Using Machine Learning for Non-Invasive Detection of Kidney Stones Based on Laboratory Test Results: A Case Study from a Saudi Arabian Hospital.

Kidney stone disease is a widespread urological disorder affecting millions globally. Timely diagnosis is crucial to avoid severe complications. Traditionally, renal stones are detected using computed tomography (CT), which, despite its effectiveness, is costly, resource-intensive, exposes patients to unnecessary radiation, and often results in delays due to radiology report wait times. This study presents a novel approach leveraging machine learning to detect renal stones early using routine laboratory test results. We utilized an extensive dataset comprising 2156 patient records from a Saudi Arabian hospital, featuring 15 attributes with challenges such as missing data and class imbalance. We evaluated various machine learning algorithms and imputation methods, including single and multiple imputations, as well as oversampling and undersampling techniques. Our results demonstrate that ensemble tree-based classifiers, specifically random forest (RF) and extra tree classifiers (ETree), outperform others with remarkable accuracy rates of 99%, recall rates of 98%, and F1 scores of 99% for RF, and 92% for ETree. This study underscores the potential of non-invasive, cost-effective laboratory tests for renal stone detection, promoting prompt and improved medical support.

Interactive notebooks for achieving learning outcomes in a graduate course: a pedagogical approach.

Social network analysis involves delicate and sophisticated mathematical concepts which are abstract and challenging to acquire by traditional methods. Many studies show that female students perform poorly in computer science-related courses compared to male students. To address these issues, this research investigates the impact of employing a web-based interactive programming tool, Jupyter notebooks, on supporting deeper conceptual understanding and, therefore, better attainment levels of the course learning outcomes in a female setting. The work also highlights the overall experience and enjoyment this tool brought to the classroom. Document analysis and questionnaire were used as data collection methods. A mixed approach was applied, mid-term exam documents were investigated qualitatively, and the questionnaire was analyzed quantitatively. Our results showed that most students correctly perceived the learning outcomes and knowledge introduced within the Jupyter environment. Moreover, the interactive nature of Jupyter enhanced engagement and brought enjoyment to the learning experience.

Influence of social media on urologic knowledge acquisition among young urologists in Saudi Arabia.

Objectives: The present study explores how young urologists in Saudi Arabia are adopting social media as a learning tool and how this new development is shaping as far as learning is concerned. Methods: A 18-item online survey via survemonkey.com was conducted. The survey was distributed through email in Saudi Arabia. The survey targeted young urologists and urology residents. The survey design and distribution was performed according to CHERRIES guidelines. Due to the quantitative nature of study data, SPSS software was used to analyze collected data. Results: A total of 104 young Saudi urologists responded to our survey. Participants were mostly familiar with the use of Twitter (86%), followed closely by YouTube (82%) and then Snapchat and Instagram (73% and 63%, respectively), a large portion (72%) of participants believe that social media has a moderate-to-high influence on their urology knowledge, YouTube was by far the most used source to watch and understand surgical skills, followed by reference books and websites, respectively. Conclusion: Social media has contributed to the spread of medical information among urology community and outside the urology community as well, with easier spread of medical knowledge to all involved by using social media, an extensive impact is achieved to both physicians and patients as well. For future work, this study should be conducted again, to monitor and compare the progression of usage among urologists.

Deep Learning Approaches for Detecting COVID-19 From Chest X-Ray Images: A Survey.

Chest X-ray (CXR) imaging is a standard and crucial examination method used for suspected cases of coronavirus disease (COVID-19). In profoundly affected or limited resource areas, CXR imaging is preferable owing to its availability, low cost, and rapid results. However, given the rapidly spreading nature of COVID-19, such tests could limit the efficiency of pandemic control and prevention. In response to this issue, artificial intelligence methods such as deep learning are promising options for automatic diagnosis because they have achieved state-of-the-art performance in the analysis of visual information and a wide range of medical images. This paper reviews and critically assesses the preprint and published reports between March and May 2020 for the diagnosis of COVID-19 via CXR images using convolutional neural networks and other deep learning architectures. Despite the encouraging results, there is an urgent need for public, comprehensive, and diverse datasets. Further investigations in terms of explainable and justifiable decisions are also required for more robust, transparent, and accurate predictions.