Congestive Heart Failure

Predicting Diabetic Macular Edema Treatment Responses Using OCT: Dataset and Methods of APTOS Competition

Diabetic macular edema (DME) significantly contributes to visual impairment in diabetic patients. Treatment responses to intravitreal therapies vary, highlighting the need for patient stratification to predict therapeutic benefits and enable personalized strategies. To our knowledge, this study is the first to explore pre-treatment stratification for predicting DME treatment responses. To advanc...

Predicting the efficacy of bevacizumab on peritumoral edema based on imaging features and machine learning.

This study proposes a novel approach to predict the efficacy of bevacizumab (BEV) in treating peritumoral edema in metastatic brain tumor patients by integrating advanced machine learning (ML) techniques with comprehensive imaging and clinical data. A retrospective analysis was performed on 300 patients who received BEV treatment from September 2013 to January 2024. The dataset incorporated 13 pre...

A Dataset and Toolkit for Multiparameter Cardiovascular Physiology Sensing on Rings

Smart rings offer a convenient way to continuously and unobtrusively monitor cardiovascular physiological signals. However, a gap remains between th...

IntelliCardiac: An Intelligent Platform for Cardiac Image Segmentation and Classification

Precise and effective processing of cardiac imaging data is critical for the identification and management of the cardiovascular diseases. We introd...

Cardiovascular function changes following lung resection: a computational model to compare afterload increase and contractility loss mechanisms

Functional limitation after lung resection surgery has been consistently documented in clinical studies, and right ventricle (RV) dysfunction has be...

Deep Learning-based Aligned Strain from Cine Cardiac MRI for Detection of Fibrotic Myocardial Tissue in Patients with Duchenne Muscular Dystrophy.

Purpose To develop a deep learning (DL) model that derives aligned strain values from cine (noncontrast) cardiac MRI and evaluate performance of these...

Machine Learning Assisted Stroke Prediction in Mechanical Circulatory Support: Predictive Role of Systemic Mitochondrial Dysfunction.

Stroke continues to be a major adverse event in advanced congestive heart failure (CHF) patients after continuous-flow left ventricular assist device ...

Risk Stratification of Left Ventricle Hypertrabeculation Versus Non-Compaction Cardiomyopathy Using Echocardiography, Magnetic Resonance Imaging, and Cardiac Computed Tomography.

Non-compaction cardiomyopathy (NCCM) is a rare, congenital form of cardiomyopathy characterized by excessive trabeculations in the left ventricle myoc...

Anatomy-derived 3D Aortic Hemodynamics Using Fluid Physics-informed Deep Learning.

Background Four-dimensional (4D) flow MRI provides assessment of thoracic aorta hemodynamic measures that are increasingly recognized as important bio...

Expert consensus document on artificial intelligence of the Italian Society of Cardiology.

Artificial intelligence (AI), a branch of computer science focused on developing algorithms that replicate intelligent behaviour, has recently been us...

Accelerated 3D-3D rigid registration of echocardiographic images obtained from apical window using particle filter

The perfect alignment of 3D echocardiographic images captured from various angles has improved image quality and broadened the field of view. This s...

FineQ: Software-Hardware Co-Design for Low-Bit Fine-Grained Mixed-Precision Quantization of LLMs

Large language models (LLMs) have significantly advanced the natural language processing paradigm but impose substantial demands on memory and compu...

NSFlow: An End-to-End FPGA Framework with Scalable Dataflow Architecture for Neuro-Symbolic AI

Neuro-Symbolic AI (NSAI) is an emerging paradigm that integrates neural networks with symbolic reasoning to enhance the transparency, reasoning capa...

Spectral Bias Correction in PINNs for Myocardial Image Registration of Pathological Data

Accurate myocardial image registration is essential for cardiac strain analysis and disease diagnosis. However, spectral bias in neural networks imp...

SCALE-Sim v3: A modular cycle-accurate systolic accelerator simulator for end-to-end system analysis

The rapid advancements in AI, scientific computing, and high-performance computing (HPC) have driven the need for versatile and efficient hardware a...

Cardiac MRI Semantic Segmentation for Ventricles and Myocardium using Deep Learning

Automated noninvasive cardiac diagnosis plays a critical role in the early detection of cardiac disorders and cost-effective clinical management. Au...

Chest X-ray Classification using Deep Convolution Models on Low-resolution images with Uncertain Labels

Deep Convolutional Neural Networks have consistently proven to achieve state-of-the-art results on a lot of imaging tasks over the past years' major...

Low-Bit Integerization of Vision Transformers using Operand Reodering for Efficient Hardware

Pre-trained vision transformers have achieved remarkable performance across various visual tasks but suffer from expensive computational and memory ...

Electrocardiogram-based deep learning to predict left ventricular systolic dysfunction in paediatric and adult congenital heart disease in the USA: a multicentre modelling study.

BACKGROUND: Left ventricular systolic dysfunction (LVSD) is independently associated with cardiovascular events in patients with congenital heart dise...

Explainable AI-Guided Efficient Approximate DNN Generation for Multi-Pod Systolic Arrays

Approximate deep neural networks (AxDNNs) are promising for enhancing energy efficiency in real-world devices. One of the key contributors behind th...