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This scoping review aims to identify and summarize the current literature on Machine learning (ML) approaches for detecting coronary artery disease (CAD) using angiography imaging. We comprehensively searched several databases and identified 23 studies that met the inclusion criteria. They employed different types of angiography imaging including computed tomography and invasive coronary angiography. Several studies have used deep learning algorithms for image classification and segmentation, and our findings show that various machine learning algorithms, such as convolutional neural networks, different types of U-Net, and hybrid approaches. Studies also varied in the outcomes measured, identifying stenosis, and assessing the severity of CAD. ML approaches can improve the accuracy and efficiency of CAD detection by using angiography. The performance of the algorithms differed depending on the dataset used, algorithm employed, and features selected for analysis. Therefore, there is a need to develop ML tools that can be easily integrated into clinical practice to aid in the diagnosis and management of CAD.

Introduction: The use of mobile applications (apps) become widespread and Provide many benefits especially in healthcare. According to the World Health Organization, osteoporosis is one of the most common diseases of elderly in the world. Like other chronic conditions, disease self-management can prove fruitful. Using a mobile application for Osteoporosis can improve patient care and self-management by encouraging patients to take a more active role in their health.Material and Methods: This study presents a systematic review of mHealth applications, available on Google Play Store, Bazaar market (as a local market) and also Apple App Store, for both the English and Persian speakers. The assessment criteria, including content, visual aids, reminders, health warnings, social and design of selected apps, were tested during July 2019.Results: Reviewing the 19 included applications showed that the most and least focus of apps content was on exercise with 84% repetition and the osteoporosis fracture that no program addressed this issue separately. Findings on reminders, health warnings, and visual aids were not very encouraging (available in 11% apps). Reminders were more common in English-speaking apps than Persian-speaking ones, and Visual aids, one of the benefits of mobile apps over paper logbooks, were provided only in2 apps. The opportunity to share data in social networks was available in 26% of apps, and in the design section, most of the apps have no significant flaws, but 74% of cases did not provide any clear instructions required for the elderly.Conclusion:The review shows that there are rather few products on offer and the ones that are available display low quality, poor performance, and evidence-based information is also insufficiently used. Further efforts are required to collect data that will support the design of validated evidence-based educational functions for mHealth apps.