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

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.

The Canadian Women's Heart Health Alliance ATLAS on the Epidemiology, Diagnosis, and Management of Cardiovascular Disease in Women - Chapter 8: Knowledge Gaps and Status of Existing Research Programs in Canada.

Despite significant progress in medical research and public health efforts, gaps in knowledge of women's heart health remain across epidemiology, presentation, management, outcomes, education, research, and publications. Historically, heart disease was viewed primarily as a condition in men and male individuals, leading to limited understanding of the unique risks and symptoms that women experience. These knowledge gaps are particularly problematic because globally heart disease is the leading cause of death for women. Until recently, sex and gender have not been addressed in cardiovascular research, including in preclinical and clinical research. Recruitment was often limited to male participants and individuals identifying as men, and data analysis according to sex or gender was not conducted, leading to a lack of data on how treatments and interventions might affect female patients and individuals who identify as women differently. This lack of data has led to suboptimal treatment and limitations in our understanding of the underlying mechanisms of heart disease in women, and is directly related to limited awareness and knowledge gaps in professional training and public education. Women are often unaware of their risk factors for heart disease or symptoms they might experience, leading to delays in diagnosis and treatments. Additionally, health care providers might not receive adequate training to diagnose and treat heart disease in women, leading to misdiagnosis or undertreatment. Addressing these knowledge gaps requires a multipronged approach, including education and policy change, built on evidence-based research. In this chapter we review the current state of existing cardiovascular research in Canada with a specific focus on women.

En dépit des avancées importantes de la recherche médicale et des efforts en santé publique, il reste des lacunes dans les connaissances sur la santé cardiaque des femmes sur les plans de l'épidémiologie, du tableau clinique, de la prise en charge, des résultats, de l'éducation, de la recherche et des publications. Du point de vue historique, la cardiopathie a d'abord été perçue comme une maladie qui touchait les hommes et les individus de sexe masculin. De ce fait, la compréhension des risques particuliers et des symptômes qu'éprouvent les femmes est limitée. Ces lacunes dans les connaissances posent particulièrement problème puisqu'à l'échelle mondiale la cardiopathie est la cause principale de décès chez les femmes. Jusqu'à récemment, la recherche en cardiologie, notamment la recherche préclinique et clinique, ne portait pas sur le sexe et le genre. Le recrutement souvent limité aux participants masculins et aux individus dont l'identité de genre correspond au sexe masculin et l'absence d'analyses de données en fonction du sexe ou du genre ont eu pour conséquence un manque de données sur la façon dont les traitements et les interventions nuisent aux patientes féminines et aux individus dont l'identité de genre correspond au sexe féminin, et ce, de façon différente. Cette absence de données a mené à un traitement sous-optimal et à des limites de notre compréhension des mécanismes sous-jacents de la cardiopathie chez les femmes, et est directement reliée à nos connaissances limitées, et à nos lacunes en formation professionnelle et en éducation du public. Le fait que les femmes ne connaissent souvent pas leurs facteurs de risque de maladies du cœur ou les symptômes qu'elles peuvent éprouver entraîne des retards de diagnostic et de traitements. De plus, le fait que les prestataires de soins de santé ne reçoivent pas la formation adéquate pour poser le diagnostic et traiter la cardiopathie chez les femmes les mène à poser un mauvais diagnostic ou à ne pas traiter suffisamment. Pour pallier ces lacunes de connaissances, il faut une approche à plusieurs volets, qui porte notamment sur l'éducation et les changements dans les politiques, et qui repose sur la recherche fondée sur des données probantes. Dans ce chapitre, nous passons en revue l'état actuel de la recherche existante sur les maladies cardiovasculaires au Canada, plus particulièrement chez les femmes.

Multiclass machine learning vs. conventional calculators for stroke/CVD risk assessment using carotid plaque predictors with coronary angiography scores as gold standard: a 500 participants study.

Machine learning (ML)-based algorithms for cardiovascular disease (CVD) risk assessment have shown promise in clinical decisions. However, they usually predict binary events using only conventional risk factors. Our overall goal was to develop the "multiclass machine learning (MCML)-based algorithms" (labelled as AtheroEdge 3.0ML) and assess whether considering carotid ultrasound imaging fused with conventional risk factors can provide better CVD/stroke risk prediction than conventional CVD risk calculators (CCVRC). Carotid ultrasound and coronary angiography were performed on 500 participants. Stenosis in the coronary arteries was used to assign participants a coronary angiographic score (CAS). CVD/stroke risk was determined using three types of MCML algorithms: (i) support vector machine (SVM), (ii) random forest (RF), and (iii) extreme gradient boost (XGBoost). The performance of CVD risk assessment using MCML and CCVRC (such as Framingham Risk Score, the Systematic Coronary Risk Evaluation score, and the Atherosclerotic CVD) was evaluated on test patients against the CAS as the gold standard for each class using the area-under-the-curve (AUC) and classification accuracy. The mean percentage improvement in AUC and the mean absolute improvement in accuracy over CCVRC using 90% training and 10% testing protocol (labelled as K10) were ~ 105% and ~ 28%, respectively. Of all the three MCML systems, RF showed the best performance. Further, carotid image phenotypes showed the most effective clinical feature in AtheroEdge 3.0ML performance. The AtheroEdge 3.0ML using carotid imaging are reliable, accurate, and superior to traditional CVD risk scoring methods for predicting the CVD/stroke risk due to coronary artery disease.

Low-Cost Office-Based Cardiovascular Risk Stratification Using Machine Learning and Focused Carotid Ultrasound in an Asian-Indian Cohort.

This study developed an office-based cardiovascular risk calculator using a machine learning (ML) algorithm that utilized a focused carotid ultrasound. The design of this study was divided into three steps. The first step involved collecting 18 office-based biomarkers consisting of six clinical risk factors (age, sex, body mass index, systolic blood pressure, diastolic blood pressure, and smoking) and 12 carotid ultrasound image-based phenotypes. The second step consisted of the design of an ML-based cardiovascular risk calculator-called "AtheroEdge Composite Risk Score 2.0" (AECRS2.0ML) for risk stratification, considering chronic kidney disease (CKD) as the surrogate endpoint of cardiovascular disease. The last step consisted of comparing AECRS2.0ML against the currently utilized office-based CVD calculators, namely the Framingham risk score (FRS) and the World Health Organization (WHO) risk scores. A cohort of 379 Asian-Indian patients with type-2 diabetes mellitus, hypertension, and chronic kidney disease (stage 1 to 5) were recruited for this cross-sectional study. From this retrospective cohort, 758 ultrasound scan images were acquired from the far walls of the left and right common carotid arteries [mean age = 55 ± 10.8 years, 67.28% males, 91.82% diabetic, 86.54% hypertensive, and 83.11% with CKD]. The mean office-based cardiovascular risk estimates using FRS and WHO calculators were 26% and 19%, respectively. AECRS2.0ML demonstrated a better risk stratification ability having a higher area-under-the-curve against FRS and WHO by ~30% (0.871 vs. 0.669) and ~ 20% (0.871 vs. 0.727), respectively. The office-based machine-learning cardiovascular risk-stratification tool (AECRS2.0ML) shows superior performance compared to currently available conventional cardiovascular risk calculators.