Medical imaging in problem-based learning and impact on the students: A cross-sectional study.

OBJECTIVE: To investigate the medical students' performance with and perception towards different multimedia medical imaging tools. METHODS: The cross-sectional study was conducted at the College of Medicine, Qassim University, Saudi Arabia, from 2019 to 2020, and comprised third year undergraduate medical students during the academic year 2019-2020. The students were divided into tow groups. Those receiving multimedia-enhanced problem-based learning sessions were in intervention group A, while those receiving traditional problem-based learning sessions were in control group B. Scores of the students in the formative assessment at the end of the sessions were compared between the groups. Students' satisfaction survey was also conducted online and analysed. Data was analysed using SPSS 21. RESULTS: Of the 130 medical students, 75(57.7%) were males and 55(42.3%) were females. A significant increase in the mean scores was observed for both male and female students in group A compared to those in group B (p<0.05). The perception survey was filled up by 100(77%) students, and open-ended comments were obtained from 88(88%) of them. Overall, 69(74%) subjects expressed satisfaction with the multimedia-enhanced problem-based learning sessions. CONCLUSIONS: Radiological and pathological images enhanced the students' understanding, interaction and critical thinking during problem-based learning sessions.

A Turf-Based Feature Selection Technique for Predicting Factors Affecting Human Health during Pandemic.

Worldwide, COVID-19 is a highly contagious epidemic that has affected various fields. Using Artificial Intelligence (AI) and particular feature selection approaches, this study evaluates the aspects affecting the health of students throughout the COVID-19 lockdown time. The research presented in this paper plays a vital role in indicating the factor affecting the health of students during the lockdown in the COVID-19 pandemic. The research presented in this article investigates COVID-19's impact on student health using feature selections. The Filter feature selection technique is used in the presented work to statistically analyze all the features in the dataset, and for better accuracy. ReliefF (TuRF) filter feature selection is tuned and utilized in such a way that it helps to identify the factors affecting students' health from a benchmark dataset of students studying during COVID-19. Random Forest (RF), Gradient Boosted Decision Trees (GBDT), Support Vector Machine (SVM), and 2- layer Neural Network (NN), helps in identifying the most critical indicators for rapid intervention. Results of the approach presented in the paper identified that the students who maintained their weight and kept themselves busy in health activities in the pandemic, such student's remained healthy through this pandemic and study from home in a positive manner. The results suggest that the 2- layer NN machine-learning algorithm showed better accuracy (90%) to predict the factors affecting on health issues of students during COVID-19 lockdown time.

Mucormycosis co-infection in COVID-19 patients: An update.

Mucormycosis (MCM) is a rare fungal disorder that has recently been increased in parallel with novel COVID-19 infection. MCM with COVID-19 is extremely lethal, particularly in immunocompromised individuals. The collection of available scientific information helps in the management of this co-infection, but still, the main question on COVID-19, whether it is occasional, participatory, concurrent, or coincidental needs to be addressed. Several case reports of these co-infections have been explained as causal associations, but the direct contribution in immunocompromised individuals remains to be explored completely. This review aims to provide an update that serves as a guide for the diagnosis and treatment of MCM patients' co-infection with COVID-19. The initial report has suggested that COVID-19 patients might be susceptible to developing invasive fungal infections by different species, including MCM as a co-infection. In spite of this, co-infection has been explored only in severe cases with common triangles: diabetes, diabetes ketoacidosis, and corticosteroids. Pathogenic mechanisms in the aggressiveness of MCM infection involves the reduction of phagocytic activity, attainable quantities of ferritin attributed with transferrin in diabetic ketoacidosis, and fungal heme oxygenase, which enhances iron absorption for its metabolism. Therefore, severe COVID-19 cases are associated with increased risk factors of invasive fungal co-infections. In addition, COVID-19 infection leads to reduction in cluster of differentiation, especially CD4+ and CD8+ T cell counts, which may be highly implicated in fungal co-infections. Thus, the progress in MCM management is dependent on a different strategy, including reduction or stopping of implicit predisposing factors, early intake of active antifungal drugs at appropriate doses, and complete elimination via surgical debridement of infected tissues.

Analyzing the Features Affecting the Performance of Teachers during Covid-19: A Multilevel Feature Selection

COVID-19 is a profoundly contagious pandemic that has taken the world by storm and has afflicted different fields of life with negative effects. It has had a substantial impact on education which is evident from the transition from Face-to-Face (F2F) teaching to online mode of education and the rigid implementation of lockdown across the globe. This study examines the factors impacting the performance of teachers during the lockdown period of COVID-19 using various feature selection algorithms and Artificial Intelligence techniques. In this paper, we have proposed a novel multilevel feature selection for the prediction of the factors affecting the teachers’ satisfaction with online teaching and learning in COVID-19. The proposed multilevel feature selection is composed of the Fast Correlation Based Filter (FCBF), Mutual Information (MI), Relieff, and Particle Swarm Optimization (PSO) feature selection. The performance of the proposed feature selection approach is evaluated through the teachers’ benchmark dataset. We used a range of measures like accuracy, precision, f-measure, and recall to evaluate the performance of the proposed approach. We applied different machine learning approaches (SVM, LGBM, and ANN) with the proposed multilevel feature selection technique. The performance of the proposed approach is also compared with existing feature selection algorithms, and the results show the improvement in the performance of feature selection in terms of accuracy, precision, recall, and F-Measure. Proposed feature selection provides more than 80% accuracy with Light Weight Gradient Boosting Machine (LGBM).

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

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

Digitalization plan in medical education during COVID-19 lockdown.

BACKGROUND: The COVID-19 pandemic has enhanced the adoption of virtual learning after the urgent suspension of traditional teaching. Different online learning strategies were established to face this learning crisis. The present descriptive cross-sectional study was conducted to reveal the different digital procedures implemented by the College of Medicine at Qassim University for better student performance and achievement. METHODS: The switch into distance-based learning was managed by the digitalization committee. Multiple online workshops were conducted to the staff and students about the value and procedures of such a shift. New procedures for online problem-based learning (PBL) sessions were designed. Students' satisfaction was recorded regarding the efficiency of live streaming educational activities and online assessment. RESULTS: The students were satisfied with the overall shift into this collaborative e-learning environment and the new successful procedures of virtual PBL sessions. The digital learning tools facilitated the performance of the students and their peer sharing of knowledge. The role of informatics computer technologies was evident in promoting the students, research skills, and technical competencies. CONCLUSIONS: The present work elaborated on the procedures and privileges of the transformation into digitalized learning, particularly the PBL sessions, which were appreciated by the students and staff. It recommended the adoption of future online theoretical courses as well as the development of informatics computer technologies.