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.

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.

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.

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.