ABSTRACT
The development of advanced technologies in artificial intelligence (AI) has expanded its applications across various fields. Machine learning (ML), a subcategory of AI, enables computers to recognize patterns within extensive datasets. Furthermore, deep learning, a specialized form of ML, processes inputs through neural network architectures inspired by biological processes. The field of clinical lipidology has experienced significant growth over the past few years, and recently, it has begun to intersect with AI. Consequently, the purpose of this narrative review is to examine the applications of AI in clinical lipidology. This review evaluates various publications concerning the diagnosis of familial hypercholesterolemia, estimation of low-density lipoprotein cholesterol (LDL-C) levels, prediction of lipid goal attainment, challenges associated with statin use, and the influence of cardiometabolic and dietary factors on the discordance between apolipoprotein B and LDL-C. Given the concerns surrounding AI techniques, such as ethical dilemmas, opacity, limited reproducibility, and methodological constraints, it is prudent to establish a framework that enables the medical community to accurately interpret and utilize these emerging technological tools.
ABSTRACT
Objective@#The role of lipoprotein(a) (Lp[a]) as a possibly causal risk factor for atherosclerotic cardiovascular disease has been well established. However, the clinical evidence regarding the association between Lp(a) levels and atrial fibrillation (AF) remains limited and inconsistent. This study aimed to analyze the association between elevated Lp(a) levels or single-nucleotide polymorphisms (SNPs) related to high levels of Lp(a) and AF. @*Methods@#This systematic review was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A literature search was performed to identify studies that evaluated the association between Lp(a) levels or SNPs related to high levels of Lp(a) and AF. Observational studies with a cross-sectional, casecontrol, or cohort design were included in this systematic review, without limitations according to language, country, or publication type. @*Results@#Eleven observational studies including 1,246,817 patients were eligible for this systematic review. Two cross-sectional studies, 5 prospective/retrospective cohort studies, and 4 Mendelian randomization studies were analyzed. Two cross-sectional studies that compared Lp(a) levels between patients with and without AF showed conflicting results.Cohort studies that evaluated the incidence of AF according to Lp(a) levels showed different results: no association (3 studies), a positive association (1 study), and an inverse relationship (1 study). Finally, Mendelian randomization studies also showed heterogeneous results (positive association: 2 studies; inverse association: 1 study; no association: 1 study). @*Conclusion@#Although there could be an association between Lp(a) levels and AF, the results of the studies published to date are contradictory and not yet definitive. Therefore, further research should clarify this issue.