Evaluation of Early Gadolinium Enhancement (EGE) and Cardiac Functional Parameters in Cine-Magnetic Resonance Imaging (MRI) on Artificial Intelligence in Patients with Acute Myocarditis: A Case-Controlled Observational Study

Artificial intelligence and myocarditis-a systematic review of current applications

Myocarditis, marked by heart muscle inflammation, poses significant clinical challenges. This study, guided by PRISMA guidelines, explores the expanding role of artificial intelligence (AI) in myocarditis, aiming to consolidate current knowledge and guide future research. Following PRISMA guidelines, a systematic review was conducted across PubMed, Cochrane Reviews, Scopus, Embase, and Web of Science databases. MeSH terms including artificial intelligence, deep learning, machine learning, myocarditis, and inflammatory cardiomyopathy were used. Inclusion criteria involved original articles utilizing AI for myocarditis, while exclusion criteria eliminated reviews, editorials, and non-AI-focused studies. The search yielded 616 articles, with 42 meeting inclusion criteria after screening. The identified articles, spanning diagnostic, survival prediction, and molecular analysis aspects, were analyzed in each subsection. Diagnostic studies showcased the versatility of AI algorithms, achieving high accuracies in myocarditis detection. Survival prediction models exhibited robust discriminatory power, particularly in emergency settings and pediatric populations. Molecular analyses demonstrated AI's potential in deciphering complex immune interactions. This systematic review provides a comprehensive overview of AI applications in myocarditis, highlighting transformative potential in diagnostics, survival prediction, and molecular understanding. Collaborative efforts are crucial for overcoming limitations and realizing AI's full potential in improving myocarditis care.

A challenging case of eosinophilic myocarditis leading to heart failure and transplantation

The large spectrum of etiologies, severities, and histologic appearances of eosinophilic myocarditis (EoM) poses challenges to its diagnosis and management. Endomyocardial biopsy is the current gold standard for diagnosis. However, cardiovascular magnetic resonance imaging is becoming more frequently used to diagnose acute myocarditis because of enhanced sensitivity when compared to histopathologic examination, and its less invasive nature. We report a complicated case of EoM in a male in his mid-thirties that led to fulminant cardiogenic shock that required immunosuppressive therapy on day 5 of admission and implantation of a left ventricular assist device (LVAD) on day 30. EoM was diagnosed on histopathologic examination of the resected fragment of the left ventricular myocardium. Nine months after the initial presentation, the patient ultimately required heart transplantation. The explanted heart showed minimal residual interstitial inflammation with evidence of mildly active intimal arteritis and patchy areas of interstitial fibrosis. In this report, we describe our patient's clinical features and correlate them with imaging and histopathologic findings to illustrate the difficulty in diagnosing EoM, particularly in this complicated patient that ultimately required heart transplantation. The diagnosis can be challenging due to the variable histopathologic features, clinical presentation, and utilization of therapeutic medications and devices.

Imaging Methods: Magnetic Resonance Imaging

Myocardial inflammation occurs following activation of the cardiac immune system, producing characteristic changes in the myocardial tissue. Cardiovascular magnetic resonance is the non-invasive imaging gold standard for myocardial tissue characterization, and is able to detect image signal changes that may occur resulting from inflammation, including edema, hyperemia, capillary leak, necrosis, and fibrosis. Conventional cardiovascular magnetic resonance for the detection of myocardial inflammation and its sequela include T2-weighted imaging, parametric T1- and T2-mapping, and gadolinium-based contrast-enhanced imaging. Emerging techniques seek to image several parameters simultaneously for myocardial tissue characterization, and to depict subtle immune-mediated changes, such as immune cell activity in the myocardium and cardiac cell metabolism. This review article outlines the underlying principles of current and emerging cardiovascular magnetic resonance methods for imaging myocardial inflammation.

Artificial intelligence and imaging: Opportunities in cardio-oncology

Cardiovascular disease is a leading cause of death in cancer survivors. It is critical to apply new predictive and early diagnostic methods in this population, as this can potentially inform cardiovascular treatment and surveillance decision-making. We discuss the application of artificial intelligence (AI) technologies to cardiovascular imaging in cardio-oncology, with a particular emphasis on prevention and targeted treatment of a variety of cardiovascular conditions in cancer patients. Recently, the use of AI-augmented cardiac imaging in cardio-oncology is gaining traction. A large proportion of cardio-oncology patients are screened and followed using left ventricular ejection fraction (LVEF) and global longitudinal strain (GLS), currently obtained using echocardiography. This use will continue to increase with new cardiotoxic cancer treatments. AI is being tested to increase precision, throughput, and accuracy of LVEF and GLS, guide point-of-care image acquisition, and integrate imaging and clinical data to optimize the prediction and detection of cardiac dysfunction. The application of AI to cardiovascular magnetic resonance imaging (CMR), computed tomography (CT; especially coronary artery calcium or CAC scans), single proton emission computed tomography (SPECT) and positron emission tomography (PET) imaging acquisition is also in early stages of analysis for prediction and assessment of cardiac tumors and cardiovascular adverse events in patients treated for childhood or adult cancer. The opportunities for application of AI in cardio-oncology imaging are promising, and if availed, will improve clinical practice and benefit patient care.

Review of multi-modality imaging update and diagnostic work up of Takotsubo cardiomyopathy

Takotsubo cardiomyopathy (TC) is an acute but reversible non-ischemic heart failure syndrome. It is characterized by a transient form of ventricular dysfunction typically manifesting as basal hyperkinesis with hypokinesia and ballooning of left ventricle mid-cavity and apex. Imaging helps in both diagnosis and follow up. Echocardiogram is the first-line modality to assess the typical contractile dysfunction in suspected patients with catheter angiography showing normal coronary arteries. Cardiac MRI is currently the modality of choice for the non-invasive initial assessment of TC and for follow up imaging. The current review focusses on historical background of TC, its pathophysiology, diagnostic work up and differential diagnosis and provides multimodality imaging work up of TC including role of echocardiogram, invasive catheterization, nuclear imaging, cardiac computed tomography and cardiac MRI including basic and advanced MRI sequences.