Applications In Cardiology Research Articles

Transforming healthcare performance monitoring - A cutting-edge approach with generalized additive profiles: GAMs for healthcare quality monitoring.

Recent findings indicate a growing trend in data analysis within healthcare using statistical process control. However, the diversity of variables involved necessitates the expansion of new process control methodologies. This study examined control chart applications in cardiology by using generalized additive models (GAMs) to construct profiles while involving multiple healthcare variables (08). Two distinct statistics: deviation (D), and Hotelling (T2) were employed for constructing control charts: a commonly used single-variable statistic for nonparametric profiles and an innovative multivariate statistic that assesses the contribution of each element to process changes. These statistics were tested for monitoring ischemic and hemorrhagic strokes in 1-year acute stroke (369) patients at the Faisalabad Institute of Cardiology. Demographic parameters (age, gender), vascular risk factors (diabetes, family history, sleep), socioeconomic variables (smoking, location), and blood pressure are included in the model. The research includes the computation of zero-state average run length (ARL) for assessing the performance of control charts. The characteristics of the proposed profile were analyzed, such as the T2 control chart, performing better than the D chart for medium-to-large shifts (δ ≥ 0.50). On the other hand, for small δ = 0.25, the D control chart produces smaller ARL values but more significant standard deviations. While both statistics contribute to profile monitoring, T2 is more effective at identifying and tracing medium and large shifts. In conclusion, such handy tools may aid healthcare performance monitoring, especially for complicated predictor-response relationships. Monitored profiles demonstrated that GAMs are useful for healthcare analysis and process monitoring.

Clinical application of clinical decision support systems in the management of patients with atrial fibrillation

Cardiology, like other branches of medicine, is increasingly faced with the need not only to optimize patient treatment, reduce financial costs, but also to improve long-term outcomes. The volume of information required for such tasks is significant, and a doctor’s time is severely limited. Additional software capable of processing large amounts of data in a short period can assist doctors. Clinical decision support system (CDSS) is a type of software that, based on numerous clinical characteristics, provides doctors with information on the most likely diagnosis, patient risk profile, most suitable therapy, and more. Nowadays, CDSS finding more and more applications in cardiology and cardiovascular medicine. The experience of its clinical use has also been accumulated in the Russian Federation. The problem of the correct choice of anticoagulant therapy remains relevant in clinical practice. In addition, despite the proven benefit of prescribing anticoagulants in patients with atrial fibrillation (AF) at a high risk, the frequency of "underprescription" of oral anticoagulants (OACs) remains relatively high. The introduction of a strategy for a personalized approach to the selection of anticoagulants, based on individual patient characteristics, can significantly improve adherence to clinical guidelines and, as a result, reduce the risk of thromboembolic complications. This article discusses the positive and negative aspects of using CDSS in the management of patients with AF, highlighting the main limitations when using them in conditions close to real clinical practice.

AI-Enhanced ECG Applications in Cardiology: Comprehensive Insights from the Current Literature with a Focus on COVID-19 and Multiple Cardiovascular Conditions.

The application of artificial intelligence (AI) in electrocardiography is revolutionizing cardiology and providing essential insights into the consequences of the COVID-19 pandemic. This comprehensive review explores AI-enhanced ECG (AI-ECG) applications in risk prediction and diagnosis of heart diseases, with a dedicated chapter on COVID-19-related complications. Introductory concepts on AI and machine learning (ML) are explained to provide a foundational understanding for those seeking knowledge, supported by examples from the literature and current practices. We analyze AI and ML methods for arrhythmias, heart failure, pulmonary hypertension, mortality prediction, cardiomyopathy, mitral regurgitation, hypertension, pulmonary embolism, and myocardial infarction, comparing their effectiveness from both medical and AI perspectives. Special emphasis is placed on AI applications in COVID-19 and cardiology, including detailed comparisons of different methods, identifying the most suitable AI approaches for specific medical applications and analyzing their strengths, weaknesses, accuracy, clinical relevance, and key findings. Additionally, we explore AI's role in the emerging field of cardio-oncology, particularly in managing chemotherapy-induced cardiotoxicity and detecting cardiac masses. This comprehensive review serves as both an insightful guide and a call to action for further research and collaboration in the integration of AI in cardiology, aiming to enhance precision medicine and optimize clinical decision-making.

Diagnostic value of a point-of-care cardiac troponin-I assay (i-STAT®) for clinical application in canine and feline cardiology

Introduction/ObjectivesThis study was performed to evaluate the diagnostic value and net benefits, including cost-effectiveness, of a point-of-care analyser (i-STAT®) for measurement of cardiac troponin I (cTnI) in dogs and cats. Animals, Materials and Methods120 dogs and 120 cats presented with signs of cardiac disease and suspected myocardial insult on cardiac assessment. This was a validation study expressed as agreement between the i-STAT® analyser and two common commercial reference methods (IMMULITE® 2000 and ACCESS® hsTnI). ResultsThe comparison between methods showed a negative bias between the i–STAT® and the two commercial cTnI assays. The bias was more evident when the i-STAT® values were compared to the traditional cTnI assay (IMMULITE® 2000), with a calculated difference of −1.14 ng/mL (dogs) and −0.96 ng/mL (cats). However, the bias was distinctly lower when the i-STAT® measurements were compared to the high-sensitivity cTnI assay (ACCESS®), namely −0.3 ng/mL in dogs and −0.17 in cats. DiscussionThe i-STAT® method can reliably detect normal, low and elevated cTnI values, which is fundamental to differentiate pets with and without myocardial damage and, with the rapid availability of results, this confirms the clinical utility of the i-STAT® method. ConclusionscTnI concentrations measured with the i-STAT® have good comparability with those obtained with both commercial assays for low and elevated cTnI values. However, results should be cautiously interpreted for high troponin values, especially if a strict cut-off value is adopted for diagnostic or prognostic purposes in critical clinical conditions, such as myocarditis or acute myocardial ischaemia.

Machine Learning Analysis in the Field of Heart Disease

Machine learning (ML) has emerged as a transformative force in cardiovascular disease research, offering innovative approaches to prevention, diagnosis, and treatment. This report provides an overview of the current landscape of ML applications in cardiology, highlighting its potential to revolutionize the field. Key ML algorithms such as Support Vector Machine (SVM), Random Forests, Adaboost, XGBoost, and Convolutional Neural Networks (CNN) are discussed for their unique capabilities in handling complex cardiovascular data and improving disease prediction and risk stratification. Notably, the integration of feature selection techniques with ML algorithms enables the identification of biomarkers and clinical indicators, facilitating personalized care and targeted interventions. The potential of ML extends to early disease diagnosis, patient stratification, and the design of new therapeutic strategies, thereby transforming cardiology into a domain of precision medicine. While the advancements are promising, there is a pressing need for further research to enhance the interpretability, generalizability, and integration of ML models into real-world healthcare settings. Collaborative efforts between ML experts, healthcare providers, and policymakers are essential to address regulatory, ethical, and privacy concerns and to fully harness the capabilities of ML in cardiovascular medicine.

A review of evaluation approaches for explainable AI with applications in cardiology

Explainable artificial intelligence (XAI) elucidates the decision-making process of complex AI models and is important in building trust in model predictions. XAI explanations themselves require evaluation as to accuracy and reasonableness and in the context of use of the underlying AI model. This review details the evaluation of XAI in cardiac AI applications and has found that, of the studies examined, 37% evaluated XAI quality using literature results, 11% used clinicians as domain-experts, 11% used proxies or statistical analysis, with the remaining 43% not assessing the XAI used at all. We aim to inspire additional studies within healthcare, urging researchers not only to apply XAI methods but to systematically assess the resulting explanations, as a step towards developing trustworthy and safe models.

Application of Artificial Intelligence in Cardiology: A Bibliometric Analysis.

Recent advancements in artificial intelligence (AI) applications in medicine have been significant over the past 30 years. To monitor current research developments, it is crucial to examine the latest trends in AI adoption across various medical fields. This bibliometric analysis focuses on AI applications in cardiology. Unlike existing literature reviews, this study specifically examines journal articles published in the last decade, sourced from both Scopus and Web of Science databases, to illustrate the recent trends in AI within cardiology. The bibliometric analysis involves a statistical and quantitative evaluation of the literature on AI application in cardiovascular medicine over a defined period. A comprehensive global literature review is conducted to identify key research areas, authors, and their interrelationships through published works. The leading institutions and most influential authors in research on the role of AI in cardiology were located in the United States, the United Kingdom, and China. This study also provides researchers with an overview of the evolution of research in AI and cardiology. The main contribution of this study is to highlight the prominent authors, countries, journals, institutions, keywords, and trends in the development of AI in cardiology.

Future Horizons: The Potential Role of Artificial Intelligence in Cardiology.

Cardiovascular diseases (CVDs) are the leading cause of premature death and disability globally, leading to significant increases in healthcare costs and economic strains. Artificial intelligence (AI) is emerging as a crucial technology in this context, promising to have a significant impact on the management of CVDs. A wide range of methods can be used to develop effective models for medical applications, encompassing everything from predicting and diagnosing diseases to determining the most suitable treatment for individual patients. This literature review synthesizes findings from multiple studies that apply AI technologies such as machine learning algorithms and neural networks to electrocardiograms, echocardiography, coronary angiography, computed tomography, and cardiac magnetic resonance imaging. A narrative review of 127 articles identified 31 papers that were directly relevant to the research, encompassing a broad spectrum of AI applications in cardiology. These applications included AI models for ECG, echocardiography, coronary angiography, computed tomography, and cardiac MRI aimed at diagnosing various cardiovascular diseases such as coronary artery disease, hypertrophic cardiomyopathy, arrhythmias, pulmonary embolism, and valvulopathies. The papers also explored new methods for cardiovascular risk assessment, automated measurements, and optimizing treatment strategies, demonstrating the benefits of AI technologies in cardiology. In conclusion, the integration of artificial intelligence (AI) in cardiology promises substantial advancements in diagnosing and treating cardiovascular diseases.

Artificial Intelligence Applications in Nuclear Cardiology

Artificial Intelligence Applications in Nuclear Cardiology

Is artificial intelligence the new kid on the block? Sustainable applications in cardiology

Is artificial intelligence the new kid on the block? Sustainable applications in cardiology

AI-based predictive modeling: applications in cardiology

Predictive analytics have emerged as a powerful tool in cardiology, revolutionizing how patient care is delivered by leveraging artificial intelligence (AI) and machine learning (ML) algorithms. Healthcare professionals can now forecast the occurrence and progression of cardiovascular diseases with unprecedented accuracy. This breakthrough technology possesses the ability to fundamentally transform the field of cardiology, facilitating early detection, personalized treatment methodologies, and improving patient outcomes. The utilization of predictive analytics based on AI and ML represents a significant advancement that can optimize resource allocation, enhance clinical decision-making, and most importantly, benefit those affected by heart conditions. As the technology continues to grow more sophisticated through ongoing research and development, its full potential to revolutionize cardiovascular medicine and benefit patients worldwide has yet to be fully realized.

Can Artificial Intelligence Revolutionize the Diagnosis and Management of the Atrial Septal Defect in Children?

Atrial septal defects (ASDs) present a significant healthcare challenge, demanding accurate and timely diagnosis and precise management to ensure optimal patient outcomes. Artificial intelligence (AI) applications in healthcare are rapidly evolving, offering promise for enhanced medical decision-making and patient care. In the context of cardiology, the integration of AI promises to provide more efficient and accurate diagnosis and personalized treatment strategies for ASD patients. In interventional cardiology, sometimes the lack of precise measurement of the cardiac rims evaluated by transthoracic echocardiography combined with the floppy aspect of the rims can mislead and result in complications. AI software can be created to generate responses for difficult tasks, like which device is the most suitable for different shapes and dimensions to prevent embolization or erosion. This paper reviews the current state of AI in healthcare and its applications in cardiology, emphasizing the specific opportunities and challenges in applying AI to ASD diagnosis and management. By exploring the capabilities and limitations of AI in ASD diagnosis and management. This paper highlights the evolution of medical practice towards a more AI-augmented future, demonstrating the capacity of AI to unlock new possibilities for healthcare professionals and patients alike.

NLRP3 inflammasome in the pathogenesis of acute myocardial infarction: a cardiologist's view

Within five years after myocardial infarction (MI), a third of patients have secondary major adverse cardiovascular events (MACEs). The first randomized clinical trials to show the effectiveness of anti-inflammatory strategies in the prevention of MACEs are CANTOS, COLCOT and LoDoCo2. These studies have identified an effective therapeutic target — the NLRP3 inflammasome. The results of COLCOT and LoDoCo2 led to colchicine becoming the first anti-inflammatory drug to be included in clinical guidelines for the treatment of patients with coronary artery disease (CAD). However, considering colchicine in routine clinical practice requires the cardiologist to have knowledge of the basic molecular mechanisms of inflammation in cardiovascular diseases. The review discusses current data on inflammation and the NLRP3 inflammasome in the pathogenesis of CAD and MI, results and issues of their application in clinical cardiology.

Exploring vectorcardiography: An extensive vectocardiogram analysis across age, sex, BMI, and cardiac conditions

BackgroundThe vectocardiogram (VCG) offers a three-dimensional view of the heart's electrical activity, yet many VCG parameters remain unexplored in diverse clinical contexts. ObjectivesThis study aims to explore the relationships between various VCG parameters and specific patient characteristics. MethodsECG signals from adults were transformed into VCGs utilizing the Kors matrix, yielding 315 parameters per patient from the P, QRS and T loops. Univariable analysis, circular statistics, and stepwise logistic regression were employed to examine the relationships between VCG parameters and factors such as age, sex, BMI, hypertension, echocardiographic ischemic heart disease (Echo-IHD), and left ventricular hypertrophy (Echo-LVH). ResultsWe included 664 adults and considered an alpha value of 0.05 and a power of 90%. The study revealed significant associations, such as age with P loop roundness index (RI) (OR = 3.825, 95% confidence interval [95%CI] = 2.079–7.04), male sex with QRS loop RI (OR = 6.08, 95%CI = 1.835–20.153), abnormal BMI with the T loop's RI (OR = 0.544, 95%CI = 0.325–0.909), hypertension with the T loop planarity index (PI) (OR = 8.01, 95%CI = 2.134–30.117), Echo-IHD with QRS loop curvature at the 4/10th segment (OR = 7.58, 95%CI = 1.954–29.458), and Echo-LVH with the T loop lag-1/10 dihedral angle (OR = 10.3, 95%CI = 1.822–58.101). In the study, several additional VCG parameters demonstrated statistically significant, albeit smaller, associations with patient demographics and cardiovascular conditions. ConclusionsThe findings enhance our understanding of the intricate relationships between VCG parameters and patient characteristics, emphasizing the potential role of VCG analysis in assessing cardiovascular diseases. These insights may guide future research and clinical applications in cardiology.

Principles of artificial intelligence and its application in cardiovascular medicine.

Artificial intelligence (AI) represents a rapidly developing field. Its use can improve diagnosis and therapy in many areas of medicine. Despite this enormous progress, many physicians perceive it as a black box and are skeptical about it. This review will present the basics of machine learning. Different classifications of artificial intelligence, such as supervised versus unsupervised and discriminative versus generative AI, are given. Analogies to human intelligence are discussed as far as algorithms are oriented toward it. In the second step, the most common models like random forest, k-means clustering, convolutional neural network, and transformers will be presented in a way that the underlying idea can be understood. Corresponding medical applications in cardiovascular medicine will be named for all models, respectively. The overview is intended to show that the term artificial intelligence covers a wide range of different concepts. It should help physicians understand the principles of AI to make up one's minds about its application in cardiology. It should also enable them to evaluate results obtained with AI's help critically.

Possibilities and Prospects of Hypoxytherapy Application in Cardiology

Despite the achievements of modern cardiology, the cardiovascular morbidity and mortality in most countries of the world remains high, which motivates the wider use of additional (alternative) preventive and therapeutic approaches, one of which is hypoxytherapy. Over several decades of using this method, a large amount of data has been accumulated that allows us to state the beneficial effect of various hypoxytherapy regimens on the clinical course of cardiovascular diseases and can potentially improve prognosis of them. The use of hypoxytherapy is associated with an improvement in blood pressure circadian rhythm, flow-dependent vasodilation, an insulin resistance decrease, a vascular wall stiffness redaction, an improvement in blood rheology, endothelial function and nitric oxide system, a decrease of proinflammatory and prothrombotic cytokines levels. A number of modern experts consider hypoxytherapy as an affordable and perspective method of prevention and treatment of cardiovascular disease, effectively supporting traditional non-drug and drug-based approaches. Contemporary data indicate serious future perspectives for an expanded study of the hypoxytherapy possibilities in cardiac patients, including through the State research programmes. This review is devoted to the discussion of the physiological effects of hypoxytherapy, the possibilities of its application in cardiological practice, including with the analysis of own data, as well as precautions during its implementation.

Artificial Intelligence in Cardiology: AnAustralian Perspective.

Significant advances have been made in artificial intelligence technology in recent years. Many health care applications have been investigated to assist clinicians and the technology is close to being integrated into routine clinical practice. The high prevalence of cardiac disease in Australia places overwhelming demands on the existing health care system, challenging its capacity to provide quality patient care. Artificial intelligence has emerged as a promising solution. This discussion paper provides an Australian perspective on the current state of artificial intelligence in cardiology, including the benefits and challenges of implementation. This paper highlights some current artificial intelligence applications in cardiology, while also detailing challenges such as data privacy, ethical considerations, and integration within existing health infrastructures. Overall, this paper aims to provide insights into the potential benefits of artificial intelligence in cardiology, while also acknowledging the barriers that need to be addressed to ensure safe and effective implementation into an Australian health system.

Flavonoids quercetin, dihydroquercetin (taxifolin): antioxidant and anti-ischemic effects, possibility of application in cardiology

Objective. Determination of the place of flavonoids quercetin and dihydroquercetin as a preventive and auxiliary agent in the treatment of cardiovascular diseases. Material and methods. The analysis of literature data was carried out, in which the results of studies on the use of flavonoids quercetin, dihydroquercetin (taxifolin) in animal experiments and in patients with cardiovascular diseases were presented, as well as data from experimental laboratory studies were analyzed. Results and discussion. Flavonoids had antioxidant, antiradical, vasoprotective, antiplatelet, anti-inflammatory effects. They slow down the development of atherosclerosis and cardiovascular diseases, and are also a means of preventing fatal cardiovascular complications. The antihypertensive, anti-ischemic, antiarrhythmic, hypolipidemic, antiplatelet effect of quercetin and dihydroquercetin has been shown. The possibility of using dihydroquercetin in the complex treatment of patients with arterial hypertension is shown. The high efficiency of the drug Bio Dihydroquercetin, which is distinguished by a special author's production technology, and is a highly active dihydroquercetin, of natural origin with proven efficacy and safety, was noted. Taking dihydroquercetin is associated with a decrease in blood viscosity, a decrease in aggregation, an improvement in the deformability of red blood cells, as well as an improvement in endothelial function and tissue perfusion. Conclusions. Flavonoids quercetin, dihydroquercetin (Dihydroquercetin) have antioxidant, vasodilator, hypotensive, antiplatelet, hypolipidemic, anti-ischemic, anti-inflammatory effects. They can be used in the comprehensive prevention and treatment of patients with cardiovascular diseases

Potential applications of Fe3+-activated Sr9Al6O18 nanophosphors for fingerprint detection, oxidative stress, and thrombosis treatment

This study reports on the synthesis of Fe3+-activated Sr9Al6O18 nanophosphors (SAO:Fe NPs) using a simple solution combustion process, which emits a pale green light and possesses excellent fluorescence properties. An in-situ powder dusting method was utilized to extract unique ridge features of latent fingerprints (LFPs) on various surfaces using ultra-violet 254 nm excitation. The results showed that SAO:Fe NPs possess high contrast, high sensitivity, and no background interference, enabling the observation of LFPs for longer periods. Poroscopy, which is the examination of sweat pores on the skin's papillary ridges, is important in the identification process, and the YOLOv8x program based on deep convolutional neural networks was used to study the features visible in FPs. The potential of SAO:Fe NPs to ameliorate oxidative stress and thrombosis was analyzed. The results showed that SAO:Fe NPs have antioxidant properties by scavenging 2,2-diphenylpicrylhydrazyl (DPPH) and normalized the stress markers in NaNO2-induced oxidative stress in Red Blood Cells (RBC). In addition, SAO:Fe inhibited platelet aggregation induced by adenosine diphosphate (ADP). Therefore, SAO:Fe NPs may have potential applications in advanced cardiology and forensic sciences. Overall, this study highlights the synthesis and potential applications of SAO:Fe NPs, which can enhance the sensitivity and specificity of fingerprint detection and provide insights into developing novel treatments for oxidative stress and thrombosis.

Artificial Intelligence in Nuclear Cardiology

Artificial intelligence (AI) encompasses a variety of computer algorithms that have a wide range of potential clinical applications in nuclear cardiology. This article will introduce core terminology and concepts for AI including classifications of AI as well as training and testing regimens. We will then highlight the potential role for AI to improve image registration and image quality. Next, we will discuss methods for AI-driven image attenuation correction. Finally, we will review advancements in machine learning and deep-learning applications for disease diagnosis and risk stratification, including efforts to improve clinical translation of this valuable technology with explainable AI models.