Latest AI and machine learning research in myocardial infarction for healthcare professionals.
BACKGROUND: The electrocardiogram (ECG) screening in athletes is essential due to the unique cardiac adaptations induced by intensive training. However, differentiating between physiological adaptations and pathological abnormalities remains a significant challenge, particularly when considering variations across different sports, ages, and genders.
Transcranial direct current stimulation (tDCS) as a non-invasive stimulation is still in the experimental stage for many psychiatric disorders even in adults. The use of tDCS provides an opportunity to influence neural networks and their functional connectivity. How tDCS affects cortical networks and how it influences the functional connectivity of the developing brain is largely unknown. The elec...
BACKGROUND: Chronic obstructive pulmonary disease (COPD), marked by dyspnea, cough, and sputum production, significantly impairs patients' quality of ...
OBJECTIVE: In this paper we develop and evaluate ECG-SMART-NET for occlusion myocardial infarction (OMI) identification. OMI is a severe form of heart...
Electrocardiogram (ECG) is a common non-invasive diagnostic tool for cardiovascular diseases. Adequate data is crucial in utilizing deep learning to a...
Arrhythmia classifiers relying on supervised deep learning models usually require a substantial amount of labeled clinical data. The distribution of t...
Recent advancements in computed tomography have significantly transformed the clinical application of this technique in diagnosing and managing corona...
Cardiogenic shock (CS) is a life-threatening complication of ST-elevation myocardial infarction (STEMI) and remains the leading cause of in-hospital ...
Biosensor-based, real-time stress detection has generated clinical interest for the purpose of driving just-in-time interventions that support recover...
BACKGROUND: The long-term success rate of atrial fibrillation (AF) ablation remains a significant clinical challenge, particularly in patients with pe...
This study presents a real-time implementation of an accelerated Hurst Contour Projection from Multiscale Multifractal Analysis (HCP-MMA) for deep lea...
Rapid respiratory rate (RR) changes in older adults may indicate serious illness. Therefore, accurately estimating RR for cardiorespiratory fitness is...
Atrial fibrillation (AF) has been a prevalent and serious arrhythmia associated with increased morbidity and mortality worldwide. The Electrocardiogra...
Integrating Artificial Intelligence (AI) with electrocardiograms (ECG) represents a transformative shift in cardiovascular medicine, marking a modern ...
Coronary CT angiography (CCTA) has seen steady progress since its inception, becoming a key player in the non-invasive assessment of coronary artery d...
INTRODUCTION: Premature Ventricular Contractions (PVCs) can be warning signs for serious cardiac conditions, and early detection is essential for prev...
The aim was to determine the profile of long-term symptoms after known and undetected SARS-CoV-2 infections and to generate tools for risk and diagnos...
Deep learning-based electrocardiogram (ECG) classification has shown impressive performance but clinical adoption has been slowed by the lack of trans...
: While early risk stratification in STEMI is essential, the threat of cardiogenic shock (CS) persists after revascularization due to reperfusion inju...
Obstructive sleep apnea-hypopnea syndrome (OSAHS) is one of the most common sleep disorders affecting nearly one billion of the global adult populatio...