Artificial Intelligence and Cardiovascular Genetics

Abstract

Polygenic diseases, which are genetic disorders caused by the combined action of multiple genes, pose unique and significant challenges for the diagnosis and management of affected patients. A major goal of cardiovascular medicine has been to understand how genetic variation leads to the clinical heterogeneity seen in polygenic cardiovascular diseases (CVDs). Recent advances and emerging technologies in artificial intelligence (AI), coupled with the ever-increasing availability of next generation sequencing (NGS) technologies, now provide researchers with unprecedented possibilities for dynamic and complex biological genomic analyses. Combining these technologies may lead to a deeper understanding of heterogeneous polygenic CVDs, better prognostic guidance, and, ultimately, greater personalized medicine. Advances will likely be achieved through increasingly frequent and robust genomic characterization of patients, as well the integration of genomic data with other clinical data, such as cardiac imaging, coronary angiography, and clinical biomarkers. This review discusses the current opportunities and limitations of genomics; provides a brief overview of AI; and identifies the current applications, limitations, and future directions of AI in genomics.

An Artificial Intelligence-Based Bio-Medical Stroke Prediction and Analytical System Using a Machine Learning Approach

“…Because of them, adults are at risk of severe disability and possibly death. If an impending stroke can be recognised or predicted in its early stages, it may be feasible to significantly mitigate its effects [8][9][10]. Several risk factors for stroke have been established through the course of a great number of investigations and clinical trials.…”

A cardiologist’s guide to machine learning in cardiovascular disease prognosis prediction

“…ML methods have been successfully applied to predict the incidence of hypertension by using polygenic risk factors [ 39 , 41 , 58 ], to predict advanced coronary calcium [ 60 ], inheritable cardiac disease[ 12 ] and to predict type II diabetes in a multi-ethnic cohort [ 47 ]. Of interest, the number of layers within neural network architectures used in genomics has generally been far less than those used for image recognition [ 77 ], and typically consist of only a few layers [ 86 ] with many hundreds to thousands of parameters [ 35 ].…”

Remote patient monitoring using artificial intelligence: Current state, applications, and challenges

“…Medical research is also benefitting from AI by helping to expedite genome sequencing and the development of new drugs and treatments from the knowledge that previously was not possible to obtain or observe from such complex data. Machine learning, a subset of AI, can potentially assist clinicians in interpreting complex data in a relatively short period using specialized algorithms (Helm et al, 2020; Krittanawong et al, 2022). They can assist with a patient assessment to help predict early deterioration of their health status and even classify their types of motion or activities (Z. Liu, Zhu, et al, 2022; Huang et al, 2022).…”