Explore the vast range of titles available.

The intersection of global broadband technology and miniaturized high-capability computing devices has led to a revolution in the delivery of healthcare and the birth of telemedicine and mobile health (mHealth). Rapid advances in handheld imaging devices with other mHealth devices such as smartphone apps and wearable devices are making great strides in the field of cardiovascular imaging like never before. Although these technologies offer a bright promise in cardiovascular imaging, it is far from straightforward. The massive data influx from telemedicine and mHealth including cardiovascular imaging supersedes the existing capabilities of current healthcare system and statistical software. Artificial intelligence with machine learning is the one and only way to navigate through this complex maze of the data influx through various approaches. Deep learning techniques are further expanding their role by image recognition and automated measurements. Artificial intelligence provides limitless opportunity to rigorously analyze data. As we move forward, the futures of mHealth, telemedicine and artificial intelligence are increasingly becoming intertwined to give rise to precision medicine.

To provide accurate predictions, current machine learning-based solutions require large, manually labeled training datasets. We implement persistent homology (PH), a topological tool for studying the pattern of data, to analyze echocardiography-based strain data and differentiate between rare diseases like constrictive pericarditis (CP) and restrictive cardiomyopathy (RCM). Patient population (retrospectively registered) included those presenting with heart failure due to CP (n = 51), RCM (n = 47), and patients without heart failure symptoms (n = 53). Longitudinal, radial, and circumferential strains/strain rates for left ventricular segments were processed into topological feature vectors using Machine learning PH workflow. In differentiating CP and RCM, the PH workflow model had a ROC AUC of 0.94 (Sensitivity = 92%, Specificity = 81%), compared with the GLS model AUC of 0.69 (Sensitivity = 65%, Specificity = 66%). In differentiating between all three conditions, the PH workflow model had an AUC of 0.83 (Sensitivity = 68%, Specificity = 84%), compared with the GLS model AUC of 0.68 (Sensitivity = 52% and Specificity = 76%). By employing persistent homology to differentiate the “pattern” of cardiac deformations, our machine-learning approach provides reasonable accuracy when evaluating small datasets and aids in understanding and visualizing patterns of cardiac imaging data in clinically challenging disease states.

Coronary artery disease is one of the leading causes of morbidity and mortality worldwide. Although it can present with an acute coronary syndrome, it is often characterised by long periods of stability, known as chronic coronary artery disease. This Review presents a comprehensive overview of the diagnosis of the disease, with a focus on cardiac imaging. We discuss various cardiac imaging modalities, including CT coronary angiography, stress echocardiogram, stress single-photon emission CT, PET, and stress cardiac magnetic resonance. We also compare the roles of anatomical (eg, CT coronary angiography) versus functional (eg, stress echocardiogram) tests and examine the potential utility of artificial intelligence in more detail.

Artificial intelligence (AI) broadly refers to analytical algorithms that iteratively learn from data, allowing computers to find hidden insights without being explicitly programmed where to look. These include a family of operations encompassing several terms like machine learning, cognitive learning, deep learning and reinforcement learning-based methods that can be used to integrate and interpret complex biomedical and healthcare data in scenarios where traditional statistical methods may not be able to perform. In this review article, we discuss the basics of machine learning algorithms and what potential data sources exist; evaluate the need for machine learning; and examine the potential limitations and challenges of implementing machine in the context of cardiovascular medicine. The most promising avenues for AI in medicine are the development of automated risk prediction algorithms which can be used to guide clinical care; use of unsupervised learning techniques to more precisely phenotype complex disease; and the implementation of reinforcement learning algorithms to intelligently augment healthcare providers. The utility of a machine learning-based predictive model will depend on factors including data heterogeneity, data depth, data breadth, nature of modelling task, choice of machine learning and feature selection algorithms, and orthogonal evidence. A critical understanding of the strength and limitations of various methods and tasks amenable to machine learning is vital. By leveraging the growing corpus of big data in medicine, we detail pathways by which machine learning may facilitate optimal development of patient-specific models for improving diagnoses, intervention and outcome in cardiovascular medicine.

Periodontitis is a highly prevalent inflammatory illness that leads to the destruction of tooth supporting tissue structures and has been associated with an increased risk of cardiovascular disease (CVD). Precision medicine, an emerging branch of medical treatment, aims can further improve current traditional treatment by personalizing care based on one’s environment, genetic makeup, and lifestyle. Genomic databases have paved the way for precision medicine by elucidating the pathophysiology of complex, heritable diseases. Therefore, the investigation of novel periodontitis-linked genes associated with CVD will enhance our understanding of their linkage and related biochemical pathways for targeted therapies. In this article, we highlight possible mechanisms of actions connecting PD and CVD. Furthermore, we delve deeper into certain heritable inflammatory-associated pathways linking the two. The goal is to gather, compare, and assess high-quality scientific literature alongside genomic datasets that seek to establish a link between periodontitis and CVD. The scope is focused on the most up to date and authentic literature published within the last 10 years, indexed and available from PubMed Central, that analyzes periodontitis-associated genes linked to CVD. Based on the comparative analysis criteria, fifty-one genes associated with both periodontitis and CVD were identified and reported. The prevalence of genes associated with both CVD and periodontitis warrants investigation to assess the validity of a potential linkage between the pathophysiology of both diseases.

Purpose of Review Myocardial regeneration is a promising alternative to heart transplantation, but the ideal stem cell type remains unknown due to conflicting results in clinical trials. Trial discrepancies may be addressed by standardizing cell handling protocols, broadening clinical endpoints, and selecting patients likely to benefit from cell therapy. Machine learning can potentially assist with these tasks. Recent Findings We introduce machine learning and review literature with the most efficacious results translatable to regenerative cardiology, such as in quality control systems during cell culturing, automated segmentation, and myocardial tissue characterization. Investigators are then cautioned on potential pitfalls and offered solutions to minimize model biasing. Summary Standardizing imaging with automated segmentation can improve the quantification of left ventricular endpoints. Additionally, myocardial textural analysis has significant potential to uncover hidden biomarkers, which may address the need for novel clinical endpoints. Lastly, phenogrouping through radiomics signatures can assist in appropriating patients likely to respond to stem cell therapy.

Background The levels of circulating troponin are principally required in addition to electrocardiograms for the effective diagnosis of acute coronary syndrome. Current standard-of-care troponin assays provide a snapshot or momentary view of the levels due to the requirement of a blood draw. This modality further restricts the number of measurements given the clinical context of the patient. In this communication, we present the development and early validation of non-invasive transdermal monitoring of cardiac troponin-I to detect its elevated state. Methods Our device relies on infrared spectroscopic detection of troponin-I through the dermis and is tested in stepwise laboratory, benchtop, and clinical studies. Patients were recruited with suspected acute coronary syndrome. Results We demonstrate a significant correlation ( r  = 0.7774, P  < 0.001, n  = 52 biologically independent samples) between optically-derived data and blood-based immunoassay measurements with and an area under receiver operator characteristics of 0.895, sensitivity of 96.3%, and specificity of 60% for predicting a clinically meaningful threshold for defining elevated Troponin I. Conclusion This preliminary work introduces the potential of a bloodless transdermal measurement of troponin-I based on molecular spectroscopy. Further, potential pitfalls associated with infrared spectroscopic mode of inquiry are outlined including requisite steps needed for improving the precision and overall diagnostic value of the device in future studies. Plain language summary The number one cause of death in the US is heart disease. With 10 million patients visiting the emergency departments in a year with chest pain, 8 million are unrelated to cardiac issues. This places a burden on hospitals leading to suboptimal patient outcomes. In patients with cardiac issues, the time clinicians take to intervene dictates reversible or irreversible heart damage. However, current markers used to test for cardiac issues require blood sampling, limiting access to and frequency of testing. This study introduces a non-invasive cardiac marker measurement device without any form of blood draw, based on measurements taken by a wearable device through the skin. Preliminary studies show high conformance to the standard of care technologies, indicating that the technology has potential to enable more rapid, frequent, accessible and non-invasive detection of cardiac issues such as heart attacks. Titus et al. develop a technological platform for the non-invasive transdermal measurement of cardiac troponin-I, a marker of myocardial injury. Preliminary testing of their device, which works via infrared spectroscopy, indicates that troponin can be detected with reasonable performance, in the absence of a blood draw.

Exponential growth in data storage and computational power is rapidly narrowing the gap between translating findings from advanced clinical informatics into cardiovascular clinical practice. Specifically, cardiovascular imaging has the distinct advantage in providing a great quantity of data for potentially rich insights, but nuanced interpretation requires a high-level skillset that few individuals possess. A subset of machine learning, deep learning (DL), is a modality that has shown promise, particularly in the areas of image recognition, computer vision, and video classification. Due to a low signal-to-noise ratio, echocardiographic data tend to be challenging to classify; however, utilization of robust DL architectures may help clinicians and researchers automate conventional human tasks and catalyze the extraction of clinically useful data from the petabytes of collected imaging data. The promise is extending far and beyond towards a contactless echocardiographic exam—a dream that is much needed in this time of uncertainty and social distancing brought on by a stunning pandemic culture. In the current review, we discuss state-of-the-art DL techniques and architectures that can be used for image and video classification, and future directions in echocardiographic research in the current era.

As the population ages, the incidence and mortality of valvular heart disease (VHD) are rising. Current diagnostic approaches depend on expert heuristics, which may miss complex phenotypes. Unsupervised machine learning (ML) offers a scalable, data-driven alternative capable of identifying hidden patterns in large, multivariable datasets which may improve phenotyping, inform prognosis, and guide therapeutic decisions. We systematically searched PubMed for eligible studies evaluating the use of unsupervised ML on aortic stenosis, mitral regurgitation, and tricuspid regurgitation and extracted data on study population, algorithmic input parameters, ML algorithm, goals and outcome of study. Across VHD categories, we identified that unsupervised learning provides more detailed insights than traditional guidelines-based severity classes in understanding patient phenotypes and outcome prediction. These insights can be personalized to guide management with transcatheter and pharmacologic approaches for asymptomatic or early-stage VHD. Prospective studies are needed to validate these novel unsupervised ML approaches.

Patients with influenza and SARS-CoV2/Coronavirus disease 2019 (COVID-19) infections have a different clinical course and outcomes. We developed and validated a supervised machine learning pipeline to distinguish the two viral infections using the available vital signs and demographic dataset from the first hospital/emergency room encounters of 3883 patients who had confirmed diagnoses of influenza A/B, COVID-19 or negative laboratory test results. The models were able to achieve an area under the receiver operating characteristic curve (ROC AUC) of at least 97% using our multiclass classifier. The predictive models were externally validated on 15,697 encounters in 3125 patients available on TrinetX database that contains patient-level data from different healthcare organizations. The influenza vs COVID-19-positive model had an AUC of 98.8%, and 92.8% on the internal and external test sets, respectively. Our study illustrates the potentials of machine-learning models for accurately distinguishing the two viral infections. The code is made available at https://github.com/ynaveena/COVID-19-vs-Influenza and may have utility as a frontline diagnostic tool to aid healthcare workers in triaging patients once the two viral infections start cocirculating in the communities.