English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Heart failure is a serious and life-threatening condition that can lead toelevated pressure in the left ventricle. Pulmonary Arterial Wedge Pressure(PAWP) is an important surrogate marker indicating high pressure in the leftventricle. PAWP is determined by Right Heart Catheterization (RHC) but it is aninvasive procedure. A non-invasive method is useful in quickly identifyinghigh-risk patients from a large population. In this work, we develop a tensorlearning-based pipeline for identifying PAWP from multimodal cardiac MagneticResonance Imaging (MRI). This pipeline extracts spatial and temporal featuresfrom high-dimensional scans. For quality control, we incorporate an epistemicuncertainty-based binning strategy to identify poor-quality training samples.To improve the performance, we learn complementary information by integratingfeatures from multimodal data: cardiac MRI with short-axis and four-chamberviews, and Electronic Health Records. The experimental analysis on a largecohort of $1346$ subjects who underwent the RHC procedure for PAWP estimationindicates that the proposed pipeline has a diagnostic value and can producepromising performance with significant improvement over the baseline inclinical practice (i.e., $\Delta$AUC $=0.10$, $\Delta$Accuracy $=0.06$, and$\Delta$MCC $=0.39$). The decision curve analysis further confirms the clinicalutility of our method.

Tensor-based Multimodal Learning for Prediction of Pulmonary Arterial Wedge Pressure from Cardiac MRI