The Relation Between Theoretical and Practical Exams for Health Sciences Students at King Saud Bin Abdulaziz University for Health Sciences- Jeddah.

Introduction: A comprehensive approach to assessment is essential to ensure that all students' learning competencies are measured accurately. Therefore, multiple methods of assessment have been developed to address this matter. This Study aims to assess the correlation between health sciences students' performance on theoretical and practical exams. Methods: A correlational study design was conducted. The academic performance of 352 students across theoretical/practical courses was tested. SPSS version 29.0 was used for analysis. Spearman's rho correlation (Rs), Wilcoxon, and Mann Whitney were computed at p<0.05. Results: The theoretical performance was strongly correlated with the practical performance of all programs pooled together (Rs (352) = 0.67, p<0.001). Also, there was a strong correlation between theoretical and practical performance for male students (Rs (181) = 0.72, p<0.001), while a moderate correlation for female students (Rs (171) = 0.53, p<0.001). Mann-Whitney test revealed significant mean performance difference by gender both at theoretical (U = 9284, p<0.0001) and practical (U = 11,373, p < 0.0001) levels. Conclusion: There were significant correlations between theoretical knowledge and practical skills across the selected four programs.; The mean student's performance was better in the practical skills than in the theoretical knowledge assessment, and female students surpassed male students in both practical and theoretical assessments in the four programs offered to both genders.

The barriers, motives, perceptions, and attitudes toward research among radiology practitioners and interns in Saudi Arabia: a cross-sectional study.

Background: Research and the use of evidence-based practices are imperative to the advancement of diagnostic imaging modalities. The aim of this study was to assess the perceptions and attitudes of radiology practitioners (i.e., Technicians, Technologists or Specialists, and Senior Specialists) and interns in King Abdulaziz Medical Cities (KAMCs), Kingdom of Saudi Arabia, toward research, and to explore the various barriers and obstacles that hinder their research efforts. Methods: A cross-sectional descriptive investigation was carried out from December 2022 to March 2023 among 112-KAMCs' radiology practitioners and interns, using previously developed and validated questionnaire comprised of five distinct sections, each serving a specific purpose, and with a non-probability convenient sampling technique. Descriptive statistics were generated for participants' demographics, and chi-square and fisher's exact tests were used to examine the association between participants' demographics and their involvement in research. Results: Among the 137 KAMCs' radiology practitioners and interns who were invited to participate, 112 responded and completed the questionnaire, resulting in an overall response rate of 81.75%. Radiology practitioners and interns from various medical imaging subspecialties were found to be involved in research to the extent of 83%, with nearly half (40.9%) of them have had publications, and 53.3% of these publications being either cross-sectional studies or retrospective clinical studies. A lack of time (66.1%), a lack of a professional supervisor support program (50.9%), and deficiency in research skills (45.5%) were common obstacles that may impede the participants' ability to conduct research. The most common motives for participants to conduct research were the desire to improve their resumes (69.6%), get accepted into postgraduate radiology programs (58%), and improve their research skills (52.7%). Conclusion: KAMCs' radiology practitioners and interns have a positive attitude toward performing research. Despite the high percentage (83%) of those involved in research, the number of publications remains low. A crucial step to advancing the profession's evidence base is engaging radiology practitioners and interns in research and encouraging radiology practitioner-led research. The study findings can serve as a valuable basis for designing developmental programs aimed at overcoming research obstacles among healthcare professionals in Saudi Arabia.

A New Weighted Deep Learning Feature Using Particle Swarm and Ant Lion Optimization for Cervical Cancer Diagnosis on Pap Smear Images.

One of the most widespread health issues affecting women is cervical cancer. Early detection of cervical cancer through improved screening strategies will reduce cervical cancer-related morbidity and mortality rates worldwide. Using a Pap smear image is a novel method for detecting cervical cancer. Previous studies have focused on whole Pap smear images or extracted nuclei to detect cervical cancer. In this paper, we compared three scenarios of the entire cell, cytoplasm region, or nucleus region only into seven classes of cervical cancer. After applying image augmentation to solve imbalanced data problems, automated features are extracted using three pre-trained convolutional neural networks: AlexNet, DarkNet 19, and NasNet. There are twenty-one features as a result of these scenario combinations. The most important features are split into ten features by the principal component analysis, which reduces the dimensionality. This study employs feature weighting to create an efficient computer-aided cervical cancer diagnosis system. The optimization procedure uses the new evolutionary algorithms known as Ant lion optimization (ALO) and particle swarm optimization (PSO). Finally, two types of machine learning algorithms, support vector machine classifier, and random forest classifier, have been used in this paper to perform classification jobs. With a 99.5% accuracy rate for seven classes using the PSO algorithm, the SVM classifier outperformed the RF, which had a 98.9% accuracy rate in the same region. Our outcome is superior to other studies that used seven classes because of this focus on the tissues rather than just the nucleus. This method will aid physicians in diagnosing precancerous and early-stage cervical cancer by depending on the tissues, rather than on the nucleus. The result can be enhanced using a significant amount of data.

A Review of Recent Advances in Brain Tumor Diagnosis Based on AI-Based Classification.

Uncontrolled and fast cell proliferation is the cause of brain tumors. Early cancer detection is vitally important to save many lives. Brain tumors can be divided into several categories depending on the kind, place of origin, pace of development, and stage of progression; as a result, tumor classification is crucial for targeted therapy. Brain tumor segmentation aims to delineate accurately the areas of brain tumors. A specialist with a thorough understanding of brain illnesses is needed to manually identify the proper type of brain tumor. Additionally, processing many images takes time and is tiresome. Therefore, automatic segmentation and classification techniques are required to speed up and enhance the diagnosis of brain tumors. Tumors can be quickly and safely detected by brain scans using imaging modalities, including computed tomography (CT), magnetic resonance imaging (MRI), and others. Machine learning (ML) and artificial intelligence (AI) have shown promise in developing algorithms that aid in automatic classification and segmentation utilizing various imaging modalities. The right segmentation method must be used to precisely classify patients with brain tumors to enhance diagnosis and treatment. This review describes multiple types of brain tumors, publicly accessible datasets, enhancement methods, segmentation, feature extraction, classification, machine learning techniques, deep learning, and learning through a transfer to study brain tumors. In this study, we attempted to synthesize brain cancer imaging modalities with automatically computer-assisted methodologies for brain cancer characterization in ML and DL frameworks. Finding the current problems with the engineering methodologies currently in use and predicting a future paradigm are other goals of this article.

The Impact of COVID-19 on Radiological Science Students and Interns at King Saud bin Abdulaziz University for Health Sciences: Cross-Sectional Study.

Introduction: The impact of the coronavirus disease (COVID-19) outbreak on many parts of our lives cannot be overstated. This study aimed to identify the psychological, physical activity, and educational effects of COVID-19 on radiological sciences students and interns at the three campuses of King Saud bin Abdulaziz University for Health Sciences (KSAU-HS), Riyadh, Jeddah, and Alahsa. Methods: A cross-sectional study was conducted between November and December 2021 among Saudi-108 radiological sciences students and interns using non-probability convenient sampling at King Saud bin Abdul-Aziz University for Health Science (KSAU-HS), Riyadh, Jeddah, and Alahsa using a validated questionnaire. Statistical analyses were conducted using Excel and JMP statistical software. Results: 102 out of 108 completed the questionnaire resulting in a 94.44% response rate. The percentage of the overall negative psychological impact was 62%. For the physical activity effects of COVID-19 among students and interns, 96% reported a decline in their physical activities. 77% of participants reported a fair impression that the students were able to achieve some of their academic goals and acquired new skills during the pandemic, and 20% reported a good impression. They achieved all their goals and developed new skills, whereas 3% reported bad impressions and needed to achieve their goals or improve their skills. Conclusion: COVID-19 had a negative psychological and physical activity impact on RADs students and interns at the three KSAU-HS campuses in the Kingdom of Saudi Arabia. Despite technical difficulties, students and interns reported positive academic outcomes from COVID-19.

Burnout Among Saudi Radiological Sciences Undergraduate Students During COVID-19 Pandemic: A Cross-Sectional Study.

Background: The novel Coronavirus Disease 2019 (COVID-19) pandemic has posed unprecedented new stressors and challenges to the applied health sciences' education. This study explored the prevalence of burnout among Saudi radiological sciences students at King Saud bin Abdulaziz University for Health Sciences during the COVID-19 pandemic. Methods: A cross-sectional study was conducted between November and December 2020 among 176-Saudi radiological sciences students, using the 16-item questionnaire of Maslach Burnout Inventory-General Survey for Students and through non-probability convenient sampling technique. The 16 items of the questionnaire were scored on a 7-point frequency rating scale ranging from 0 (never) to 6 (every day) and consisted of three distinct burnout dimensions/subscales: a) emotional exhaustion (5-items), cynicism (5-items), and professional efficacy (6-items). The means of individual items that make up each scale of burnout were calculated, and statistical analysis was performed using the Mann-Whitney U-test. Results/Observations/Findings: From the 176-radiological sciences students approached, 96 (54.5%) completed the questionnaire. The percentage of students who were at moderate to high risk of burnout was 70.8% for emotional exhaustion, 75% for cynicism, and 74% for professional efficacy subscales. Emotional exhaustion was significantly higher among fourth-year students (P = 0.042), than third-year students. Cynicism was significantly higher among fourth-year female students (P = 0.035), than third-year female students. The professional efficacy was significantly lower among fourth-year female students (P = 0.007) than males. Conclusion: Our study shows 73.3% moderate to high burnout rates among Saudi radiological sciences students during the COVID-19 pandemic. Burnout increases as students advance to the fourth year. A block/modular curriculum structure for fourth-year courses may be necessary to reduce burnout among fourth-year students. Academic counseling can ease students' emotional stress and reduce burnout risk.

Patient-Based Dose Audit for Common Radiographic Examinations With Digital Radiology Systems: A Retrospective Cross-Sectional Study.

This study aims to audit radiation doses of adult patients who underwent common diagnostic X-ray examinations and compare dose area product (DAP) values with the established International Diagnostic Reference Level (IDRLs). Retrospective cross-sectional records of 339-patients who underwent 699-radiographic examinations between October 2018 and March 2019 were obtained. Patient-related factors, exposure, and DAP data were recorded for the six most common examinations. The mean and 75th percentile of DAPs were recorded and compared to IDRLs values. The 75th percentiles of the locally measured DAPs were below IDRLs for all examinations except for lateral lumbar, AP, and lateral thoracic spine, in which DAP-75th-percentile exceeded all IDRLs by up to 40.7%, 2.8%, 365.5%, respectively. Considering the type of detector used, the mean of the locally measured DAPs significantly exceeded the UK DRLs for the lateral thoracic spine and lateral lumbar spine. Locally measured DAP values were below the IDRLs except for thoracic and lumbar spine projections, which significantly exceeded.