Traditional health systems mostly rely on rules created by experts to offer adaptive
interventions to patients. However, with recent advances in artificial intelligence (AI) and …

Machine learning has become a key driver of the digital health revolution. That comes with a
fair share of high hopes and hype. We conducted a scoping review on machine learning in …

This paper proposes an automated diagnostic tool namely, Auto-MyIn, for diagnosing
myocardial infarction (MI) using multiple convolutional neural networks (CNN). Rather than …

A U-shaped contraction pattern was shown to be associated with a better Cardiac
resynchronization therapy (CRT) response. The main goal of this study is to automatically …

This study aimed to investigate the diagnostic performance of machine learning-based
radiomics analysis to diagnose coronary artery disease status and risk from rest/stress …

Directed by 2 decades of technological processes and remodeling, the dynamic quality of
healthcare data combined with the progress of computational power has allowed for rapid …

As the convergence of AI and the Internet of Medical Things (IoMT) continues to gain
momentum, the Artificial Intelligence of Internet of Medical Things (AIoMT) paradigm has …

Purpose Deep learning has shown great promise as the backbone of clinical decision
support systems. Synthetic data generated by generative models can enhance the …

Objectives This study investigated the discriminability of quantitative radiomics features
extracted from cardiac magnetic resonance (CMR) images for hypertrophic cardiomyopathy …

Cardiac amyloidosis is an uncommon restrictive cardiomyopathy featuring an unregulated
amyloid protein deposition that impairs organic function. Early cardiac amyloidosis …