Congestive Heart Failure

Cardiomyopathy Diagnosis Model from Endomyocardial Biopsy Specimens: Appropriate Feature Space and Class Boundary in Small Sample Size Data

As the number of patients with heart failure increases, machine learning (ML) has garnered attention in cardiomyopathy diagnosis, driven by the shortage of pathologists. However, endomyocardial biopsy specimens are often small sample size and require techniques such as feature extraction and dimensionality reduction. This study aims to determine whether texture features are effective for feature...

Evaluation of state-of-the-art deep learning models in the segmentation of the heart ventricles in parasternal short-axis echocardiograms

Previous studies on echocardiogram segmentation are focused on the left ventricle in parasternal long-axis views. In this study, deep-learning models were evaluated on the segmentation of the ventricles in parasternal short-axis echocardiograms (PSAX-echo). Segmentation of the ventricles in complementary echocardiogram views will allow the computation of important metrics with the potential to a...

A Deep Bayesian Nonparametric Framework for Robust Mutual Information Estimation

Mutual Information (MI) is a crucial measure for capturing dependencies between variables, but exact computation is challenging in high dimensions w...

Task-oriented Uncertainty Collaborative Learning for Label-Efficient Brain Tumor Segmentation

Multi-contrast magnetic resonance imaging (MRI) plays a vital role in brain tumor segmentation and diagnosis by leveraging complementary information...

CORDIC Is All You Need

Artificial intelligence necessitates adaptable hardware accelerators for efficient high-throughput million operations. We present pipelined architec...

Estimating Blood Pressure with a Camera: An Exploratory Study of Ambulatory Patients with Cardiovascular Disease

Hypertension is a leading cause of morbidity and mortality worldwide. The ability to diagnose and treat hypertension in the ambulatory population is...

Artificial Intelligence-Guided Lung Ultrasound by Nonexperts.

IMPORTANCE: Lung ultrasound (LUS) aids in the diagnosis of patients with dyspnea, including those with cardiogenic pulmonary edema, but requires techn...

A Non-contrast Head CT Foundation Model for Comprehensive Neuro-Trauma Triage

Recent advancements in AI and medical imaging offer transformative potential in emergency head CT interpretation for reducing assessment times and i...

T1-PILOT: Optimized Trajectories for T1 Mapping Acceleration

Cardiac T1 mapping provides critical quantitative insights into myocardial tissue composition, enabling the assessment of pathologies such as fibros...

RURANET++: An Unsupervised Learning Method for Diabetic Macular Edema Based on SCSE Attention Mechanisms and Dynamic Multi-Projection Head Clustering

Diabetic Macular Edema (DME), a prevalent complication among diabetic patients, constitutes a major cause of visual impairment and blindness. Althou...

On Aggregation Queries over Predicted Nearest Neighbors

We introduce Aggregation Queries over Nearest Neighbors (AQNNs), a novel type of aggregation queries over the predicted neighborhood of a designated...

Subclass Classification of Gliomas Using MRI Fusion Technique

Glioma, the prevalent primary brain tumor, exhibits diverse aggressiveness levels and prognoses. Precise classification of glioma is paramount for t...

TabulaTime: A Novel Multimodal Deep Learning Framework for Advancing Acute Coronary Syndrome Prediction through Environmental and Clinical Data Integration

Acute Coronary Syndromes (ACS), including ST-segment elevation myocardial infarctions (STEMI) and non-ST-segment elevation myocardial infarctions (N...

Denoising, segmentation and volumetric rendering of optical coherence tomography angiography (OCTA) image using deep learning techniques: a review

Optical coherence tomography angiography (OCTA) is a non-invasive imaging technique widely used to study vascular structures and micro-circulation d...

A Flexible Precision Scaling Deep Neural Network Accelerator with Efficient Weight Combination

Deploying mixed-precision neural networks on edge devices is friendly to hardware resources and power consumption. To support fully mixed-precision ...

Artificial intelligence-guided detection of under-recognised cardiomyopathies on point-of-care cardiac ultrasonography: a multicentre study.

BACKGROUND: Point-of-care ultrasonography (POCUS) enables cardiac imaging at the bedside and in communities but is limited by abbreviated protocols an...

ScarNet: A Novel Foundation Model for Automated Myocardial Scar Quantification from LGE in Cardiac MRI

Background: Late Gadolinium Enhancement (LGE) imaging is the gold standard for assessing myocardial fibrosis and scarring, with left ventricular (LV...

Calcium Binding Affinity in the Mutational Landscape of Troponin-C: Free Energy Calculation, Coevolution Modeling and Machine Learning

Mutation in calcium-binding proteins (CBPs) can significantly influence Ca2+ binding affinity (BA), resulting in substantial impairment in the signali...

A Machine Learning Approach to Predicting Dyspnea with Noninvasive Biomarkers

Dyspnea is the subjective sensation of breathing discomfort. This symptom is highly prevalent in patients with chronic and critical illness, and its p...

Uncovering genetic architecture of the heart via genetic association studies of unsupervised deep learning derived endophenotypes

Recent genome-wide association studies (GWAS) have effectively linked genetic variants to quantitative traits derived from time-series cardiac magneti...