The performance of artificial intelligence (AI) for brain MRI can improve if enough data are made available. Generative adversarial networks (GANs) showed a lot of potential to generate synthetic MRI data that can capture the distribution of real MRI. Besides, GANs are also popular for segmentation, noise removal, and super-resolution of brain MRI images. This scoping review aims to explore how GANs methods are being used on brain MRI data, as reported in the literature. The review describes the different applications of GANs for brain MRI, presents the most commonly used GANs architectures, and summarizes the publicly available brain MRI datasets for advancing the research and development of GANs-based approaches. This review followed the guidelines of PRISMA-ScR to perform the study search and selection. The search was conducted on five popular scientific databases. The screening and selection of studies were performed by two independent reviewers, followed by validation by a third reviewer. Finally, the data were synthesized using a narrative approach. This review included 139 studies out of 789 search results. The most common use case of GANs was the synthesis of brain MRI images for data augmentation. GANs were also used to segment brain tumors and translate healthy images to diseased images or CT to MRI and vice versa. The included studies showed that GANs could enhance the performance of AI methods used on brain MRI imaging data. However, more efforts are needed to transform the GANs-based methods in clinical applications.

Background Cardiac arrest is a life-threatening cessation of activity in the heart. Early prediction of cardiac arrest is important, as it allows for the necessary measures to be taken to prevent or intervene during the onset. Artificial intelligence (AI) technologies and big data have been increasingly used to enhance the ability to predict and prepare for the patients at risk. Objective This study aims to explore the use of AI technology in predicting cardiac arrest as reported in the literature. Methods A scoping review was conducted in line with the guidelines of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) extension for scoping reviews. Scopus, ScienceDirect, Embase, the Institute of Electrical and Electronics Engineers, and Google Scholar were searched to identify relevant studies. Backward reference list checks of the included studies were also conducted. Study selection and data extraction were independently conducted by 2 reviewers. Data extracted from the included studies were synthesized narratively. Results Out of 697 citations retrieved, 41 studies were included in the review, and 6 were added after backward citation checking. The included studies reported the use of AI in the prediction of cardiac arrest. Of the 47 studies, we were able to classify the approaches taken by the studies into 3 different categories: 26 (55%) studies predicted cardiac arrest by analyzing specific parameters or variables of the patients, whereas 16 (34%) studies developed an AI-based warning system. The remaining 11% (5/47) of studies focused on distinguishing patients at high risk of cardiac arrest from patients who were not at risk. Two studies focused on the pediatric population, and the rest focused on adults (45/47, 96%). Most of the studies used data sets with a size of <10,000 samples (32/47, 68%). Machine learning models were the most prominent branch of AI used in the prediction of cardiac arrest in the studies (38/47, 81%), and the most used algorithm was the neural network (23/47, 49%). K-fold cross-validation was the most used algorithm evaluation tool reported in the studies (24/47, 51%). Conclusions AI is extensively used to predict cardiac arrest in different patient settings. Technology is expected to play an integral role in improving cardiac medicine. There is a need for more reviews to learn the obstacles to the implementation of AI technologies in clinical settings. Moreover, research focusing on how to best provide clinicians with support to understand, adapt, and implement this technology in their practice is also necessary.

Artificial Intelligence (AI) technologies are increasingly being used to enhance kidney transplant outcomes. In this review, we explore the use of AI in kidney transplantation (KT) in the existing literature. Four databases were searched to identify a total of 33 eligible studies. AI technologies were used to help in diagnostic, predictive and medication management purposes for kidney transplant patients. AI is an emerging tool in KT, however, there is a research gap exploring the limitations associated with implementing AI technologies in the field. Research is also needed to recognize clinical educational needs and other barriers to promote adoption and standardization of care for KT patients amongst clinicians.

A variety of climatic factors are known to influence respiratory health, usually through their impact on air quality. High temperatures, for example, are often associated with an increase in ground levels of ozone, which in turn negatively impact respiratory function (Thurston and Ito 1999; WHO 2000). In New York City, high temperatures were correlated with increased hospital admissions for both chronic airway obstruction and asthma (as well as some cardiovascular problems) (Lin et al. 2009). Humidity levels are often positively correlated with allergenic spores (e.g., Hasnain et al. 2012) and other particulates (e.g., Kulshrestha et al. 2012) and thus can trigger asthmatic attacks or other forms of respiratory distress. More recently, investigators have begun to examine the relationship between dust-sand storms and respiratory health. For example, Waness et al. (2011) cited evidence suggesting that exposure to dust-sand storms in the Middle East may contribute to pulmonary alveolar proteinosis and silicosis. In this study, we conducted a detailed correlational analysis between various climatic variables, the density of airborne particulate matter, and the incidence of hospital admissions related to respiratory problems.Inhalation of air particulates may result in serious health problems, most notably with the respiratory system. Due to a variety of factors (traffic, construction, weather patterns), the air in Doha is of poor quality and is significantly over targets for PM2.5 and PM10 (WHO, 2014 rankings). We examined the levels of particulate matter to determine if there was a daily or weekly pattern. We then compared the number of air particulates in the summer and winter and attempted to determine if small (0.5–2.5 mg/l) or large (>2.5 mg) particulate matter was related to weather patterns and resulted in increased use of the nebulizer at a local clinic. We found that particle number (both small and large) was highest around 6 am and lowest around 12 pm, a pattern that didn't change between months. The number of large and small particles was generally lower on Friday when human activity was lowest. The temperature decreased in winter while humidity increased; wind speed remained relatively constant. Data suggest that nebulizer use was higher in the winter and lower in the summer although the reasons for this are speculative. There was no relationship found between the number of people using the nebulizer and the number of air particles.

BACKGROUND Artificial Intelligence technologies and big data have been increasingly used to enhance kidney transplant experts’ ability to make critical decisions and manage the care plan for their patients. OBJECTIVE To explore the use of AI technologies in the field of kidney transplantation as reported in the literature. METHODS Embase, CINAHL, PubMed and Google Scholar were used in the search. Backward reference list checking of included studies was also conducted. Study selection and data extraction was done independently by three reviewers. Data extracted was synthesized in a narrative approach. RESULTS Of 505 citations retrieved from the databases, 33 unique studies are included in this review. Artificial intelligence (AI) technologies in the included studies were used to help with diagnosis (n= 16), used as a prediction tool (n=15) and, also for supporting appropriate prescription for kidney transplant patients (n = 2). The population who benefited from the technique included patients who underwent kidney transplantation procedure (n = 24) and those who are potential candidate (n=6). The most prominent AI branch used in kidney transplantation care was machine learning with Random Forest (n=11) being the most used AI model, followed by Linear Regression (n=6). CONCLUSIONS Conclusion: AI is extensively being used in the field of kidney transplant. However, there is a gap in research on the limitation and obstacles associated with implementing AI technologies in kidney transplant. There is a need for more research to identify educational needs and standardized practice for clinicians who wish to apply AI technologies in critical transplantation-related decisions.