Detecting Coronary Artery Disease from Computed Tomography Images Using a Deep Learning Technique

In recent times, coronary artery disease (CAD) has become one of the leading causes of morbidity and mortality across the globe. Diagnosing the presence and severity of CAD in individuals is essential for choosing the best course of treatment. Presently, computed tomography (CT) provides high spatial resolution images of the heart and coronary arteries in a short period. On the other hand, there are many challenges in analyzing cardiac CT scans for signs of CAD. Research studies apply machine learning (ML) for high accuracy and consistent performance to overcome the limitations. It allows excellent visualization of the coronary arteries with high spatial resolution. Convolutional neural networks (CNN) are widely applied in medical image processing to identify diseases. However, there is a demand for efficient feature extraction to enhance the performance of ML techniques. The feature extraction process is one of the factors in improving ML techniques’ efficiency. Thus, the study intends to develop a method to detect CAD from CT angiography images. It proposes a feature extraction method and a CNN model for detecting the CAD in minimum time with optimal accuracy. Two datasets are utilized to evaluate the performance of the proposed model. The present work is unique in applying a feature extraction model with CNN for CAD detection. The experimental analysis shows that the proposed method achieves 99.2% and 98.73% prediction accuracy, with F1 scores of 98.95 and 98.82 for benchmark datasets. In addition, the outcome suggests that the proposed CNN model achieves the area under the receiver operating characteristic and precision-recall curve of 0.92 and 0.96, 0.91 and 0.90 for datasets 1 and 2, respectively. The findings highlight that the performance of the proposed feature extraction and CNN model is superior to the existing models

Efficacy of povidone-iodine nasal rinse and mouth wash in COVID-19 management: a prospective, randomized pilot clinical trial (povidone-iodine in COVID-19 management)

Abstract Objectives/Hypothesis To assess the efficacy of 0.23% povidone-iodine (PVP-I) nasal rinses and mouth washes on detectability of the coronavirus disease 2019 (COVID-19) virus and cycle threshold (Ct) values in nasopharyngeal swabs. Study design This was an open-label, prospective, randomized, placebo-controlled clinical trial. Setting The study was conducted in King Saud University Medical City, Riyadh, Saudi Arabia, from August 2021 to July 2022. Methods Participants diagnosed with SARS-CoV-2 were randomly assigned to one of three groups, with participants receiving either 0.23% PVP-I, 0.9% normal saline (NS) nasal rinses and mouth washes, or no intervention (control group). Nasopharyngeal swabs were taken 4, 8, 12, and 18 days after the first swab to measure the detectability of the virus and the Ct. Results A total of 19 participants were involved in this study. The mean viral survival was 9.8, 12, and 12.6 days for the PVP-I, NS, and control groups, respectively, with a statistically significant difference (p = 0.046). The Ct mean values were 23 ± 3.4, 23.5 ± 6.3, and 26.3 ± 5.9 at the time of recruitment and 25.2 ± 3.5, 15 ± 11.7, and 26.9 ± 6.4 after 4 days for the PVP-I, NS, and control groups, respectively. Conclusions When used continuously at a concentration of 0.23%, PVP-I showed promising results in terms of decreasing the pandemic burden by reducing the period of infectiousness and viral load. However, the use of PVP-I did not result in significantly different changes in the quality-of-life parameters in recently vaccinated and mild COVID-19 patients

Development of type 2 diabetes mellitus after gestational diabetes in a cohort in KSA: Prevalence and risk factors

Objectives: KSA has the highest prevalence of diabetes mellitus among Middle Eastern countries with a prevalence range of 21%–24%. Gestational diabetes (GDM) is a well-known risk factor for type 2 diabetes mellitus (T2DM). GDM is associated with a 7-fold increased risk of T2DM. Thus, this research assessed the prevalence and risk factors associated with the development T2DM in a cohort of patients with GDM in KSA. Methods: The medical records of patients with GDM who visited the outpatient clinics of a tertiary care hospital from 2011 to 2014 were included in this study. Patients with a prior diagnosis of diabetes mellitus before pregnancy and those with GDM who did not have postpartum diabetes screening were excluded. Results: A total of 123 women with GDM and underwent postpartum diabetes screening, 82 (67%) developed T2DM based on follow-up records. Approximately 45% (37/82) of patients who developed T2DM were screened ≤6 months after delivery, whereas 55%(45/82) were screened >6 months after delivery. Older patients, patients who had a higher number of pregnancies (gravidity and parity), and patients with previous GDM were more likely to develop T2DM. Conclusion: In KSA, women who developed GDM, particularly those who are older, multigravid, and multiparous and who have a prior history of GDM, are at an increased risk of developing T2DM. Postpartum diabetes screening of patients with GDM within the recommended period need to be improved. Keywords: Gestational diabetes, Postpartum, Risk factors, Screening, Type 2 diabete

Additional file 1 of Efficacy of povidone-iodine nasal rinse and mouth wash in COVID-19 management: a prospective, randomized pilot clinical trial (povidone-iodine in COVID-19 management)

Supplementary Material

Lifestyle habits and obesity indices among male adolescents in Riyadh, Saudi Arabia

Abstract Obesity among adolescents is a global health apprehension which requires early prevention. The aim of this study was to determine the association between lifestyle habits including physical activity, sedentary behaviors and eating habits with obesity indices of body mass index (BMI) and waist-to-height ratio (WHtR) among male adolescents in Riyadh, Saudi Arabia. We randomly selected 471 secondary school male adolescents aged 14–18 years. A pre-validated self-reported questionnaire was used to record the data on physical activity level, sedentary behaviors, sleep duration and eating habits. The International Obesity Task Force (IOTF) cutoff values for adolescents under 18 years of age were used to define overweight and obesity. Total energy expenditure was calculated using metabolic equivalent-minutes per week. Anthropometry including weight, height, BMI, waist circumference, waist/height ratio (WHtR), were assessed. 53.7% and 48.4% of the adolescents were overweight/obese and had abdominal obesity; respectively. Those with overweight and obesity or above 50% of WHtR were much less active in terms of METs-min/week from vigorous-intensity sports, sum of all METs-min/week from all vigorous-intensity physical activity, total METs-min/week from all physical activity compared with non-obese adolescents and below 50% of WHtR. The present study identified the lifestyle habits that were associated with obesity and may represent valid targets for the prevention and management of obesity among Saudi adolescents. Knowledge of the factors that contribute to obesity could be used in preventive programs for the control of obesity among adolescents