Latest AI and machine learning research in congestive heart failure for healthcare professionals.
Retinal fluids, detectable through optical coherence tomography (OCT), are key biomarkers for retinal diseases such as diabetic macular edema and age-related macular degeneration, guiding treatment decisions and monitoring response to therapy. Automated segmentation of retinal fluids could support large-scale clinical research and the development of clinical decision support tools. Recent ophthalm...
Background: Mechanical ventricular unloading and systemic circulatory support with left ventricular assist devices (LVADs) enable myocardial recovery in a subset of advanced heart failure (HF) patients, but predictors and mechanisms of recovery are not well understood. Integrating clinical and molecular data may improve identification of patients most likely to recover and uncover biologically rel...
Backgound: Accurate assessment of diastolic function and left ventricular (LV) filling pressure is central to heart failure diagnosis and risk stratif...
Multimodal learning has the potential to improve clinical prediction by integrating complementary data sources, but the incremental value of imaging b...
Introduction: Accurate stratification of hard atherosclerotic cardiovascular disease (ASCVD) risk remains challenging despite advances in prevention. ...
Radiotherapy-induced normal tissue injury is a clinically important complication, and accurate segmentation of injury regions from medical images coul...
Retinal Cysts are formed by leakage and accumulation of fluid in the retina due to the incompetence of retinal vasculature. These cystic spaces have s...
Clinicians lack precision medicine tools to estimate individualized treatment effects for patients with heart failure (HF). Causal machine learning le...
Cardiovascular modeling has rapidly advanced over the past few decades due to the rising needs for health tracking and early detection of cardiovascul...
Background: Transthyretin cardiomyopathy (ATTR-CM) is a progressive, potentially fatal disease requiring accurate risk stratification. Echocardiograph...
Aims: Dynamic left ventricular outflow tract obstruction (LVOTO) is a hemodynamically significant complication following transcatheter aortic valve re...
Identification and quantification of myocardial scar is important for diagnosis and prognosis of cardiovascular diseases. However, reliable scar segme...
Hypertrophic cardiomyopathy (HCM) requires accurate risk stratification to inform decisions regarding ICD therapy and follow-up management. Current es...
Implicit neural representations (INRs) have emerged as a powerful framework for continuous image representation learning. In Functa-based approaches, ...
Sleep disturbances lead to risk for cardiovascular (CV), metabolic, and neurological diseases. While in-lab polysomnography (PSG) is the gold standard...
Computer-aided segmentation of brain tumors from MRI data is of crucial significance to clinical decision-making in diagnosis, treatment planning, and...
Background: Pediatric dilated cardiomyopathy (DCM) is a rare, progressive heart disease with variable outcomes that range from recovery to heart trans...
Background: Anemia is nearly ubiquitous in hospitalized patients with congestive heart failure (CHF), yet little data informs the decision to transfus...
Introduction: Cardiovascular magnetic resonance (CMR) is the reference standard for quantifying cardiac structure and function, yet widely applicable ...
Abstract Background: Echocardiography (echo) notes contain valuable prognostic information for patients in the intensive care unit (ICU). However, the...