Multi-modality cardiac imaging plays a key role in the management of patients with
cardiovascular diseases. It allows a combination of complementary anatomical …

The aim of the presented review is to summarize the literature data on the accuracy and
clinical applicability of artificial intelligence (AI) models as a valuable alternative to the …

The sole use of single modality data often fails to capture the complex heterogeneity among
patients, including the variability in resistance to anti-HER2 therapy and outcomes of …

Abstract Background and Objective: Survival prediction of heart failure patients is critical to
improve the prognostic management of the cardiovascular disease. The existing survival …

Inspired by the McGurk effect, studies on multimodal data fusion start with audio-visual
speech recognition tasks. Multimodal data fusion research was not popular for a period of …

Multi-modal fusion approaches aim to integrate information from different data sources.
Unlike natural datasets, such as in audio-visual applications, where samples consist of …

The rising prevalence of cardiovascular disease presents an escalating challenge for
current health services, which are grappling with increasing demands. Innovative changes …

Traditional quality control practices in heavy rail manufacturing primarily rely on experiential
knowledge and numerical simulations. However, these methods come with significant …

The rapid evolution of neural networks and deep learning has revolutionized various fields,
with clinical cardiology being no exception. As traditional methods in cardiology encounter …

Multimodal medical imaging plays a pivotal role in clinical diagnosis and research, as it
combines information from various imaging modalities to provide a more comprehensive …