Current cardiology reports
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Point-of-care ultrasound using small ultrasound devices has expanded beyond emergency and critical care medicine to many other subspecialties. Awareness of the strengths and limitations of the technology and knowledge of the appropriate settings and common indications for point-of-care ultrasound is important. ⋯ Point-of-care ultrasound is widely embraced as an extension of the physical exam and is employed in acute care and medical education settings. Echocardiography laboratories involved in education must individualize training to the intended scope of practice of the user. Advances in artificial intelligence may assist in image acquisition and interpretation by novice users. Point-of-care ultrasound is widely available in a variety of clinical settings. The field has advanced substantially in the past 2 decades and will likely continue to expand with advancement in technology, reduced cost, and improved opportunities to assist new users.
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The aim of this structured review is to summarize the current research applications and opportunities arising from artificial intelligence (AI) and texture analysis with regard to cardiac imaging. ⋯ Current research findings suggest tremendous potential for AI in cardiac imaging, especially with regard to objective image analyses, overcoming the limitations of an observer-dependent subjective image interpretation. Researchers have used this technique across multiple imaging modalities, for instance to detect myocardial scars in cardiac MR imaging, to predict contrast enhancement in non-contrast studies, and to improve image acquisition and reconstruction. AI in medical imaging has the potential to provide novel, much-needed applications for improving patient care pertaining to the cardiovascular system. While several shortcomings are still present in the current methodology, AI may serve as a resourceful assistant to radiologists and clinicians alike.
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The clinical and incremental value of functional imaging with 18F-fluorodeoxyglucose-positron emission tomography/computed tomography (FDG PET/CT) for the diagnosis and management of patients with suspected native and prosthetic valve infective endocarditis (IE). ⋯ The diagnosis of IE is challenging because of the highly variable clinical presentations, especially in the case of prosthetic valve endocarditis (PVE). FDG PET/CT has been shown to play an important role for the diagnosis of PVE as a major Duke criterion. Whether FDG PET/CT could play a similar role in patients with suspected native valve endocarditis (NVE) is less well established. It is increasingly recognized that IE is a multisystem disorder, and identification of extra-cardiac manifestations on whole-body FDG PET/CT impacts management and prognosis of patients with IE. Finally, FDG PET/CT provides incremental prognostic value over other clinical and para-clinical parameters, enabling prediction of in-hospital mortality, IE recurrence, hospitalization, and new onset heart failure and embolic events. FDG PET/CT plays a key role in the investigation of patients with suspected IE, enabling detection of valvular infection and extra-cardiac manifestations of the infection which has important prognostic implications.
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The purpose of this review is to discuss the updated guideline recommendations on management of dyslipidemia for prevention and treatment of cardiovascular disease. ⋯ The American College of Cardiology/American Heart Association (ACC/AHA) and European Society of Cardiology/European Atherosclerosis Society (ESC/EAS) published revised cholesterol management guidelines in 2018 and 2019, respectively, to reflect new evidence in the field. Broadly speaking, both emphasize refining cardiovascular disease risk estimation and aggressively lowering low-density lipoprotein-cholesterol (LDL-C) with statin and non-statin agents to curb cardiovascular risk. While they share the same guiding principles, there are important differences in the recommendations from both societies including how they define risk categories and goals for LDL-C lowering. This review summarizes current methods of managing dyslipidemia with a focus on the common themes and notable differences between the 2018 ACC/AHA and 2019 ESC/EAS cholesterol management guidelines.
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To (i) review the concept of artificial intelligence (AI); (ii) summarize recent developments in artificial intelligence-enabled electrocardiogram (AI-ECG); (iii) address notable inherent limitations and challenges of AI-ECG; and (iv) discuss the future direction of the field. ⋯ Advancements in machine learning and computing methods have led to application of AI-ECG and potential new applications to patient care. Further study is needed to verify previous findings in diverse populations as well as begin to confront the limitations needed for clinical implementation. Nearly one century after the Nobel Prize was awarded to Willem Einthoven for demonstrating that an electrocardiogram (ECG) could record the electrical signature of the heart, the ECG remains one of the most important diagnostic tests in modern medicine. We now stand at the edge of true ECG innovation. Simultaneous advancements in computing power, wireless technology, digitized data availability, and machine learning have led to the birth of AI-ECG algorithms with novel capabilities and real potential for clinical application. AI has the potential to improve diagnostic accuracy and efficiency by providing fully automated, unbiased, and unambiguous ECG analysis along with promising new findings that may unlock new value in the ECG. These breakthroughs may cause a paradigm shift in clinical workflow as well as patient monitoring and management.