Unlocking the potential of artificial intelligence in sports cardiology: does it have a role in evaluating athlete's heart?

The integration of artificial intelligence (AI) technologies is evolving in different fields of cardiology and in particular in sports cardiology. Artificial intelligence offers significant opportunities to enhance risk assessment, diagnosis, treatment planning, and monitoring of athletes. This article explores the application of AI in various aspects of sports cardiology, including imaging techniques, genetic testing, and wearable devices. The use of machine learning and deep neural networks enables improved analysis and interpretation of complex datasets. However, ethical and legal dilemmas must be addressed, including informed consent, algorithmic fairness, data privacy, and intellectual property issues. The integration of AI technologies should complement the expertise of physicians, allowing for a balanced approach that optimizes patient care and outcomes. Ongoing research and collaborations are vital to harness the full potential of AI in sports cardiology and advance our management of cardiovascular health in athletes.

[Precision medicine enhances personalized medicine in cardiology]. / Präzisionsmedizin vertieft die personalisierte Medizin in der Kardiologie.

Personalized medicine and precision medicine, frequently used synonymously, shall be clearly differentiated. Accordingly, personalization in cardiac medicine is based on the clinical presentation of a patient, as well as his/her cardiovascular risk factors and comorbidities, electrocardiography, imaging, and biomarkers for myocardial load and ischemia. Personalization is based on large clinical trials with detailed subgroup analyses and is practiced on the basis of guidelines. Further in depth personalization is achieved by precision medicine, which is based on innovative imaging for myocardial structure, coronary morphology, and electrophysiology. From the clinical perspective, genome analyses are relevant for comparatively rare monogenetic cardiovascular diseases. While these as well as transcriptome and metabolome analyses play a significant role in cardiovascular research with great translation potential, they have not yet been broadly introduced in the diagnosis, prevention, and treatment of complex cardiovascular diseases. Furthermore, digital technologies have considerable potential in cardiovascular precision medicine. On the one hand, this is based on the frequency of the diseases with the availability of Big Data and, on the other hand, on the availability of bio-signals and sensors of those signals in cardiovascular diseases.

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.

Radiation exposure in interventional cardiology: extremities doses.

X-rays are widely used in interventional cardiology (IC). Medical staff is exposed to ionising radiations with difficulties to accurately estimate the absorbed dose, on the other hand, it is well known that eye lens and extremities are the most exposed. In most IC units, radiological monitoring is performed by measuring the personal dose equivalent with a dosemeter worn under the operator's apron. The ambient dose equivalent is, usually, also measured. Furthermore, doses to the lens and extremities are often not measured because of the absence or difficulty of wearing the appropriate dosemeters. The main aim of our study is to estimate the extremities doses, of the interventional cardiologists, from the personal dose equivalent, the patient's received doses or to the ambient dose equivalent. For this purpose, we use a radiological monitoring, of four (04) interventional cardiologists, carried out at Algiers hospital. A Monte Carlo calculation is performed for comparison. This paper reports the preliminary results of this study.

Artificial Intelligence in Cardiology: Applications and Obstacles.

Artificial intelligence (AI) technology is poised to alter the flow of daily life, and in particular, medicine, where it may eventually complement the physician's work in diagnosing and treating disease. Despite the recent frenzy and uptick in AI research over the past decade, the integration of AI into medical practice is in its early stages. Cardiology stands to benefit due to its many diagnostic modalities and diverse treatments. AI methods have been applied to various domains within cardiology: imaging, electrocardiography, wearable devices, risk prediction, and disease classification. While many AI-based approaches have been developed that perform equal to or better than the state-of-the-art, few prospective randomized studies have evaluated their use. Furthermore, obstacles at the intersection of medicine and AI remain unsolved, including model understanding, bias, model evaluation, relevance and reproducibility, and legal and ethical dilemmas. We summarize recent and current applications of AI in cardiology, followed by a discussion of the aforementioned complications.

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.

What Is New in Risk Assessment in Nuclear Cardiology?

Nuclear cardiology techniques allow in-depth evaluation of cardiac patients. A body of literature has established the use of nuclear cardiology. The results obtained with traditional cameras have been reinforced by those obtained with a series of innovations that have revolutionized the field of nuclear cardiology. This article highlights the role of nuclear cardiology in the risk assessment of patients with cardiac disease and sheds light on advancements of nuclear imaging techniques in the cardiovascular field. Patient risk stratification has a key role in modern precision medicine. Nuclear cardiac imaging techniques may quantitatively investigate major disease mechanisms of different cardiac pathologies.

Effectiveness of staff radiation protection devices for interventional cardiology procedures.

PURPOSE: To evaluate the effectiveness of currently available radioprotective (RP) devices in reducing the dose to interventional cardiology staff, especially to the eye lens and brain. METHODS: The performances of five RP devices (masks, caps, patient drapes, staff lead and lead-free aprons and Zero-Gravity (ZG) suspended radiation protection system) were assessed by means of Monte Carlo (MC) simulations. A geometry representative of an interventional cardiology setup was modelled and several configurations, including beam projections and staff distance from the source, were investigated. In addition, measurements on phantoms were performed for masks and drapes. RESULTS: An average dose reduction of 65% and 25% to the eyes and the brain respectively was obtained for the masks by MC simulations but a strong influence of the design was observed. The cap effectiveness for the brain ranges on average between 13% and 37%. Nevertheless, it was shown that only some upper parts of the brain were protected. There was no significant difference between the effectiveness of lead and lead-free aprons. Of all the devices, the ZG system offered the highest protection to the brain and eye lens and a protection level comparable to the apron for the organs normally covered. CONCLUSION: All investigated devices showed potential for dose reduction to specific organs. However, for masks, caps and drapes, it strongly depends on the design, exposure conditions and staff position. Therefore, for a clinical use, it is recommended to evaluate their effectiveness in the planned conditions of use.

Establishing a priori and a posteriori predictive models to assess patients' peak skin dose in interventional cardiology. Part 2: results of the VERIDIC project.

BACKGROUND: Optimizing patient exposure in interventional cardiology is key to avoid skin injuries. PURPOSE: To establish predictive models of peak skin dose (PSD) during percutaneous coronary intervention (PCI), chronic total occlusion percutaneous coronary intervention (CTO), and transcatheter aortic valve implantation (TAVI) procedures. MATERIAL AND METHODS: A total of 534 PCI, 219 CTO, and 209 TAVI were collected from 12 hospitals in eight European countries. Independent associations between PSD and clinical and technical dose determinants were examined for those procedures using multivariate statistical analysis. A priori and a posteriori predictive models were built using stepwise multiple linear regressions. A fourfold cross-validation was performed, and models' performance was evaluated using the root mean square error (RMSE), mean absolute percentage error (MAPE), coefficient of determination (R²), and linear correlation coefficient (r). RESULTS: Multivariate analysis proved technical parameters to overweight clinical complexity indices with PSD mainly affected by fluoroscopy time, tube voltage, tube current, distance to detector, and tube angulation for PCI. For CTO, these were body mass index, tube voltage, and fluoroscopy contribution. For TAVI, these parameters were sex, fluoroscopy time, tube voltage, and cine acquisitions. When benchmarking the predictive models, the correlation coefficients were r = 0.45 for the a priori model and r = 0.89 for the a posteriori model for PCI. These were 0.44 and 0.67, respectively, for the CTO a priori and a posteriori models, and 0.58 and 0.74, respectively, for the TAVI a priori and a posteriori models. CONCLUSION: A priori predictive models can help operators estimate the PSD before performing the intervention while a posteriori models are more accurate estimates and can be useful in the absence of skin dose mapping solutions.

Patient exposure dose in interventional cardiology per clinical and technical complexity levels. Part 1: results of the VERIDIC project.

BACKGROUND: Patients can be exposed to high skin doses during complex interventional cardiology (IC) procedures. PURPOSE: To identify which clinical and technical parameters affect patient exposure and peak skin dose (PSD) and to establish dose reference levels (DRL) per clinical complexity level in IC procedures. MATERIAL AND METHODS: Validation and Estimation of Radiation skin Dose in Interventional Cardiology (VERIDIC) project analyzed prospectively collected patient data from eight European countries and 12 hospitals where percutaneous coronary intervention (PCI), chronic total occlusion PCI (CTO), and transcatheter aortic valve implantation (TAVI) procedures were performed. A total of 62 clinical complexity parameters and 31 technical parameters were collected, univariate regressions were performed to identify those parameters affecting patient exposure and define DRL accordingly. RESULTS: Patient exposure as well as clinical and technical parameters were collected for a total of 534 PCI, 219 CTO, and 209 TAVI. For PCI procedures, body mass index (BMI), number of stents ≥2, and total stent length >28 mm were the most prominent clinical parameters, which increased the PSD value. For CTO, these were total stent length >57 mm, BMI, and previous anterograde or retrograde technique that failed in the same session. For TAVI, these were male sex, BMI, and number of diseased vessels. DRL values for Kerma-area product (PKA), air kerma at patient entrance reference point (Ka,r), fluoroscopy time (FT), and PSD were stratified, respectively, for 14 clinical parameters in PCI, 10 in CTO, and four in TAVI. CONCLUSION: Prior knowledge of the key factors influencing the PSD will help optimize patient radiation protection in IC.

Successfully implemented artificial intelligence and machine learning applications in cardiology: State-of-the-art review.

The omnipresence and deep impact of artificial intelligence (AI) in today's society are undeniable. While the technology has already established itself as a powerful tool in several industries, more recently it has also started to change the practice of medicine. The aim of this review is to provide healthcare providers working in the field of cardiovascular medicine with an overview of AI and machine learning (ML) algorithms that have passed the initial tests and made it into contemporary clinical practice. The following domains where AI/ML could revolutionize cardiology are covered: (i) signal processing, (ii) image processing, (iii) clinical risk stratification, (iv) natural language processing, and (v) fundamental clinical discoveries.

2022 Artificial intelligence primer for the nuclear cardiologist.

Driven by advances in computing power, the past decade has seen rapid developments in artificial intelligence (AI) which now offers potential enhancements to every aspect of nuclear cardiology workflow including acquisition, reconstruction, segmentation, direct image analysis, and interpretation; as well as facilitating clinical and imaging big-data integration for superior personalized risk stratification. To understand the relevance and potential of AI in their field, this review provides a primer for nuclear cardiologists in 2022. The aim is to explain terminology and provide a summary of key current implementations, challenges, and future aspirations of AI-based enhancements to nuclear cardiology.

Coordinating Cardiology clinics randomized trial of interventions to improve outcomes (COORDINATE) - Diabetes: rationale and design.

Several medications that are proven to reduce cardiovascular events exist for individuals with type 2 diabetes mellitus (T2DM) and atherosclerotic cardiovascular disease, however they are substantially underused in clinical practice. Clinician, patient, and system-level barriers all contribute to these gaps in care; yet, there is a paucity of high quality, rigorous studies evaluating the role of interventions to increase utilization. The COORDINATE-Diabetes trial randomized 42 cardiology clinics across the United States to either a multifaceted, site-specific intervention focused on evidence-based care for patients with T2DM or standard of care. The multifaceted intervention comprised the development of an interdisciplinary care pathway for each clinic, audit-and-feedback tools and educational outreach, in addition to patient-facing tools. The primary outcome is the proportion of individuals with T2DM prescribed three key classes of evidence-based medications (high-intensity statin, angiotensin converting enzyme inhibitor or angiotensin receptor blocker, and either a sodium/glucose cotransporter-2 inhibitor (SGLT-2i) inhibitor or glucagon-like peptide 1 receptor agonist (GLP-1RA) and will be assessed at least 6 months after participant enrollment. COORDINATE-Diabetes aims to identify strategies that improve the implementation and adoption of evidence-based therapies.

[Computed tomography and magnetic resonance imaging in cardiac diagnostics-how to choose the right modality : A guide based on the new guidelines of the European Society of Cardiology (ESC)]. / Computer- und Magnetresonanztomographie in der Herzdiagnostik ­ welche Modalität ist die richtige? : Eine Orientierungshilfe auf Basis der neuen Leitlinien der European Society of Cardiology (ESC).

In the guideline updates of the European Society of Cardiology (ESC), noninvasive radiological cross-sectional imaging is taking an increasingly prominent role, while at the same time invasive diagnostic approaches are becoming less important. Especially for the diagnosis and treatment of chronic and acute coronary syndromes, there are fundamental changes in clinical routine. In addition, cross-sectional imaging also offers an alternative to diagnostic algorithms for other cardiac pathologies, especially echocardiography, which is increasingly used in the differential diagnosis of cardiac diseases. The radiologist should be aware of the recommendations of the current guidelines and encourage their establishment in clinical practice. This paper summarizes the indications of cross-sectional cardiac imaging with focus on new recommendations in the ESC guidelines and addresses specific strengths and weaknesses of each modality.

[e-Health, telemedicine and applications in cardiology: state of the art in Italy and experience of the cardiology unit of the G.B. Grassi Hospital of Rome]. / e-Health, telemedicina e applicazioni in Cardiologia: stato dell'arte in Italia ed esperienza dell'U.O.C. di Cardiologia dell'Ospedale G.B. Grassi di Roma.

e-Health (electronic health) refers to the use of information and communication technologies (ICT) to promote organizational change and facilitate new healthcare skills. In the last few years, several telemedicine services using ICT have been launched and the updating of related regulations has started, also for the increase in demand for services, their complexity and the need to offer adequate care to the patient. In cardiology, the experiences concern mainly patients suffering from heart failure or the carriers of cardiac implantable electronic devices (CIEDs), and few experiences are described in ischemic heart disease. In this article we present the design, the implementation and the results of the telemedicine service at the Cardiology Unit of the G.B. Grassi Hospital in Rome, concerning follow-up televisits for patients with heart failure, ischemic heart disease and for management of treatment plans, telemonitoring and telecontrol of CIEDs carriers and teleconsultation in ischemic heart disease. The considerations of this review, the experiences reported and the speed of digital evolution make the implementation of existing practices and the redesign of new pathways necessary.

Setting up regional diagnostic reference levels for pediatric interventional cardiology in Latin America and the Caribbean countries: preliminary results and identified challenges.

The goal of the present study was to propose a set of preliminary regional diagnostic reference levels (DRLs) for pediatric interventional cardiology (IC) procedures in Latin America and the Caribbean countries, classified by age and weight groups. The study was conducted in the framework of the Optimization of Protection in Pediatric Interventional Radiology in Latin America and the Caribbean program coordinated by the World Health Organization and the Pan American Health Organization in cooperation with the International Atomic Energy Agency. The first step of the program was focused on pediatric IC. Dose data from diagnostic and therapeutic procedures were collected between December 2020 and December 2021. Regional DRLs were set as the third quartile of patient dose data (kerma area product) collected in 18 hospitals from 10 countries in an initial sample of 968 procedures. DRLs were set for four age bands and five weight ranges. The values obtained for the four age bands (<1 yr, 1 to <5 yr, 5 to <10 yr and 10 to <16 yr) were 2.9, 6.1, 8.8 and 14.4 Gy cm2for diagnostic procedures, and 4.0, 5.0, 10.0 and 38.1 Gy cm2for therapeutic procedures, respectively. The values obtained for the five weight bands (<5 kg, 5 to <15 kg, 15 to <30 kg, 30 to <50 kg and 50 to <80 kg) were 3.0, 4.5, 8.1, 9.2 and 26.8 Gy cm2for diagnostic procedures and 3.7, 4,3, 7.3, 16.1 and 53.4 Gy cm2for therapeutic procedures, respectively. While initial data were collected manually as patient dose management systems (DMSs) were not available in most of the hospitals involved in the program, a centralized automatic DMS for the collection and management of patient dose indicators has now been introduced and is envisaged to increase the sample size. The possibility of alerting on high dose values and introducing corrective actions will help in optimization.

COMPARISON OF TWO TYPES EYE LENS DOSEMETERS USED IN INTERVENTIONAL CARDIOLOGY AND RADIOLOGY.

The aim of this contribution is to provide an overview of comparison of two types of eye lens dosemeter systems. The comparison was performed at the Department of Intravenous Cardiology using patient and physician phantoms and supplemented by dose simulation using the Monte Carlo method. The tests were performed in several specific geometries and in addition to eye lens dosemeters the value of a personal dosemeter located at the reference point was also monitored. The value of Hp(3) and Hp(10) was monitored. It is clear from the results that film dosemeters achieve more correct results in most cases. It is probably due to a better correction for the angle of exposure. This assumption must be verified by more detailed measurements in laboratory conditions.

Application of Medical Knowledge Graphs in Cardiology and Cardiovascular Medicine: A Brief Literature Review.

A knowledge graph is defined as a collection of interlinked descriptions of concepts, relationships, entities and events. Medical knowledge graphs have been the most recent advances in technology, therapy and medicine. Nowadays, a number of specific uses and applications rely on knowledge graphs. The application of the knowledge graph, another form of artificial intelligence (AI) in cardiology and cardiovascular medicine, is a new concept, and only a few studies have been carried out on this particular aspect. In this brief literature review, the use and importance of disease-specific knowledge graphs in exploring various aspects of Kawasaki disease were described. A vision of individualized knowledge graphs (iKGs) in cardiovascular medicine was also discussed. Such iKGs would be based on a modern informatics platform of exchange and inquiry that could comprehensively integrate biologic knowledge with medical histories and health outcomes of individual patients. This could transform how clinicians and scientists discover, communicate and apply new knowledge. In addition, we also described how a study based on the comprehensive longitudinal evaluation of dietary factors associated with acute myocardial infarction and fatal coronary heart disease used a knowledge graph to show the dietary factors associated with cardiovascular diseases in Nurses' Health Study data. To conclude, in this fast-developing world, medical knowledge graphs have emerged as attractive methods of data storage and hypothesis generation. They could be a major and effective tool in cardiology and cardiovascular medicine and play an important role in reaching effective clinical decisions during treatment and management of patients in the cardiology department.