Congestive Heart Failure

Using Artificial Intelligence to Assess Treatment-Effect Heterogeneity in Pragmatic Cardiovascular Trials: Insights from TRANSFORM-HF

Background and Aims: Pragmatic clinical trials are designed to assess interventions in real-world settings, and their broad inclusion criteria and clinical variability create valuable opportunities for exploring heterogeneity of treatment effects. In this context, TRANSFORM-HF was a pragmatic trial that found no overall survival difference between torsemide and furosemide in patients hospitalized ...

Deep learning enables diagnosis of atrial cardiomyopathy from routine 12-lead electrocardiogram

BackgroundAtrial cardiomyopathy (AtCM) is both a cause and a consequence of atrial fibrillation and flutter (AF) and can lead to ischemic stroke. Imaging derived left atrial (LA) structure and function are used to diagnose AtCM. Considering the tight coupling of heart structure and rhythm generation, this information might also be derived from 12-lead electrocardiogram (ECG), which is low-cost and...

Utilizing artificial intelligence and medical experts to identify predictors for common diagnoses in dyspneic adults: A cross-sectional study of consecutive emergency department patients from Southern Sweden.

OBJECTIVE: Half of all adult emergency department (ED) visits with a complaint of dyspnea involve acute heart failure (AHF), exacerbation of chronic o...

Metabolomic machine learning predictor for arsenic-associated hypertension risk in male workers.

Arsenic (As)-induced hypertension is a significant public health concern, highlighting the need for early risk prediction. This study aimed to develop...

Efficacy of Image Similarity as a Metric for Augmenting Small Dataset Retinal Image Segmentation

Synthetic images are an option for augmenting limited medical imaging datasets to improve the performance of various machine learning models. A comm...

CineMyoPS: Segmenting Myocardial Pathologies from Cine Cardiac MR

Myocardial infarction (MI) is a leading cause of death worldwide. Late gadolinium enhancement (LGE) and T2-weighted cardiac magnetic resonance (CMR)...

SD-Acc: Accelerating Stable Diffusion through Phase-aware Sampling and Hardware Co-Optimizations

The emergence of diffusion models has significantly advanced generative AI, improving the quality, realism, and creativity of image and video genera...

MTCNet: Motion and Topology Consistency Guided Learning for Mitral Valve Segmentationin 4D Ultrasound

Mitral regurgitation is one of the most prevalent cardiac disorders. Four-dimensional (4D) ultrasound has emerged as the primary imaging modality fo...

Physics-Informed Neural ODEs for Temporal Dynamics Modeling in Cardiac T1 Mapping

Spin-lattice relaxation time ($T_1$) is an important biomarker in cardiac parametric mapping for characterizing myocardial tissue and diagnosing car...

Evaluating the Prognostic Value of Kansas City Cardiomyopathy Questionnaire (KCCQ) Scores for 6-Month Readmissions in Southeast Asian Populations With Heart Failure.

BACKGROUND: Heart failure (HF) is a prevalent cause of hospital readmissions. Our study aims to determine the correlation between the Kansas City Card...

Precision medicine applications in dilated cardiomyopathy: Advancing personalized care.

Dilated cardiomyopathy (DCM) is a prevalent cardiac disorder affecting 1 in 250-500 individuals, characterized by ventricular dilation and impaired sy...

Machine learning risk-prediction model for in-hospital mortality in Takotsubo cardiomyopathy.

BACKGROUND: Takotsubo cardiomyopathy (TC) is an acute heart failure syndrome characterized by transient left ventricular dysfunction, often triggered ...

Patient Similarity Computation for Clinical Decision Support: An Efficient Use of Data Transformation, Combining Static and Time Series Data

Patient similarity computation (PSC) is a fundamental problem in healthcare informatics. The aim of the patient similarity computation is to measure...

Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data

Postoperative stroke remains a critical complication in elderly surgical intensive care unit (SICU) patients, contributing to prolonged hospitalizat...

Machine learning applied to wearable fitness tracker data and the risk of hospitalizations and cardiovascular events.

BACKGROUND: Wearable fitness trackers generate extensive physiological and activity data, offering potential to monitor health and predict outcomes. M...

Deep Learning Algorithms to Predict Differential Renal Function <40% in Unilateral Hydronephrosis Based on Key Parameters of Urinary Tract Ultrasound.

OBJECTIVE: To identify the correlation between ultrasound findings and the incidence of differential renal function (DRF) <40%, we conducted an analys...

From Acquisition to Prognosis: The Role of AI in Cardiac Magnetic Resonance Imaging Evaluation of Ischemic Cardiomyopathy.

Acute and chronic ischemic cardiomyopathy (ICM) still represents a leading cause of morbidity and mortality. Cardiac magnetic resonance (CMR) imaging ...

Machine Learning Assessment of Gestational Age in Accelerated Maturation, Delayed Maturation, Villous Edema, Chorangiosis, and Intrauterine Fetal Demise.

CONTEXT.—: Assessment of placental villous maturation is among the most common tasks in perinatal pathology. However, the significance of abnormalitie...

AI driven cardiovascular risk prediction using NLP and Large Language Models for personalized medicine in athletes.

The performance and long-term health of athletes are significantly influenced by their cardiovascular resilience and associated risk factors. This stu...

Eigenhearts: Cardiac diseases classification using eigenfaces approach.

In the realm of cardiovascular medicine, medical imaging plays a crucial role in accurately classifying cardiac diseases and making precise diagnoses....