Level of Awareness Regarding Interventional Radiology Among Medical Students at Northern Border University in Arar, Saudi Arabia.

Introduction Interventional radiology (IR) is a highly specialized field of radiology that employs advanced imaging techniques like MRIs, CT scans, X-rays, and ultrasounds to detect and treat a variety of medical disorders. By using minimally invasive procedures, interventional radiologists can access the body's internal organs and tissues with minimal discomfort and reduced risks compared to traditional surgical techniques. Some common IR procedures include angioplasty, embolization, biopsy, and stent placement, among others. Overall, IR is an innovative and effective approach to medical care that offers numerous benefits to patients. As this specialty expands, there is a huge demand for increasing staff. However, due to a lack of awareness, this increased demand could not be fulfilled. Objective The objective is to assess medical students' knowledge regarding IR and compare this knowledge between male and female students. Materials and methods This cross-sectional study was carried out at Northern Border University's College of Medicine in Arar, Saudi Arabia. The study aimed to assess the medical students' knowledge of IR. All students enrolled in the clinical years at Northern Border University were included in the study, and a self-administered online questionnaire was used to collect data. The minimum sample size required was 169. Appropriate statistical analysis was applied to the collected data, and a p-value of less than 0.05 was considered significant. Results One hundred and seventy-two participants in all who met the inclusion criteria answered the study's questionnaire. The fourth-year students represented the highest percentage of the sample, with 65 participants (37.8%), followed by 54 (31.4%) fifth-year students and 53 (30.8%) sixth-year students. The study found that 66 participants (38.4%) rated their knowledge of IR as adequate, while only 8 (4.7%) considered it excellent. The participants' self-rated knowledge of IR did not significantly differ across male and female groups. Conclusion The study's findings suggest that medical students have limited knowledge of IR and that there is no discernible difference in the knowledge and interest of males and females in this subject. Further research and targeted educational interventions may be necessary to improve the medical students' overall knowledge and interest in IR.

Updates on the Prognosis of Giant Cell Arteritis: A Systematic Review.

Giant cell arteritis (GCA) is considered the most common type of vasculitis, especially in people aged 50 years or older, and imaging studies have helped predict its systemic nature. We conducted this review to highlight the results of the recently published articles considering the prognosis of giant cell arteritis (GCA). We searched for the relevant literature in SCOPUS, PubMed, Web of Science, and Science Direct and were included. We used Rayyan (Rayyan Systems, Cambridge, Massachusetts) throughout this systematic approach. The search resulted in twelve studies with 2600 patients with GCA diagnosis; most of them, 1853 (71.3%), were females. This systematic review found that most of the GCA patients experienced at least one relapse episode, primarily in patients younger than 75 years, with dependency on glucocorticoids, female sex, and involvement of large vessel vasculitis. We also found that stroke in GCA patients was associated with a bad prognosis. Therefore, we think more prospective studies are needed to enhance particular patient outcomes, and new therapeutic approaches using accessible biotherapies like tocilizumab and other similar medications are required.

Prevalence and Awareness of Obesity and Bariatric Surgeries in the Northern Borders Region, Saudi Arabia.

Introduction Obesity is a complex health issue affecting millions worldwide, characterized by excessive body fat accumulation, often leading to various health complications. Bariatric surgeries are effective interventions for severe obesity, assisting patients in attaining substantial weight reduction and enhancing their overall well-being. This study aimed to assess obesity patterns and bariatric surgery prevalence in the Northern Borders region of Saudi Arabia to increase community knowledge and awareness about obesity and bariatric surgery. Methods This cross-sectional study included 386 residents in the Northern Borders region, Saudi Arabia. The participants completed a previously validated self-administered electronic questionnaire, and the confidentiality of the collected data was ensured. Results Nearly 58.3% of the participants (31-40 years), with a predominance of females, had a body mass index (BMI) >30, and 33.7% had undergone bariatric surgery. Most participants (92.5%) were aware that obesity is associated with significant medical issues, 98.2% appreciated that there is a surgical method to reduce weight, and 58.8% indicated that the procedure was not safe. Additionally, the majority of the respondents (57.0%) were not sure about the complications of weight-loss surgeries, and only 28.0% knew that surgeries for obesity and their complications may lead to death. Significant associations were found between age, education level, and BMI concerning the knowledge of obesity/bariatric surgery (p=0.003, 0.001, 0.002), respectively. However, gender and work status did not show such associations (p> 0.05). Conclusion Our study highlighted a lack of knowledge among the community regarding the safety, potential complications, and survival outcomes associated with obesity and bariatric surgery that could be due to ignorance and reluctance to pursue bariatric surgery to overcome morbid obesity. Significantly, the study found a relationship between age, education level, BMI, and knowledge of obesity and bariatric surgery.

Identification and Prediction of Chronic Diseases Using Machine Learning Approach.

Nowadays, humans face various diseases due to the current environmental condition and their living habits. The identification and prediction of such diseases at their earlier stages are much important, so as to prevent the extremity of it. It is difficult for doctors to manually identify the diseases accurately most of the time. The goal of this paper is to identify and predict the patients with more common chronic illnesses. This could be achieved by using a cutting-edge machine learning technique to ensure that this categorization reliably identifies persons with chronic diseases. The prediction of diseases is also a challenging task. Hence, data mining plays a critical role in disease prediction. The proposed system offers a broad disease prognosis based on patient's symptoms by using the machine learning algorithms such as convolutional neural network (CNN) for automatic feature extraction and disease prediction and K-nearest neighbor (KNN) for distance calculation to find the exact match in the data set and the final disease prediction outcome. A collection of disease symptoms has been performed for the preparation of the data set along with the person's living habits, and details related to doctor consultations are taken into account in this general disease prediction. Finally, a comparative study of the proposed system with various algorithms such as Naïve Bayes, decision tree, and logistic regression has been demonstrated in this paper.

A Large-Scale Dataset and Deep Learning Model for Detecting and Counting Olive Trees in Satellite Imagery.

Since the Pre-Roman era, olive trees have a significant economic and cultural value. In 2019, the Al-Jouf region, in the north of the Kingdom of Saudi Arabia, gained a global presence by entering the Guinness World Records, with the largest number of olive trees in the world. Olive tree detecting and counting from a given satellite image are a significant and difficult computer vision problem. Because olive farms are spread out over a large area, manually counting the trees is impossible. Moreover, accurate automatic detection and counting of olive trees in satellite images have many challenges such as scale variations, weather changes, perspective distortions, and orientation changes. Another problem is the lack of a standard database of olive trees available for deep learning applications. To address these problems, we first build a large-scale olive dataset dedicated to deep learning research and applications. The dataset consists of 230 RGB images collected over the territory of Al-Jouf, KSA. We then propose an efficient deep learning model (SwinTUnet) for detecting and counting olive trees from satellite imagery. The proposed SwinTUnet is a Unet-like network which consists of an encoder, a decoder, and skip connections. Swin Transformer block is the fundamental unit of SwinTUnet to learn local and global semantic information. The results of an experimental study on the proposed dataset show that the SwinTUnet model outperforms the related studies in terms of overall detection with a 0.94% estimation error.

An Effective Data Science Technique for IoT-Assisted Healthcare Monitoring System with a Rapid Adoption of Cloud Computing.

Patients are required to be observed and treated continually in some emergency situations. However, due to time constraints, visiting the hospital to execute such tasks is challenging. This can be achieved using a remote healthcare monitoring system. The proposed system introduces an effective data science technique for IoT supported healthcare monitoring system with the rapid adoption of cloud computing that enhances the efficiency of data processing and the accessibility of data in the cloud. Many IoT sensors are employed, which collect real healthcare data. These data are retained in the cloud for the processing of data science. In the Healthcare Monitoring-Data Science Technique (HM-DST), initially, an altered data science technique is introduced. This algorithm is known as the Improved Pigeon Optimization (IPO) algorithm, which is employed for grouping the stored data in the cloud, which helps in improving the prediction rate. Next, the optimum feature selection technique for extraction and selection of features is illustrated. A Backtracking Search-Based Deep Neural Network (BS-DNN) is utilized for classifying human healthcare. The proposed system's performance is finally examined with various healthcare datasets of real time and the variations are observed with the available smart healthcare systems for monitoring.

Machine Algorithm for Heartbeat Monitoring and Arrhythmia Detection Based on ECG Systems.

Cardiac arrhythmia is an illness in which a heartbeat is erratic, either too slow or too rapid. It happens as a result of faulty electrical impulses that coordinate the heartbeats. Sudden cardiac death can occur as a result of certain serious arrhythmia disorders. As a result, the primary goal of electrocardiogram (ECG) investigation is to reliably perceive arrhythmias as life-threatening to provide a suitable therapy and save lives. ECG signals are waveforms that denote the electrical movement of the human heart (P, QRS, and T). The duration, structure, and distances between various peaks of each waveform are utilized to identify heart problems. The signals' autoregressive (AR) analysis is then used to obtain a specific selection of signal features, the parameters of the AR signal model. Groups of retrieved AR characteristics for three various ECG kinds are cleanly separated in the training dataset, providing high connection classification and heart problem diagnosis to each ECG signal within the training dataset. A new technique based on two-event-related moving averages (TERMAs) and fractional Fourier transform (FFT) algorithms is suggested to better evaluate ECG signals. This study could help researchers examine the current state-of-the-art approaches employed in the detection of arrhythmia situations. The characteristic of our suggested machine learning approach is cross-database training and testing with improved characteristics.

Attitude of medical students at King Saud Bin Abdulaziz University for Health Sciences toward family medicine as a future specialty.

BACKGROUND: Owing to the rising population of the Kingdom of Saudi Arabia, the need for family physicians is growing. The number of family physicians who would be available in the health service in future is dependent on the attitudes of medical students because their choice of specialty is a major factor in satisfying this demand. The aim of the study was to evaluate the attitudes of medical students to family medicine as a future career. MATERIALS AND METHODS: This cross-sectional study was conducted at King Saud Bin Abdulaziz University for Health Sciences. A total of 308 students were randomly selected from problem-based learning groups. Data were collected using a 25-item validated questionnaire, and Excel and SPSS were used for data entry and analysis. Mean and standard deviation were used to describe numerical data and frequencies and percentages to describe categorical data. P < 0.05 was considered statistically significant. RESULTS: A total of 308 medical students, 201 (65.3%) of whom were male, completed the questionnaire. Majority of the students (229 [74.3%]) agreed that family physicians make important contributions to medicine although family medicine was one of the least preferred specialties of the students. CONCLUSIONS: Most students were aware of the importance of family medicine; however, only a few of them chose this specialty. Further studies should be conducted to identify the factors that influence medical students' decisions in their choice of family medicine as a future career.