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.

Deep learning approach for cardiovascular disease risk stratification and survival analysis on a Canadian cohort.

The quantification of carotid plaque has been routinely used to predict cardiovascular risk in cardiovascular disease (CVD) and coronary artery disease (CAD). To determine how well carotid plaque features predict the likelihood of CAD and cardiovascular (CV) events using deep learning (DL) and compare against the machine learning (ML) paradigm. The participants in this study consisted of 459 individuals who had undergone coronary angiography, contrast-enhanced ultrasonography, and focused carotid B-mode ultrasound. Each patient was tracked for thirty days. The measurements on these patients consisted of maximum plaque height (MPH), total plaque area (TPA), carotid intima-media thickness (cIMT), and intraplaque neovascularization (IPN). CAD risk and CV event stratification were performed by applying eight types of DL-based models. Univariate and multivariate analysis was also conducted to predict the most significant risk predictors. The DL's model effectiveness was evaluated by the area-under-the-curve measurement while the CV event prediction was evaluated using the Cox proportional hazard model (CPHM) and compared against the DL-based concordance index (c-index). IPN showed a substantial ability to predict CV events (p < 0.0001). The best DL system improved by 21% (0.929 vs. 0.762) over the best ML system. DL-based CV event prediction showed a ~ 17% increase in DL-based c-index compared to the CPHM (0.86 vs. 0.73). CAD and CV incidents were linked to IPN and carotid imaging characteristics. For survival analysis and CAD prediction, the DL-based system performs superior to ML-based models.

Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm: A Review.

Cardiovascular disease (CVD) related mortality and morbidity heavily strain society. The relationship between external risk factors and our genetics have not been well established. It is widely acknowledged that environmental influence and individual behaviours play a significant role in CVD vulnerability, leading to the development of polygenic risk scores (PRS). We employed the PRISMA search method to locate pertinent research and literature to extensively review artificial intelligence (AI)-based PRS models for CVD risk prediction. Furthermore, we analyzed and compared conventional vs. AI-based solutions for PRS. We summarized the recent advances in our understanding of the use of AI-based PRS for risk prediction of CVD. Our study proposes three hypotheses: i) Multiple genetic variations and risk factors can be incorporated into AI-based PRS to improve the accuracy of CVD risk predicting. ii) AI-based PRS for CVD circumvents the drawbacks of conventional PRS calculators by incorporating a larger variety of genetic and non-genetic components, allowing for more precise and individualised risk estimations. iii) Using AI approaches, it is possible to significantly reduce the dimensionality of huge genomic datasets, resulting in more accurate and effective disease risk prediction models. Our study highlighted that the AI-PRS model outperformed traditional PRS calculators in predicting CVD risk. Furthermore, using AI-based methods to calculate PRS may increase the precision of risk predictions for CVD and have significant ramifications for individualized prevention and treatment plans.

A Pharmaceutical Paradigm for Cardiovascular Composite Risk Assessment Using Novel Radiogenomics Risk Predictors in Precision Explainable Artificial Intelligence Framework: Clinical Trial Tool.

BACKGROUND: Cardiovascular disease (CVD) is challenging to diagnose and treat since symptoms appear late during the progression of atherosclerosis. Conventional risk factors alone are not always sufficient to properly categorize at-risk patients, and clinical risk scores are inadequate in predicting cardiac events. Integrating genomic-based biomarkers (GBBM) found in plasma/serum samples with novel non-invasive radiomics-based biomarkers (RBBM) such as plaque area, plaque burden, and maximum plaque height can improve composite CVD risk prediction in the pharmaceutical paradigm. These biomarkers consider several pathways involved in the pathophysiology of atherosclerosis disease leading to CVD. OBJECTIVE: This review proposes two hypotheses: (i) The composite biomarkers are strongly correlated and can be used to detect the severity of CVD/Stroke precisely, and (ii) an explainable artificial intelligence (XAI)-based composite risk CVD/Stroke model with survival analysis using deep learning (DL) can predict in preventive, precision, and personalized (aiP3) framework benefiting the pharmaceutical paradigm. METHOD: The PRISMA search technique resulted in 214 studies assessing composite biomarkers using radiogenomics for CVD/Stroke. The study presents a XAI model using AtheroEdgeTM 4.0 to determine the risk of CVD/Stroke in the pharmaceutical framework using the radiogenomics biomarkers. CONCLUSIONS: Our observations suggest that the composite CVD risk biomarkers using radiogenomics provide a new dimension to CVD/Stroke risk assessment. The proposed review suggests a unique, unbiased, and XAI model based on AtheroEdgeTM 4.0 that can predict the composite risk of CVD/Stroke using radiogenomics in the pharmaceutical paradigm.

Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment.

Motivation: The price of medical treatment continues to rise due to (i) an increasing population; (ii) an aging human growth; (iii) disease prevalence; (iv) a rise in the frequency of patients that utilize health care services; and (v) increase in the price. Objective: Artificial Intelligence (AI) is already well-known for its superiority in various healthcare applications, including the segmentation of lesions in images, speech recognition, smartphone personal assistants, navigation, ride-sharing apps, and many more. Our study is based on two hypotheses: (i) AI offers more economic solutions compared to conventional methods; (ii) AI treatment offers stronger economics compared to AI diagnosis. This novel study aims to evaluate AI technology in the context of healthcare costs, namely in the areas of diagnosis and treatment, and then compare it to the traditional or non-AI-based approaches. Methodology: PRISMA was used to select the best 200 studies for AI in healthcare with a primary focus on cost reduction, especially towards diagnosis and treatment. We defined the diagnosis and treatment architectures, investigated their characteristics, and categorized the roles that AI plays in the diagnostic and therapeutic paradigms. We experimented with various combinations of different assumptions by integrating AI and then comparing it against conventional costs. Lastly, we dwell on three powerful future concepts of AI, namely, pruning, bias, explainability, and regulatory approvals of AI systems. Conclusions: The model shows tremendous cost savings using AI tools in diagnosis and treatment. The economics of AI can be improved by incorporating pruning, reduction in AI bias, explainability, and regulatory approvals.

Management of Patients with Asymptomatic Carotid Stenosis May Need to Be Individualized: A Multidisciplinary Call for Action.

The optimal management of patients with asymptomatic carotid stenosis (ACS) is the subject of extensive debate. According to the 2017 European Society for Vascular Surgery guidelines, carotid endarterectomy should (Class IIa; Level of Evidence: B) or carotid artery stenting may be considered (Class IIb; Level of Evidence: B) in the presence of one or more clinical/imaging characteristics that may be associated with an increased risk of late ipsilateral stroke (e.g., silent embolic infarcts on brain computed tomography/magnetic resonance imaging, progression in the severity of ACS, a history of contralateral transient ischemic attack/stroke, microemboli detection on transcranial Doppler, etc.), provided documented perioperative stroke/death rates are <3% and the patient's life expectancy is >5 years. Besides these clinical/imaging characteristics, there are additional individual, ethnic/racial or social factors that should probably be evaluated in the decision process regarding the optimal management of these patients, such as individual patient needs/patient choice, patient compliance with best medical treatment, patient sex, culture, race/ethnicity, age and comorbidities, as well as improvements in imaging/operative techniques/outcomes. The present multispecialty position paper will present the rationale why the management of patients with ACS may need to be individualized.

Is stenting for atherosclerotic renal stenosis an effective technique?

INTRODUCTION: One of the treatments for renal artery stenosis is endovascular intervention, but its effectiveness is controversial. The present study aims to analyze the experience of a working group in the endovascular treatment of selected patients with severe obstructive atherosclerotic lesions of the renal arteries, and to characterize early and late results. METHODS: This is a retrospective study of symptomatic patients with atherosclerotic renal artery stenosis who underwent endoluminal therapy between May 12, 1999 and March 12, 2015 at two institutions. Statistical analysis was performed using the PASW Statistics program. RESULTS: A total of 99 patients were treated, mean age 66 years and 76.8% male. The mean degree of stenosis measured by renal Doppler echocardiography was 83% and 64.6% were ostial lesions. Mean preoperative creatinine level was higher than the postoperative mean: 1.3 vs. 1.2 mg/dl (p=0.014). The number of antihypertensive drugs in the preoperative period was higher than in the postoperative period: 2.0 vs. 1.3 (p=0.001). The mean follow-up was 40 months (0-164). The mean peak systolic velocity over time in the postoperative period was 77 cm/s (40-250). The restenosis rate was 8%, and 30-day mortality was 0%. CONCLUSIONS: The results demonstrated that the endovascular technique has a beneficial effect on blood pressure and renal function in selected patients, and is a safe technique associated with a high rate of technical success and few complications.

Activator protein-1 in carotid plaques is related to cerebrovascular symptoms and cholesteryl ester content.

INTRODUCTION: Transcription factor activator protein-1 regulates genes involved in inflammation and repair. The aim of this study was to determine whether transcription factor activator protein-1 activity in carotid plaques is related to symptoms, lipid accumulation, or extracellular matrix composition. METHODS: Twenty-eight atherosclerotic carotid plaques were removed by endarterectomy and divided into two groups based on the presence or absence of ipsilateral symptoms (<1 month ago). Activator protein-1 DNA binding activity was assessed, and subunit (c-Jun, JunD, JunB, c-Fos, FosB, Fra-1, Fra-2) protein levels analyzed by immunoblotting. Distribution of c-Jun in plaques was analyzed by immunohistochemistry. RESULTS: Plaques associated with symptoms had increased activator protein-1 activity and increased expression of c-Jun and JunD, as compared to asymptomatic plaques. Fra-1 and Fra-2 were present in equal amounts in both groups, whereas JunB, FosB, and c-Fos were undetectable. Activator protein-1 activity correlated with cholesteryl ester and elastin in plaques and decreased with age. Activator protein-1 activity did not correlate with collagen, calcified tissue, or proteoglycan content. CONCLUSIONS: Activator protein-1 is increased in plaques associated with symptoms. The correlation between activator protein-1 and cholesteryl esters suggests that high activator protein-1 is a marker of plaque vulnerability. Activator protein-1 expression can also reflect the activation of repair processes.