Artificial intelligence in atherosclerotic disease: Applications and trends

Modeling Techniques and Boundary Conditions in Abdominal Aortic Aneurysm Analysis: Latest Developments in Simulation and Integration of Machine Learning and Data-Driven Approaches

Research on abdominal aortic aneurysms (AAAs) primarily focuses on developing a clear understanding of the initiation, progression, and treatment of AAA through improved model accuracy. High-fidelity hemodynamic and biomechanical predictions are essential for clinicians to optimize preoperative planning and minimize therapeutic risks. Computational fluid dynamics (CFDs), finite element analysis (FEA), and fluid-structure interaction (FSI) are widely used to simulate AAA hemodynamics and biomechanics. However, the accuracy of these simulations depends on the utilization of realistic and sophisticated boundary conditions (BCs), which are essential for properly integrating the AAA with the rest of the cardiovascular system. Recent advances in machine learning (ML) techniques have introduced faster, data-driven surrogates for AAA modeling. These approaches can accelerate segmentation, predict hemodynamics and biomechanics, and assess disease progression. However, their reliability depends on high-quality training data derived from CFDs and FEA simulations, where BC modeling plays a crucial role. Accurate BCs can enhance ML predictions, increasing the clinical applicability. This paper reviews existing BC models, discussing their limitations and technical challenges. Additionally, recent advancements in ML and data-driven techniques are explored, discussing their current states, future directions, common algorithms, and limitations.

Advances in the treatment of atherosclerotic plaque based on nanomaterials

Atherosclerosis is the leading cause of cardiovascular disease worldwide, posing not only a significant threat to cardiovascular health but also impairing the function of multiple organs, with severe cases potentially being life-threatening. Consequently, the effective treatment of atherosclerosis is of paramount importance in reducing the mortality associated with cardiovascular diseases. With the advancement of nanomedicine and a deeper understanding of the pathological mechanisms underlying atherosclerosis, nanomaterials have emerged as promising platforms for precise diagnosis and targeted therapeutic strategies. These materials offer notable advantages, including targeted drug delivery, enhanced bioavailability, improved drug stability, and controlled release. This review provides an overview of the mechanisms underlying atherosclerotic plaque development and examines nanomaterial-based therapeutic approaches for managing atherosclerotic plaques, including therapies targeting cholesterol metabolism, anti-inflammatory strategies, macrophage clearance, and immunotherapy. Additionally, the paper discusses the current technical challenges associated with the clinical transformation of these therapies. Finally, the potential future integration of nanomaterials, smart nanomaterials, and artificial intelligence in the treatment of atherosclerosis is also explored.

The Heart of Transformation: Exploring Artificial Intelligence in Cardiovascular Disease

The application of artificial intelligence (AI) and machine learning (ML) in medicine and healthcare has been extensively explored across various areas. AI and ML can revolutionize cardiovascular disease management by significantly enhancing diagnostic accuracy, disease prediction, workflow optimization, and resource utilization. This review summarizes current advancements in AI and ML concerning cardiovascular disease, including their clinical investigation and use in primary cardiac imaging techniques, common cardiovascular disease categories, clinical research, patient care, and outcome prediction. We analyze and discuss commonly used AI and ML models, algorithms, and methodologies, highlighting their roles in improving clinical outcomes while addressing current limitations and future clinical applications. Furthermore, this review emphasizes the transformative potential of AI and ML in cardiovascular practice by improving clinical decision making, reducing human error, enhancing patient monitoring and support, and creating more efficient healthcare workflows for complex cardiovascular conditions.

A Body Shape Index as a Simple Anthropometric Marker for the Risk of Cardiovascular Events

PURPOSE OF REVIEW

To provide an overview of the predictive value of A Body Shape Index (ABSI) for the risk of cardiovascular events.

RECENT FINDINGS

ABSI has been reported to have an association with development of cardiovascular diseases, and its usefulness for predicting major cardiovascular events including cardiovascular mortality, nonfatal coronary syndrome and nonfatal stroke has been investigated. The formula for ABSI includes waist circumference, which is not included in the conventional calculation of body mass index (BMI), along with BMI and height. High ABSI is independently associated with a high incidence of cardiovascular events. ABSI with specific cutoff values can be a useful tool for cardiovascular risk stratification by detecting the presence of abdominal obesity and it can be used for evaluation of the risk of cardiovascular events. Nonetheless, other factors such as race, gender, age, and physical, environmental and socioeconomic purviews also need be taken into consideration.

Impact of Guideline-Directed Statin Prescriptions on Cardiovascular Outcomes by Race in a Real-World Primary Prevention Cohort

Background

Data on real-world statin prescription in large, private health care networks and impacts on primary prevention of atherosclerotic cardiovascular disease (ASCVD) outcomes across race are scarce.

Objectives

The purpose of this study was to investigate the impact of statin prescription on ASCVD outcomes within and across race in a large, nongovernmental health care system.

Methods

Statin prescription in Black and White patients without ASCVD was evaluated (2013-2019). Guideline-directed statin intensity was defined as at least "moderate" for intermediate and high-risk patients. Statin prescription at index and ASCVD outcomes at follow-up (myocardial infarction/revascularization, stroke, mortality) were assessed via electronic health care records using International Classification of Diseases-9 and 10 codes. Cox regression models, adjusted for CVD risk factors, were used to calculate HRs for association between statin prescription and incident ASCVD events across race.

Results

Among 270,079 patients, 7.6% (n = 20,477) and 92.4% (n = 249,602) identified as Black and White, respectively. Significantly fewer Black patients were prescribed statin therapy than White patients (13.6% vs 19.0%; P < 0.001). At a mean follow-up of 6 years, patients with "no statin" prescription vs guideline-directed statin intensity showed increased ASCVD in Black patients (HR: 1.40 [95% CI: 1.05-1.86]), and White patients (HR: 1.32 [95% CI: 1.21-1.45]; P < 0.05) and all-cause mortality. Intermediate and high-risk Black patients faced a 17% higher risk of mortality compared to White patients. However, the interaction between race and statin prescription was not a significant predictor of incident ASCVD events.

Conclusions

Statins remain underprescribed. Although Black patients received proportionally less statin prescription than White patients, this was not associated with higher risk of mortality in Black patients.

Revealing the nature of cardiovascular disease using DERGA, a novel data ensemble refinement greedy algorithm

BACKGROUND

The study aimed to determine the most crucial parameters associated with CVD and employ a novel data ensemble refinement procedure to uncover the optimal pattern of these parameters that can result in a high prediction accuracy.

METHODS AND RESULTS

Data were collected from 369 patients in total, 281 patients with CVD or at risk of developing it, compared to 88 otherwise healthy individuals. Within the group of 281 CVD or at-risk patients, 53 were diagnosed with coronary artery disease (CAD), 16 with end-stage renal disease, 47 newly diagnosed with diabetes mellitus 2 and 92 with chronic inflammatory disorders (21 rheumatoid arthritis, 41 psoriasis, 30 angiitis). The data were analyzed using an artificial intelligence-based algorithm with the primary objective of identifying the optimal pattern of parameters that define CVD. The study highlights the effectiveness of a six-parameter combination in discerning the likelihood of cardiovascular disease using DERGA and Extra Trees algorithms. These parameters, ranked in order of importance, include Platelet-derived Microvesicles (PMV), hypertension, age, smoking, dyslipidemia, and Body Mass Index (BMI). Endothelial and erythrocyte MVs, along with diabetes were the least important predictors. In addition, the highest prediction accuracy achieved is 98.64%. Notably, using PMVs alone yields a 91.32% accuracy, while the optimal model employing all ten parameters, yields a prediction accuracy of 0.9783 (97.83%).

CONCLUSIONS

Our research showcases the efficacy of DERGA, an innovative data ensemble refinement greedy algorithm. DERGA accelerates the assessment of an individual's risk of developing CVD, allowing for early diagnosis, significantly reduces the number of required lab tests and optimizes resource utilization. Additionally, it assists in identifying the optimal parameters critical for assessing CVD susceptibility, thereby enhancing our understanding of the underlying mechanisms.

Artificial Intelligence in Cardiovascular Disease Prevention: Is it Ready for Prime Time?

PURPOSE OF REVIEW

This review evaluates how Artificial Intelligence (AI) enhances atherosclerotic cardiovascular disease (ASCVD) risk assessment, allows for opportunistic screening, and improves adherence to guidelines through the analysis of unstructured clinical data and patient-generated data. Additionally, it discusses strategies for integrating AI into clinical practice in preventive cardiology.

RECENT FINDINGS

AI models have shown superior performance in personalized ASCVD risk evaluations compared to traditional risk scores. These models now support automated detection of ASCVD risk markers, including coronary artery calcium (CAC), across various imaging modalities such as dedicated ECG-gated CT scans, chest X-rays, mammograms, coronary angiography, and non-gated chest CT scans. Moreover, large language model (LLM) pipelines are effective in identifying and addressing gaps and disparities in ASCVD preventive care, and can also enhance patient education. AI applications are proving invaluable in preventing and managing ASCVD and are primed for clinical use, provided they are implemented within well-regulated, iterative clinical pathways.

Familial hypercholesterolemia in children and the importance of early treatment

PURPOSE OF REVIEW

Familial hypercholesterolemia leads to elevated levels of low-density lipoprotein cholesterol (LDL-C) from birth onwards due to a pathogenetic variation in genes in cholesterol metabolism. Early screening to identify and subsequently treat children with familial hypercholesterolemia is crucial to reduce the risk of premature atherosclerotic cardiovascular disease (ASCVD). This review focuses on recent insights in the field of pediatric familial hypercholesterolemia.

RECENT FINDINGS

Screening in childhood and early initiation of optimal lipid-lowering therapy (LLT) have shown promising outcomes in the prevention of ASCVD. In addition, cost-effectiveness research has demonstrated highly favorable results. With the availability of novel therapies, familial hypercholesterolemia has become a well treatable disease.

SUMMARY

Children with familial hypercholesterolemia benefit from early detection and optimal treatment of their elevated LDL-C levels.

Artificial Intelligence in Cardiology and Atherosclerosis in the Context of Precision Medicine: A Scoping Review

Cardiovascular diseases remain the main cause of death worldwide which makes it essential to better understand, diagnose, and treat atherosclerosis. Artificial intelligence (AI) and novel technological solutions offer us new possibilities and enable the practice of individually tailored medicine. The study was performed using the PRISMA protocol. As of January 10, 2023, the analysis has been based on a review of 457 identified articles in PubMed and MEDLINE databases. The search covered reviews, original articles, meta-analyses, comments, and editorials published in the years 2009-2023. In total, 123 articles met inclusion criteria. The results were divided into the subsections presented in the review (genome-wide association studies, radiomics, and other studies). This paper presents actual knowledge concerning atherosclerosis, in silico, and big data analyses in cardiology that affect the way medicine is practiced in order to create an individual approach and adjust the therapy of atherosclerosis.

Tailored risk assessment and forecasting in intermittent claudication

BACKGROUND

Guidelines recommend cardiovascular risk reduction and supervised exercise therapy as the first line of treatment in intermittent claudication, but implementation challenges and poor patient compliance lead to significant variation in management and therefore outcomes. The development of a precise risk stratification tool is proposed through a machine-learning algorithm that aims to provide personalized outcome predictions for different management strategies.

METHODS

Feature selection was performed using the least absolute shrinkage and selection operator method. The model was developed using a bootstrapped sample based on patients with intermittent claudication from a vascular centre to predict chronic limb-threatening ischaemia, two or more revascularization procedures, major adverse cardiovascular events, and major adverse limb events. Algorithm performance was evaluated using the area under the receiver operating characteristic curve. Calibration curves were generated to assess the consistency between predicted and actual outcomes. Decision curve analysis was employed to evaluate the clinical utility. Validation was performed using a similar dataset.

RESULTS

The bootstrapped sample of 10 000 patients was based on 255 patients. The model was validated using a similar sample of 254 patients. The area under the receiver operating characteristic curves for risk of progression to chronic limb-threatening ischaemia at 2 years (0.892), risk of progression to chronic limb-threatening ischaemia at 5 years (0.866), likelihood of major adverse cardiovascular events within 5 years (0.836), likelihood of major adverse limb events within 5 years (0.891), and likelihood of two or more revascularization procedures within 5 years (0.896) demonstrated excellent discrimination. Calibration curves demonstrated good consistency between predicted and actual outcomes and decision curve analysis confirmed clinical utility. Logistic regression yielded slightly lower area under the receiver operating characteristic curves for these outcomes compared with the least absolute shrinkage and selection operator algorithm (0.728, 0.717, 0.746, 0.756, and 0.733 respectively). External calibration curve and decision curve analysis confirmed the reliability and clinical utility of the model, surpassing traditional logistic regression.

CONCLUSION

The machine-learning algorithm successfully predicts outcomes for patients with intermittent claudication across various initial treatment strategies, offering potential for improved risk stratification and patient outcomes.

An attention-based deep learning method for right ventricular quantification using 2D echocardiography: Feasibility and accuracy

AIM

To test the feasibility and accuracy of a new attention-based deep learning (DL) method for right ventricular (RV) quantification using 2D echocardiography (2DE) with cardiac magnetic resonance imaging (CMR) as reference.

METHODS AND RESULTS

We retrospectively analyzed images from 50 adult patients (median age 51, interquartile range 32-62 42% women) who had undergone CMR within 1 month of 2DE. RV planimetry of the myocardial border was performed in end-diastole (ED) and end-systole (ES) for eight standardized 2DE RV views with calculation of areas. The DL model comprised a Feature Tokenizer module and a stack of Transformer layers. Age, gender and calculated areas were used as inputs, and the output was RV volume in ED/ES. The dataset was randomly split into training, validation and testing subsets (35, 5 and 10 patients respectively). Mean RVEDV, RVESV and RV ejection fraction (EF) were 163 ± 70 mL, 82 ± 42 mL and 51% ± 8% respectively without differences among the subsets. The proposed method achieved good prediction of RV volumes (R2  = .953, absolute percentage error [APE] = 9.75% ± 6.23%) and RVEF (APE = 7.24% ± 4.55%). Per CMR, there was one patient with RV dilatation and three with RV dysfunction in the testing dataset. The DL model detected RV dilatation in 1/1 case and RV dysfunction in 4/3 cases.

CONCLUSIONS

An attention-based DL method for 2DE RV quantification showed feasibility and promising accuracy. The method requires validation in larger cohorts with wider range of RV size and function. Further research will focus on the reduction of the number of required 2DE to make the method clinically applicable.