Artificial intelligence for cardiovascular disease risk assessment in personalised framework: a scoping review

Abstract

Background: The field of precision medicine endeavors to transform the healthcare industry by advancing individualised strategies for diagnosis, treatment modalities, and predictive assessments. This is achieved by utilizing extensive multidimensional biological datasets encompassing diverse components, such as an individual's genetic makeup, functional attributes, and environmental influences. Artificial intelligence (AI) systems, namely machine learning (ML) and deep learning (DL), have exhibited remarkable efficacy in predicting the potential occurrence of specific cancers and cardiovascular diseases (CVD).

Methods: We conducted a comprehensive scoping review guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. Our search strategy involved combining key terms related to CVD and AI using the Boolean operator AND. In August 2023, we conducted an extensive search across reputable scholarly databases including Google Scholar, PubMed, IEEE Xplore, ScienceDirect, Web of Science, and arXiv to gather relevant academic literature on personalised medicine for CVD. Subsequently, in January 2024, we extended our search to include internet search engines such as Google and various CVD websites. These searches were further updated in March 2024. Additionally, we reviewed the reference lists of the final selected research articles to identify any additional relevant literature.

Findings: A total of 2307 records were identified during the process of conducting the study, consisting of 564 entries from external sites like arXiv and 1743 records found through database searching. After 430 duplicate articles were eliminated, 1877 items that remained were screened for relevancy. In this stage, 1241 articles remained for additional review after 158 irrelevant articles and 478 articles with insufficient data were removed. 355 articles were eliminated for being inaccessible, 726 for being written in a language other than English, and 281 for not having undergone peer review. Consequently, 121 studies were deemed suitable for inclusion in the qualitative synthesis. At the intersection of CVD, AI, and precision medicine, we found important scientific findings in our scoping review. Intricate pattern extraction from large, complicated genetic datasets is a skill that AI algorithms excel at, allowing for accurate disease diagnosis and CVD risk prediction. Furthermore, these investigations have uncovered unique genetic biomarkers linked to CVD, providing insight into the workings of the disease and possible treatment avenues. The construction of more precise predictive models and personalised treatment plans based on the genetic profiles of individual patients has been made possible by the revolutionary advancement of CVD risk assessment through the integration of AI and genomics.

Interpretation: The systematic methodology employed ensured the thorough examination of available literature and the inclusion of relevant studies, contributing to the robustness and reliability of the study's findings. Our analysis stresses a crucial point in terms of the adaptability and versatility of AI solutions. AI algorithms designed in non-CVD domains such as in oncology, often include ideas and tactics that might be modified to address cardiovascular problems.

Funding: No funding received.

Keywords: Artificial intelligence; Cardiovascular diseases; Deep learning; Personalised medicine; Precision medicine.

Fig. 1

The 8 P's of PM. Each “P” stands for the fundamental aspect that has contributed to developing and advancing the ground-breaking field of PM. The predictive aspect harnesses data and AI to forecast patients' disease risks and treatment responses.^, Preventive strategies focus on proactive interventions to mitigate disease risks, while participatory engagement empowers patients to be active partners in their healthcare decisions.^, Precision modifies the treatments to meet patients' needs and preferences.^,,, , , , , Pharmacogenomics focuses on understanding how an individual's genetic makeup affects their drug response, leading to optimised drug selection and dosages.^, Patient empowerment provides patients with the knowledge, skills, and resources necessary to make informed decisions about their health. Prognostic models use AI to predict disease progression and patient outcomes, guiding personalised insights into a patient's health status. Finally, ensuring privacy safeguards patient data confidentiality, fostering trust in the PM journey.

Fig. 2

Venn diagram depicting AI, ML, and DL hierarchy.

Fig. 3

Sankey diagram depicting the keywords used per database for initial paper identification and subsequent exclusion criteria.

Fig. 4

PRISMA flowchart for systematic paper selection and quality assessment.

Fig. 5

Milestones in the journey of PM.

Fig. 6

CVD risk factors and data acquisition.

Fig. 7

Five-step personalized treatment for CVDs.