-of-the-art categories of solutions, as well as our recommended solution, motivations behind each approach, their advantages over each category to guide both clinicians and AI researchers to proceed with the best techniques and practices based on their applications. Inspired from different settings and extensions, we then review through different datasets inducing medical and non-medical images, the variety of splits, and the evaluation protocols proposed so far. Finally, we discuss the recent applications and future directions of ZSL. We aim to convey a useful intuition through this paper towards the goal of handling complex learning tasks more similar to the way humans learn. We mainly focus on two applications in the current modern yet challenging era: coping with an early and fast diagnosis of COVID-19 cases, and also encouraging the readers to develop other similar AI-based automated detection / recognition systems using ZSL.
Rezaei Mahdi, Shahidi Mahsa

2020-Oct-02

Autonomous Vehicles, COVID-19 Pandemic, Chest X-Ray (CXR), Deep Learning, Machine Learning, SARS-CoV-2, Semantic Embedding, Supervised Annotation, Zero-Shot Learning

General

Redesigning COVID 19 Care with Network Medicine and Machine Learning: A review.

In Mayo Clinic proceedings. Innovations, quality & outcomes
Emerging evidence regarding COVID 19 highlights the role of individual resistance and immune function in both susceptibility to infection as well as severity of disease. Multiple factors influence the response of the human host when exposed to viral pathogens. Influencing an individual's susceptibility to infection include such factors as nutritional status, physical and psychosocial stressors, obesity, protein calorie malnutrition, emotional resilience, single nucleotide polymorphisms (SNPs), environmental toxins-including air pollution and first- and second-hand tobacco smoke, sleep habits, sedentary lifestyle, drug-induced nutritional deficiencies and drug-induced immunomodulatory effects, availability of nutrient dense food and empty calories. This review examines the network of interacting co-factors that influence the host-pathogen relationship, which in turn determine one's susceptibility to viral infections like COVID 19. It then evaluates the role of machine learning, including predictive analytics and random forest modeling, to help clinicians assess patients' risk of developing active infection and devise a comprehensive approach to prevention and treatment.
Halamka John, Cerrato Paul, Perlman Adam

2020-Oct-05

Radiology

Deep learning for automatic quantification of lung abnormalities in COVID-19 patients: first experience and correlation with clinical parameters.

In European journal of radiology open

Rationale and objectives : To demonstrate the first experience of a deep learning-based algorithm for automatic quantification of lung parenchymal abnormalities in chest CT of COVID-19 patients and to correlate quantitative results with clinical and laboratory parameters.

Materials and methods : We retrospectively included 60 consecutive patients (mean age, 61 ± 12 years; 18 females) with proven COVID-19 infection undergoing chest CT between March and May 2020. Clinical and laboratory data (within 24 hours before/after chest CT) were recorded. Prototype software using a deep learning algorithm was applied for automatic segmentation and quantification of lung opacities. Percentage of opacity (PO, ground-glass and consolidations) and percentage of high opacity (PHO, consolidations), were defined as 100 times the volume of segmented abnormalities divided by the volume of the lung mask.

Results : Automatic CT analysis of the lung was feasible in all patients (n = 60). The median time to accomplish automatic evaluation was 120 s (IQR: 118-128 s). In four cases (7%), manual corrections were necessary. Patients with need for mechanical ventilation had a significantly higher PO (median 44%, IQR: 23-58% versus 13%, IQR: 10-24%; p = 0.001) and PHO (median: 11%, IQR: 6-21% versus 3%, IQR: 2-7%, p = 0.002) compared to those without. The PO and PHO moderately correlated with c-reactive protein (r = 0.49-0.60, both p < 0.001) and leucocyte count (r = 0.30-0.40, both p = 0.05). PO had a negative correlation with SO₂ (r=-0.50, p = 0.001).

Conclusion : Preliminary experience indicates the feasibility of a rapid, automatic quantification tool of lung parenchymal abnormalities in COVID-19 patients using deep learning, with results correlating with laboratory and clinical parameters.

Mergen Victor, Kobe Adrian, Blüthgen Christian, Euler André, Flohr Thomas, Frauenfelder Thomas, Alkadhi Hatem, Eberhard Matthias

2020-Oct-06

COVID-19, computed tomography, deep learning, lung infection

General

Mortality prediction model for the triage of COVID-19, pneumonia, and mechanically ventilated ICU patients: A retrospective study.

In Annals of medicine and surgery (2012)

Rationale : Prediction of patients at risk for mortality can help triage patients and assist in resource allocation.

Objectives : Develop and evaluate a machine learning-based algorithm which accurately predicts mortality in COVID-19, pneumonia, and mechanically ventilated patients.

Methods : Retrospective study of 53,001 total ICU patients, including 9166 patients with pneumonia and 25,895 mechanically ventilated patients, performed on the MIMIC dataset. An additional retrospective analysis was performed on a community hospital dataset containing 114 patients positive for SARS-COV-2 by PCR test. The outcome of interest was in-hospital patient mortality.

Results : When trained and tested on the MIMIC dataset, the XGBoost predictor obtained area under the receiver operating characteristic (AUROC) values of 0.82, 0.81, 0.77, and 0.75 for mortality prediction on mechanically ventilated patients at 12-, 24-, 48-, and 72- hour windows, respectively, and AUROCs of 0.87, 0.78, 0.77, and 0.734 for mortality prediction on pneumonia patients at 12-, 24-, 48-, and 72- hour windows, respectively. The predictor outperformed the qSOFA, MEWS and CURB-65 risk scores at all prediction windows. When tested on the community hospital dataset, the predictor obtained AUROCs of 0.91, 0.90, 0.86, and 0.87 for mortality prediction on COVID-19 patients at 12-, 24-, 48-, and 72- hour windows, respectively, outperforming the qSOFA, MEWS and CURB-65 risk scores at all prediction windows.

Conclusions : This machine learning-based algorithm is a useful predictive tool for anticipating patient mortality at clinically useful timepoints, and is capable of accurate mortality prediction for mechanically ventilated patients as well as those diagnosed with pneumonia and COVID-19.

Ryan Logan, Lam Carson, Mataraso Samson, Allen Angier, Green-Saxena Abigail, Pellegrini Emily, Hoffman Jana, Barton Christopher, McCoy Andrea, Das Ritankar

2020-Nov

Artificial intelligence, COVID-19, Machine learning, Mortality prediction, SARS-CoV-2

General

Alexa, What classes do I have today? The use of Artificial Intelligence via Smart Speakers in Education.

In Procedia computer science
Looking back to the rumours from the early 2000's, when the world of technology bloomed together with the curiosity towards what was next to come, by 2020, robots should have assisted and supported almost every task from our daily life. While this may seem as a Sci-Fi movie scenario, it is partially a tangible reality, that we quickly got used to, thanks to the introduction of smart speakers. As the world changes, so does the future of our students. In this respects, the evolution of the technology comes up with specific environments for educational purpose. Building smart learning environments supported by e-learning platforms is an important area of research in education domain within our days. The evolution of these smart learning environments is justified by some events (Covid19) that force students to learn remotely. The paper proposes a software application component using Alexa smart speaker, that integrates different services (Amazon Web Services, Microsoft Services) for a proper virtual environment platform, for both students and teachers. It addresses the main concerns of the current educational system, and provides a smart solution through the use of Artificial Intelligence based tools. The proposed approach not only achieves unifying data and knowledge-share mechanisms in a remotely mode, but it brings also a good learning experience, increasing the effectiveness and the efficiency of the learning process.
Şerban Camelia, Todericiu Ioana-Alexandra

2020

artificial intelligence, e-learning, smart speakers, virtual learning environments

General

Machine learning for coronavirus covid-19 detection from chest x-rays.

In Procedia computer science
At the end of 2019, a new form of Coronavirus, called COVID-19, has widely spread in the world. To quickly screen patients with the aim to detect this new form of pulmonary disease, in this paper we propose a method aimed to automatically detect the COVID-19 disease by analysing medical images. We exploit supervised machine learning techniques building a model considering a data-set freely available for research purposes of 85 chest X-rays. The experiment shows the effectiveness of the proposed method in the discrimination between the COVID-19 disease and other pulmonary diseases.
Brunese Luca, Martinelli Fabio, Mercaldo Francesco, Santone Antonella

2020

COVID-19, Coronavirus, artificial intelligence, machine learning, medical images, x-ray

Cardiology

Digital cardiovascular care in COVID-19 pandemic: A potential alternative?

In Journal of cardiac surgery ; h5-index 21.0

BACKGROUND : Cardiovascular patients are at increased risk of acquiring coronavirus disease 2019 (COVID-19) infection while their visit to healthcare facilities. There is a need for alternative tools for optimal monitoring and management of cardiovascular patients in the present pandemic situation. Digital health care may prove to be a new revolutionary tool to protect cardiovascular patients from coronavirus disease by avoiding routine visits to health care facilities that are already overwhelmed with COVID-19 patients.

METHODS : To evaluate the role of digital health care in the present era of the COVID-19 pandemic, we have reviewed the published literature on digital health services providing cardiovascular care.

RESULTS AND CONCLUSION : Digital health including telemedicine services, robotic telemedicine carts, use of artificial intelligence and machine learning, use of digital gadgets like smartwatches and web-based applications may be a safe alternative for the management of cardiovascular patients in the present pandemic situation.

Kaushik Atul, Patel Surendra, Dubey Kalika

2020-Oct-10

COVID-19 pandemic, artificial intelligence, cardiovascular care, digital health, telemedicine

General

Unsupervised explainable AI for simultaneous molecular evolutionary study of forty thousand SARS-CoV-2 genomes

bioRxiv Preprint
Unsupervised AI (artificial intelligence) can obtain novel knowledge from big data without particular models or prior knowledge and is highly desirable for unveiling hidden features in big data. SARS-CoV-2 poses a serious threat to public health and one important issue in characterizing this fast-evolving virus is to elucidate various aspects of their genome sequence changes. We previously established unsupervised AI, a BLSOM (batch-learning SOM), which can analyze five million genomic sequences simultaneously. The present study applied the BLSOM to the oligonucleotide compositions of forty thousand SARS-CoV-2 genomes. While only the oligonucleotide composition was given, the obtained clusters of genomes corresponded primarily to known main clades and internal divisions in the main clades. Since the BLSOM is explainable AI, it reveals which features of the oligonucleotide composition are responsible for clade clustering. The BLSOM has powerful image display capabilities and enables efficient knowledge discovery about viral evolutionary processes.
Ikemura, T.; Wada, K.; Wada, Y.; Iwasaki, Y.; Abe, T.

2020-10-12

General

Prediction of COVID-19 Severity from Chest CT and Laboratory Measurements: Evaluation of a Machine Learning Approach.

In JMIR medical informatics ; h5-index 23.0

BACKGROUND : Most of mortality of COVID-19 were from severe patients.

OBJECTIVE : Effective treatment of these severe cases remains a challenge due to a lack of early detection.

METHODS : A total set of 27 severe and 151 non-severe clinical and computerized tomography (CT) records from 46 COVID-19 patients (10 severe, 36 non-severe) was collected for building the model. Using a recently published convolutional neural network (CNN), we managed to extract features from CT images. A machine learning model which combines these features with clinical laboratory results was also trained.

RESULTS : Herein, we presented a prediction model, combining the radiological outcome with the clinical biochemical indexes, to identify the severe cases. The prediction model yields a cross-validated AUROC score of 0.93 and F1 score of 0.89, which improved 6% and 15%, respectively, from the models with laboratory tests features only. In addition, we developed a statistical model for forecasting severity based on patients' laboratory tests results before turning into severe cases, with an AUROC score of 0.81.

CONCLUSIONS : To our knowledge, this is the first report on predicting COVID-19 patient's severity progression, as well as severity forecasting, through a combination analysis of laboratory tests and CT images.

CLINICALTRIAL :

Zhu Fang, Li Daowei, Zhang Qiang, Tan Yue, Yue Yuanyi, Bai Yuhan, Li Jimeng, Li Jiahang, Feng Xinghuo, Chen Shiyu, Xu Youjun, Xiao Si-Yu, Sun Muyan, Li Xiaona

2020-Sep-21

Radiology

Dynamic evaluation of lung involvement during coronavirus disease-2019 (COVID-19) with quantitative lung CT.

In Emergency radiology

PURPOSE : To identify and quantify lung changes associated with coronavirus disease-2019 (COVID-19) with quantitative lung CT during the disease.

METHODS : This retrospective study reviewed COVID-19 patients who underwent multiple chest CT scans during their disease course. Quantitative lung CT was used to determine the nature and volume of lung involvement. A semi-quantitative scoring system was also used to evaluate lung lesions.

RESULTS : This study included eighteen cases (4 cases in mild type, 10 cases in moderate type, 4 cases in severe type, and without critical type cases) with confirmed COVID-19. Patients had a mean hospitalized period of 24.1 ± 7.1 days (range: 14-38 days) and underwent an average CT scans of 3.9 ± 1.6 (range: 2-8). The total volumes of lung abnormalities reached a peak of 8.8 ± 4.1 days (range: 2-14 days). The ground-glass opacity (GGO) volume percentage was higher than the consolidative opacity (CO) volume percentage on the first CT examination (Z = 2.229, P = 0.026), and there was no significant difference between the GGO volume percentage and that of CO at the peak stage (Z = - 0.628, P = 0.53). The volume percentage of lung involvement identified by AI demonstrated a strong correlation with the total CT scores at each stage (r = 0.873, P = 0.0001).

CONCLUSIONS : Quantitative lung CT can automatically identify the nature of lung involvement and quantify the dynamic changes of lung lesions on CT during COVID-19. For patients who recovered from COVID-19, GGO was the predominant imaging feature on the initial CT scan, while GGO and CO were the main appearances at peak stage.

Ma Chun, Wang Xiao-Ling, Xie Dong-Mei, Li Yu-Dan, Zheng Yong-Ji, Zhang Hai-Bing, Ming Bing

2020-Oct-10

Artificial intelligence, Coronavirus, Lung, Pneumonia, Tomography, X-ray, viral

General

A comprehensive study on classification of COVID-19 on computed tomography with pretrained convolutional neural networks.

In Scientific reports ; h5-index 158.0
The use of imaging data has been reported to be useful for rapid diagnosis of COVID-19. Although computed tomography (CT) scans show a variety of signs caused by the viral infection, given a large amount of images, these visual features are difficult and can take a long time to be recognized by radiologists. Artificial intelligence methods for automated classification of COVID-19 on CT scans have been found to be very promising. However, current investigation of pretrained convolutional neural networks (CNNs) for COVID-19 diagnosis using CT data is limited. This study presents an investigation on 16 pretrained CNNs for classification of COVID-19 using a large public database of CT scans collected from COVID-19 patients and non-COVID-19 subjects. The results show that, using only 6 epochs for training, the CNNs achieved very high performance on the classification task. Among the 16 CNNs, DenseNet-201, which is the deepest net, is the best in terms of accuracy, balance between sensitivity and specificity, [Formula: see text] score, and area under curve. Furthermore, the implementation of transfer learning with the direct input of whole image slices and without the use of data augmentation provided better classification rates than the use of data augmentation. Such a finding alleviates the task of data augmentation and manual extraction of regions of interest on CT images, which are adopted by current implementation of deep-learning models for COVID-19 classification.
Pham Tuan D

2020-Oct-09

Radiology

Development and evaluation of an artificial intelligence system for COVID-19 diagnosis.

In Nature communications ; h5-index 260.0
Early detection of COVID-19 based on chest CT enables timely treatment of patients and helps control the spread of the disease. We proposed an artificial intelligence (AI) system for rapid COVID-19 detection and performed extensive statistical analysis of CTs of COVID-19 based on the AI system. We developed and evaluated our system on a large dataset with more than 10 thousand CT volumes from COVID-19, influenza-A/B, non-viral community acquired pneumonia (CAP) and non-pneumonia subjects. In such a difficult multi-class diagnosis task, our deep convolutional neural network-based system is able to achieve an area under the receiver operating characteristic curve (AUC) of 97.81% for multi-way classification on test cohort of 3,199 scans, AUC of 92.99% and 93.25% on two publicly available datasets, CC-CCII and MosMedData respectively. In a reader study involving five radiologists, the AI system outperforms all of radiologists in more challenging tasks at a speed of two orders of magnitude above them. Diagnosis performance of chest x-ray (CXR) is compared to that of CT. Detailed interpretation of deep network is also performed to relate system outputs with CT presentations. The code is available at https://github.com/ChenWWWeixiang/diagnosis_covid19 .
Jin Cheng, Chen Weixiang, Cao Yukun, Xu Zhanwei, Tan Zimeng, Zhang Xin, Deng Lei, Zheng Chuansheng, Zhou Jie, Shi Heshui, Feng Jianjiang

2020-Oct-09

General

Machine learning techniques for sequence-based prediction of viral-host interactions between SARS-CoV-2 and human proteins.

In Biomedical journal

BACKGROUND : COVID-19 (Coronavirus Disease-19), a disease caused by the SARS-CoV-2 virus, has been declared as a pandemic by the World Health Organization on March 11, 2020. Over 15 million people have already been affected worldwide by COVID-19, resulting in more than 0.6 million deaths. Protein-protein interactions (PPIs) play a key role in the cellular process of SARS-CoV-2 virus infection in the human body. Recently a study has reported some SARS-CoV-2 proteins that interact with several human proteins while many potential interactions remain to be identified.

METHOD : In this article, various machine learning models are built to predict the PPIs between the virus and human proteins that are further validated using biological experiments. The classification models are prepared based on different sequence-based features of human proteins like amino acid composition, pseudo amino acid composition, and conjoint triad.

RESULT : We have built an ensemble voting classifier using SVM^Radial, SVM^Polynomial, and Random Forest technique that gives a greater accuracy, precision, specificity, recall, and F1 score compared to all other models used in the work. A total of 1326 potential human target proteins of SARS-CoV-2 have been predicted by the proposed ensemble model and validated using gene ontology and KEGG pathway enrichment analysis. Several repurposable drugs targeting the predicted interactions are also reported.

CONCLUSION : This study may encourage the identification of potential targets for more effective anti-COVID drug discovery.

Dey Lopamudra, Chakraborty Sanjay, Mukhopadhyay Anirban

2020-Sep-03

COVID-19, Classifier ensemble, Machine learning, Protein–protein interaction, SARS-CoV-2, Supervised classification

General

Evaluation of a genetic risk score for severity of COVID-19 using human chromosomal-scale length variation.

In Human genomics

INTRODUCTION : The course of COVID-19 varies from asymptomatic to severe in patients. The basis for this range in symptoms is unknown. One possibility is that genetic variation is partly responsible for the highly variable response. We evaluated how well a genetic risk score based on chromosomal-scale length variation and machine learning classification algorithms could predict severity of response to SARS-CoV-2 infection.

METHODS : We compared 981 patients from the UK Biobank dataset who had a severe reaction to SARS-CoV-2 infection before 27 April 2020 to a similar number of age-matched patients drawn for the general UK Biobank population. For each patient, we built a profile of 88 numbers characterizing the chromosomal-scale length variability of their germ line DNA. Each number represented one quarter of the 22 autosomes. We used the machine learning algorithm XGBoost to build a classifier that could predict whether a person would have a severe reaction to COVID-19 based only on their 88-number classification.

RESULTS : We found that the XGBoost classifier could differentiate between the two classes at a significant level (p = 2 · 10^-11) as measured against a randomized control and (p = 3 · 10^-14) as measured against the expected value of a random guessing algorithm (AUC = 0.5). However, we found that the AUC of the classifier was only 0.51, too low for a clinically useful test.

CONCLUSION : Genetics play a role in the severity of COVID-19, but we cannot yet develop a useful genetic test to predict severity.

Toh Christopher, Brody James P

2020-Oct-09

COVID-19, Genetic risk score, Machine learning, UK biobank

General

Prognostic Assessment of COVID-19 in ICU by Machine Learning Methods: A Retrospective Study.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Patients with coronavirus disease (COVID-19) in ICU have a high mortality rate, and how to early assess the prognosis and carry out precise treatment is of great significance.

OBJECTIVE : To use machine learning to construct a model for the analysis of risk factors and prediction of death among ICU patients with COVID-19.

METHODS : In this retrospective study, 123 COVID-19 patients inthe ICU of Vulcan Hill Hospital were selected from the database, and data were randomly divided into a training data set (n = 98) and test data set (n = 25) with a 4:1 ratio. Significance tests, analysis of correlation and factor analysis were used to screen the 100 potential risk factors individually. Conventional logistic regression methods and four machine learning algorithms were used to construct the risk prediction model for the prognosis of COVID-19 patients in ICU. Performance of these machine learning models was measured by the area under the receiver operating characteristic curve (AUC). Model interpretation and model evaluation of the risk prediction model, such as calibration curve, SHAP, LIME, etc., were performed to ensure its stability and reliability.The outcome is based on the ICU death recorded from the database.

RESULTS : Layer-by-layer screening of 100 potential risk factors finallyrevealed 8 important risk factors that were included in the risk prediction model: lymphocyte percentage (LYM%), prothrombin time (PT), lactate dehydrogenase (LDH), total bilirubin (T-Bil), percentage of eosinophils (EOS%), creatinine(Cr), neutrophil percentage (NEUT%), albumin (ALB) level. Finally, eXtreme Gradient Boosting (XGBoost) established by 8 important risk factors showed the best recognition ability in the training set of 5-fold cross validation (AUC=0.86) and the verification queue (AUC=0.92). The calibration curve showed that the risk predicted by the model was in good agreement with the actual risk. In addition, using SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) algorithms, feature interpretation and sample prediction interpretation algorithms of the XGBoost black box model were implemented. Additionally, the model has been translated into an online risk calculator that is freely available for the public usage ( http://114.251.235.51:1226/index).

CONCLUSIONS : The 8 factors XGBoost model predicts risk of death in ICU patients with COVID-19 well，which initially demonstrates stability and can be used effectively to predict COVID-19 prognosis in ICU patients.

CLINICALTRIAL :

Pan Pan, Li Yichao, Xiao Yongjiu, Han Bingchao, Su Mingliang, Li Yansheng, Zhang Siqi, Jiang Dapeng, Chen Xia, Zhou Fuquan, Ma Ling, Bao Pengtao, Su Longxiang, Xie Lixin

2020-Oct-08

General

Computer-aided prediction and design of IL-6 inducing peptides: IL-6 plays a crucial role in COVID-19.

In Briefings in bioinformatics
Interleukin 6 (IL-6) is a pro-inflammatory cytokine that stimulates acute phase responses, hematopoiesis and specific immune reactions. Recently, it was found that the IL-6 plays a vital role in the progression of COVID-19, which is responsible for the high mortality rate. In order to facilitate the scientific community to fight against COVID-19, we have developed a method for predicting IL-6 inducing peptides/epitopes. The models were trained and tested on experimentally validated 365 IL-6 inducing and 2991 non-inducing peptides extracted from the immune epitope database. Initially, 9149 features of each peptide were computed using Pfeature, which were reduced to 186 features using the SVC-L1 technique. These features were ranked based on their classification ability, and the top 10 features were used for developing prediction models. A wide range of machine learning techniques has been deployed to develop models. Random Forest-based model achieves a maximum AUROC of 0.84 and 0.83 on training and independent validation dataset, respectively. We have also identified IL-6 inducing peptides in different proteins of SARS-CoV-2, using our best models to design vaccine against COVID-19. A web server named as IL-6Pred and a standalone package has been developed for predicting, designing and screening of IL-6 inducing peptides (https://webs.iiitd.edu.in/raghava/il6pred/).
Dhall Anjali, Patiyal Sumeet, Sharma Neelam, Usmani Salman Sadullah, Raghava Gajendra P S

2020-Oct-09

COVID-19, Interleukin 6 (IL-6), computer-aided prediction, machine learning, pro-inflammatory cytokine

General

Predicting Coronavirus Disease 2019 Infection Risk and Related Risk Drivers in Nursing Homes: A Machine Learning Approach.

In Journal of the American Medical Directors Association

OBJECTIVE : Inform coronavirus disease 2019 (COVID-19) infection prevention measures by identifying and assessing risk and possible vectors of infection in nursing homes (NHs) using a machine-learning approach.

DESIGN : This retrospective cohort study used a gradient boosting algorithm to evaluate risk of COVID-19 infection (ie, presence of at least 1 confirmed COVID-19 resident) in NHs.

SETTING AND PARTICIPANTS : The model was trained on outcomes from 1146 NHs in Massachusetts, Georgia, and New Jersey, reporting COVID-19 case data on April 20, 2020. Risk indices generated from the model using data from May 4 were prospectively validated against outcomes reported on May 11 from 1021 NHs in California.

METHODS : Model features, pertaining to facility and community characteristics, were obtained from a self-constructed dataset based on multiple public and private sources. The model was assessed via out-of-sample area under the receiver operating characteristic curve (AUC), sensitivity, and specificity in the training (via 10-fold cross-validation) and validation datasets.

RESULTS : The mean AUC, sensitivity, and specificity of the model over 10-fold cross-validation were 0.729 [95% confidence interval (CI) 0.690‒0.767], 0.670 (95% CI 0.477‒0.862), and 0.611 (95% CI 0.412‒0.809), respectively. Prospective out-of-sample validation yielded similar performance measures (AUC 0.721; sensitivity 0.622; specificity 0.713). The strongest predictors of COVID-19 infection were identified as the NH's county's infection rate and the number of separate units in the NH; other predictors included the county's population density, historical Centers of Medicare and Medicaid Services cited health deficiencies, and the NH's resident density (in persons per 1000 square feet). In addition, the NH's historical percentage of non-Hispanic white residents was identified as a protective factor.

CONCLUSIONS AND IMPLICATIONS : A machine-learning model can help quantify and predict NH infection risk. The identified risk factors support the early identification and management of presymptomatic and asymptomatic individuals (eg, staff) entering the NH from the surrounding community and the development of financially sustainable staff testing initiatives in preventing COVID-19 infection.

Sun Christopher L F, Zuccarelli Eugenio, Zerhouni El Ghali A, Lee Jason, Muller James, Scott Karen M, Lujan Alida M, Levi Retsef

2020-Aug-27

COVID-19, Nursing homes, health policy, infection prevention, long-term care facility, machine-learning, risk modeling

General

Digital phenotyping to enhance substance use treatment during the COVID-19 pandemic: Viewpoint.

In JMIR mental health
The COVID-19 pandemic has required transitioning many clinical addiction treatment programs to telephonic or virtual visits. Novel solutions are needed to enhance substance use treatment during a time when many patients are disconnected from clinical care and social supports. Digital phenotyping, which leverages the unique functionality of smartphones sensors (GPS, social behavior, and typing patterns), can buttress clinical treatment in a remote, scalable fashion. Specifically, digital phenotyping has the potential to improve relapse prediction and intervention, relapse detection, and overdose intervention. Digital phenotyping may enhance relapse prediction through coupling machine learning algorithms with the enormous wealth of collected behavioral data. Activity based analysis in real time potentially can be used to prevent relapse by warning substance users when they approach locational triggers such as bars or liquor stores. Wearable devices detect when someone has relapsed to substances through measuring physiological changes such as electrodermal activity and locomotion. Despite its initial promise, privacy, security and barriers to access are important issues to address.
Hsu Michael, Ahern David K, Suzuki Joji

2020-Sep-25

Radiology

Using Artificial Intelligence for COVID-19 Chest X-ray Diagnosis.

In Federal practitioner : for the health care professionals of the VA, DoD, and PHS

Background : Coronavirus disease-19 (COVID-19), caused by a novel member of the coronavirus family, is a respiratory disease that rapidly reached pandemic proportions with high morbidity and mortality. In only a few months, it has had a dramatic impact on society and world economies. COVID-19 has presented numerous challenges to all aspects of health care, including reliable methods for diagnosis, treatment, and prevention. Initial efforts to contain the spread of the virus were hampered by the time required to develop reliable diagnostic methods. Artificial intelligence (AI) is a rapidly growing field of computer science with many applications for health care. Machine learning is a subset of AI that uses deep learning with neural network algorithms. It can recognize patterns and achieve complex computational tasks often far quicker and with increased precision than can humans.

Methods : In this article, we explore the potential for the simple and widely available chest X-ray (CXR) to be used with AI to diagnose COVID-19 reliably. Microsoft CustomVision is an automated image classification and object detection system that is a part of Microsoft Azure Cognitive Services. We utilized publicly available CXR images for patients with COVID-19 pneumonia, pneumonia from other etiologies, and normal CXRs as a dataset to train Microsoft CustomVision.

Results : Our trained model overall demonstrated 92.9% sensitivity (recall) and positive predictive value (precision), with results for each label showing sensitivity and positive predictive value at 94.8% and 98.9% for COVID-19 pneumonia, 89% and 91.8% for non-COVID-19 pneumonia, 95% and 88.8% for normal lung. We then validated the program using CXRs of patients from our institution with confirmed COVID-19 diagnoses along with non-COVID-19 pneumonia and normal CXRs. Our model performed with 100% sensitivity, 95% specificity, 97% accuracy, 91% positive predictive value, and 100% negative predictive value.

Conclusions : We have used a readily available, commercial platform to demonstrate the potential of AI to assist in the successful diagnosis of COVID-19 pneumonia on CXR images. The findings have implications for screening and triage, initial diagnosis, monitoring disease progression, and identifying patients at increased risk of morbidity and mortality. Based on the data, a website was created to demonstrate how such technologies could be shared and distributed to others to combat entities such as COVID-19 moving forward.

Borkowski Andrew A, Viswanadhan Narayan A, Thomas L Brannon, Guzman Rodney D, Deland Lauren A, Mastorides Stephen M

2020-Sep

General

Potential limitations in COVID-19 machine learning due to data source variability: a case study in the nCov2019 dataset.

In Journal of the American Medical Informatics Association : JAMIA

OBJECTIVE : Lack of representative COVID-19 data is a bottleneck for reliable and generalizable machine learning. Data sharing is insufficient without data quality, where source variability plays an important role. We showcase and discuss potential biases from data source variability for COVID-19 machine learning.

MATERIALS AND METHODS : We used the publicly available nCov2019 dataset, including patient level data from several countries. We aimed to the discovery and classification of severity subgroups using symptoms and comorbidities.

RESULTS : Cases from the two countries with the highest prevalence were divided into separate subgroups with distinct severity manifestations. This variability can reduce the representativeness of training data with respect the model target populations and increase model complexity at risk of overfitting.

CONCLUSION : Data source variability is a potential contributor to bias in distributed research networks. We call for systematic assessment and reporting of data source variability and data quality in COVID-19 data sharing, as key information for reliable and generalizable machine learning.

Sáez Carlos, Romero Nekane, Conejero J Alberto, García-Gómez Juan M

2020-Oct-07

COVID-19, biases, data quality, data sharing, dataset shift, distributed research networks, heterogeneity, machine learning, multi site data, variability

General

Feasibility of Asynchronous and Automated Telemedicine in Otolaryngology: Prospective Cross-Sectional Study.

In JMIR medical informatics ; h5-index 23.0

BACKGROUND : COVID-19 often causes respiratory symptoms, making otolaryngology offices one of the most susceptible places for community transmission of the virus. Thus, telemedicine may benefit both patients and physicians.

OBJECTIVE : This study aims to explore the feasibility of telemedicine for the diagnosis of all otologic disease types.

METHODS : A total of 177 patients were prospectively enrolled, and the patient's clinical manifestations with otoendoscopic images were written in the electrical medical records. Asynchronous diagnoses were made for each patient to assess Top-1 and Top-2 accuracy, and we selected 20 cases to conduct a survey among four different otolaryngologists to assess the accuracy, interrater agreement, and diagnostic speed. We also constructed an experimental automated diagnosis system and assessed Top-1 accuracy and diagnostic speed.

RESULTS : Asynchronous diagnosis showed Top-1 and Top-2 accuracies of 77.40% and 86.44%, respectively. In the selected 20 cases, the Top-2 accuracy of the four otolaryngologists was on average 91.25% (SD 7.50%), with an almost perfect agreement between them (Cohen kappa=0.91). The automated diagnostic model system showed 69.50% Top-1 accuracy. Otolaryngologists could diagnose an average of 1.55 (SD 0.48) patients per minute, while the machine learning model was capable of diagnosing on average 667.90 (SD 8.3) patients per minute.

CONCLUSIONS : Asynchronous telemedicine in otology is feasible owing to the reasonable Top-2 accuracy when assessed by experienced otolaryngologists. Moreover, enhanced diagnostic speed while sustaining the accuracy shows the possibility of optimizing medical resources to provide expertise in areas short of physicians.

Cha Dongchul, Shin Seung Ho, Kim Jungghi, Eo Tae Seong, Na Gina, Bae Seong Hoon, Jung Jinsei, Kim Sung Huhn, Moon In Seok, Choi Jae Young, Park Yu Rang

2020-Sep-22

General

Machine Learning to Predict Mortality and Critical Events in COVID-19 Positive New York City Patients: A Cohort Study.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Coronavirus disease 2019 (COVID-19) has infected millions of patients worldwide and has been responsible for several hundred thousand fatalities. This has necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods for achieving this are lacking.

OBJECTIVE : We analyze Electronic Health Records from COVID-19 positive hospitalized patients admitted to the Mount Sinai Health System in New York City (NYC). We present machine learning models for making predictions about the hospital course over clinically meaningful time horizons based on patient characteristics at admission. We assess performance of these models at multiple hospitals and time points.

METHODS : We utilized XGBoost and baseline comparator models, for predicting in-hospital mortality and critical events at time windows of 3, 5, 7 and 10 days from admission. Our study population included harmonized electronic health record (EHR) data from five hospitals in NYC for 4,098 COVID-19+ patients admitted from March 15, 2020 to May 22, 2020. Models were first trained on patients from a single hospital (N=1514) before or on May 1, externally validated on patients from four other hospitals (N=2201) before or on May 1, and prospectively validated on all patients after May 1 (N=383). Finally, we establish model interpretability to identify and rank variables that drive model predictions.

RESULTS : On cross-validation, the XGBoost classifier outperformed baseline models, with area under the receiver operating characteristic curve (AUC-ROC) for mortality at 0.89 at 3 days, 0.85 at 5 and 7 days, and 0.84 at 10 days; XGBoost also performed well for critical event prediction with AUC-ROC of 0.80 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. In external validation, XGBoost achieved an AUC-ROC of 0.88 at 3 days, 0.86 at 5 days, 0.86 at 7 days, and 0.84 at 10 days for mortality prediction. Similarly, XGBoost achieved an AUC-ROC of 0.78 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. Trends in performance on prospective validation sets were similar. At 7 days, acute kidney injury on admission, elevated LDH, tachypnea, and hyperglycemia were the strongest drivers of critical event prediction, while higher age, anion gap, and C-reactive protein were the strongest drivers for mortality prediction.

CONCLUSIONS : We trained and validated (both externally and prospectively) machine-learning models for mortality and critical events at different time horizons. These models identify at-risk patients, as well as uncover underlying relationships predicting outcomes.

CLINICALTRIAL :

Vaid Akhil, Somani Sulaiman, Russak Adam J, De Freitas Jessica K, Chaudhry Fayzan F, Paranjpe Ishan, Johnson Kipp W, Lee Samuel J, Miotto Riccardo, Richter Felix, Zhao Shan, Beckmann Noam D, Naik Nidhi, Kia Arash, Timsina Prem, Lala Anuradha, Paranjpe Manish, Golden Eddye, Danieletto Matteo, Singh Manbir, Meyer Dara, O’Reilly Paul F, Huckins Laura, Kovatch Patricia, Finkelstein Joseph, Freeman Robert M, Argulian Edgar, Kasarskis Andrew, Percha Bethany, Aberg Judith A, Bagiella Emilia, Horowitz Carol R, Murphy Barbara, Nestler Eric J, Schadt Eric E, Cho Judy H, Cordon-Cardo Carlos, Fuster Valentin, Charney Dennis S, Reich David L, Bottinger Erwin P, Levin Matthew A, Narula Jagat, Fayad Zahi A, Just Allan C, Charney Alexander W, Nadkarni Girish N, Glicksberg Benjamin

2020-Oct-02

General

Increased risk of COVID-19 infection and mortality in people with mental disorders: analysis from electronic health records in the United States.

In World psychiatry : official journal of the World Psychiatric Association (WPA)
Concerns have been expressed that persons with a pre-existing mental disorder may represent a population at increased risk for COVID-19 infection and with a higher likelihood of adverse outcomes of the infection, but there is no systematic research evidence in this respect. This study assessed the impact of a recent (within past year) diagnosis of a mental disorder - including attention-deficit/hyperactivity disorder (ADHD), bipolar disorder, depression and schizophrenia - on the risk for COVID-19 infection and related mortality and hospitalization rates. We analyzed a nation-wide database of electronic health records of 61 million adult patients from 360 hospitals and 317,000 providers, across 50 states in the US, up to July 29, 2020. Patients with a recent diagnosis of a mental disorder had a significantly increased risk for COVID-19 infection, an effect strongest for depression (adjusted odds ratio, AOR=7.64, 95% CI: 7.45-7.83, p<0.001) and schizophrenia (AOR=7.34, 95% CI: 6.65-8.10, p<0.001). Among patients with a recent diagnosis of a mental disorder, African Americans had higher odds of COVID-19 infection than Caucasians, with the strongest ethnic disparity for depression (AOR=3.78, 95% CI: 3.58-3.98, p<0.001). Women with mental disorders had higher odds of COVID-19 infection than males, with the strongest gender disparity for ADHD (AOR=2.03, 95% CI: 1.73-2.39, p<0.001). Patients with both a recent diagnosis of a mental disorder and COVID-19 infection had a death rate of 8.5% (vs. 4.7% among COVID-19 patients with no mental disorder, p<0.001) and a hospitalization rate of 27.4% (vs. 18.6% among COVID-19 patients with no mental disorder, p<0.001). These findings identify individuals with a recent diagnosis of a mental disorder as being at increased risk for COVID-19 infection, which is further exacerbated among African Americans and women, and as having a higher frequency of some adverse outcomes of the infection. This evidence highlights the need to identify and address modifiable vulnerability factors for COVID-19 infection and to prevent delays in health care provision in this population.
Wang QuanQiu, Xu Rong, Volkow Nora D

2020-Oct-07

ADHD, COVID‐19, access to care, bipolar disorder, depression, discrimination, ethnic disparity, gender disparity, hospitalization, mental disorders, mortality, risk of infection, schizophrenia

General

Issues associated with deploying CNN transfer learning to detect COVID-19 from chest X-rays.

In Physical and engineering sciences in medicine
Covid-19 first occurred in Wuhan, China in December 2019. Subsequently, the virus spread throughout the world and as of June 2020 the total number of confirmed cases are above 4.7 million with over 315,000 deaths. Machine learning algorithms built on radiography images can be used as a decision support mechanism to aid radiologists to speed up the diagnostic process. The aim of this work is to conduct a critical analysis to investigate the applicability of convolutional neural networks (CNNs) for the purpose of COVID-19 detection in chest X-ray images and highlight the issues of using CNN directly on the whole image. To accomplish this task, we use 12-off-the-shelf CNN architectures in transfer learning mode on 3 publicly available chest X-ray databases together with proposing a shallow CNN architecture in which we train it from scratch. Chest X-ray images are fed into CNN models without any preprocessing to replicate researches used chest X-rays in this manner. Then a qualitative investigation performed to inspect the decisions made by CNNs using a technique known as class activation maps (CAM). Using CAMs, one can map the activations contributed to the decision of CNNs back to the original image to visualize the most discriminating region(s) on the input image. We conclude that CNN decisions should not be taken into consideration, despite their high classification accuracy, until clinicians can visually inspect and approve the region(s) of the input image used by CNNs that lead to its prediction.
Majeed Taban, Rashid Rasber, Ali Dashti, Asaad Aras

2020-Oct-06

COVID-19, Class activation maps, Convolutional neural network, Coronavirus, Deep learning

Public Health

Machine learning based early warning system enables accurate mortality risk prediction for COVID-19.

In Nature communications ; h5-index 260.0
Soaring cases of coronavirus disease (COVID-19) are pummeling the global health system. Overwhelmed health facilities have endeavored to mitigate the pandemic, but mortality of COVID-19 continues to increase. Here, we present a mortality risk prediction model for COVID-19 (MRPMC) that uses patients' clinical data on admission to stratify patients by mortality risk, which enables prediction of physiological deterioration and death up to 20 days in advance. This ensemble model is built using four machine learning methods including Logistic Regression, Support Vector Machine, Gradient Boosted Decision Tree, and Neural Network. We validate MRPMC in an internal validation cohort and two external validation cohorts, where it achieves an AUC of 0.9621 (95% CI: 0.9464-0.9778), 0.9760 (0.9613-0.9906), and 0.9246 (0.8763-0.9729), respectively. This model enables expeditious and accurate mortality risk stratification of patients with COVID-19, and potentially facilitates more responsive health systems that are conducive to high risk COVID-19 patients.
Gao Yue, Cai Guang-Yao, Fang Wei, Li Hua-Yi, Wang Si-Yuan, Chen Lingxi, Yu Yang, Liu Dan, Xu Sen, Cui Peng-Fei, Zeng Shao-Qing, Feng Xin-Xia, Yu Rui-Di, Wang Ya, Yuan Yuan, Jiao Xiao-Fei, Chi Jian-Hua, Liu Jia-Hao, Li Ru-Yuan, Zheng Xu, Song Chun-Yan, Jin Ning, Gong Wen-Jian, Liu Xing-Yu, Huang Lei, Tian Xun, Li Lin, Xing Hui, Ma Ding, Li Chun-Rui, Ye Fei, Gao Qing-Lei

2020-10-06

General

Reference ontology and database annotation of the COVID-19 Open Research Dataset (CORD-19)

bioRxiv Preprint
The COVID-19 Open Research Dataset (CORD-19) was released in March 2020 to allow the machine learning and wider research community to develop techniques to answer scientific questions on COVID-19. The data set consists of a large collection of scientific literature, including over 100,000 full text papers. Annotating training data to normalise variability in biological entities can improve the performance of downstream analysis and interpretation. To facilitate and enhance the use of the CORD-19 data in these applications, in late March 2020 we performed a comprehensive annotation process using named entity recognition tool, TERMite, along with a number of large reference ontologies and vocabularies including domains of genes, proteins, drugs and virus strains. The additional annotation has identified and tagged over 45 million entities within the corpus made up of 62,746 unique biomedical entities. The latest updated version of the annotated data, as well as older versions, is made openly available under GPL-2.0 License for the community to use at: https://github.com/SciBiteLabs/CORD19
Giles, O.; Huntley, R.; Karlsson, A.; Lomax, J.; Malone, J.

2020-10-07

General

M3Lung-Sys: A Deep Learning System for Multi-Class Lung Pneumonia Screening from CT Imaging

ArXiv Preprint
To counter the outbreak of COVID-19, the accurate diagnosis of suspected cases plays a crucial role in timely quarantine, medical treatment, and preventing the spread of the pandemic. Considering the limited training cases and resources (e.g, time and budget), we propose a Multi-task Multi-slice Deep Learning System (M3Lung-Sys) for multi-class lung pneumonia screening from CT imaging, which only consists of two 2D CNN networks, i.e., slice- and patient-level classification networks. The former aims to seek the feature representations from abundant CT slices instead of limited CT volumes, and for the overall pneumonia screening, the latter one could recover the temporal information by feature refinement and aggregation between different slices. In addition to distinguish COVID-19 from Healthy, H1N1, and CAP cases, our M 3 Lung-Sys also be able to locate the areas of relevant lesions, without any pixel-level annotation. To further demonstrate the effectiveness of our model, we conduct extensive experiments on a chest CT imaging dataset with a total of 734 patients (251 healthy people, 245 COVID-19 patients, 105 H1N1 patients, and 133 CAP patients). The quantitative results with plenty of metrics indicate the superiority of our proposed model on both slice- and patient-level classification tasks. More importantly, the generated lesion location maps make our system interpretable and more valuable to clinicians.
Xuelin Qian, Huazhu Fu, Weiya Shi, Tao Chen, Yanwei Fu, Fei Shan, Xiangyang Xue

2020-10-07

General

Clinical Characteristics and Outcomes of Severe and Critical Patients With 2019 Novel Coronavirus Disease (COVID-19) in Wenzhou: A Retrospective Study.

In Frontiers in medicine
Information about severe cases of 2019 novel coronavirus disease (COVID-19) infection is scarce. The aim of this study was to report the clinical characteristics and outcomes of severe and critical patients with confirmed COVID-19 infection in Wenzhou city. In this single-centered, retrospective cohort study, we consecutively enrolled 37 RT-PCR confirmed positive severe or critical patients from January 28 to February 16, 2020 in a tertiary hospital. Outcomes were followed up until 28-day mortality. Fifteen severe and 22 critical adult patients with the COVID-19 infection were included. Twenty-six (68.4%) were men. Echocardiography data results suggest that normal or increased cardiac output and diastolic dysfunction are the most common manifestations. Compared with severe patients, critical patients were older, more likely to exhibit low platelet counts and high blood urea nitrogen, and were in hospital for longer. Most patients had organ dysfunction during hospitalization, including 11 (29.7%) with ARDS, 8 (21.6%) with acute kidney injury, 17 (45.9%) with acute cardiac injury, and 33 (89.2%) with acute liver dysfunction. Eighteen (48.6%) patients were treated with high-flow ventilation, 9 (13.8%) with non-invasive ventilation, 10 (15.4%) with invasive mechanical ventilation, 7 (18.9%) with prone position ventilation, 6 (16.2%) with extracorporeal membrane oxygenation (ECMO), and 3 (8.1%) with renal replacement therapy. Only 1 (2.7%) patient had died in the 28-day follow up in our study. All patients had bilateral infiltrates on their chest CT scan. Twenty-one (32.3%) patients presented ground glass opacity (GGO) with critical patients more localized in the periphery and the center. The mortality of critical patients with the COVID-19 infection is low in our study. Cardiac function was enhanced in the early stage and less likely to develop into acute cardiac injury, but most patients suffered with acute liver injury. The CT imaging presentations of COVID-19 in critical patients were more likely with consolidation and bilateral lung involvement.
Qian Song-Zan, Hong Wan-Dong, Lingjie-Mao Chenfeng-Lin, Zhendong-Fang Pan

2020

COVID-19, critically ill, infection, outcome, severity

Radiology

The Performance of Deep Neural Networks in Differentiating Chest X-Rays of COVID-19 Patients From Other Bacterial and Viral Pneumonias.

In Frontiers in medicine
Chest radiography is a critical tool in the early detection, management planning, and follow-up evaluation of COVID-19 pneumonia; however, in smaller clinics around the world, there is a shortage of radiologists to analyze large number of examinations especially performed during a pandemic. Limited availability of high-resolution computed tomography and real-time polymerase chain reaction in developing countries and regions of high patient turnover also emphasizes the importance of chest radiography as both a screening and diagnostic tool. In this paper, we compare the performance of 17 available deep learning algorithms to help identify imaging features of COVID19 pneumonia. We utilize an existing diagnostic technology (chest radiography) and preexisting neural networks (DarkNet-19) to detect imaging features of COVID-19 pneumonia. Our approach eliminates the extra time and resources needed to develop new technology and associated algorithms, thus aiding the front-line healthcare workers in the race against the COVID-19 pandemic. Our results show that DarkNet-19 is the optimal pre-trained neural network for the detection of radiographic features of COVID-19 pneumonia, scoring an overall accuracy of 94.28% over 5,854 X-ray images. We also present a custom visualization of the results that can be used to highlight important visual biomarkers of the disease and disease progression.
Elgendi Mohamed, Nasir Muhammad Umer, Tang Qunfeng, Fletcher Richard Ribon, Howard Newton, Menon Carlo, Ward Rabab, Parker William, Nicolaou Savvas

2020

artificial intelligence, chest X-ray radiography, convolutional neural networks, corona virus, image classification, neural network, transfer learning

Public Health

Using Nominal Group Technique to Elucidate a COVID-19 Research Agenda for Maternal and Child Health (MCH) Populations.

In International journal of MCH and AIDS
As the global impact of the COVID-19 pandemic continues to evolve, robust data describing its effect on maternal and child health (MCH) remains limited. The aim of this study was to elucidate an agenda for COVID-19 research with particular focus on its impact within MCH populations. This was achieved using the Nominal Group Technique through which researchers identified and ranked 12 research topics across various disciplines relating to MCH in the setting of COVID-19. Proposed research topics included vaccine development, genomics, and artificial intelligence among others. The proposed research priorities could serve as a template for a vigorous COVID-19 research agenda by the NIH and other national funding agencies in the US.
Ikedionwu Chioma A, Dongarwar Deepa, Yusuf Korede K, Maiyegun Sitratullah O, Ibrahimi Sahra, Salihu Hamisu M

2020

Artificial intelligence, Big data, COVID-19, Coronavirus, MCH, Maternal and child health, Pandemics

General

The investigation of multiresolution approaches for chest X-ray image based COVID-19 detection.

In Health information science and systems
COVID-19 is a novel virus, which has a fast spreading rate, and now it is seen all around the world. The case and death numbers are increasing day by day. Some tests have been used to determine the COVID-19. Chest X-ray and chest computerized tomography (CT) are two important imaging tools for determination and monitoring of COVID-19. And new methods have been searching for determination of the COVID-19. In this paper, the investigation of various multiresolution approaches in detection of COVID-19 is carried out. Chest X-ray images are used as input to the proposed approach. As recent trend in machine learning shifts toward the deep learning, we would like to show that the traditional methods such as multiresolution approaches are still effective. To this end, the well-known multiresolution approaches namely Wavelet, Shearlet and Contourlet transforms are used to decompose the chest X-ray images and the entropy and the normalized energy approaches are employed for feature extraction from the decomposed chest X-ray images. Entropy and energy features are generally accompanied with the multiresolution approaches in texture recognition applications. The extreme learning machines (ELM) classifier is considered in the classification stage of the proposed study. A dataset containing 361 different COVID-19 chest X-ray images and 200 normal (healthy) chest X-ray images are used in the experimental works. The performance evaluation is carried out by employing various metric namely accuracy, sensitivity, specificity and precision. As deep learning is mentioned, a comparison between proposed multiresolution approaches and deep learning approaches is also carried out. To this end, deep feature extraction and fine-tuning of pretrained convolutional neural networks (CNNs) are considered. For deep feature extraction, pretrained, ResNet50 model is employed. For classification of the deep features, the Support Vector Machines (SVM) classifier is used. The ResNet50 model is also used in the fine-tuning. The experimental works show that multiresolution approaches produced better performance than the deep learning approaches. Especially, Shearlet transform outperformed at all. 99.29% accuracy score is obtained by using Shearlet transform.
Ismael Aras M, Şengür Abdulkadir

2020-Dec

COVID-19, Chest X-ray images, Contourlet, Deep learning, Multiresolution approaches, Shearlet, Wavelet

General

Review on Diagnosis of COVID-19 from Chest CT Images Using Artificial Intelligence.

In Computational and mathematical methods in medicine
The COVID-19 diagnostic approach is mainly divided into two broad categories, a laboratory-based and chest radiography approach. The last few months have witnessed a rapid increase in the number of studies use artificial intelligence (AI) techniques to diagnose COVID-19 with chest computed tomography (CT). In this study, we review the diagnosis of COVID-19 by using chest CT toward AI. We searched ArXiv, MedRxiv, and Google Scholar using the terms "deep learning", "neural networks", "COVID-19", and "chest CT". At the time of writing (August 24, 2020), there have been nearly 100 studies and 30 studies among them were selected for this review. We categorized the studies based on the classification tasks: COVID-19/normal, COVID-19/non-COVID-19, COVID-19/non-COVID-19 pneumonia, and severity. The sensitivity, specificity, precision, accuracy, area under the curve, and F1 score results were reported as high as 100%, 100%, 99.62, 99.87%, 100%, and 99.5%, respectively. However, the presented results should be carefully compared due to the different degrees of difficulty of different classification tasks.
Ozsahin Ilker, Sekeroglu Boran, Musa Musa Sani, Mustapha Mubarak Taiwo, Uzun Ozsahin Dilber

2020

General

Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers-A study to show how popularity is affecting accuracy in social media.

In Applied soft computing
COVID-19 originally known as Corona VIrus Disease of 2019, has been declared as a pandemic by World Health Organization (WHO) on 11th March 2020. Unprecedented pressures have mounted on each country to make compelling requisites for controlling the population by assessing the cases and properly utilizing available resources. The rapid number of exponential cases globally has become the apprehension of panic, fear and anxiety among people. The mental and physical health of the global population is found to be directly proportional to this pandemic disease. The current situation has reported more than twenty four million people being tested positive worldwide as of 27th August, 2020 Therefore, it's the need of the hour to implement different measures to safeguard the countries by demystifying the pertinent facts and information. This paper aims to bring out the fact that tweets containing all handles related to COVID-19 and WHO have been unsuccessful in guiding people around this pandemic outbreak appositely. This study analyses two types of tweets gathered during the pandemic times. In one case, around twenty three thousand most re-tweeted tweets within the time span from1st Jan 2019 to 23rd March 2020 have been analysed and observation says that the maximum number of the tweets portrays neutral or negative sentiments. On the other hand, a dataset containing 226668 tweets collected within the time span between December 2019 and May 2020 have been analysed which contrastingly show that there were a maximum number of positive and neutral tweets tweeted by netizens. The research demonstrates that though people have tweeted mostly positive regarding COVID-19, yet netizens were busy engrossed in re-tweeting the negative tweets and that no useful words could be found in WordCloud or computations using word frequency in tweets. The claims have been validated through a proposed model using deep learning classifiers with admissible accuracy up to 81%. Apart from these the authors have proposed the implementation of a Gaussian membership function based fuzzy rule base to correctly identify sentiments from tweets. The accuracy for the said model yields up to a permissible rate of 79%.
Chakraborty Koyel, Bhatia Surbhi, Bhattacharyya Siddhartha, Platos Jan, Bag Rajib, Hassanien Aboul Ella

2020-Sep-28

00-01, 99-00, COVID-19, Deep learning, Emotional intelligence, Fuzzy rule, Gaussian membership function, Sentiment analysis, Tweets, WHO

General

Health is the Motive and Digital is the Instrument.

In Journal of the Indian Institute of Science
The coronavirus crisis has seen an unprecedented response from India and the world. If the viral outbreak has exposed gross inadequacies in the healthcare systems of nations both rich and poor, it has stirred a digital healthcare revolution that has been building since the past decade. We have seen how this new era of digital health evolved over the years since healthcare started getting increasingly unaffordable in the western countries forcing a relook in their strategies to explosion of digital innovations in mobile telephony and applications, internet, wearable devices, artificial intelligence, robotics, big data and genomics. The single biggest trigger for the digital shift has indeed been the COVID-19 pandemic this year, more so in India with astonishing response from the private enterprise and the proactive push from the government so evident. However, the full potential of this digital revolution cannot be realized as long as core structural reforms in public healthcare do not take place along with significant boost in digital infrastructure. The way digital technologies have helped facilitate strategy and response to the global pandemic and with predictions of more zoonotic outbreaks impending in the coming years, it has become imperative for the world to increasingly adopt and integrate digital innovations to make healthcare more accessible, interconnected and affordable.
Seethalakshmi S, Nandan Rahul

2020-Sep-27

General

Deep Learning Approaches for COVID-19 Detection Based on Chest X-ray Images.

In Expert systems with applications
COVID-19 is a novel virus that causes infection in both the upper respiratory tract and the lungs. The numbers of cases and deaths have increased on a daily basis on the scale of a global pandemic. Chest X-ray images have proven useful for monitoring various lung diseases and have recently been used to monitor the COVID-19 disease. In this paper, deep-learning-based approaches, namely deep feature extraction, fine-tuning of pretrained convolutional neural networks (CNN), and end-to-end training of a developed CNN model, have been used in order to classify COVID-19 and normal (healthy) chest X-ray images. For deep feature extraction, pretrained deep CNN models (ResNet18, ResNet50, ResNet101, VGG16, and VGG19) were used. For classification of the deep features, the Support Vector Machines (SVM) classifier was used with various kernel functions, namely Linear, Quadratic, Cubic, and Gaussian. The aforementioned pretrained deep CNN models were also used for the fine-tuning procedure. A new CNN model is proposed in this study with end-to-end training. A dataset containing 180 COVID-19 and 200 normal (healthy) chest X-ray images was used in the study's experimentation. Classification accuracy was used as the performance measurement of the study. The experimental works reveal that deep learning shows potential in the detection of COVID-19 based on chest X-ray images. The deep features extracted from the ResNet50 model and SVM classifier with the Linear kernel function produced a 94.7% accuracy score, which was the highest among all the obtained results. The achievement of the fine-tuned ResNet50 model was found to be 92.6%, whilst end-to-end training of the developed CNN model produced a 91.6% result. Various local texture descriptors and SVM classifications were also used for performance comparison with alternative deep approaches; the results of which showed the deep approaches to be quite efficient when compared to the local texture descriptors in the detection of COVID-19 based on chest X-ray images.
Ismael Aras M, Şengür Abdulkadir

2020-Sep-28

COVID-19, chest X-ray images, convolutional neural networks, deep learning, local texture descriptors

Internal Medicine

Development and Validation of the Quick COVID-19 Severity Index: A Prognostic Tool for Early Clinical Decompensation.

In Annals of emergency medicine ; h5-index 53.0

STUDY OBJECTIVE : The goal of this study is to create a predictive, interpretable model of early hospital respiratory failure among emergency department (ED) patients admitted with coronavirus disease 2019 (COVID-19).

METHODS : This was an observational, retrospective, cohort study from a 9-ED health system of admitted adult patients with severe acute respiratory syndrome coronavirus 2 (COVID-19) and an oxygen requirement less than or equal to 6 L/min. We sought to predict respiratory failure within 24 hours of admission as defined by oxygen requirement of greater than 10 L/min by low-flow device, high-flow device, noninvasive or invasive ventilation, or death. Predictive models were compared with the Elixhauser Comorbidity Index, quick Sequential [Sepsis-related] Organ Failure Assessment, and the CURB-65 pneumonia severity score.

RESULTS : During the study period, from March 1 to April 27, 2020, 1,792 patients were admitted with COVID-19, 620 (35%) of whom had respiratory failure in the ED. Of the remaining 1,172 admitted patients, 144 (12.3%) met the composite endpoint within the first 24 hours of hospitalization. On the independent test cohort, both a novel bedside scoring system, the quick COVID-19 Severity Index (area under receiver operating characteristic curve mean 0.81 [95% confidence interval {CI} 0.73 to 0.89]), and a machine-learning model, the COVID-19 Severity Index (mean 0.76 [95% CI 0.65 to 0.86]), outperformed the Elixhauser mortality index (mean 0.61 [95% CI 0.51 to 0.70]), CURB-65 (0.50 [95% CI 0.40 to 0.60]), and quick Sequential [Sepsis-related] Organ Failure Assessment (0.59 [95% CI 0.50 to 0.68]). A low quick COVID-19 Severity Index score was associated with a less than 5% risk of respiratory decompensation in the validation cohort.

CONCLUSION : A significant proportion of admitted COVID-19 patients progress to respiratory failure within 24 hours of admission. These events are accurately predicted with bedside respiratory examination findings within a simple scoring system.

Haimovich Adrian D, Ravindra Neal G, Stoytchev Stoytcho, Young H Patrick, Wilson Francis P, van Dijk David, Schulz Wade L, Taylor R Andrew

2020-Oct

General

COVIDomaly: A Deep Convolutional Autoencoder Approach for Detecting Early Cases of COVID-19

ArXiv Preprint
As of September 2020, the COVID-19 pandemic continues to devastate the health and well-being of the global population. With more than 33 million confirmed cases and over a million deaths, global health organizations are still a long way from fully containing the pandemic. This pandemic has raised serious questions about the emergency preparedness of health agencies, not only in terms of treatment of an unseen disease, but also in identifying its early symptoms. In the particular case of COVID-19, several studies have indicated that chest radiography images of the infected patients show characteristic abnormalities. However, at the onset of a given pandemic, such as COVID-19, there may not be sufficient data for the affected cases to train models for their robust detection. Hence, supervised classification is ill-posed for this problem because the time spent in collecting large amounts of infected peoples' data could lead to the loss of human lives and delays in preventive interventions. Therefore, we formulate this problem within a one-class classification framework, in which the data for healthy patients is abundantly available, whereas no training data is present for the class of interest (COVID-19 in our case). To solve this problem, we present COVIDomaly, a convolutional autoencoder framework to detect unseen COVID-19 cases from the chest radiographs. We tested two settings on a publicly available dataset (COVIDx) by training the model on chest X-rays from (i) only healthy adults, and (ii) healthy and other non-COVID-19 pneumonia, and detected COVID-19 as an anomaly. After performing 3-fold cross validation, we obtain a pooled ROC-AUC of 0.7652 and 0.6902 in the two settings respectively. These results are very encouraging and pave the way towards research for ensuring emergency preparedness in future pandemics, especially the ones that could be detected from chest X-rays.
Faraz Khoshbakhtian, Ahmed Bilal Ashraf, Shehroz S. Khan

2020-10-06

General

COVID-19 and Media Datasets: Period- and location-specific textual data mining.

In Data in brief
The vocabulary used in news on a disease such as COVID-19 changes according the period [4]. This aspect is discussed on the basis of MEDISYS-sourced media datasets via two studies. The first focuses on terminology extraction and the second on period prediction according to the textual content using machine learning approaches.
Roche Mathieu

2020-Sep-30

COVID-19, Classification, Corpus, NLP, Terminology Extraction, Text-Mining

General

Reference ontology and database annotation of the COVID-19 Open Research Dataset (CORD-19)

bioRxiv Preprint
The COVID-19 Open Research Dataset (CORD-19) was released in March 2020 to allow the machine learning and wider research community to develop techniques to answer scientific questions on COVID-19. The data set consists of a large collection of scientific literature, including over 100,000 full text papers. Annotating training data to normalise variability in biological entities can improve the performance of downstream analysis and interpretation. To facilitate and enhance the use of the CORD-19 data in these applications, in late March 2020 we performed a comprehensive annotation process using named entity recognition tool, TERMite, along with a number of large reference ontologies and vocabularies including domains of genes, proteins, drugs and virus strains. The additional annotation has identified and tagged over 45 million entities within the corpus made up of 62,746 unique biomedical entities. The latest updated version of the annotated data, as well as older versions, is made openly available under GPL-2.0 License for the community to use at: https://github.com/SciBiteLabs/CORD19 .
Giles, O.; Huntley, R.; Karlsson, A.; Lomax, J.; Malone, J.

2020-10-05

Radiology

Effectiveness of COVID-19 diagnosis and management tools: A review.

In Radiography (London, England : 1995)

OBJECTIVE : To review the available literature concerning the effectiveness of the COVID-19 diagnostic tools.

BACKGROUND : With the absence of specific treatment/vaccines for the coronavirus COVID-19, the most appropriate approach to control this infection is to quarantine people and isolate symptomatic people and suspected or infected cases. Although real-time reverse transcription-polymerase chain reaction (RT-PCR) assay is considered the first tool to make a definitive diagnosis of COVID-19 disease, the high false negative rate, low sensitivity, limited supplies and strict requirements for laboratory settings might delay accurate diagnosis. Computed tomography (CT) has been reported as an important tool to identify and investigate suspected patients with COVID-19 disease at early stage.

KEY FINDINGS : RT-PCR shows low sensitivity (60-71%) in diagnosing patients with COVID-19 infection compared to the CT chest. Several studies reported that chest CT scans show typical imaging features in all patients with COVID-19. This high sensitivity and initial presentation in CT chest can be helpful in rectifying false negative results obtained from RT-PCR. As COVID-19 has similar manifestations to other pneumonia diseases, artificial intelligence (AI) might help radiologists to differentiate COVID-19 from other pneumonia diseases.

CONCLUSION : Although CT scan is a powerful tool in COVID-19 diagnosis, it is not sufficient to detect COVID-19 alone due to the low specificity (25%), and challenges that radiologists might face in differentiating COVID-19 from other viral pneumonia on chest CT scans. AI might help radiologists to differentiate COVID-19 from other pneumonia diseases.

IMPLICATION FOR PRACTICE : Both RT-PCR and CT tests together would increase sensitivity and improve quarantine efficacy, an impact neither could achieve alone.

Alsharif W, Qurashi A

2020-Sep-21

Artificial intelligence, CT scan, Consolidation, Crazy-paving, Ground-glass opacification, RT-PCR

General

Practicing precision medicine with intelligently integrative clinical and multi-omics data analysis.

In Human genomics
Precision medicine aims to empower clinicians to predict the most appropriate course of action for patients with complex diseases like cancer, diabetes, cardiomyopathy, and COVID-19. With a progressive interpretation of the clinical, molecular, and genomic factors at play in diseases, more effective and personalized medical treatments are anticipated for many disorders. Understanding patient's metabolomics and genetic make-up in conjunction with clinical data will significantly lead to determining predisposition, diagnostic, prognostic, and predictive biomarkers and paths ultimately providing optimal and personalized care for diverse, and targeted chronic and acute diseases. In clinical settings, we need to timely model clinical and multi-omics data to find statistical patterns across millions of features to identify underlying biologic pathways, modifiable risk factors, and actionable information that support early detection and prevention of complex disorders, and development of new therapies for better patient care. It is important to calculate quantitative phenotype measurements, evaluate variants in unique genes and interpret using ACMG guidelines, find frequency of pathogenic and likely pathogenic variants without disease indicators, and observe autosomal recessive carriers with a phenotype manifestation in metabolome. Next, ensuring security to reconcile noise, we need to build and train machine-learning prognostic models to meaningfully process multisource heterogeneous data to identify high-risk rare variants and make medically relevant predictions. The goal, today, is to facilitate implementation of mainstream precision medicine to improve the traditional symptom-driven practice of medicine, and allow earlier interventions using predictive diagnostics and tailoring better-personalized treatments. We strongly recommend automated implementation of cutting-edge technologies, utilizing machine learning (ML) and artificial intelligence (AI) approaches for the multimodal data aggregation, multifactor examination, development of knowledgebase of clinical predictors for decision support, and best strategies for dealing with relevant ethical issues.
Ahmed Zeeshan

2020-Oct-02

Artificial intelligence, Clinics, Genomics, Integrative analysis, Machine learning, Metabolomics, Precision medicine

General

Blockchain in Healthcare: Insights on COVID-19.

In International journal of environmental research and public health ; h5-index 73.0
The SARS-CoV2 pandemic has impacted risk management globally. Blockchain has been increasingly applied to healthcare management, as a strategic tool to strengthen operative protocols and to create the proper basis for an efficient and effective evidence-based decisional process. We aim to validate blockchain in healthcare, and to suggest a trace-route for a COVID19-safe clinical practice. The use of blockchain in combination with artificial intelligence systems allows the creation of a generalizable predictive system that could contribute to the containment of pandemic risk on national territory. A SWOT analysis of the adoption of a blockchain-based prediction model in healthcare and SARS-CoV-2 infection has been carried out to underline opportunities and limits to its adoption. Blockchain could play a strategic role in future digital healthcare: specifically, it may work to improve COVID19-safe clinical practice. The main concepts, and particularly those related to clinical workflow, obtainable from different blockchain-based models have been reported here and critically discussed.
Fusco Antonio, Dicuonzo Grazia, Dell’Atti Vittorio, Tatullo Marco

2020-Sep-30

COVID-19, artificial intelligence, blockchain, global health, healthcare management

Pathology

In-silico drug repurposing study predicts the combination of pirfenidone and melatonin as a promising candidate therapy to reduce SARS-CoV-2 infection progression and respiratory distress caused by cytokine storm.

In PloS one ; h5-index 176.0
From January 2020, COVID-19 is spreading around the world producing serious respiratory symptoms in infected patients that in some cases can be complicated by the severe acute respiratory syndrome, sepsis and septic shock, multiorgan failure, including acute kidney injury and cardiac injury. Cost and time efficient approaches to reduce the burthen of the disease are needed. To find potential COVID-19 treatments among the whole arsenal of existing drugs, we combined system biology and artificial intelligence-based approaches. The drug combination of pirfenidone and melatonin has been identified as a candidate treatment that may contribute to reduce the virus infection. Starting from different drug targets the effect of the drugs converges on human proteins with a known role in SARS-CoV-2 infection cycle. Simultaneously, GUILDify v2.0 web server has been used as an alternative method to corroborate the effect of pirfenidone and melatonin against the infection of SARS-CoV-2. We have also predicted a potential therapeutic effect of the drug combination over the respiratory associated pathology, thus tackling at the same time two important issues in COVID-19. These evidences, together with the fact that from a medical point of view both drugs are considered safe and can be combined with the current standard of care treatments for COVID-19 makes this combination very attractive for treating patients at stage II, non-severe symptomatic patients with the presence of virus and those patients who are at risk of developing severe pulmonary complications.
Artigas Laura, Coma Mireia, Matos-Filipe Pedro, Aguirre-Plans Joaquim, Farrés Judith, Valls Raquel, Fernandez-Fuentes Narcis, de la Haba-Rodriguez Juan, Olvera Alex, Barbera Jose, Morales Rafael, Oliva Baldo, Mas Jose Manuel

2020

General

A Novel Strategy for the Development of Vaccines for SARS-CoV-2 (COVID-19) and Other Viruses Using AI and Viral Shell Disorder.

In Journal of proteome research
A model that predicts levels of coronavirus (CoV) respiratory and fecal-oral transmission potentials based on the shell disorder has been built using neural network (artificial intelligence, AI) analysis of the percentage of disorder (PID) in the nucleocapsid, N, and membrane, M, proteins of the inner and outer viral shells, respectively. Using primarily the PID of N, SARS-CoV-2 is grouped as having intermediate levels of both respiratory and fecal-oral transmission potentials. Related studies, using similar methodologies, have found strong positive correlations between virulence and inner shell disorder among numerous viruses, including Nipah, Ebola, and Dengue viruses. There is some evidence that this is also true for SARS-CoV-2 and SARS-CoV, which have N PIDs of 48% and 50%, and case-fatality rates of 0.5-5% and 10.9%, respectively. The underlying relationship between virulence and respiratory potentials has to do with the viral loads of vital organs and body fluids, respectively. Viruses can spread by respiratory means only if the viral loads in saliva and mucus exceed certain minima. Similarly, a patient is likelier to die when the viral load overwhelms vital organs. Greater disorder in inner shell proteins has been known to play important roles in the rapid replication of viruses by enhancing the efficiency pertaining to protein-protein/DNA/RNA/lipid bindings. This paper suggests a novel strategy in attenuating viruses involving comparison of disorder patterns of inner shells (N) of related viruses to identify residues and regions that could be ideal for mutation. The M protein of SARS-CoV-2 has one of the lowest M PID values (6%) in its family, and therefore, this virus has one of the hardest outer shells, which makes it resistant to antimicrobial enzymes in body fluid. While this is likely responsible for its greater contagiousness, the risks of creating an attenuated virus with a more disordered M are discussed.
Goh Gerard Kian-Meng, Dunker A Keith, Foster James A, Uversky Vladimir N

2020-Oct-02

Nipah, antibody, attenuate, coronavirus, covid, disorder, ebola, function, shell, immune, intrinsic, matrix, nucleocapsid, nucleoprotein, protein, shell, structure, vaccine, viral, virulence

General

Artificial intelligence in pulmonary medicine: computer vision, predictive model and COVID-19.

In European respiratory review : an official journal of the European Respiratory Society
Artificial intelligence (AI) is transforming healthcare delivery. The digital revolution in medicine and healthcare information is prompting a staggering growth of data intertwined with elements from many digital sources such as genomics, medical imaging and electronic health records. Such massive growth has sparked the development of an increasing number of AI-based applications that can be deployed in clinical practice. Pulmonary specialists who are familiar with the principles of AI and its applications will be empowered and prepared to seize future practice and research opportunities. The goal of this review is to provide pulmonary specialists and other readers with information pertinent to the use of AI in pulmonary medicine. First, we describe the concept of AI and some of the requisites of machine learning and deep learning. Next, we review some of the literature relevant to the use of computer vision in medical imaging, predictive modelling with machine learning, and the use of AI for battling the novel severe acute respiratory syndrome-coronavirus-2 pandemic. We close our review with a discussion of limitations and challenges pertaining to the further incorporation of AI into clinical pulmonary practice.
Khemasuwan Danai, Sorensen Jeffrey S, Colt Henri G

2020-Sep-30

General

Evaluation of Country Dietary Habits Using Machine Learning Techniques in Relation to Deaths from COVID-19.

In Healthcare (Basel, Switzerland)
COVID-19 disease has affected almost every country in the world. The large number of infected people and the different mortality rates between countries has given rise to many hypotheses about the key points that make the virus so lethal in some places. In this study, the eating habits of 170 countries were evaluated in order to find correlations between these habits and mortality rates caused by COVID-19 using machine learning techniques that group the countries together according to the different distribution of fat, energy, and protein across 23 different types of food, as well as the amount ingested in kilograms. Results shown how obesity and the high consumption of fats appear in countries with the highest death rates, whereas countries with a lower rate have a higher level of cereal consumption accompanied by a lower total average intake of kilocalories.
García-Ordás María Teresa, Arias Natalia, Benavides Carmen, García-Olalla Oscar, Benítez-Andrades José Alberto

2020-Sep-29

COVID-19, K-Means, KCal, countries, deaths, fat, machine learning, protein

Cardiology

Application of Artificial Intelligence Trilogy Accelerates Survey Efficacy for Severe Acute Respiratory Syndrome Coronavirus 2 Infection within Smart Quarantine Stations.

In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : As the coronavirus disease (COVID-19) epidemic worsens, the burden of quarantine stations (Q stations) outside of emergency rooms (ERs) at every hospital increases daily. To prepare for the screening workload inside Q stations, all staff with medical licenses are required to support the working shift. Therefore, the need to simplify the workflow and decision-making process for physicians and surgeons from all subspecialist fields is necessary.

OBJECTIVE : To demonstrate how the NCKUH AI trilogy of smart Q station diversion, AI-assisted image interpretation, and a built-in clinical decision-making algorithm improves medical care and reduces quarantine processing time.

METHODS : This observational study on the emerging COVID-19 pandemic included constitutively 643 patients. The artificial intelligence (AI) trilogy, i.e., 1) smart Q station diversion, 2) AI-assisted image interpretation, and 3) a built-in clinical decision-making algorithm on a tablet computer, was applied to shorten the quarantine survey and reduce processing time during the COVID-19 pandemic.

RESULTS : The use of the AI trilogy facilitated the processing of suspected cases, with or without symptoms, travel, occupation, and contact or clustering histories, which were performed with a tablet computer device. A separate AI-mode function that could quickly recognize pulmonary infiltrates on chest X-rays was merged into the smart clinical assisting system (SCAS), and this model was subsequently trained with COVID-19 pneumonia cases from the GitHub open source dataset. The detection rates were 93.2% and 45.5% in posteroanterior and anteroposterior chest X-rays, respectively. The SCAS algorithm was continuously adjusted based on the frequently updated Taiwan Center for Disease Control public safety guidelines for faster clinical decision making. Our ex vivo study demonstrated the efficiency of 75% alcohol disinfection on the tablet computer surface for a 20-μL positive SARS-CoV-2 virus solution. The positive rate of a real-time polymerase chain reaction was 100% and became 75% and 0% after one and two disinfection procedures (n=4), respectively. To further analyze the effect of the AI application in the Q station, we subdivided the Q station into with or without AI groups. Compared with the conventional ER track (n=281), the survey time at the clinical Q station (n=1520) was significantly shortened [median survey time (95% confidence interval; CI) at the ER: 153 (108.5-205) min vs. at the clinical Q station: 35 (24-56) min; p<0.0001]. Furthermore, the use of the AI application in the Q station reduced the survey time in the Q station [median survey time (95% CI) without AI: 100.5 (40.3-152.5) min vs. with AI in the Q station: 34 (24-53) min; p<0.0001].

CONCLUSIONS : The AI trilogy improves medical care workflow safely by shortening the quarantine survey and reducing processing time, especially during an emerging epidemic infectious disease.

CLINICALTRIAL :

Liu Ping-Yen, Tsai Yi-Shan, Chen Po-Lin, Tsai Huey-Pin, Hsu Ling-Wei, Wang Chi-Shiang, Lee Nan-Yao, Huang Mu-Shiang, Wu Yun-Chiao, Ko Wen-Chien, Yang Yi-Ching, Chiang Jung-Hsien, Shen Meng-Ru

2020-Sep-16

Internal Medicine

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

In Molecular psychiatry ; h5-index 103.0
An amendment to this paper has been published and can be accessed via a link at the top of the paper.
Wang Quan Qiu, Kaelber David C, Xu Rong, Volkow Nora D

2020-Sep-30

Surgery

Frontiers of Robotic Gastroscopy: A Comprehensive Review of Robotic Gastroscopes and Technologies.

In Cancers
Upper gastrointestinal (UGI) tract pathology is common worldwide. With recent advancements in robotics, innovative diagnostic and treatment devices have been developed and several translational attempts made. This review paper aims to provide a highly pictorial critical review of robotic gastroscopes, so that clinicians and researchers can obtain a swift and comprehensive overview of key technologies and challenges. Therefore, the paper presents robotic gastroscopes, either commercial or at a progressed technology readiness level. Among them, we show tethered and wireless gastroscopes, as well as devices aimed for UGI surgery. The technological features of these instruments, as well as their clinical adoption and performance, are described and compared. Although the existing endoscopic devices have thus far provided substantial improvements in the effectiveness of diagnosis and treatment, there are certain aspects that represent unwavering predicaments of the current gastroenterology practice. A detailed list includes difficulties and risks, such as transmission of communicable diseases (e.g., COVID-19) due to the doctor-patient proximity, unchanged learning curves, variable detection rates, procedure-related adverse events, endoscopists' and nurses' burnouts, limited human and/or material resources, and patients' preferences to choose non-invasive options that further interfere with the successful implementation and adoption of routine screening. The combination of robotics and artificial intelligence, as well as remote telehealth endoscopy services, are also discussed, as viable solutions to improve existing platforms for diagnosis and treatment are emerging.
Marlicz Wojciech, Ren Xuyang, Robertson Alexander, Skonieczna-Żydecka Karolina, Łoniewski Igor, Dario Paolo, Wang Shuxin, Plevris John N, Koulaouzidis Anastasios, Ciuti Gastone

2020-Sep-28

artificial intelligence, gastric cancer, gastroscopy, machine learning, robotic gastroscopy

General

A multimodal deep learning-based drug repurposing approach for treatment of COVID-19.

In Molecular diversity
Recently, various computational methods have been proposed to find new therapeutic applications of the existing drugs. The Multimodal Restricted Boltzmann Machine approach (MM-RBM), which has the capability to connect the information about the multiple modalities, can be applied to the problem of drug repurposing. The present study utilized MM-RBM to combine two types of data, including the chemical structures data of small molecules and differentially expressed genes as well as small molecules perturbations. In the proposed method, two separate RBMs were applied to find out the features and the specific probability distribution of each datum (modality). Besides, RBM was used to integrate the discovered features, resulting in the identification of the probability distribution of the combined data. The results demonstrated the significance of the clusters acquired by our model. These clusters were used to discover the medicines which were remarkably similar to the proposed medications to treat COVID-19. Moreover, the chemical structures of some small molecules as well as dysregulated genes' effect led us to suggest using these molecules to treat COVID-19. The results also showed that the proposed method might prove useful in detecting the highly promising remedies for COVID-19 with minimum side effects. All the source codes are accessible using https://github.com/LBBSoft/Multimodal-Drug-Repurposing.git.
Hooshmand Seyed Aghil, Zarei Ghobadi Mohadeseh, Hooshmand Seyyed Emad, Azimzadeh Jamalkandi Sadegh, Alavi Seyed Mehdi, Masoudi-Nejad Ali

2020-Sep-30

COVID-19, Deep learning, Drug repurposing, Multimodal data fusion, Restricted Boltzmann machine

General

Exploring the Potential of Artificial Intelligence and Machine Learning to Combat COVID-19 and Existing Opportunities for LMIC: A Scoping Review.

In Journal of primary care & community health

BACKGROUND : In the face of the current time-sensitive COVID-19 pandemic, the limited capacity of healthcare systems resulted in an emerging need to develop newer methods to control the spread of the pandemic. Artificial Intelligence (AI), and Machine Learning (ML) have a vast potential to exponentially optimize health care research. The use of AI-driven tools in LMIC can help in eradicating health inequalities and decrease the burden on health systems.

METHODS : The literature search for this Scoping review was conducted through the PubMed database using keywords: COVID-19, Artificial Intelligence (AI), Machine Learning (ML), and Low Middle-Income Countries (LMIC). Forty-three articles were identified and screened for eligibility and 13 were included in the final review. All the items of this Scoping review are reported using guidelines for PRISMA extension for scoping reviews (PRISMA-ScR).

RESULTS : Results were synthesized and reported under 4 themes. (a) The need of AI during this pandemic: AI can assist to increase the speed and accuracy of identification of cases and through data mining to deal with the health crisis efficiently, (b) Utility of AI in COVID-19 screening, contact tracing, and diagnosis: Efficacy for virus detection can a be increased by deploying the smart city data network using terminal tracking system along-with prediction of future outbreaks, (c) Use of AI in COVID-19 patient monitoring and drug development: A Deep learning system provides valuable information regarding protein structures associated with COVID-19 which could be utilized for vaccine formulation, and (d) AI beyond COVID-19 and opportunities for Low-Middle Income Countries (LMIC): There is a lack of financial, material, and human resources in LMIC, AI can minimize the workload on human labor and help in analyzing vast medical data, potentiating predictive and preventive healthcare.

CONCLUSION : AI-based tools can be a game-changer for diagnosis, treatment, and management of COVID-19 patients with the potential to reshape the future of healthcare in LMIC.

Naseem Maleeha, Akhund Ramsha, Arshad Hajra, Ibrahim Muhammad Talal

COVID-19, artificial intelligence, low middle-income countries, machine learning, pandemic

General

Outcomes associated with SARS-CoV-2 viral clades in COVID-19.

In medRxiv : the preprint server for health sciences
Background The COVID-19 epidemic of 2019-20 is due to the novel coronavirus SARS-CoV-2. Following first case description in December, 2019 this virus has infected over 10 million individuals and resulted in at least 500,000 deaths world-wide. The virus is undergoing rapid mutation, with two major clades of sequence variants emerging. This study sought to determine whether SARS-CoV-2 sequence variants are associated with differing outcomes among COVID-19 patients in a single medical system. Methods Whole genome SARS-CoV-2 RNA sequence was obtained from isolates collected from patients registered in the University of Washington Medicine health system between March 1 and April 15, 2020. Demographic and baseline medical data along with outcomes of hospitalization and death were collected. Statistical and machine learning models were applied to determine if viral genetic variants were associated with specific outcomes of hospitalization or death. Findings Full length SARS-CoV-2 sequence was obtained 190 subjects with clinical outcome data. 35 (18.4%) were hospitalized and 14 (7.4%) died from complications of infection. A total of 289 single nucleotide variants were identified. Clustering methods demonstrated two major viral clades, which could be readily distinguished by 12 polymorphisms in 5 genes. A trend toward higher rates of hospitalization of patients with Clade 2 was observed (p=0.06). Machine learning models utilizing patient demographics and co-morbidities achieved area-under-the-curve (AUC) values of 0.93 for predicting hospitalization. Addition of viral clade or sequence information did not significantly improve models for outcome prediction. Conclusion SARS-CoV-2 shows substantial sequence diversity in a community-based sample. Two dominant clades of virus are in circulation. Among patients sufficiently ill to warrant testing for virus, no significant difference in outcomes of hospitalization or death could be discerned between clades in this sample. Major risk factors for hospitalization and death for either major clade of virus include patient age and comorbid conditions.
Nakamichi Kenji, Shen Jolie Zhu, Lee Cecilia S, Lee Aaron Y, Roberts Emma Adaline, Simonson Paul D, Roychoudhury Pavitra, Andriesen Jessica G, Randhawa April K, Mathias Patrick C, Greninger Alex, Jerome Keith R, Van Gelder Russell N

2020-Sep-25

Pathology

AI for radiographic COVID-19 detection selects shortcuts over signal.

In medRxiv : the preprint server for health sciences
Artificial intelligence (AI) researchers and radiologists have recently reported AI systems that accurately detect COVID-19 in chest radiographs. However, the robustness of these systems remains unclear. Using state-of-the-art techniques in explainable AI, we demonstrate that recent deep learning systems to detect COVID-19 from chest radiographs rely on confounding factors rather than medical pathology, creating an alarming situation in which the systems appear accurate, but fail when tested in new hospitals.
DeGrave Alex J, Janizek Joseph D, Lee Su-In

2020-Sep-14

General

Improvement and Multi-Population Generalizability of a Deep Learning-Based Chest Radiograph Severity Score for COVID-19.

In medRxiv : the preprint server for health sciences

PURPOSE : To improve and test the generalizability of a deep learning-based model for assessment of COVID-19 lung disease severity on chest radiographs (CXRs) from different patient populations.

MATERIALS AND METHODS : A published convolutional Siamese neural network-based model previously trained on hospitalized patients with COVID-19 was tuned using 250 outpatient CXRs. This model produces a quantitative measure of COVID-19 lung disease severity (pulmonary x-ray severity (PXS) score). The model was evaluated on CXRs from four test sets, including 3 from the United States (patients hospitalized at an academic medical center (N=154), patients hospitalized at a community hospital (N=113), and outpatients (N=108)) and 1 from Brazil (patients at an academic medical center emergency department (N=303)). Radiologists from both countries independently assigned reference standard CXR severity scores, which were correlated with the PXS scores as a measure of model performance (Pearson r). The Uniform Manifold Approximation and Projection (UMAP) technique was used to visualize the neural network results.

RESULTS : Tuning the deep learning model with outpatient data improved model performance in two United States hospitalized patient datasets (r=0.88 and r=0.90, compared to baseline r=0.86). Model performance was similar, though slightly lower, when tested on the United States outpatient and Brazil emergency department datasets (r=0.86 and r=0.85, respectively). UMAP showed that the model learned disease severity information that generalized across test sets.

CONCLUSIONS : Performance of a deep learning-based model that extracts a COVID-19 severity score on CXRs improved using training data from a different patient cohort (outpatient versus hospitalized) and generalized across multiple populations.

Li Matthew D, Arun Nishanth T, Aggarwal Mehak, Gupta Sharut, Singh Praveer, Little Brent P, Mendoza Dexter P, Corradi Gustavo C A, Takahashi Marcelo S, Ferraciolli Suely F, Succi Marc D, Lang Min, Bizzo Bernardo C, Dayan Ittai, Kitamura Felipe C, Kalpathy-Cramer Jayashree

2020-Sep-18

General

Tailoring Time Series Models For Forecasting Coronavirus Spread: Case Studies of 187 Countries.

In Computational and structural biotechnology journal
When will the coronavirus end? Are the current precautionary measures effective? To answer these questions it is important to forecast regularly and accurately the spread of COVID-19 infections. Different time series forecasting models have been applied in the literature to tackle the pandemic situation. The current research efforts developed few of these models and validates its accuracy for selected countries. It becomes difficult to draw an objective comparison between the performance of these models at a global scale. This is because, the time series trend for the infection differs between the countries depending on the strategies adopted by the healthcare organizations to decrease the spread. Consequently, it is important to develop a tailored model for a country that allows healthcare organizations to better judge the effect of the undertaken precautionary measures, and provision more efficiently the needed resources to face this disease. This paper addresses this void. We develop and compare the performance of the time series models in the literature in terms of root mean squared error and mean absolute percentage error.
Ismail Leila, Materwala Huned, Znati Taieb, Turaev Sherzod, Khan Moien A B

2020-Sep-24

COVID-19, Coronavirus, Epidemic transmission, Forecasting models, Machine learning models, Pandemic, Time series models

General

The Emerging Role of Artificial Intelligence in the Fight Against COVID-19.

In European urology ; h5-index 128.0
The coronavirus disease 2019 (COVID-19) pandemic has generated large volumes of clinical data that can be an invaluable resource towards answering a number of important questions for this and future pandemics. Artificial intelligence can have an important role in analysing such data to identify populations at higher risk of COVID-19-related urological pathologies and to suggest treatments that block viral entry into cells by interrupting the angiotensin-converting enzyme 2-transmembrane serine protease 2 (ACE2-TMPRSS2) pathway.
Ghose Aruni, Roy Sabyasachi, Vasdev Nikhil, Olsburgh Jonathon, Dasgupta Prokar

2020-Sep-17

Radiology

Using machine learning of clinical data to diagnose COVID-19: a systematic review and meta-analysis.

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

BACKGROUND : The recent Coronavirus Disease 2019 (COVID-19) pandemic has placed severe stress on healthcare systems worldwide, which is amplified by the critical shortage of COVID-19 tests.

METHODS : In this study, we propose to generate a more accurate diagnosis model of COVID-19 based on patient symptoms and routine test results by applying machine learning to reanalyzing COVID-19 data from 151 published studies. We aim to investigate correlations between clinical variables, cluster COVID-19 patients into subtypes, and generate a computational classification model for discriminating between COVID-19 patients and influenza patients based on clinical variables alone.

RESULTS : We discovered several novel associations between clinical variables, including correlations between being male and having higher levels of serum lymphocytes and neutrophils. We found that COVID-19 patients could be clustered into subtypes based on serum levels of immune cells, gender, and reported symptoms. Finally, we trained an XGBoost model to achieve a sensitivity of 92.5% and a specificity of 97.9% in discriminating COVID-19 patients from influenza patients.

CONCLUSIONS : We demonstrated that computational methods trained on large clinical datasets could yield ever more accurate COVID-19 diagnostic models to mitigate the impact of lack of testing. We also presented previously unknown COVID-19 clinical variable correlations and clinical subgroups.

Li Wei Tse, Ma Jiayan, Shende Neil, Castaneda Grant, Chakladar Jaideep, Tsai Joseph C, Apostol Lauren, Honda Christine O, Xu Jingyue, Wong Lindsay M, Zhang Tianyi, Lee Abby, Gnanasekar Aditi, Honda Thomas K, Kuo Selena Z, Yu Michael Andrew, Chang Eric Y, Rajasekaran Mahadevan Raj, Ongkeko Weg M

2020-Sep-29

COVID-19, Diagnostic model, Machine learning

Public Health

Exploring U.S. Shifts in Anti-Asian Sentiment with the Emergence of COVID-19.

In International journal of environmental research and public health ; h5-index 73.0
Background: Anecdotal reports suggest a rise in anti-Asian racial attitudes and discrimination in response to COVID-19. Racism can have significant social, economic, and health impacts, but there has been little systematic investigation of increases in anti-Asian prejudice. Methods: We utilized Twitter's Streaming Application Programming Interface (API) to collect 3,377,295 U.S. race-related tweets from November 2019-June 2020. Sentiment analysis was performed using support vector machine (SVM), a supervised machine learning model. Accuracy for identifying negative sentiments, comparing the machine learning model to manually labeled tweets was 91%. We investigated changes in racial sentiment before and following the emergence of COVID-19. Results: The proportion of negative tweets referencing Asians increased by 68.4% (from 9.79% in November to 16.49% in March). In contrast, the proportion of negative tweets referencing other racial/ethnic minorities (Blacks and Latinx) remained relatively stable during this time period, declining less than 1% for tweets referencing Blacks and increasing by 2% for tweets referencing Latinx. Common themes that emerged during the content analysis of a random subsample of 3300 tweets included: racism and blame (20%), anti-racism (20%), and daily life impact (27%). Conclusion: Social media data can be used to provide timely information to investigate shifts in area-level racial sentiment.
Nguyen Thu T, Criss Shaniece, Dwivedi Pallavi, Huang Dina, Keralis Jessica, Hsu Erica, Phan Lynn, Nguyen Leah H, Yardi Isha, Glymour M Maria, Allen Amani M, Chae David H, Gee Gilbert C, Nguyen Quynh C

2020-Sep-25

big data, content analysis, minority groups, racial bias, social media

Public Health

Early prediction of mortality risk among patients with severe COVID-19, using machine learning.

In International journal of epidemiology ; h5-index 76.0

BACKGROUND : Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 infection, has been spreading globally. We aimed to develop a clinical model to predict the outcome of patients with severe COVID-19 infection early.

METHODS : Demographic, clinical and first laboratory findings after admission of 183 patients with severe COVID-19 infection (115 survivors and 68 non-survivors from the Sino-French New City Branch of Tongji Hospital, Wuhan) were used to develop the predictive models. Machine learning approaches were used to select the features and predict the patients' outcomes. The area under the receiver operating characteristic curve (AUROC) was applied to compare the models' performance. A total of 64 with severe COVID-19 infection from the Optical Valley Branch of Tongji Hospital, Wuhan, were used to externally validate the final predictive model.

RESULTS : The baseline characteristics and laboratory tests were significantly different between the survivors and non-survivors. Four variables (age, high-sensitivity C-reactive protein level, lymphocyte count and d-dimer level) were selected by all five models. Given the similar performance among the models, the logistic regression model was selected as the final predictive model because of its simplicity and interpretability. The AUROCs of the external validation sets were 0.881. The sensitivity and specificity were 0.839 and 0.794 for the validation set, when using a probability of death of 50% as the cutoff. Risk score based on the selected variables can be used to assess the mortality risk. The predictive model is available at [https://phenomics.fudan.edu.cn/risk_scores/].

CONCLUSIONS : Age, high-sensitivity C-reactive protein level, lymphocyte count and d-dimer level of COVID-19 patients at admission are informative for the patients' outcomes.

Hu Chuanyu, Liu Zhenqiu, Jiang Yanfeng, Shi Oumin, Zhang Xin, Xu Kelin, Suo Chen, Wang Qin, Song Yujing, Yu Kangkang, Mao Xianhua, Wu Xuefu, Wu Mingshan, Shi Tingting, Jiang Wei, Mu Lina, Tully Damien C, Xu Lei, Jin Li, Li Shusheng, Tao Xuejin, Zhang Tiejun, Chen Xingdong

2020-Sep-23

COVID-19, death, fatality rate, machine learning, predictive model

General

Detection Methods of COVID-19.

In SLAS technology
Since being first detected in China, coronavirus disease 2019 (COVID-19) has spread rapidly across the world, triggering a global pandemic with no viable cure in sight. As a result, national responses have focused on the effective minimization of the spread. Border control measures and travel restrictions have been implemented in a number of countries to limit the import and export of the virus. The detection of COVID-19 is a key task for physicians. The erroneous results of early laboratory tests and their delays led researchers to focus on different options. Information obtained from computed tomography (CT) and radiological images is important for clinical diagnosis. Therefore, it is worth developing a rapid method of detection of viral diseases through the analysis of radiographic images. We propose a novel method of detection of COVID-19. The purpose is to provide clinical decision support to healthcare workers and researchers. The article is to support researchers working on early detection of COVID-19 as well as similar viral diseases.
Echtioui Amira, Zouch Wassim, Ghorbel Mohamed, Mhiri Chokri, Hamam Habib

2020-Sep-30

CNN, COVID-19, convolutional neural network, deep learning, diagnosis

General

Risk Factors for Mortality in Critically Ill Patients with COVID-19 in Huanggang, China: A Single-Centre Multivariate Pattern Analysis.

In Journal of medical virology
To date, the coronavirus disease 2019 (COVID-19) has a worldwide distribution. Risk factors for mortality in critically ill patients, especially detailed self-evaluation indicators and laboratory-examination indicators, have not been well described. In this paper, a total of 192 critically ill patients (142 were discharged and 50 died in the hospital) with COVID-19 were included. Self-evaluation indicators including demographics, baseline characteristics and symptoms and detailed lab-examination indicators were extracted. Data were first compared between survivors and non-survivors. Multivariate pattern analysis (MVPA) was performed to identify possible risk factors for mortality of COVID-19 patients. MVPA achieved a relatively high classification accuracy of 93% when using both self-evaluation indicators and laboratory-examination indicators. Several self-evaluation factors related to COVID-19 were highly associated with mortality, including age, duration (time from illness onset to admission), and the Barthel index score. When the duration, age and Barthel index increased by one day, one year and one point, the mortality increased by 3.6%, 2.4% and 0.9% respectively. Laboratory-examination indicators including C-reactive protein (CRP), white blood cell (WBC) count, platelet count, fibrin degradation products (FDP), oxygenation index (OI), lymphocyte count and D-dimer were also risk factors. Among them, duration was the strongest predictor of all-cause mortality. Several self-evaluation indicators that can simply be obtained by questionnaires and without clinical examination were the risk factors of all-cause mortality in critically ill COVID-19 patients. The prediction model can be used by individuals to improve health awareness, and by clinicians to identify high-risk individuals. This article is protected by copyright. All rights reserved.
Chen Yinyin, Linli Zeqiang, Lei Yuting, Yang Yiya, Liu Zhipeng, Xia Youchun, Liang Yumei, Zhu Huabo, Guo Shuixia

2020-Sep-30

COVID-19, Clinical indicators, Machine learning, Risk factor, Self-evaluation

General

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

In International journal of medical informatics ; h5-index 49.0

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

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

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

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

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

2020-Sep-23

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

General

Leveraging Computational Modeling to Understand Infectious Diseases.

In Current pathobiology reports

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

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

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

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

2020-Sep-24

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

General

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

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

2020-Sep-23

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

General

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

In The Lancet. Digital health

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

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

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

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

Funding : National Institutes of Health.

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

2020-Oct

Radiology

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

In The Lancet. Digital health

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

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

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

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

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

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

2020-Oct

General

Artificial intelligence in COVID-19 drug repurposing.

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

2020-Sep-18

General

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

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

2020-Sep-22

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

General

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

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

2020-Sep-22

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

General

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

In Frontiers in psychology ; h5-index 92.0
The COVID-19 disease and the systemic responses to it has impacted lives, routines and procedures at an unprecedented level. While medical care and emergency response present immediate needs, the implications of this pandemic will likely be far-reaching. Most practices that the clinical research within neuroscience and music field rely on, take place in hospitals or closely connected clinical settings which have been hit hard by the contamination. So too have its preventive and treatment measures. This means that clinical research protocols may have been altered, postponed or put in complete jeopardy. In this context, we would like to present and discuss the problems arising under the current crisis. We do so by critically approaching an online discussion facilitated by an expert panel in the field of music and neuroscience. This effort is hoped to provide an efficient basis to orient ourselves as we begin to map the needs and elements in this field of research as we further propose ideas and solutions on how to overcome, or at least ease the problems and questions we encounter or will encounter, with foresight. Among others, we hope to answer questions on technical or social problems that can be expected, possible solutions and preparatory steps to take in order to improve or ease research implementation, ethical implications and funding considerations. Finally, we further hope to facilitate the process of creating new protocols in order to minimize the impact of this crisis on essential research which may have the potential to relieve health systems.
Papatzikis Efthymios, Zeba Fathima, Särkämö Teppo, Ramirez Rafael, Grau-Sánchez Jennifer, Tervaniemi Mari, Loewy Joanne

2020

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

General

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

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

2020-Sep-22

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

General

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

In Neuropsychiatric disease and treatment

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

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

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

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

Ge Fenfen, Zhang Di, Wu Lianhai, Mu Hongwei

2020

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

General

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

bioRxiv Preprint
The global spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and the associated disease COVID-19, requires therapeutic interventions that can be rapidly translated to clinical care. Unfortunately, traditional drug discovery methods have a >90% failure rate and can take 10-15 years from target identification to clinical use. In contrast, drug repurposing can significantly accelerate translation. We developed a quantitative high-throughput screen to identify efficacious single agents and combination therapies against SARS-CoV-2. Quantitative high-content morphological profiling was coupled with an AI-based machine learning strategy to classify features of cells for infection and stress. This assay detected multiple antiviral mechanisms of action (MOA), including inhibition of viral entry, propagation, and modulation of host cellular responses. From a library of 1,425 FDA-approved compounds and clinical candidates, we identified 16 dose-responsive compounds with antiviral effects. In particular, we discovered that lactoferrin is an effective inhibitor of SARS-CoV-2 infection with an IC50 of 308 nM and that it potentiates the efficacy of both remdesivir and hydroxychloroquine. Lactoferrin also stimulates an antiviral host cell response and retains inhibitory activity in iPSC-derived alveolar epithelial cells, a model for the primary site of infection. Given its safety profile in humans, these data suggest that lactoferrin is a readily translatable therapeutic adjunct for COVID-19. Additionally, several commonly prescribed drugs were found to exacerbate viral infection and warrant clinical investigation. We conclude that morphological profiling for drug repurposing is an effective strategy for the selection and optimization of drugs and drug combinations as viable therapeutic options for COVID-19 pandemic and other emerging infectious diseases.
Mirabelli, C.; Wotring, J. W.; Zhang, C. J.; McCarty, S. M.; Fursmidt, R.; Frum, T.; Kadambi, N. S.; Amin, A. T.; O’Meara, T. R.; Pretto-Kernahan, C. D.; Spence, J. R.; Huang, J.; Alysandratos, K. D.; Kotton, D. N.; Handelman, S. K.; Wobus, C. E.; Weatherwax, K. J.; Mashour, G. A.; O’Meara, M. J.; Sexton, J. Z.

2020-09-28

General

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

In PloS one ; h5-index 176.0
The study aims to understand Twitter users' discourse and psychological reactions to COVID-19. We use machine learning techniques to analyze about 1.9 million Tweets (written in English) related to coronavirus collected from January 23 to March 7, 2020. A total of salient 11 topics are identified and then categorized into ten themes, including "updates about confirmed cases," "COVID-19 related death," "cases outside China (worldwide)," "COVID-19 outbreak in South Korea," "early signs of the outbreak in New York," "Diamond Princess cruise," "economic impact," "Preventive measures," "authorities," and "supply chain." Results do not reveal treatments and symptoms related messages as prevalent topics on Twitter. Sentiment analysis shows that fear for the unknown nature of the coronavirus is dominant in all topics. Implications and limitations of the study are also discussed.
Xue Jia, Chen Junxiang, Chen Chen, Zheng Chengda, Li Sijia, Zhu Tingshao

2020

General

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

In Journal of medical Internet research ; h5-index 88.0

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

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

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

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

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

CLINICALTRIAL :

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

2020-Sep-14

Surgery

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

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

2020

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

Oncology

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

In International journal of environmental research and public health ; h5-index 73.0
The possibility to use widespread and simple chest X-ray (CXR) imaging for early screening of COVID-19 patients is attracting much interest from both the clinical and the AI community. In this study we provide insights and also raise warnings on what is reasonable to expect by applying deep learning to COVID classification of CXR images. We provide a methodological guide and critical reading of an extensive set of statistical results that can be obtained using currently available datasets. In particular, we take the challenge posed by current small size COVID data and show how significant can be the bias introduced by transfer-learning using larger public non-COVID CXR datasets. We also contribute by providing results on a medium size COVID CXR dataset, just collected by one of the major emergency hospitals in Northern Italy during the peak of the COVID pandemic. These novel data allow us to contribute to validate the generalization capacity of preliminary results circulating in the scientific community. Our conclusions shed some light into the possibility to effectively discriminate COVID using CXR.
Tartaglione Enzo, Barbano Carlo Alberto, Berzovini Claudio, Calandri Marco, Grangetto Marco

2020-Sep-22

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

Radiology

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

In International journal of molecular sciences ; h5-index 102.0
In December 2019, physicians reported numerous patients showing pneumonia of unknown origin in the Chinese region of Wuhan. Following the spreading of the infection over the world, The World Health Organization (WHO) on 11 March 2020 declared the novel severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) outbreak a global pandemic. The scientific community is exerting an extraordinary effort to elucidate all aspects related to SARS-CoV-2, such as the structure, ultrastructure, invasion mechanisms, replication mechanisms, or drugs for treatment, mainly through in vitro studies. Thus, the clinical in vivo data can provide a test bench for new discoveries in the field of SARS-CoV-2, finding new solutions to fight the current pandemic. During this dramatic situation, the normal scientific protocols for the development of new diagnostic procedures or drugs are frequently not completely applied in order to speed up these processes. In this context, interdisciplinarity is fundamental. Specifically, a great contribution can be provided by the association and interpretation of data derived from medical disciplines based on the study of images, such as radiology, nuclear medicine, and pathology. Therefore, here, we highlighted the most recent histopathological and imaging data concerning the SARS-CoV-2 infection in lung and other human organs such as the kidney, heart, and vascular system. In addition, we evaluated the possible matches among data of radiology, nuclear medicine, and pathology departments in order to support the intense scientific work to address the SARS-CoV-2 pandemic. In this regard, the development of artificial intelligence algorithms that are capable of correlating these clinical data with the new scientific discoveries concerning SARS-CoV-2 might be the keystone to get out of the pandemic.
Scimeca Manuel, Urbano Nicoletta, Bonfiglio Rita, Montanaro Manuela, Bonanno Elena, Schillaci Orazio, Mauriello Alessandro

2020-Sep-22

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

Radiology

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

In Journal of thoracic imaging

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

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

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

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

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

2020-Sep-22

Radiology

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

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

2020-Nov

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

General

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

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

2020-Aug-27

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

General

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

In Revista da Associacao Medica Brasileira (1992)
The respiratory disease caused by the coronavirus SARS-CoV-2 (COVID-19) is a pandemic that produces a large number of simultaneous patients with severe symptoms and in need of special hospital care, overloading the infrastructure of health services. All of these demands generate the need to ration equipment and interventions. Faced with this imbalance, how, when, and who decides, there is the impact of the stressful systems of professionals who are at the front line of care and, in the background, issues inherent to human subjectivity. Along this path, the idea of using artificial intelligence algorithms to replace health professionals in the decision-making process also arises. In this context, there is the ethical question of how to manage the demands produced by the pandemic. The objective of this work is to reflect, from the point of view of medical ethics, on the basic principles of the choices made by the health teams, during the COVID-19 pandemic, whose resources are scarce and decisions cause anguish and restlessness. The ethical values for the rationing of health resources in an epidemic must converge to some proposals based on fundamental values such as maximizing the benefits produced by scarce resources, treating people equally, promoting and recommending instrumental values, giving priority to critical situations. Naturally, different judgments will occur in different circumstances, but transparency is essential to ensure public trust. In this way, it is possible to develop prioritization guidelines using well-defined values and ethical recommendations to achieve fair resource allocation.
Neves Nedy M B C, Bitencourt Flávia B C S N, Bitencourt Almir G V

2020

General

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

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

2020-Sep

Oncology

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

In BMJ supportive & palliative care ; h5-index 29.0

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

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

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

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

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

2020-Sep-22

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

General

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

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

2020-Sep-22

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

Cardiology

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

In PloS one ; h5-index 176.0
Worldwide, testing capacity for SARS-CoV-2 is limited and bottlenecks in the scale up of polymerase chain reaction (PCR-based testing exist. Our aim was to develop and evaluate a machine learning algorithm to diagnose COVID-19 in the inpatient setting. The algorithm was based on basic demographic and laboratory features to serve as a screening tool at hospitals where testing is scarce or unavailable. We used retrospectively collected data from the UCLA Health System in Los Angeles, California. We included all emergency room or inpatient cases receiving SARS-CoV-2 PCR testing who also had a set of ancillary laboratory features (n = 1,455) between 1 March 2020 and 24 May 2020. We tested seven machine learning models and used a combination of those models for the final diagnostic classification. In the test set (n = 392), our combined model had an area under the receiver operator curve of 0.91 (95% confidence interval 0.87-0.96). The model achieved a sensitivity of 0.93 (95% CI 0.85-0.98), specificity of 0.64 (95% CI 0.58-0.69). We found that our machine learning algorithm had excellent diagnostic metrics compared to SARS-CoV-2 PCR. This ensemble machine learning algorithm to diagnose COVID-19 has the potential to be used as a screening tool in hospital settings where PCR testing is scarce or unavailable.
Goodman-Meza David, Rudas Akos, Chiang Jeffrey N, Adamson Paul C, Ebinger Joseph, Sun Nancy, Botting Patrick, Fulcher Jennifer A, Saab Faysal G, Brook Rachel, Eskin Eleazar, An Ulzee, Kordi Misagh, Jew Brandon, Balliu Brunilda, Chen Zeyuan, Hill Brian L, Rahmani Elior, Halperin Eran, Manuel Vladimir

2020

Public Health

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

In Journal of medical Internet research ; h5-index 88.0
[This corrects the article DOI: 10.2196/20285.].
Liu Dianbo, Clemente Leonardo, Poirier Canelle, Ding Xiyu, Chinazzi Matteo, Davis Jessica, Vespignani Alessandro, Santillana Mauricio

2020-Sep-22

Public Health

Machine Learning Maps Research Needs in COVID-19 Literature.

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

2020-Sep-16

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

Radiology

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

In European journal of radiology open

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

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

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

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

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

2020

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

General

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

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

2020-Sep-17

Covid-19, Machine learning, SIR model, Tamilnadu

General

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

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

2020-Sep-16

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

General

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

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

2020-Sep-06

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

General

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

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

2020-09-23

General

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

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

2020

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

Radiology

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

In Annals of translational medicine

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

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

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

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

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

2020-Aug

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

General

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

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

2020

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

General

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

In Applied energy ; h5-index 131.0
Being heavily dependent to oil products (mainly gasoline and diesel), the French transport sector is the main emitter of Particulate Matter (PMs) whose critical levels induce harmful health effects for urban inhabitants. We selected three major French cities (Paris, Lyon, and Marseille) to investigate the relationship between the Coronavirus Disease 19 (COVID-19) outbreak and air pollution. Using Artificial Neural Networks (ANNs) experiments, we have determined the concentration of PM_2.5 and PM₁₀ linked to COVID-19-related deaths. Our focus is on the potential effects of Particulate Matter (PM) in spreading the epidemic. The underlying hypothesis is that a pre-determined particulate concentration can foster COVID-19 and make the respiratory system more susceptible to this infection. The empirical strategy used an innovative Machine Learning (ML) methodology. In particular, through the so-called cutting technique in ANNs, we found new threshold levels of PM_2.5 and PM₁₀ connected to COVID-19: 17.4 µg/m³ (PM_2.5) and 29.6 µg/m³ (PM₁₀) for Paris; 15.6 µg/m³ (PM_2.5) and 20.6 µg/m³ (PM₁₀) for Lyon; 14.3 µg/m³ (PM_2.5) and 22.04 µg/m³ (PM₁₀) for Marseille. Interestingly, all the threshold values identified by the ANNs are higher than the limits imposed by the European Parliament. Finally, a Causal Direction from Dependency (D2C) algorithm is applied to check the consistency of our findings.
Magazzino Cosimo, Mele Marco, Schneider Nicolas

2020-Dec-01

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

General

Short-term forecasting of the coronavirus pandemic.

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

2020-Sep-12

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

General

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

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

2020-09-21

General

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

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

2020-Sep-18

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

Radiology

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

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

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

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

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

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

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

2020-Sep-15

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

Pathology

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

In Cell reports ; h5-index 119.0
To predict the tropism of human coronaviruses, we profile 28 SARS-CoV-2 and coronavirus-associated receptors and factors (SCARFs) using single-cell transcriptomics across various healthy human tissues. SCARFs include cellular factors both facilitating and restricting viral entry. Intestinal goblet cells, enterocytes, and kidney proximal tubule cells appear highly permissive to SARS-CoV-2, consistent with clinical data. Our analysis also predicts non-canonical entry paths for lung and brain infections. Spermatogonial cells and prostate endocrine cells also appear to be permissive to SARS-CoV-2 infection, suggesting male-specific vulnerabilities. Both pro- and anti-viral factors are highly expressed within the nasal epithelium, with potential age-dependent variation, predicting an important battleground for coronavirus infection. Our analysis also suggests that early embryonic and placental development are at moderate risk of infection. Lastly, SCARF expression appears broadly conserved across a subset of primate organs examined. Our study establishes a resource for investigations of coronavirus biology and pathology.
Singh Manvendra, Bansal Vikas, Feschotte Cédric

2020-Sep-03

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

Public Health

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

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

2020-Sep-18

Public Health

Artificial Intelligence for COVID-19: A Rapid Review.

In Journal of medical Internet research ; h5-index 88.0

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

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

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

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

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

CLINICALTRIAL :

Chen Jiayang, See Kay Choong

2020-Sep-15

Radiology

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

In European radiology ; h5-index 62.0

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

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

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

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

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

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

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

2020-Sep-18

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

General

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

In Journal of thoracic disease ; h5-index 52.0

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

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

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

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

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

2020-Aug

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

Radiology

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

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

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

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

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

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

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

2020-Sep-17

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

General

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

In European journal of clinical investigation

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

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

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

Lagzian Mohammad, Dadkhah Mehdi, Mehraeen AmirReza

2020-Sep-16

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

Radiology

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

In Abdominal radiology (New York)

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

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

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

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

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

2020-Sep-16

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

General

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

In Journal of medical Internet research ; h5-index 88.0

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

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

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

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

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

CLINICALTRIAL :

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

2020-Sep-13

General

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

In Journal of medical Internet research ; h5-index 88.0

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

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

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

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

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

CLINICALTRIAL :

Jeon Jouhyun, Baruah Gaurav, Sarabadani Sarah, Palanica Adam

2020-Sep-13

Public Health

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

In Journal of medical Internet research ; h5-index 88.0

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

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

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

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

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

CLINICALTRIAL :

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

2020-Sep-14

Internal Medicine

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

In Molecular psychiatry ; h5-index 103.0
The global pandemic of COVID-19 is colliding with the epidemic of opioid use disorders (OUD) and other substance use disorders (SUD) in the United States (US). Currently, there is limited data on risks, disparity, and outcomes for COVID-19 in individuals suffering from SUD. This is a retrospective case-control study of electronic health records (EHRs) data of 73,099,850 unique patients, of whom 12,030 had a diagnosis of COVID-19. Patients with a recent diagnosis of SUD (within past year) were at significantly increased risk for COVID-19 (adjusted odds ratio or AOR = 8.699 [8.411-8.997], P < 10^-30), an effect that was strongest for individuals with OUD (AOR = 10.244 [9.107-11.524], P < 10^-30), followed by individuals with tobacco use disorder (TUD) (AOR = 8.222 ([7.925-8.530], P < 10^-30). Compared to patients without SUD, patients with SUD had significantly higher prevalence of chronic kidney, liver, lung diseases, cardiovascular diseases, type 2 diabetes, obesity and cancer. Among patients with recent diagnosis of SUD, African Americans had significantly higher risk of COVID-19 than Caucasians (AOR = 2.173 [2.01-2.349], P < 10^-30), with strongest effect for OUD (AOR = 4.162 [3.13-5.533], P < 10^-25). COVID-19 patients with SUD had significantly worse outcomes (death: 9.6%, hospitalization: 41.0%) than general COVID-19 patients (death: 6.6%, hospitalization: 30.1%) and African Americans with COVID-19 and SUD had worse outcomes (death: 13.0%, hospitalization: 50.7%) than Caucasians (death: 8.6%, hospitalization: 35.2%). These findings identify individuals with SUD, especially individuals with OUD and African Americans, as having increased risk for COVID-19 and its adverse outcomes, highlighting the need to screen and treat individuals with SUD as part of the strategy to control the pandemic while ensuring no disparities in access to healthcare support.
Wang Quan Qiu, Kaelber David C, Xu Rong, Volkow Nora D

2020-Sep-14

Radiology

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

In European journal of radiology ; h5-index 47.0

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

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

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

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

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

2020-Aug-28

COVID-19, Computed tomography, Machine learning, Software

General

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

In Psychological science ; h5-index 93.0
How can we nudge people to not engage in unethical behaviors, such as hoarding and violating social-distancing guidelines, during the COVID-19 pandemic? Because past research on antecedents of unethical behavior has not provided a clear answer, we turned to machine learning to generate novel hypotheses. We trained a deep-learning model to predict whether or not World Values Survey respondents perceived unethical behaviors as justifiable, on the basis of their responses to 708 other items. The model identified optimism about the future of humanity as one of the top predictors of unethicality. A preregistered correlational study (N = 218 U.S. residents) conceptually replicated this finding. A preregistered experiment (N = 294 U.S. residents) provided causal support: Participants who read a scenario conveying optimism about the COVID-19 pandemic were less willing to justify hoarding and violating social-distancing guidelines than participants who read a scenario conveying pessimism. The findings suggest that optimism can help reduce unethicality, and they document the utility of machine-learning methods for generating novel hypotheses.
Sheetal Abhishek, Feng Zhiyu, Savani Krishna

2020-Sep-14

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

Oncology

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

In Immunology and cell biology
Big data has become a central part of medical research, as well as modern life generally. "Omics" technologies include genomics, proteomics, microbiomics, and increasingly other omics. These have been driven by rapid advances in laboratory techniques and equipment. Crucially, improved information handling capabilities have allowed concepts such as artificial intelligence and machine learning to enter the research world. The Covid-19 pandemic has shown how quickly information can be generated and analysed using such approaches, but also showed its limitations. This review will look at how "omics" has begun to be translated into clinical practice. While there appears almost limitless potential in using big data for "precision" or "personalised" medicine, the reality is that this remains largely aspirational. Oncology is the only field of medicine that is widely adopting such technologies, and even in this field uptake is irregular. There are practical and ethical reasons for this lack of translation of increasingly affordable techniques into the clinic. Undoubtedly there will be increasing use of large datasets from traditional (e.g. tumour samples, patient genomics) and non-traditional (e.g. smartphone) sources. It is perhaps the greatest challenge of the healthcare sector over the coming decade to integrate these resources in an effective, practical and ethical way.
D’Adamo Gemma L, Widdop James T, Giles Edward M

2020-Sep-13

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

Public Health

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

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

2020

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

General

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

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

2020-Nov

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

General

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

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

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

General

Beyond Infection: Integrating Competence into Reservoir Host Prediction.

In Trends in ecology & evolution
Most efforts to predict novel reservoirs of zoonotic pathogens use information about host exposure and infection rather than competence, defined as the ability to transmit pathogens. Better obtaining and integrating competence data into statistical models as covariates, as the response variable, and through postmodel validation should improve predictive research.
Becker Daniel J, Seifert Stephanie N, Carlson Colin J

2020-Sep-10

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

General

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

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

2020-09-15

General

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

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

2020-09-14

Public Health

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

In PloS one ; h5-index 176.0
India locked down 1.3 billion people on March 25, 2020, in the wake of COVID-19 pandemic. The economic cost of it was estimated at USD 98 billion, while the social costs are still unknown. This study investigated how government formed reactive policies to fight coronavirus across its policy sectors. Primary data was collected from the Press Information Bureau (PIB) in the form press releases of government plans, policies, programme initiatives and achievements. A text corpus of 260,852 words was created from 396 documents from the PIB. An unsupervised machine-based topic modelling using Latent Dirichlet Allocation (LDA) algorithm was performed on the text corpus. It was done to extract high probability topics in the policy sectors. The interpretation of the extracted topics was made through a nudge theoretic lens to derive the critical policy heuristics of the government. Results showed that most interventions were targeted to generate endogenous nudge by using external triggers. Notably, the nudges from the Prime Minister of India was critical in creating herd effect on lockdown and social distancing norms across the nation. A similar effect was also observed around the public health (e.g., masks in public spaces; Yoga and Ayurveda for immunity), transport (e.g., old trains converted to isolation wards), micro, small and medium enterprises (e.g., rapid production of PPE and masks), science and technology sector (e.g., diagnostic kits, robots and nano-technology), home affairs (e.g., surveillance and lockdown), urban (e.g. drones, GIS-tools) and education (e.g., online learning). A conclusion was drawn on leveraging these heuristics are crucial for lockdown easement planning.
Debnath Ramit, Bardhan Ronita

2020

Public Health

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

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

2020-Sep-10

Public Health

Medical Student Training in eHealth: Scoping Review.

In JMIR medical education

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

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

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

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

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

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

2020-Sep-11

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

Public Health

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

In Innovation (New York, N.Y.)
The outbreak of COVID-19 seriously challenges every government with regard to capacity and management of public health systems facing the catastrophic emergency. Culture and anti-epidemic policy do not necessarily conflict with each other. All countries and governments should be more tolerant to each other in seeking cultural and political consensus to overcome this historically tragic pandemic together.
Li Jianping, Guo Kun, Viedma Enrique Herrera, Lee Heesoek, Liu Jiming, Zhong Ning, Autran Monteiro Gomes Luiz Flavio, Filip Florin Gheorghe, Fang Shu-Cherng, Özdemir Mujgan Sagir, Liu Xiaohui, Lu Guoqing, Shi Yong

2020-Aug-28

General

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

In F1000Research
Background: Community containment is one of the common methods used to mitigate infectious disease outbreaks. The effectiveness of such a method depends on how strictly it is applied and the timing of its implementation. An early start and being strict is very effective; however, at the same time, it impacts freedom and economic opportunity. Here we created a simulation model to understand the effect of the starting day of community containment on the ﬁnal outcome, that is, the number of those infected, hospitalized and those that died, as we followed the dynamics of COVID-19 pandemic. Methods: We used a stochastic recursive simulation method to apply disease outbreak dynamics measures of COVID-19 as an example to simulate disease spread. Parameters are allowed to be randomly assigned between higher and lower values obtained from published COVID-19 literature. Results: We simulated the dynamics of COVID-19 spread, calculated the number of active infections, hospitalizations and deaths as the outcome of our simulation and compared these results with real world data. We also represented the details of the spread in a network graph structure, and shared the code for the simulation model to be used for examining other variables. Conclusions: Early implementation of community containment has a big impact on the ﬁnal outcome of an outbreak.
Mohsen Attayeb, Alarabi Ahmed

2020

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

Cardiology

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

In Nutrition, metabolism, and cardiovascular diseases : NMCD

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

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

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

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

2020-Jul-31

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

General

Customer experiences in the age of artificial intelligence.

In Computers in human behavior ; h5-index 125.0
Artificial intelligence (AI) is revolutionising the way customers interact with brands. There is a lack of empirical research into AI-enabled customer experiences. Hence, this study aims to analyse how the integration of AI in shopping can lead to an improved AI-enabled customer experience. We propose a theoretical model drawing on the trust-commitment theory and service quality model. An online survey was distributed to customers who have used an AI- enabled service offered by a beauty brand. A total of 434 responses were analysed using partial least squares-structural equation modelling. The findings indicate the significant role of trust and perceived sacrifice as factors mediating the effects of perceived convenience, personalisation and AI-enabled service quality. The findings also reveal the significant effect of relationship commitment on AI-enabled customer experience. This study contributes to the existing literature by revealing the mediating effects of trust and perceived sacrifice and the direct effect of relationship commitment on AI-enabled customer experience. In addition, the study has practical implications for retailers deploying AI in services offered to their customers.
Ameen Nisreen, Tarhini Ali, Reppel Alexander, Anand Amitabh

2021-Jan

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

General

COVID-19 pandemic changes the food consumption patterns.

In Trends in food science & technology

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

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

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

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

2020-Sep-02

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

General

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

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

2020-Sep-02

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

General

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

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

2020-Aug-18

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

Surgery

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

In Indian journal of otolaryngology and head and neck surgery : official publication of the Association of Otolaryngologists of India
Computerization of health care is the only model to sustain safe health care in this COVID era particularly in overpopulated nations with limited health care providers/systems like India. Accordingly incorporation of computer-based algorithms and artificial intelligence seems very robust and practical models to assist the physician. The advantages of Computerized algorithms to facilitate better screening, diagnosis or follow-up and use of Artificial Intelligence (AI) to aid in medical diagnosis are discussed.
Katyayan Angira, Katyayan Adri, Mishra Anupam

2020-Sep-01

Artificial intelligence, COVID-19, Computer algorithms

General

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

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

2020-Aug-29

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

General

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

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

2020-Aug-28

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

General

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

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

2020-09-10

General

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

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

2020-Aug-28

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

General

COVID-19 Pandemic Cyclic Lockdown Optimization Using Reinforcement Learning

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

2020-09-10

Surgery

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

In Critical care explorations

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

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

Setting : Tertiary care ICU and academic laboratory.

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

Interventions : None.

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

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

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

2020-Sep

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

General

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

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

2020-09-10

Radiology

RadLex Normalization in Radiology Reports

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

2020-09-10

Ophthalmology

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

In Progress in retinal and eye research ; h5-index 55.0
The simultaneous maturation of multiple digital and telecommunications technologies in 2020 has created an unprecedented opportunity for ophthalmology to adapt to new models of care using tele-health supported by digital innovations. These digital innovations include artificial intelligence (AI), 5th generation (5G) telecommunication networks and the Internet of Things (IoT), creating an inter-dependent ecosystem offering opportunities to develop new models of eye care addressing the challenges of COVID-19 and beyond. Ophthalmology has thrived in some of these areas partly due to its many image-based investigations. Tele-health and AI provide synchronous solutions to challenges facing ophthalmologists and healthcare providers worldwide. This article reviews how countries across the world have utilised these digital innovations to tackle diabetic retinopathy, retinopathy of prematurity, age-related macular degeneration, glaucoma, refractive error correction, cataract and other anterior segment disorders. The review summarises the digital strategies that countries are developing and discusses technologies that may increasingly enter the clinical workflow and processes of ophthalmologists. Furthermore as countries around the world have initiated a series of escalating containment and mitigation measures during the COVID-19 pandemic, the delivery of eye care services globally has been significantly impacted. As ophthalmic services adapt and form a "new normal", the rapid adoption of some of telehealth and digital innovation during the pandemic is also discussed. Finally, challenges for validation and clinical implementation are considered, as well as recommendations on future directions.
Olivia Li Ji-Peng, Liu Hanruo, Ting Darren S J, Jeon Sohee, Chan R V Paul, Kim Judy E, Sim Dawn A, Thomas Peter B M, Lin Haotian, Chen Youxin, Sakomoto Taiji, Loewenstein Anat, Lam Dennis S C, Pasquale Louis R, Wong Tien Y, Lam Linda A, Ting Daniel S W

2020-Sep-05

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

Public Health

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

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

2020-Sep-03

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

General

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

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

2020-Sep-03

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

General

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

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

2020-Sep-02

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

General

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

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

2021-Feb

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

General

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

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

2020-09-09

General

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

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

2020-09-08

General

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

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

2020-09-07

General

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

In Diabetes research and clinical practice ; h5-index 50.0
We describe our experience in the remote management of women with gestational diabetes mellitus during the COVID-19 pandemic. We used a mobile phone application with artificial intelligence that automatically classifies and analyses the data (ketonuria, diet transgressions, and blood glucose values), making adjustment recommendations regarding the diet or insulin treatment.
Albert Lara, Capel Ismael, García-Sáez Gema, Martín-Redondo Pablo, Hernando M Elena, Rigla Mercedes

2020-Sep-03

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

Oncology

Scholarly Publishing in the Wake of COVID-19.

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

2020-Oct-01

General

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

In Computer methods and programs in biomedicine

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

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

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

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

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

2020-Aug-18

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

General

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

In Environmental science and pollution research international
This study uses two different approaches to explore the relationship between pollution emissions, economic growth, and COVID-19 deaths in India. Using a time series approach and annual data for the years from 1980 to 2018, stationarity and Toda-Yamamoto causality tests were performed. The results highlight unidirectional causality between economic growth and pollution. Then, a D2C algorithm on proportion-based causality is applied, implementing the Oryx 2.0.8 protocol in Apache. The underlying hypothesis is that a predetermined pollution concentration, caused by economic growth, could foster COVID-19 by making the respiratory system more susceptible to infection. We use data (from January 29 to May 18, 2020) on confirmed deaths (total and daily) and air pollution concentration levels for 25 major Indian cities. We verify a ML causal link between PM_2.5, CO₂, NO₂, and COVID-19 deaths. The implications require careful policy design.
Mele Marco, Magazzino Cosimo

2020-Sep-04

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

General

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

In Health informatics journal ; h5-index 25.0
The rapid spread of the COVID-19 virus around the world poses a real threat to public safety. Some COVID-19 symptoms are similar to other viral chest diseases, which makes it challenging to develop models for effective detection of COVID-19 infection. This article advocates a model to differentiate between COVID-19 and other four viral chest diseases under uncertainty environment using the viruses primary symptoms and CT scans. The proposed model is based on a plithogenic set, which provides higher accurate evaluation results in an uncertain environment. The proposed model employs the best-worst method (BWM) and the technique in order of preference by similarity to ideal solution (TOPSIS). Besides, this study discusses how smart Internet of Things technology can assist medical staff in monitoring the spread of COVID-19. Experimental evaluation of the proposed model was conducted on five different chest diseases. Evaluation results demonstrate that the proposed model effectiveness in detecting the COVID-19 in all five cases achieving detection accuracy of up to 98%.
Abdel-Basst Mohamed, Mohamed Rehab, Elhoseny Mohamed

2020-Sep-04

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

Pathology

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

In Viruses ; h5-index 58.0
Until vaccines and effective therapeutics become available, the practical solution to transit safely out of the current coronavirus disease 19 (CoVID-19) lockdown may include the implementation of an effective testing, tracing and tracking system. However, this requires a reliable and clinically validated diagnostic platform for the sensitive and specific identification of SARS-CoV-2. Here, we report on the development of a de novo, high-resolution and comparative genomics guided reverse-transcribed loop-mediated isothermal amplification (LAMP) assay. To further enhance the assay performance and to remove any subjectivity associated with operator interpretation of results, we engineered a novel hand-held smart diagnostic device. The robust diagnostic device was further furnished with automated image acquisition and processing algorithms and the collated data was processed through artificial intelligence (AI) pipelines to further reduce the assay run time and the subjectivity of the colorimetric LAMP detection. This advanced AI algorithm-implemented LAMP (ai-LAMP) assay, targeting the RNA-dependent RNA polymerase gene, showed high analytical sensitivity and specificity for SARS-CoV-2. A total of ~200 coronavirus disease (CoVID-19)-suspected NHS patient samples were tested using the platform and it was shown to be reliable, highly specific and significantly more sensitive than the current gold standard qRT-PCR. Therefore, this system could provide an efficient and cost-effective platform to detect SARS-CoV-2 in resource-limited laboratories.
Rohaim Mohammed A, Clayton Emily, Sahin Irem, Vilela Julianne, Khalifa Manar E, Al-Natour Mohammad Q, Bayoumi Mahmoud, Poirier Aurore C, Branavan Manoharanehru, Tharmakulasingam Mukunthan, Chaudhry Nouman S, Sodi Ravinder, Brown Amy, Burkhart Peter, Hacking Wendy, Botham Judy, Boyce Joe, Wilkinson Hayley, Williams Craig, Whittingham-Dowd Jayde, Shaw Elisabeth, Hodges Matt, Butler Lisa, Bates Michelle D, La Ragione Roberto, Balachandran Wamadeva, Fernando Anil, Munir Muhammad

2020-Sep-01

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

Public Health

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

In International journal of environmental research and public health ; h5-index 73.0
The spread of COVID-19 is not evenly distributed. Neighborhood environments may structure risks and resources that produce COVID-19 disparities. Neighborhood built environments that allow greater flow of people into an area or impede social distancing practices may increase residents' risk for contracting the virus. We leveraged Google Street View (GSV) images and computer vision to detect built environment features (presence of a crosswalk, non-single family home, single-lane roads, dilapidated building and visible wires). We utilized Poisson regression models to determine associations of built environment characteristics with COVID-19 cases. Indicators of mixed land use (non-single family home), walkability (sidewalks), and physical disorder (dilapidated buildings and visible wires) were connected with higher COVID-19 cases. Indicators of lower urban development (single lane roads and green streets) were connected with fewer COVID-19 cases. Percent black and percent with less than a high school education were associated with more COVID-19 cases. Our findings suggest that built environment characteristics can help characterize community-level COVID-19 risk. Sociodemographic disparities also highlight differential COVID-19 risk across groups of people. Computer vision and big data image sources make national studies of built environment effects on COVID-19 risk possible, to inform local area decision-making.
Nguyen Quynh C, Huang Yuru, Kumar Abhinav, Duan Haoshu, Keralis Jessica M, Dwivedi Pallavi, Meng Hsien-Wen, Brunisholz Kimberly D, Jay Jonathan, Javanmardi Mehran, Tasdizen Tolga

2020-Sep-01

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

General

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

In Environmental pollution (Barking, Essex : 1987)
Air pollution can increase the risk of respiratory diseases, enhancing the susceptibility to viral and bacterial infections. Some studies suggest that small air particles facilitate the spread of viruses and also of the new coronavirus, besides the direct person-to-person contagion. However, the effects of the exposure to particulate matter and other contaminants on SARS-CoV-2 has been poorly explored. Here we examined the possible reasons why the new coronavirus differently impacted on Italian regional and provincial populations. With the help of artificial intelligence, we studied the importance of air pollution for mortality and positivity rates of the SARS-CoV-2 outbreak in Italy. We discovered that among several environmental, health, and socio-economic factors, air pollution and fine particulate matter (PM2.5), as its main component, resulted as the most important predictors of SARS-CoV-2 effects. We also found that the emissions from industries, farms, and road traffic - in order of importance - might be responsible for more than 70% of the deaths associated with SARS-CoV-2 nationwide. Given the major contribution played by air pollution (much more important than other health and socio-economic factors, as we discovered), we projected that, with an increase of 5-10% in air pollution, similar future pathogens may inflate the epidemic toll of Italy by 21-32% additional cases, whose 19-28% more positives and 4-14% more deaths. Our findings, demonstrating that fine-particulate (PM2.5) pollutant level is the most important factor to predict SARS-CoV-2 effects that would worsen even with a slight decrease of air quality, highlight that the imperative of productivity before health and environmental protection is, indeed, a short-term/small-minded resolution.
Cazzolla Gatti Roberto, Velichevskaya Alena, Tateo Andrea, Amoroso Nicola, Monaco Alfonso

2020-Aug-21

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

General

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

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

2020-Aug-05

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

General

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

In Journal of medical Internet research ; h5-index 88.0

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

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

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

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

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

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

2020-09-03

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

General

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

In Journal of medical Internet research ; h5-index 88.0

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

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

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

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

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

CLINICALTRIAL :

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

2020-Sep-01

General

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

In Expert opinion on drug discovery ; h5-index 34.0

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

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

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

Mayburd Anatoly

2020-Sep-02

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

Radiology

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

In Diagnostic and interventional radiology (Ankara, Turkey)

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

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

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

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

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

2020-Sep-02

General

Coronavirus herd immunity optimizer (CHIO).

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

2020-Aug-27

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

Oncology

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

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

2020-Aug-30

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

Radiology

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

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

2020-Nov

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

Ophthalmology

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

In Computers in biology and medicine

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

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

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

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

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

2020-Aug-20

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

Public Health

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

In Journal of medical Internet research ; h5-index 88.0

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

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

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

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

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

Oliver Nuria, Barber Xavier, Roomp Kirsten, Roomp Kristof

2020-09-10

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

General

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

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

2020-Aug-26

COVID-19, Coronavirus, Lockdown, Machine learning

General

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

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

2020-Oct-15

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

General

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

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

2020-Aug-26

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