Pharmacy education in developing countries faces many challenges. An assessment of the challenges and opportunities for the future of pharmacy education in Saudi Arabia has not been conducted. The purpose of the study was to ascertain the views and opinions of pharmacy education stakeholders regarding the current issues challenging pharmacy education, and to discuss the future of pharmacy education in Saudi Arabia. A total of 48 participants attended a one-day meeting in October 2011, designed especially for the purpose of this study. The participants were divided into six round-table discussion sessions with eight persons in each group. Six major themes were explored in these sessions, including the need to improve pharmacy education, program educational outcomes, adoption of an integrated curriculum, the use of advanced teaching methodologies, the need to review assessment methods, and challenges and opportunities to improve pharmacy experiential training. The round-table discussion sessions were videotaped and transcribed verbatim and analyzed by two independent researchers. Participants agreed that pharmacy education in the country needs improvement. Participants agreed on the need for clear, measureable, and national educational outcomes for pharmacy programs in the Kingdom. Participants raised the importance of collaboration between faculty members and departments to design and implement an integrated curriculum. They also emphasized the use of new teaching methodologies focusing on student self-learning and active learning. Assessments were discussed with a focus on the use of new tools, confidentiality of examinations, and providing feedback to students. Several points were raised regarding the opportunities to improve pharmacy experiential training, including the need for more experiential sites and qualified preceptors, addressing variations in training quality between experiential sites, the need for accreditation of experiential sites, and the use of technology to track experiential activities and assessments. Several challenges for improving pharmacy education in Saudi Arabia were discussed by stakeholders. To tackle these challenges facing most pharmacy schools in the Kingdom, national efforts need to be considered by involving all stakeholders.
In recent years, the consumption of social media content to keep up with global news and to verify its authenticity has become a considerable challenge. Social media enables us to easily access news anywhere, anytime, but it also gives rise to the spread of fake news, thereby delivering false information. This also has a negative impact on society. Therefore, it is necessary to determine whether or not news spreading over social media is real. This will allow for confusion among social media users to be avoided, and it is important in ensuring positive social development. This paper proposes a novel solution by detecting the authenticity of news through natural language processing techniques. Specifically, this paper proposes a novel scheme comprising three steps, namely, stance detection, author credibility verification, and machine learning-based classification, to verify the authenticity of news. In the last stage of the proposed pipeline, several machine learning techniques are applied, such as decision trees, random forest, logistic regression, and support vector machine (SVM) algorithms. For this study, the fake news dataset was taken from Kaggle. The experimental results show an accuracy of 93.15%, precision of 92.65%, recall of 95.71%, and F1-score of 94.15% for the support vector machine algorithm. The SVM is better than the second best classifier, i.e., logistic regression, by 6.82%.
Background: Left ventricle (LV) segmentation using a cardiac magnetic resonance imaging (MRI) dataset is critical for evaluating global and regional cardiac functions and diagnosing cardiovascular diseases. LV clinical metrics such as LV volume, LV mass and ejection fraction (EF) are frequently extracted based on the LV segmentation from short-axis MRI images. Manual segmentation to assess such functions is tedious and time-consuming for medical experts to diagnose cardiac pathologies. Therefore, a fully automated LV segmentation technique is required to assist medical experts in working more efficiently. Method: This paper proposes a fully convolutional network (FCN) architecture for automatic LV segmentation from short-axis MRI images. Several experiments were conducted in the training phase to compare the performance of the network and the U-Net model with various hyper-parameters, including optimization algorithms, epochs, learning rate, and mini-batch size. In addition, a class weighting method was introduced to avoid having a high imbalance of pixels in the classes of image’s labels since the number of background pixels was significantly higher than the number of LV and myocardium pixels. Furthermore, effective image conversion with pixel normalization was applied to obtain exact features representing target organs (LV and myocardium). The segmentation models were trained and tested on a public dataset, namely the evaluation of myocardial infarction from the delayed-enhancement cardiac MRI (EMIDEC) dataset. Results: The dice metric, Jaccard index, sensitivity, and specificity were used to evaluate the network’s performance, with values of 0.93, 0.87, 0.98, and 0.94, respectively. Based on the experimental results, the proposed network outperforms the standard U-Net model and is an advanced fully automated method in terms of segmentation performance. Conclusion: This proposed method is applicable in clinical practice for doctors to diagnose cardiac diseases from short-axis MRI images.
The human gastrointestinal (GI) tract is an important part of the body. According to World Health Organization (WHO) research, GI tract infections kill 1.8 million people each year. In the year 2019, almost 5 million individuals were detected with gastrointestinal disease. Radiation therapy has the potential to improve cure rates in GI cancer patients. Radiation oncologists direct x-ray beams at the tumour while avoiding the stomach and intestines. The current objective is to direct the x-ray beam toward the malignancy while avoiding the stomach and intestines in order to improve dose delivery to the tumour. This study offered a technique for segmenting GI tract organs (small bowel, big intestine, and stomach) to assist radio oncologists to treat cancer patients more quickly and accurately. The suggested model is a U-Net model designed from scratch and used for the segmentation of a small size of images to extract the local features more efficiently. Furthermore, in the proposed model, six transfer learning models were employed as the backbone of the U-Net topology. The six transfer learning models used are Inception V3, SeResNet50, VGG19, DenseNet121, InceptionResNetV2, and EfficientNet B0. The suggested model was analysed with model loss, dice coefficient, and IoU. The results specify that the suggested model outperforms all transfer learning models, with performance parameter values as 0.122 model loss, 0.8854 dice coefficient, and 0.8819 IoU.
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