Data science is likely to lead to major changes in cardiovascular imaging. Problems with
timing, efficiency, and missed diagnoses occur at all stages of the imaging chain. The …

Recent findings Cardiovascular disease (CVD) is the leading cause of mortality and poses
challenges for healthcare providers globally. Risk-based approaches for the management of …

This study proposes an efficient neural network with convolutional layers to classify
significantly class-imbalanced clinical data. The data is curated from the National Health and …

In daily clinical practice, clinicians integrate available data to ascertain the diagnostic and
prognostic probability of a disease or clinical outcome for their patients. For patients with …

Nowadays, cardiovascular deaths and diseases have increased at a fast rate worldwide.
The early prediction of this disease is necessary to prevent the deaths. Detection of heart …

Aims To analyse the predictive capacity of 15 machine learning methods for estimating
cardiovascular risk in a cohort and to compare them with other risk scales. Methods We …

Purpose of review The ripples of artificial intelligence are being felt in various sectors of
human life. Machine learning, a subset of artificial intelligence, extracts information from …

In this review, we describe the technical aspects of artificial intelligence (AI) in cardiac
imaging, starting with radiomics, basic algorithms of deep learning and application tasks of …

Accurate prediction of cardiovascular disease (CVD) requires multifaceted information
consisting of not only a patient's medical history, but genomic data, symptoms, lifestyle, and …

Aims: The role of inflammation markers in myocarditis is unclear. We assessed the
diagnostic and prognostic correlates of C-reactive protein (CRP) at diagnosis in patients with …