Clinical trials

This multicenter retrospective study aims to develop, validate, and clinically apply an artificial intelligence (AI)-powered electrocardiography (ECG) algorithm for the diagnosis and prognostic prediction of cardiovascular diseases, including coronary artery disease and heart failure. By utilizing encrypted raw 12-lead ECG text data and deep neural network models, the study evaluates the effectiveness of AI-enhanced ECG in detecting cardiac abnormalities and predicting clinical outcomes.

This multicenter retrospective study aims to develop, validate, and clinically apply an artificial intelligence (AI)-powered electrocardiography (ECG) algorithm for the diagnosis and prognostic prediction of cardiovascular diseases, including coronary artery disease and heart failure. By utilizing encrypted raw 12-lead ECG text data and deep neural network models, the study evaluates the effectiveness of AI-enhanced ECG in detecting cardiac abnormalities and predicting clinical outcomes.

This pilot study is designed to evaluate the clinical performance and utility of the SmartECG-EF software in patients undergoing evaluations at Inha University Hospital. The primary objective is to assess how effectively the device assists clinicians in evaluating cardiac function and related clinical parameters. The study will prospectively enroll eligible participants to gather real-world clinical data, contributing to enhanced diagnostic accuracy and patient management.

Sarcopenic dysphagia is defined as swallowing difficulty among the elderly due to the loss of whole body skeletal and swallowing muscle mass and function. However, the pathophysiology and dynamics of swallowing in sarcopenic dysphagia have been poorly investigated. Therefore, the present study aims to investigate the characteristics of sarcopenic dysphagia using the Videofluoroscopic study (VFSS) focusing on each phase of dysphagia, and surface Electromyography (surface EMG) to assess suprahyoid muscle activity. In sarcopenic dysphagia, impairments will occur in both the oral and pharyngeal phases, particularly affecting bolus formation, premature bolus spillage and laryngeal elevation during swallowing due to the loss of swallowing muscle mass and function. These changes will be considered to have led to an change of duration and amplitude of suprahyoid muscle activity measured via surface EMG.

This study investigates patients with Embolic Stroke of Undetermined Source (ESUS) who have received an Implantable Cardiac Monitor (ICM). The main purpose is to evaluate the predictive value of an Artificial Intelligence ECG analysis tool, named SmartECG-AF. Participants will be classified into two groups based on the AI analysis: a "High Risk" group and a "Low to Intermediate Risk" (control) group. The study aims to compare the incidence rate of atrial fibrillation (AF) events over time between these two groups. Additionally, the study will analyze the relationship between the AI-predicted risk levels and the occurrence of major cardiovascular events during the follow-up period.

The purpose of this study is to compare two commonly used types of medicine for treating atrial fibrillation (also called AF), a condition that causes an irregular heart rhythm. This study is for adults who have been diagnosed with AF within the past year. Researchers want to find out which medicine is more effective, safer, and provides a better quality of life for patients needing to control their heart rhythm. Participants who agree to join the study will be randomly assigned (like flipping a coin) to one of two groups: Group 1: Will receive the medicine Dronedarone. Group 2: Will receive a standard medicine from the 'Class Ic' group (such as flecainide or propafenone). The study will follow participants for at least 12 months. Researchers will compare how well each medicine works to prevent AF from coming back. They will also carefully track any side effects and changes in participants' quality of life during the study.