Dyslipidemia in children as a risk factor for cardiovascular diseases

Revolutionizing Cardiology through Artificial Intelligence-Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment-A Comprehensive Review of the Past 5 Years

BACKGROUND

Artificial intelligence (AI) can radically change almost every aspect of the human experience. In the medical field, there are numerous applications of AI and subsequently, in a relatively short time, significant progress has been made. Cardiology is not immune to this trend, this fact being supported by the exponential increase in the number of publications in which the algorithms play an important role in data analysis, pattern discovery, identification of anomalies, and therapeutic decision making. Furthermore, with technological development, there have appeared new models of machine learning (ML) and deep learning (DP) that are capable of exploring various applications of AI in cardiology, including areas such as prevention, cardiovascular imaging, electrophysiology, interventional cardiology, and many others. In this sense, the present article aims to provide a general vision of the current state of AI use in cardiology.

RESULTS

We identified and included a subset of 200 papers directly relevant to the current research covering a wide range of applications. Thus, this paper presents AI applications in cardiovascular imaging, arithmology, clinical or emergency cardiology, cardiovascular prevention, and interventional procedures in a summarized manner. Recent studies from the highly scientific literature demonstrate the feasibility and advantages of using AI in different branches of cardiology.

CONCLUSIONS

The integration of AI in cardiology offers promising perspectives for increasing accuracy by decreasing the error rate and increasing efficiency in cardiovascular practice. From predicting the risk of sudden death or the ability to respond to cardiac resynchronization therapy to the diagnosis of pulmonary embolism or the early detection of valvular diseases, AI algorithms have shown their potential to mitigate human error and provide feasible solutions. At the same time, limits imposed by the small samples studied are highlighted alongside the challenges presented by ethical implementation; these relate to legal implications regarding responsibility and decision making processes, ensuring patient confidentiality and data security. All these constitute future research directions that will allow the integration of AI in the progress of cardiology.

Vitamin D is not associated with body composition and metabolic profile among Brazilian children with 25-hydroxyvitamin D ≥ 75 nmol/L: A cross-sectional study

BACKGROUND AND AIMS

The association of vitamin D with cardiovascular disease risk factors among children remains inconclusive, and there is a lack of studies that evaluate children with optimal serum 25-hydroxyvitamin D [25(OH)D]. Thus, this study aimed to analyze the relationship between serum 25(OH)D and body composition and metabolic profile among Brazilian children with sufficient serum 25(OH)D.

METHODS AND RESULTS

A cross-sectional study was conducted with 88 Brazilian children aged 4-11 years. Self-reported race, physical activity, anthropometry (body mass and height), body composition (waist circumference, body fat percentage, fat free mass, triceps and subscapular skinfolds), biochemical profile [lipid fractions, fasting glucose and 25(OH)D] and blood pressure data were collected. No difference was found in sex, self-reported race, physical activity, age, anthropometry, body composition, biochemical parameters and blood pressure between children with 25(OH)D 75-99 and ≥ 100 nmol/L. In addition, there was no association between serum vitamin D and body composition and metabolic profile.

CONCLUSIONS

Serum 25(OH)D was not associated with body composition and metabolic profile among Brazilian children with sufficient serum 25(OH)D. Further studies among children with serum levels ≥ 75 nmol/L are needed to confirm this finding.