CovMediScanX: A medical imaging solution for COVID-19 diagnosis from chest X-ray images.

INTRODUCTION: Radiologists have extensively employed the interpretation of chest X-rays (CXR) to identify visual markers indicative of COVID-19 infection, offering an alternative approach for the screening of infected individuals. This research article presents CovMediScanX, a deep learning-based framework designed for a rapid and automated diagnosis of COVID-19 from CXR scan images. METHODS: The proposed approach encompasses gathering and preprocessing CXR image datasets, training deep learning-based custom-made Convolutional Neural Network (CNN), pre-trained and hybrid transfer learning models, identifying the highest-performing model based on key evaluation metrics, and embedding this model into a web interface called CovMediScanX, designed for radiologists to detect the COVID-19 status in new CXR images. RESULTS: The custom-made CNN model obtained a remarkable testing accuracy of 94.32% outperforming other models. CovMediScanX, employing the custom-made CNN underwent evaluation with an independent dataset also. The images in the independent dataset are sourced from a scanning machine that is entirely different from those used for the training dataset, highlighting a clear distinction of datasets in their origins. The evaluation outcome highlighted the framework's capability to accurately detect COVID-19 cases, showcasing encouraging results with a precision of 73% and a recall of 84% for positive cases. However, the model requires further enhancement, particularly in improving its detection of normal cases, as evidenced by lower precision and recall rates. CONCLUSION: The research proposes CovMediScanX framework that demonstrates promising potential in automatically identifying COVID-19 cases from CXR images. While the model's overall performance on independent data needs improvement, it is evident that addressing bias through the inclusion of diverse data sources during training could further enhance accuracy and reliability.

Factors associated with health-related quality of life in children with sickle cell disease.

BACKGROUND: Sickle cell disease is an inherited haematological condition with life-threatening consequences. It can affect all aspects of the lives of children with the condition, including biopsychosocial and cognitive aspects. These children tend to have a low health-related quality of life (HRQoL). AIM: To identify factors associated with HRQoL in Omani children with sickle cell disease. METHOD: The study was a secondary analysis of data from a randomised controlled trial conducted with 72 parent-and-child dyads who were recruited from two tertiary hospitals in Oman. The aim of the original study was to examine the effects of an educational programme on the knowledge and self-efficacy of parents of children with sickle cell disease. As part of that study, parents and children completed two questionnaires on HRQoL, one generic and one specific to sickle cell disease. RESULTS: Parents' knowledge of sickle cell disease, parents' self-efficacy in managing their child's symptoms, parents' age, children's age and treatment with hydroxyurea were found to affect children's HRQoL. CONCLUSION: Healthcare providers need to include biopsychosocial and cognitive aspects of HRQoL in their assessments of children with sickle cell disease. Programmes designed to enhance parents' and children's knowledge and self-efficacy, as well as measures designed to ensure that children receive treatment with hydroxyurea, are likely to improve the HRQoL of children with sickle cell disease.