Deep Learning ResNet101 Deep Features of Portable Chest X-Ray Accurately Classify COVID-19 Lung Infection

This study is designed to develop Artificial Intelligence (AI) based analysis tool that could accurately detect COVID-19 lung infections based on portable chest x-rays (CXRs). The frontline physicians and radiologists suffer from grand challenges for COVID-19 pandemic due to the suboptimal image quality and the large volume of CXRs. In this study, AI-based analysis tools were developed that can precisely classify COVID-19 lung infection. Publicly available datasets of COVID-19 (N = 1525), non-COVID-19 normal (N = 1525), viral pneumonia (N = 1342) and bacterial pneumonia (N = 2521) from the Italian Society of Medical and Interventional Radiology (SIRM), Radiopaedia, The Cancer Imaging Archive (TCIA) and Kaggle repositories were taken. A multi-approach utilizing deep learning ResNet101 with and without hyperparameters optimization was employed. Additionally, the fea-tures extracted from the average pooling layer of ResNet101 were used as input to machine learning (ML) algorithms, which twice trained the learning algorithms. The ResNet101 with optimized parameters yielded improved performance to default parameters. The extracted features from ResNet101 are fed to the k-nearest neighbor (KNN) and support vector machine (SVM) yielded the highest 3-class classification performance of 99.86% and 99.46%, respectively. The results indicate that the proposed approach can be bet-ter utilized for improving the accuracy and diagnostic efficiency of CXRs. The proposed deep learning model has the potential to improve further the efficiency of the healthcare systems for proper diagnosis and prognosis of COVID-19 lung infection.

Effect of COVID-19 on patients health-seeking behaviour in Zulfi City

Introduction: During the ongoing COVID-19 pandemic, all perspectives of life were affected by the situation, and as a result, all health services worldwide were overwhelmed, which led to the exhaustion of hospital beds and intensive care units, workforce and resources. This research was done to determine the health-seeking behaviour during the COVID pandemic COVID-19 in the Al Zulfi area and its relation to the monthly number of patient visits to primary health centre before and after the pandemic and the roles and regulations for health-care services. Materials and Methods: This was an observational, cross-sectional study to study the effect of the COVID-19 pandemic on patients' health-seeking behaviour in Zulfi city. Results: Results were obtained from 567 participants;the finding was a decline in the number of patients visiting the health facilities by 65.6% compared in 2019. There were more declines in males than in females (18.5% vs. 15.9%, respectively). Conclusion: Despite the strong impact of COVID-19 on healthcare, the Kingdom of Saudi Arabia is one of the strongest countries in facing this pandemic, providing the best care, educating society and minimizing losses. Under these circumstances, patients' visits to health centres in Zulfi decreased, complications appeared for some patients who rescheduled their appointments, healthcare became electronic, and the patients were satisfied with those services.

Post COVID-19 Pandemic Generalized Anxiety Status of Health Professional undergraduate students

Aim: To assess the psychological impact of COVID-19 on mental status of undergraduate medical students, after reopening of educational institutions. Method(s): This descriptive, online cross-sectional study was conducted on medical students of Combined Military Hospital Lahore Medical College & Institute of Dentistry, Lahore Pakistan for 6 months from 1st June to 30th November, 2021. Generalized Anxiety Disorder Scale (GAD-7) was used as the study tool. Data was analyzed by using statistical software SPSS - 23. Frequencies and percentages were used for descriptive variables. A univariate analysis was utilized to identify the noticeable associations between traits of sample and the anxiety level in current scenario of COVID-19 pandemic. Finally multivariate logistic regression analyses, odds ratio (OR), and Spearman's correlation coefficient, r, was used to evaluate the association between COVID-19-related stressors and anxiety level. Result(s): Of the 324 respondents, majority were females residing in urban areas with their parents. Severe anxiety was experienced by 23.8% of students. Female respondents were found to have more anxiety as compared to males (OR = 1.81;95% CI = 1.173 - 2.815). Moreover, respondents having a relative or an acquaintance infected with COVID-19 reported to have more anxiety (OR = 3.007, 95% CI = 2.377 - 3.804). Conclusion(s): A significant number of students are experiencing anxiety in the post COVID-19 phase, especially those that had an acquaintance infected with COVID-19. There is a need to look after student's mental health and to implement appropriate psychological strategies and interventions to deal with this level of psychological distress in the students at this phase of pandemic for optimum training of future health professionals. Copyright © 2022 Lahore Medical And Dental College. All rights reserved.

Seeing the unforeseen: an insight into kingdom-wide psychological impact of COVID-19 pandemic.

OBJECTIVE: The aim of the manuscript was to measure the levels of psychological stress, both acute and post-traumatic in the Saudi Arabian population during the situation resulting from the COVID-19 outbreak. MATERIALS AND METHODS: A cross-sectional survey was carried out among people of Saudi Arabia (SA) to measure levels of psychological stress, both acute and post-traumatic during the COVID-19 outbreak. Data were collected from five regions in SA using validated questionnaires including Kessler Psychological Distress Scale (K10) and Impact of Events Scale (IES) through social media channels from March 2021 to January 2022. RESULTS: The total number of participants was 1,560. Most of participants (60.2%) were females. Around 53.6% of the sample were aged between 16-24 years old. The majority of participants (87.3%) was Saudi national. About 82% of participants was from Eastern (40.1%) and Western (42.2%) regions, followed by those from Central, Northern, and Southern. More than 60% of them had a college degree or above. The mean K 10 score was 28.33 for the sample which was above the cut-off of 25, implying significant levels of acute stress in the sample. IES values showed a mean of 28.19, well above the cut-off of significant Post Traumatic Stress Disorder (PTSD) symptoms (24). K-10 and IES scores revealed that about 76.7% of the participants suffered from significant acute stress and 59.1% suffered from symptoms of PTSD during the COVID-19 era. CONCLUSIONS: The nationwide study emphasizes the fact that the Saudi population was found to be extremely stressed and traumatized during the COVID-19 pandemic era and calls for effective.

Prediction of COVID-19 severity from clinical and biochemical markers: a single-center study from Saudi Arabia.

OBJECTIVE: It is known that the severity of COVID-19 is linked to the prognosis of patients; therefore, an early identification is required for patients who are likely to develop severe or critical COVID-19 disease. The purpose of this study is to propose a statistical method for identifying the severity of COVID-19 disease by using clinical and biochemical laboratory markers. PATIENTS AND METHODS: A total of 48 clinically and laboratory-confirmed cases of COVID-19 were obtained from King Fahad Hospital, Medina (KFHM) between 27th April 2020 to 25th May 2020. The patients' demographics and severity of COVID-19 disease were assessed using 39 clinical and biochemical features. After excluding the demographics, 35 predicting features were included in the analysis (diabetes, chronic disease, viral and bacterial co-infections, PCR cycle number, ICU admission, clot formation, cardiac enzymes elevation, hematology profile, sugar levels in the blood, as well as liver and kidney tests, etc.). Logistic regression, stepwise logistic regression, L-2 logistic regression, L-2 stepwise logistic regression, and L-2 best subset logistic regression were applied to model the features. The consistency index was used with kernel Support-Vector Machines (SVM) for the identification of associated markers. RESULTS: L-2 best subset logistic regression technique outperformed all other fitted models for modeling COVID-19 disease severity by achieving an accuracy of 88% over the test data. Consistency index over L-2 best subset logistic regression identified 14 associated markers that can best predict the COVID-19 severity among COVID-19 patients. CONCLUSIONS: By combining a variety of laboratory markers with L-2 best subset logistic regression, the current study has proposed a highly accurate and clinically interpretable model of predicting COVID-19 severity.

Impact of weather conditions on incidence and mortality of COVID-19 pandemic in Africa.

OBJECTIVE: The weather-related conditions change the ecosystem and pose a threat to social, economic and environmental development. It creates unprecedented or unanticipated human health problems in various places or times of the year. Africa is the world's second largest and most populous continent and has relatively changeable weather conditions. The present study aims to investigate the impact of weather conditions, heat and humidity on the incidence and mortality of COVID-19 pandemic in various regions of Africa. MATERIALS AND METHODS: In this study, 16 highly populated countries from North, South, East, West, and Central African regions were selected. The data on COVID-19 pandemic including daily new cases and new deaths were recorded from World Health Organization. The daily temperature and humidity figures were obtained from the weather web "Time and Date". The daily cases, deaths, temperature and humidity were recorded from the date of appearance of first case of "Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)" in the African region, from Feb 14 to August 2, 2020. RESULTS: In African countries, the daily basis mean temperature from Feb 14, 2020 to August 2, 2020 was 26.16±0.12°C, and humidity was 57.41±0.38%. The overall results revealed a significant inverse correlation between humidity and the number of cases (r= -0.192, p<0.001) and deaths (r= -0.213, p<0.001). Similarly, a significant inverse correlation was found between temperature and the number of cases (r= -0.25, p<0.001) and deaths (r=-0.18, p<0.001). Furthermore, the regression results showed that with 1% increase in humidity the number of cases and deaths was significantly reduced by 3.6% and 3.7% respectively. Congruently, with 1°C increase in temperature, the number of cases and deaths was also significantly reduced by 15.1% and 10.5%, respectively. CONCLUSIONS: Increase in relative humidity and temperature was associated with a decrease in the number of daily cases and deaths due to COVID-19 pandemic in various African countries. The study findings on weather events and COVID-19 pandemic have an impact at African regional levels to project the incidence and mortality trends with regional weather events which will enhance public health readiness and assist in planning to fight against this pandemic.

Effect of heat and humidity on the incidence and mortality due to COVID-19 pandemic in European countries.

OBJECTIVE: The weather allied conditions have an impact on air, water, soil, food, ecosystem, feelings, behaviors, and pattern of health and disease. The present study aims to investigate the impact of heat and humidity on the daily basis incidence and mortality due to COVID-19 pandemic in European countries. MATERIALS AND METHODS: We selected 10 European countries, Russia, United Kingdom, Spain, Italy, Germany, Turkey, France, Belgium, Netherlands and Belarus. This region has a relatively low temperature and high humidity, and has homogenous European ethnicity with almost similar socioeconomic culture and health care system. The data on COVID-19 pandemic including daily new cases and new deaths were recorded from World Health Organization (WHO). The information on daily temperature and humidity was obtained from world climate web "Time and Date". The daily cases, deaths, temperature and humidity were recorded from the date of appearance of first case of "Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)" in the European region, from Jan 27, 2020 to July 17, 2020. RESULTS: In 10 European countries, (Russia, United Kingdom, Spain, Italy, Germany, Turkey, France, Belgium, Netherlands and Belarus), the daily basis mean temperature from Jan 27, 2020 to July 17, 2020 was 17.07±0.18°C, and humidity was 54.78±0.47%. The overall results revealed a significant inverse correlation between humidity and the number of cases (r= -0.134, p<0.001) and deaths (r= -0.126, p<0.001). Moreover, an increase in temperature was linked with an increase in the number of cases (r=0.062, p=0.013) and deaths (r=0.118, p<0.001). The regression analysis results further revealed that with an increase of 1% humidity the number of cases (ß = -15.90, p<0.001) and deaths (ß=-1.56, p<0.001) reduced significantly. Whereas, with an increase of 1°C in temperature the number of cases (ß = 20.65, p<0.001) and deaths (ß = 3.71, p<0.001) increased significantly. CONCLUSIONS: Increase in relative humidity was associated with a decrease in the number of daily cases and deaths, however, a rise in temperature was allied with an upsurge in the number of daily cases and daily deaths due to COVID-19 pandemic in European countries. The study findings on weather events and COVID-19 pandemic have an impact at European regional levels to project the incidence and mortality trends with regional weather events to enhance public health readiness and assist in planning to fight against this pandemic situation.