Applications In Cardiology Research Articles

In vivo endoscopic ultrasound imaging with a rotational-scanning, all-optical ultrasound probe.

All-optical ultrasound manipulates ultrasound waves based on laser and photonics technologies, providing an alternative approach for pulse-echo ultrasound imaging. However, its endoscopic imaging capability is limited ex vivo by the multifiber connection between the endoscopic probe and the console. Here, we report on all-optical ultrasound for in vivo endoscopic imaging using a rotational-scanning probe that relies on a small laser sensor to detect echo ultrasound waves. The acoustically induced lasing frequency change is measured via heterodyne detection by beating the two orthogonally polarized laser modes, enabling a stable output of ultrasonic responses and immunity to low-frequency thermal and mechanical disturbances. We miniaturize its optical driving and signal interrogation unit and synchronously rotate it with the imaging probe. This specialized design leaves a single-fiber connection to the proximal end and allows fast rotational scanning of the probe. As a result, we used a flexible, miniature all-optical ultrasound probe for in vivo rectal imaging with a B-scan rate of 1 Hz and a pullback range of ∼7 cm. This can visualize the gastrointestinal and extraluminal structures of a small animal. This imaging modality offers an imaging depth of 2 cm at a central frequency of ∼20 MHz, showing promise for high-frequency ultrasound imaging applications in gastroenterology and cardiology.

Artificial intelligence applied to the electrocardiogram, or is there really a needle in a haystack?

Artificial intelligence applied to the standard Ecg (Ai-Ecg) is able to enormously enhance the performance of a diagnostic test that has been in use for over 100 years. The tool can in fact identify diseases, cardiac and non-cardiac, hidden or foreseeable, often asymptomatic, previously undetectable. Here we describe some of the main applications in cardiology: sub-clinical atrial fibrillation and asymptomatic left ventricular dysfunction. However, the Ai-Ecg still needs validation in non-experimental contexts: only in that case can its potential be considered verified and therefore usable in clinical practice.

Evaluation of photogrammetry for medical application in cardiology.

Background: In the field of medicine, photogrammetry has played for long time a marginal role due to the significant amount of work required that made it impractical for an extended medical use. Developments in digital photogrammetry occurred in the recent years, that have steadily increased the interest and application of this technique. The present study aims to compare photogrammetry reconstruction of heart with computed tomography (CT) as a reference. Methods: The photogrammetric reconstructions of digital images from ECG imaging derived images were performed. In particular, the ventricles of 15 patients with Brugada syndrome were reconstructed by using the free Zephyr Lite software. In order to evaluate the accuracy of the technique, measurements on the reconstructions were compared to patient-specific CT scan imported in ECG imaging software UZBCIT. Result: The results showed that digital photogrammetry in the context of ventricle reconstruction is feasible. The photogrammetric derived measurements of ventricles were not statistically different from CT scan measurements. Furthermore, the analysis showed high correlation of photogrammetry reconstructions with CT scan and a correlation coefficient close to 1. Conclusion: It is possible to reproduce digital objects by photogrammetry if the process described in this study is performed. The reconstruction of the ventricles from CT scan was very close to the values of the respective photogrammetric reconstruction.

Artificial Intelligence Applications in Cardiology

Artificial Intelligence Applications in Cardiology

Simulation of a novel, non-invasive radiation detector to measure the arterial input function for dynamic positron emission tomography.

Dynamic positron emission tomography (dPET) is a nuclear medicine imaging technique providing functional images for organs of interest with applications in oncology, cardiology, and drug discovery. This technique requires the acquisition of the time-course arterial plasma activity concentration, called the arterial input function (AIF), which is conventionally acquired via arterial bloodsampling. The aim of this study was to (A) optimize the geometry for a novel and cost efficient non-invasive detector called NID designed to measure the AIF for dPET scans through Monte Carlo simulations and (B) develop a clinical data analysis chain to successfully separate the arterial component of a simulated AIF signal from the venouscomponent. The NID was optimized by using an in-house Geant4-based software package. The sensitive volume of the NID consists of a band of 10 cm long and 1mm in diameter scintillating fibers placed over a wrist phantom. The phantom was simulated as a cylinder, 10 cm long and 6.413 cm in diameter comprised of polyethylene with two holes placed through it to simulate the patient's radial artery and vein. This phantom design was chosen to match the wrist phantom used in our previous proof of concept work. Two geometries were simulated with different arrangements of scintillating fibers. The first design used a single layer of 64 fibers. The second used two layers, an inner layer with 29 fibers and an outer layer with 30 fibers. Four positron emitting radioisotopes were simulated: 18 F, 11 C, 15 O, and 68 Ga with 100 million simulated decay events per run. The total and intrinsic efficiencies of both designs were calculated as well as the full width half maximum (FWHM) of the signal. In addition, contribution by the annihilation photons versus positrons to the signal was investigated. The results obtained from the two simulated detector models were compared. A clinical data analysis chain using an expectation maximization maximum likelihood algorithm was tested. This analysis chain will be used to separate arterial counts from the totalsignal. The second NID design with two layers of scintillating fibers had a higher efficiency for all simulations with a maximum increase of 17% total efficiency for 11 C simulation. All simulations had a significant annihilation photon contribution. The signal for 18 F and 11 C was almost entirely due to photons. The clinical data analysis chain was within 1% of the true value for 434 out of 440 trials. Further experimental studies to validate these simulations will berequired. The design of the NID was optimized and its efficiency increased through Monte Carlo simulations. A clinical data analysis chain was successfully developed to separate the arterial component of an AIF signal from the venous component. The simulations show that the NID can be used to accurately measure the AIF non-invasively for dPETscans.

Methods for Mathematical Analysis of Simulated and Real Fractal Processes with Application in Cardiology

In the article, a comparative analysis is performed regarding the accuracy parameter in determining the degree of self-similarity of fractal processes between the following methods: Variance-Time plot, Rescaled Range (R/S), Wavelet-based, Detrended Fluctuation Analysis (DFA) and Multifractal Detrended Fluctuation Analysis (MFDFA). To evaluate the methods, fractal processes based of Fractional Gaussian Noise were simulated and the dependence between the length of the simulated process and the degree of self-similarity was investigated by calculating the Hurst exponent (H > 0.5). It was found that the Wavelet-based, DFA and MFDFA methods, with a process length greater than 214 points, have a relative error of the Hurst exponent is less than 1%. A methodology for the Wavelet-based method related to determining the size of the scale and the wavelet algorithm was proposed, and it was investigated in terms of the exact determination of the Hurst exponent of two algorithms: Haar and Daubechies with different number of coefficients and different values of the scale. Based on the analysis, it was determined that the Daubechies algorithm with 10 coefficients and scale (i = 2, j = 10) has a relative error of less than 0.5%. The three most accurate methods are applied to the study of real cardiac signals of two groups of people: healthy and unhealthy (arrhythmia) subjects. The results of the statistical analysis, using the t-test, show that the proposed methods can distinguish the two studied groups and can be used for diagnostic purposes.

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 (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.

Bioelectronic medicine and its applications in cardiology.

Bioelectronic medicine and its applications in cardiology.

Barriers and facilitators to the implementation of additive manufacturing in cardiology: A qualitative study

BackgroundAdditive manufacturing (AM) is a fast-developing technology with possible applications in cardiology. Existing research has identified two general factors that can influence implementing AM in cardiology: economics and technology. ObjectiveIn this study we aimed to identify barriers and facilitators to implementing AM in cardiology. MethodsWe conducted a multiple case study of two Swedish cardiac surgery departments representing implementers and non-implementers of AM. We interviewed key stakeholders (n=8) who had been or were involved in implementing AM in cardiology or AM in general at the hospitals: cardiologists, physicians working with AM but not specialized in cardiology such as radiologists, company representatives, and individuals involved in the 3D-printing facilities. A combination of an inductive and deductive approach was used to analyze the interviews. ResultsSeveral barriers and facilitators influenced implementing AM in cardiology. Most barriers (n=4) were related to innovation factors, whereas most facilitators (n=4) were related to healthcare professionals. No barriers and facilitators were related to patients. ConclusionOur findings show that AM in cardiology is in its very early phases in both hospitals and mostly the work of a few individuals. In the two hospitals studied, there were some unique differences in terms of barriers that could explain the low level of implementation. These barriers could be important to address when supporting implementation of AM at hospitals where AM use is still low.

Identifying the individual metabolic abnormities from a systemic perspective using whole-body PET imaging

IntroductionDistinct physiological states arise from complex interactions among the various organs present in the human body. PET is a non-invasive modality with numerous successful applications in oncology, neurology, and cardiology. However, while PET imaging has been applied extensively in detecting focal lesions or diseases, its potential in detecting systemic abnormalities is seldom explored, mostly because total-body imaging was not possible until recently.MethodsIn this context, the present study proposes a framework capable of constructing an individual metabolic abnormality network using a subject’s whole-body 18F-FDG SUV image and a normal control database. The developed framework was evaluated in the patients with lung cancer, the one discharged after suffering from Covid-19 disease, and the one that had gastrointestinal bleeding with the underlying cause unknown.ResultsThe framework could successfully capture the deviation of these patients from healthy subjects at the level of both system and organ. The strength of the altered network edges revealed the abnormal metabolic connection between organs. The overall deviation of the network nodes was observed to be highly correlated to the organ SUV measures. Therefore, the molecular connectivity of glucose metabolism was characterized at a single subject level.ConclusionThe proposed framework represents a significant step toward the use of PET imaging for identifying metabolic dysfunction from a systemic perspective. A better understanding of the underlying biological mechanisms and the physiological interpretation of the interregional connections identified in the present study warrant further research.

A biosensing system using a multiparameter nonlinear dynamic analysis of cardiomyocyte beating for drug-induced arrhythmia recognition

Cardiovascular disease is the number one cause of death in humans. Therefore, cardiotoxicity is one of the most important adverse effects assessed by arrhythmia recognition in drug development. Recently, cell-based techniques developed for arrhythmia recognition primarily employ linear methods such as time-domain analysis that detect and compare individual waveforms and thus fall short in some applications that require automated and efficient arrhythmia recognition from large datasets. We carried out the first report to develop a biosensing system that integrated impedance measurement and multiparameter nonlinear dynamic algorithm (MNDA) analysis for drug-induced arrhythmia recognition and classification. The biosensing system cultured cardiomyocytes as physiologically relevant models, used interdigitated electrodes to detect the mechanical beating of the cardiomyocytes, and employed MNDA analysis to recognize drug-induced arrhythmia from the cardiomyocyte beating recording. The best performing MNDA parameter, approximate entropy, enabled the system to recognize the appearance of sertindole- and norepinephrine-induced arrhythmia in the recording. The MNDA reconstruction in phase space enabled the system to classify the different arrhythmias and quantify the severity of arrhythmia. This new biosensing system utilizing MNDA provides a promising and alternative method for drug-induced arrhythmia recognition and classification in cardiological and pharmaceutical applications.

Trends in cardiology and oncology artificial intelligence publications

Study objectiveTo determine whether there has been growth in publications on the use of artificial intelligence in cardiology and oncology, we assessed historical trends in publications related to artificial intelligence applications in cardiology and oncology, which are the two fields studying the leading causes of death worldwide. Upward trends in publications may indicate increasing interest in the use of artificial intelligence in these crucial fields. Design/settingTo evaluate evidence of increasing publications on the use of artificial intelligence in cardiology and oncology, historical trends in related publications on PubMed (the biomedical repository most frequently used by clinicians and scientists in these fields) were reviewed. ResultsFindings indicated that research output related to artificial intelligence (and its subcategories) generally increased over time, particularly in the last five years. With some initial degree of vacillation in publication trends, a slight qualitative inflection was noted in approximately 2015, in general publications and especially for oncology and cardiology, with subsequent consistent exponential growth. Publications predominantly focused on “machine learning” (n = 20,301), which contributed to the majority of the accelerated growth in the field, compared to “artificial intelligence” (n = 4535), “natural language processing” (n = 2608), and “deep learning” (n = 4459). ConclusionTrends in the general biomedical literature and particularly in cardiology and oncology indicated exponential growth over time. Further exponential growth is expected in future years, as awareness and cross-disciplinary collaboration and education increase. Publications specifically on machine learning will likely continue to lead the way.

Novel Artificial Intelligence Applications in Cardiology: Current Landscape, Limitations, and the Road to Real-World Applications.

Cardiovascular diseases are the leading cause of death globally and contribute significantly to the cost of healthcare. Artificial intelligence (AI) is poised to reshape cardiology. Using supervised and unsupervised learning, the two main branches of AI, several applications have been developed in recent years to improve risk prediction, allow large-scale analysis of medical data, and phenotype patients for personalized medicine. In this review, we examine the key advances in AI in cardiology and its limitations regarding bias in the data, standardization in reporting, data access, and model trust and accountability in cases of error. Finally, we discuss implementation methods to unleash AI's potential in making healthcare more accurate and efficient. Several steps need to be followed and challenges overcome in order to successfully integrate AI in clinical practice and ensure its longevity.

Epicardial adipose tissue in contemporary cardiology.

Interest in epicardial adipose tissue (EAT) is growing rapidly, and research in this area appeals to a broad, multidisciplinary audience. EAT is unique in its anatomy and unobstructed proximity to the heart and has a transcriptome and secretome very different from that of other fat depots. EAT has physiological and pathological properties that vary depending on its location. It can be highly protective for the adjacent myocardium through dynamic brown fat-like thermogenic function and harmful via paracrine or vasocrine secretion of pro-inflammatory and profibrotic cytokines. EAT is a modifiable risk factor that can be assessed with traditional and novel imaging techniques. Coronary and left atrial EAT are involved in the pathogenesis of coronary artery disease and atrial fibrillation, respectively, and it also contributes to the development and progression of heart failure. In addition, EAT might have a role in coronavirus disease 2019 (COVID-19)-related cardiac syndrome. EAT is a reliable potential therapeutic target for drugs with cardiovascular benefits such as glucagon-like peptide 1 receptor agonists and sodium–glucose co-transporter 2 inhibitors. This Review provides a comprehensive and up-to-date overview of the role of EAT in cardiovascular disease and highlights the translational nature of EAT research and its applications in contemporary cardiology.

Human colorectal cancer tissue assessment using optical coherence tomography catheter and deep learning.

Optical coherence tomography (OCT) can differentiate normal colonic mucosa from neoplasia, potentially offering a new mechanism of endoscopic tissue assessment and biopsy targeting, with a high optical resolution and an imaging depth of ~1 mm. Recent advances in convolutional neural networks (CNN) have enabled application in ophthalmology, cardiology, and gastroenterology malignancy detection with high sensitivity and specificity. Here, we describe a miniaturized OCT catheter and a residual neural network (ResNet)-based deep learning model manufactured and trained to perform automatic image processing and real-time diagnosis of the OCT images. The OCT catheter has an outer diameter of 3.8 mm, a lateral resolution of ~7μm, and an axial resolution of ~6μm. A customized ResNet is utilized to classify OCT catheter colorectal images. An area under the receiver operating characteristic (ROC) curve (AUC) of 0.975 is achieved to distinguish between normal and cancerous colorectal tissue images.

A Primer on the Present State and Future Prospects for Machine Learning and Artificial Intelligence Applications in Cardiology.

The artificial intelligence (AI) revolution is well underway, including in the medical field, and has dramatically transformed our lives. An understanding of the basics of AI applications, their development, and challenges to their clinical implementation is important for clinicians to fully appreciate the possibilities of AI. Such a foundation would ensure that clinicians have a good grasp and realistic expectations for AI in medicine and prevent discrepancies between the promised and real-world impact. When quantifying the track record for AI applications in cardiology, we found that a substantial number of AI systems are never deployed in clinical practice, although there certainly are many success stories. Successful implementations shared the following: they came from clinical areas where large amount of training data was available; were deployable into a single diagnostic modality; prediction models generally had high performance in external validation; and most were developed as part of collaborations with medical device manufacturers who had substantial experience with implementation of new clinical technology. When looking into the current processes used for developing AI-based systems, we suggest that expanding the analytic framework to address potential deployment and implementation issues at project outset will improve the rate of successful implementation, and will be a necessary next step for AI to achieve its full potential in cardiovascular medicine.

Nuclear Cardiology Data Analyzed Using Machine Learning.

Machine learning has become popular in clinical practice, and the amount of research that uses artificial intelligence is rapidly increasing. In contrast to conventional statistical and rule-based methods, machine learning creates algorithms based only on combinations of input and output databases. Basic understanding of the internal workings of artificial intelligence, its structures and need for appropriate databases, as well as its strengths and weaknesses is important for efficient machine learning application. The cardiological applications of machine learning include diagnosing coronary artery diseases and heart failure, and examples are addressed herein. A preliminary application of machine learning to a 123I-metaiodobenzylguanidine-based risk model appears promising, and further studies using similar approaches are anticipated. Nuclear medicine physicians and cardiologists should play key roles in developing machine learning-based methods to ensure practical and reliable decisions.

The Recent Advances of Mobile Healthcare in Cardiology Practice

Background:Digitalization of healthcare led to the optimization of monitoring, diagnostics, and treatment of the range of disorders. Taking into account recent situation with COVID-19 pandemics, digital technologies allowed to improve management of viral infections via remote monitoring and diagnostics of infected patients. Up to date, various mobile health applications (apps) have been proposed, including apps for the patients diagnosed with cardiovascular pathologies.Objective:The presented review aimed at the analyses of a range of mHealth solutions used to improve primary cardiac care. In addition, we studied the factors driving and hindering the wide introduction of mHealth services in the clinics.Methods:The work was based on the guidelines of the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement. The publication search was carried out using PubMed, Web of Science, Cochrane Library, Scopus, and Google Scholar databases. Studies published during the period from 2014 until January 2022 were selected for the analysis. The evaluation of risk of bias for the included studies was conducted using the Cochrane Collaboration Risk of Bias tool.Results and Discussion:An overall 5513 studies were assessed for eligibility after which 39 studies were included.. The main trend in the mobile health for cardiological applications is the use of different types of wearable devices and Artificial Intelligence-platforms. In fact, mobile technology allows remotely to monitor, interpret, and analyze biomedical data collected from the patient.Conclusion:The results of this literature search demonstrated that patients diagnosed with cardiovascular disorders can potentially benefit from the application of mHealth in cardiology. However, despite the proven advantages of mHealth for cardiology, there are many challenges and concerns regarding effectiveness, safety, reliability and the lack of official regulation and guidelines from official organizations. Such issues require solutions and further work towards a wide implementation of mHealth technologies in cardiac practice.

Device for modeling of intra-arterial circulation: application in experimental cardiology

Abstract Purpose of study Using an original device for modeling of intra-arterial blood circulation, to study the features of intravascular hemodynamics with a regular heart rhythm and with various rhythm disturbances. Material and methods We used an original device developed by us to simulate intra-arterial circulation (Document of invention No. RU 202780 U1). The main part of the device is a glass tube of a rotameter with a length of 365 mm, an inlet end with a diameter of 20 mm, an outlet end of 16.5 mm, which is a model of an arterial vessel. Flexible silicone tubes are attached to the rotameter on both sides, with free ends connected to an electric pump, with various modes of operation (imitation of pulse waves with regular rhythm, premature ventricular contractions (PVCs), atrial fibrillation (AF). An aqueous solution of glycerin was introduced into a closed system diluted with water corresponding to the viscosity of the blood. A 5 mm long silk thread was alternately installed inside the tube, an intravascular piezoelectric crystal pressure probe connected to an oscilloscope. Also it was injected inside the tube a dye - clerical ink. Results With electric pump, we simulated the spreading of the pulse wave in regular heart rate, PVCs and AF. We observed the effect of a pressure wave (deflection of a silk thread, the appearance of a turbulent flow when using a dye) on the walls of the rotameter, with the formation of standing, reflected waves during the PVCs and AF. The pressure probe registered an increase in pressure inside the tube during the spread of the 1st post-extrasystolic contraction wave up to 58%, the wave after a long pause of more than 2 seconds with AF by 44% compared to the regular rhythm. Conclusion A device for modeling of intra-arterial circulation allows a wide range of experimental work in cardiology, normal and pathological physiology, and biophysics. Funding Acknowledgement Type of funding sources: None.

Artificial intelligence in pediatric cardiology: taking baby steps in the big world of data.

Artificial intelligence (AI) has changed virtually every aspect of modern life, and medicine is no exception. Pediatric cardiology is both a perceptual and a cognitive subspecialty that involves complex decision-making, so AI is a particularly attractive tool for this medical discipline. This review summarizes the foundational work and incremental progress made as AI applications have emerged in pediatric cardiology since 2020. AI-based algorithms can be useful for pediatric cardiology in many areas, including: (1) clinical examination and diagnosis, (2) image processing, (3) planning and management of cardiac interventions, (4) prognosis and risk stratification, (5) omics and precision medicine, and (6) fetal cardiology. Most AI initiatives showcased in medical journals seem to work well in silico, but progress toward implementation in actual clinical practice has been more limited. Several barriers to implementation are identified, some encountered throughout medicine generally, and others specific to pediatric cardiology. Despite barriers to acceptance in clinical practice, AI is already establishing a durable role in pediatric cardiology. Its potential remains great, but to fully realize its benefits, substantial investment to develop and refine AI for pediatric cardiology applications will be necessary to overcome the challenges of implementation.