ABSTRACT
(1) Introduction: The objective of this study was to evaluate the patient experience with the Mawid application during the COVID-19 pandemic in Al Hassa, Saudi Arabia. (2) Methodology: A quantitative cross-sectional survey was designed to evaluate the patient experience with the Mawid app during the COVID-19 pandemic in Al Hassa, Saudi Arabia. A total of 146 respondents completed the questionnaire. (3) Results: More than half of the participants (65.8%) opined that application was easy to use. Furthermore, 65.1% of the participants considered it to be very easy and easy to search for the required information; and 63.7% of the respondents reflected that it was easy to book an appointment. There was a statistically significant difference between the ease of searching for the required information (p-value = 0.006); the ease of undoing an unwanted move and gender (p-value = 0.049); the ease of searching for the required information and educational level (p-value = 0.048); the ease of booking an appointment and educational level (p = 0.049); and the ease of searching for the required information and the labor sector of the respondents (p value= 0.049) among the genders. No significant differences were identified among the age groups. (4) Conclusions: Overall, most participants suggested that the Mawid app was easy to use and had a potentially useful set of features to help mitigate and manage the COVID-19 pandemic in Al Hassa, Saudi Arabia.
ABSTRACT
COVID-19 crisis has placed medical systems over the world under unprecedented and growing pressure. Medical imaging processing can help in the diagnosis, treatment, and early detection of diseases. It has been considered as one of the modern technologies applied to fight against the COVID-19 crisis. Although several artificial intelligence, machine learning, and deep learning techniques have been deployed in medical image processing in the context of COVID-19 disease, there is a lack of research considering systematic literature review and categorization of published studies in this field. A systematic review locates, assesses, and interprets research outcomes to address a predetermined research goal to present evidence-based practical and theoretical insights. The main goal of this study is to present a literature review of the deployed methods of medical image processing in the context of the COVID-19 crisis. With this in mind, the studies available in reliable databases were retrieved, studied, evaluated, and synthesized. Based on the in-depth review of literature, this study structured a conceptual map that outlined three multi-layered folds: data gathering and description, main steps of image processing, and evaluation metrics. The main research themes were elaborated in each fold, allowing the authors to recommend upcoming research paths for scholars. The outcomes of this review highlighted that several methods have been adopted to classify the images related to the diagnosis and detection of COVID-19. The adopted methods have presented promising outcomes in terms of accuracy, cost, and detection speed.