Academic Articles & Research Papers

Deep learning for cardiovascular medicine: a practical primer

Deep learning (DL) is a branch of machine learning (ML) showing increasing promise in medicine, to assist in data classification, novel disease phenotyping and complex decision making. Deep learning is a form of ML typically implemented via...

Deep Learning for Cardiac Image Segmentation: A Review

Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalit...

A deep learning-driven pipeline for differentiating hypertrophic cardiomyopathy from cardiac amyloidosis using 2D multi-view echocardiography

Abstract Hypertrophic Cardiomyopathy (HCM) and Cardiac Amyloidosis (CA) are two cardiac conditions that can advance to heart failure if left untreated, which present considerable diagnostic challenges due to their overlapping echocardiograp...

An Automated Image-Based Dietary Assessment System for Mediterranean Foods

Goal: The modern way of living has significantly influenced the daily diet. The ever-increasing number of people with obesity, diabetes and cardiovascular diseases stresses the need to find tools that could help in the daily intake of the n...

A Cardiovascular Disease Prediction Model Based on Routine Physical Examination Indicators Using Machine Learning Methods: A Cohort Study

BackgroundCardiovascular diseases (CVD) are currently the leading cause of premature death worldwide. Model-based early detection of high-risk populations for CVD is the key to CVD prevention. Thus, this research aimed to use machine learni...

Identification and Predictors for Cardiovascular Disease Risk Equivalents Among Adults With Diabetes.

Machine learning in cardiovascular medicine: are we there yet?

Artificial intelligence (AI) broadly refers to analytical algorithms that iteratively learn from data, allowing computers to find hidden insights without being explicitly programmed where to look. These include a family of operations encomp...

Machine Learning and the Future of Cardiovascular Care

The role of physicians has always been to synthesize the data available to them to identify diagnostic patterns that guide treatment and follow response. Today, increasingly sophisticated machine learning algorithms may grow to support clin...

Development and web-based implementation of a machine learning model for predicting one-year adverse outcomes in acute ischaemic stroke

BackgroundThis study aimed to develop an interpretable and clinically applicable machine learning prediction model to assess the risk of adverse outcomes within one year following acute ischaemic stroke, and to establish a corresponding web...

Impact of environmental factors on cardiovascular health in Castilla-La Mancha: the CarVasCLiMa cohort study protocol.

Using generalized random forests to characterize vulnerability to adverse health outcomes following wildfire smoke exposure in California

Background: As the health burden attributable to wildfire activity increases under climate change, it is crucial to determine which subgroups face heightened vulnerability to wildfire smoke. Marginalized communities may experience dispropor...

Classification of bioactive peptides: A systematic benchmark of models and encodings

Bioactive peptides are short amino acid chains possessing biological activity and exerting physiological effects relevant to human health. Despite their therapeutic value, their identification remains a major problem, as it mainly relies on...

Identification of arachidonic acid metabolism-related diagnostic markers in heart failure based on bioinformatics analysis and machine learning

BackgroundHeart failure (HF) represents the terminal phase of multiple cardiovascular conditions and is associated with significant morbidity and mortality rates. Arachidonic acid (AA), an essential fatty acid, plays a crucial role in modul...

Artificial intelligence in cardiology

Precision Prevention of Premature Cardiovascular Disease Among African Men: The Triple-Risk Convergence Framework Integrating Psychosocial, Metabolic, and Inherited Cardiovascular Susceptibility.

Low-Field MRI Reduces Cost and Environmental Impact While Maintaining Diagnostic Performance in Resource-Limited Settings: A Narrative Review With Cardiovascular Focus.

Risk of Ischemic Heart Disease in Patients With Postpancreatectomy Diabetes and Pancreatic Cancer: A Population‐Based Study

Background Postpancreatectomy diabetes can be caused by resection of functioning pancreatic tissue and is associated with postoperative pancreatic islet cell mass loss and subsequent endocrine dysfunction. Diabetes is a well‐known risk fact...

Sex-based differences in characteristics, management, and outcomes in heart failure with reduced ejection fraction

Heart failure with reduced ejection fraction (HFrEF) exhibits significant sex-based differences in clinical presentation, management, and outcomes. This study aimed to evaluate these differences using data from the Swedish Heart Failure Reg...

Artificial Intelligence in Precision Cardiovascular Medicine

Detection and Classification of Abnormities of First Heart Sound Using Empirical Wavelet Transform

It is expected that an automatic detection and classification algorithm for the abnormities of first heart sound (S1) can realize computer artificial intelligence diagnosis of some relative cardiovascular disease. Few studies have focused o...