Congestive Heart Failure

Comprehensive 3D Optical Coherence Tomography Dataset for AMD and DME: Facilitating Deep-Learning-Based 3D Segmentation.

Age-related macular degeneration (AMD) and diabetic macular edema (DME) are vision-threatening pathologies for which optical coherence tomography (OCT) provides high-resolution three-dimensional imaging, facilitating comprehensive diagnostic evaluation. Three-dimensional (3D) visualization and precise 3D segmentation of lesions enable accurate assessment of morphology, dimensions, and spatial rela...

Derivation and validation of a machine learning-driven score to predict the diagnostic yield of endomyocardial biopsy.

Despite its low diagnostic yield, endomyocardial biopsy (EMB) remains the gold standard for establishing a definitive diagnosis in many cardiomyopathies. We developed and validated a machine-learning-based score to predict the likelihood of diagnostic EMB using non-invasive data. We retrospectively analyzed 775 heart failure patients who underwent EMB. A random forest algorithm was selected for sc...

Sex differences in hemodynamics and remodeling patterns uncovered by automated Machine-Learning 3D echocardiography in aortic stenosis with preserved ejection fraction.

Women with aortic stenosis (AS) are underdiagnosed and undertreated compared to men and face a higher mortality risk despite similar symptoms and fewe...

Cardiac MR function analysis with DL-based super resolution reconstruction: application in the clinical setting.

To assess differences in volumetry, image quality and acquisition time between balanced steady-state free precession cine sequences acquired using (a)...

Quantitative Assessment of Fluorescein Angiography Leakage via Deep Learning in Pediatric Uveitis: Correlation with Clinical Parameters.

PURPOSE: Develop a deep learning algorithm for automated segmentation of retinal vascular and macular edema (ME) leakage in fluorescein angiography (F...

Sequential glioblastoma segmentation via topological data analysis and spatial adjacency.

Segmenting glioblastoma in medical imaging remains challenging due to the tumor's irregular shape, heterogeneous texture, and poorly defined boundarie...

Real-world performance of an AI system for diabetic retinopathy screening.

Diabetic retinopathy (DR) is a leading cause of preventable blindness, and the growing global burden of diabetes is placing increasing pressure on oph...

A large language model for complex cardiology care.

The scarcity of subspecialist medical expertise poses a considerable challenge for healthcare delivery. This issue is particularly acute in cardiology...

Intravitreal faricimab in patients with refractory diabetic macular edema: 6-month fluid analysis using artificial intelligence.

PURPOSE: Diabetic macular edema (DME) is a leading cause of vision loss in patients with diabetes. In developed countries, intravitreal (IVT) anti-vas...

RVO-ME: A Dual-Task OCT Dataset for Segmentation and Detection of Macular Lesions in Retinal Vein Occlusion.

Retinal vein occlusion (RVO) is one of the most common vision-threatening retinal diseases, with macular edema (ME) as its primary complication. Optic...

Artificial Intelligence-Assisted Detection of Amyloid-Related Imaging Abnormalities: Promise and Pitfalls.

The advent of anti-amyloid therapies (AATs) for Alzheimer disease (AD) has elevated the importance of MRI surveillance for amyloid-related imaging abn...

ECG and PPG Signals-Based Premature Ventricular Contraction Detection Methods: A Review, Key Challenges, and Future Directions.

Premature ventricular contraction (PVC) is a common cardiac arrhythmia, and its timely and automated detection is crucial for preventing life-threaten...

Development and multicentre validation of an artificial intelligence electrocardiogram model for ventricular remodeling in repaired tetralogy of Fallot.

AIMS: Periodic cardiac MRI (CMR) is recommended to identify adverse ventricular remodelling in repaired tetralogy of Fallot (TOF), but access to CMR i...

Endoscopic Diagnosis of Eosinophilic Esophagitis Using a Multi-Task U-Net: A Pilot Study.

PURPOSE: Endoscopically identifying eosinophilic esophagitis (EoE) is difficult due to its rare incidence and subtle morphology. We aimed to develop a...

Establishment and validation of a machine learning model to stratify malnutrition risk in hospitalized older patients with chronic heart failure.

BACKGROUND AND OBJECTIVES: Malnutrition among older hospitalized adults with chronic heart failure (CHF) is associated with adverse clinical outcomes,...

Automatic lymphedema segmentation in T2-STIR MRI using an unsupervised clustering method.

PURPOSE: To develop and evaluate an unsupervised artificial intelligence (AI)-based method for the automated segmentation and quantitative assessment ...

COL14A1 drives ischemic cardiac injury in PCOS: an artificial intelligence-identified biomarker.

BACKGROUND: Polycystic ovary syndrome (PCOS) and ischemic cardiomyopathy (ICM) share metabolic and cardiovascular risk factors, including insulin resi...

A novel explainable deep-learning approach for network analysis of epistatic interactions.

Epistatic interactions of gene loci often determine complex trait phenotypes and may indicate the underlying molecular mechanisms of traits and diseas...

Prognostic Value of Artificial Intelligence-Enabled Electrocardiography-Derived Diastolic Dysfunction Grading and Trajectory in Patients Undergoing Transcatheter Aortic Valve Replacement.

BACKGROUND: Artificial intelligence (AI)-enabled electrocardiography has emerged as a tool for detecting cardiac dysfunction. The prognostic relevance...

First-Trimester Machine Learning to Predict Preeclampsia in Normotensive Pregnancies by American Heart Association Guidelines.

This study aimed to determine whether unsupervised machine learning can identify phenotypically distinct subgroups at increased risk for preeclampsia ...