Clinical trials: Electrocardiography Education
High-risk Cardiac Patients
Registry Study for the Evaluation of High-risk Cardiac Patients by WILLEM AI-based ECG Platform
The WILLEM Registry is a large-scale, single-group, observational, registry study to collect continuous clinical evidence of Willem in real-world settings.
- Trial name
- WILLEMRegistry (NCT07333547)
- Testing
- Willem AI ECG assessment
- Where
- 5 sites in United States, Ecuador and 1 more
- Volunteers
- 200,000 people, healthy volunteers welcome
- Sponsor
- Idoven 1903 S.L.
Heart Failure, Heart Failure With Reduced Ejection Fraction
AI-ECG for Time-Resolved Prediction of HFrEF
This study aims to develop and validate a deep learning-based electrocardiogram (ECG) model for predicting the future risk of heart failure with reduced ejection fraction (HFrEF).
- Conditions
- Heart Failure, Heart Failure With Reduced Ejection Fraction, Left Ventricular Dysfunction, Cardiac Remodeling
- Trial name
- NCT07519434
- Where
- China
- Volunteers
- 286,709 people, healthy volunteers welcome
- Sponsor
- Shanghai Zhongshan Hospital
Nursing Education
Virtual Reality Versus Computer-Based Simulation in Electrocardiogram Education
The goal of this clinical trial is to compare the effects of immersive virtual reality (VR) simulation and computer-based simulation (CBS) with traditional laboratory practice on electrocardiogram (ECG) knowledge, practical…
- Trial name
- VR-CBS-ECG (NCT07468942)
- Testing
- Immersive Virtual Reality Simulation, Computer-Based Simulation, Traditional Laboratory Practice
- Where
- Turkey (Türkiye)
- Volunteers
- 90 people, healthy volunteers welcome
- Sponsor
- Koç University
Heart Disease, Electrocardiograph
DigitHeart Echo Study
Eligible patients will be interviewed by research staff to explain the trial design and rationale. Written informed consent will be obtained from patients who voluntarily agree to participate.
- Conditions
- Heart Disease, Electrocardiograph, Echocardiography
- Trial name
- DigitHeart-SHD (NCT07558109)
- Testing
- echocardiography
- Where
- Hong Kong
- Volunteers
- 478 people, healthy volunteers welcome
- Sponsor
- The University of Hong Kong
Arterial Hypertension, Heart Rhythm Disorders
Screening for Cardiac and Cardiac-associated Pathology Using Single-channel Electrocardiogram
It is a prospective, controlled, single-center, observational, non-randomized study. The study is planned to include at least 4000 patients 18 years old and older in the training sample and 1000 patients over 18 years old in the…
- Conditions
- Arterial Hypertension, Heart Rhythm Disorders, Valvular Heart Diseases, Heart Failure
- Trial name
- NCT07396792
- Volunteers
- 4,000 people, healthy volunteers welcome
- Sponsor
- I.M. Sechenov First Moscow State Medical University
Artificial Intelligence, Electrocardiogram
Artificial Intelligence-enabled ECG Detection of Congenital Heart Disease in Children: a Novel Diagnostic Tool
Congenital heart disease (CHD) is the most common congenital disease in children. The early detection, diagnosis and treatment of CHD in children is of great significance to improve the prognosis and reduce the mortality of…
- Conditions
- Artificial Intelligence, Electrocardiogram, Deep Learning, Congenital Heart Disease in Children
- Trial name
- AI-ECG-CHD (NCT06383546)
- Where
- China
- Volunteers
- healthy volunteers welcome
- Sponsor
- Xinhua Hospital, Shanghai Jiao Tong University School of Medicine
Aortic Valve Stenosis, Mitral Regurgitation
Phono- and Electrocardiogram Assisted Detection of Valvular Disease
The diagnosis of valvular heart disease (VHD), or its absence, invariably requires cardiac imaging. A familiar and inexpensive tool to assist in the diagnosis or exclusion of significant VHD could both expedite access to…
- Conditions
- Aortic Valve Stenosis, Mitral Regurgitation, Heart Murmurs, Valvular Heart Disease
- Trial name
- PEA-Valve (NCT03458806)
- Testing
- AS Algorithm 1, AS Algorithm 2, MR Algorithm 1
- Where
- United States
- Volunteers
- 156 people, healthy volunteers welcome
- Sponsor
- University of California, San Francisco
Myocardial Infarction or Chest Pain
ROMIAE (Rule-Out Acute Myocardial Infarction Using Artificial Intelligence Electrocardiogram Analysis) Trial
This study is a prospective multicenter observational study for external validation and model advancement of a deep learning based 12-lead electrocardiogram analysis algorithm targeting adult patients presenting to the emergency…
- Trial name
- ROMIAE (NCT05435391)
- Where
- South Korea
- Volunteers
- 8,814 people
- Sponsor
- CHA University
Heart Failure
Artificial Intelligence (AI) Analysis of Synchronized Phonocardiography (PCG) and Electrocardiogram(ECG)
The diagnosis of depressed left ventricular ejection fraction (dLVEF) (EF<50%) depends on golden standard ultrasound cardiography (UCG).
- Trial name
- NCT06009718
- Where
- 3 sites in China
- Volunteers
- 3,000 people
- Sponsor
- Ruijin Hospital
Hypertension, Pulmonary, Heart Failure With Reduced Ejection Fraction
Deep Learning Detection of Pulmonary Hypertension and Low Ejection Fraction Via Digital Stethoscope and 3-Lead ECG
This is a prospective, observational study evaluating whether heart sounds (phonocardiograms) and three-lead electrocardiograms (ECGs) recorded using the Eko CORE 500 digital stethoscope can help detect pulmonary hypertension…
- Trial name
- PH ELEFT 2-0 (NCT07087613)
- Testing
- Eko CORE 500 Digital Stethoscope
- Where
- 4 sites in United States
- Volunteers
- 3,850 people
- Sponsor
- Eko Devices, Inc.
Embolic Stroke of Undetermined Source
AI-Based Prediction of Atrial Fibrillation in ESUS Patients With ICM
This study investigates patients with Embolic Stroke of Undetermined Source (ESUS) who have received an Implantable Cardiac Monitor (ICM).
- Trial name
- SMART-ESUS (NCT07347691)
- Where
- 5 sites in South Korea
- Volunteers
- 92 people
- Sponsor
- Inha University Hospital
Artificial Intelligence (AI), Artificial Intelligence (AI) in Diagnosis
A Deep-Learning-Enabled Electrocardiogram for Detecting Pulmonary Hypertension
This study aims to validate the use of an artificial intelligence-enabled electrocardiogram (AI-ECG) to screen for elevated PAP.
- Conditions
- Artificial Intelligence (AI), Artificial Intelligence (AI) in Diagnosis, Hypertension, Pulmonary
- Trial name
- ADDPH (NCT07079592)
- Testing
- AI-ECG Guidance
- Where
- Taiwan
- Volunteers
- 8,666 people
- Sponsor
- National Defense Medical Center, Taiwan
Pulmonary Hypertension
Development of an Algorithm to Detect Pulmonary Hypertension Using an Electronic Stethoscope
The major goal of the study is to determine whether phonocardiography (using the Eko DUO stethoscope which can capture a three lead ECG reading) can present features that relate to the presence of PH diagnosed by echocardiography…
- Trial name
- NCT05873387
- Testing
- Use of Eko DUO stethoscope (paired ECG/PCG)
- Where
- 3 sites in United States
- Volunteers
- 2,420 people, healthy volunteers welcome
- Sponsor
- Eko Devices, Inc.
Pulmnary Hypertension
Detecting Pulmonary Hypertension With the Eko CORE 500 Digital Stethoscope
This prospective, observational study will evaluate whether synchronized heart sound (phonocardiogram, PCG) and three-lead electrocardiogram (ECG) recordings (entered as separate interventions in PRS, though collected together in…
- Trial name
- EKO-PH (NCT07136623)
- Testing
- Eko CORE 500 phonocardiogram (PCG) recording, Eko CORE 500 three-lead electrocardiogram (ECG) recording
- Where
- 2 sites in United States
- Volunteers
- 1,513 people, healthy volunteers welcome
- Sponsor
- Eko Devices, Inc.
Heart Failure, Pulmonary Arterial Hypertension
Non-invasive Pulmonary Artery Prediction (ADOPTS)
A proprietary machine-learning algorithm has been developed to model continuous pulmonary artery pressure (PAP), a physiologic marker of cardiopulmonary function.
- Trial name
- ADOPTS (NCT05641675)
- Testing
- right heart catheterization
- Where
- United States
- Volunteers
- 25 people
- Sponsor
- Silverleaf Medical Sciences INC
Pulmonary Hypertension (Diagnosis)
Applying Artificial Intelligence to the 12 Lead ECG for the Diagnosis of Pulmonary Hypertension: an Observational Study
The goal of this observational study is to apply Artificial Intelligence (AI) and machine learning technology to the resting 12-lead electrocardiogram (ECG) and assess whether it can assist doctors in the early diagnosis of…
- Trial name
- NCT05942859
- Testing
- Artificial Intelligence and Machine Learning technology
- Where
- United Kingdom
- Volunteers
- 600 people, healthy volunteers welcome
- Sponsor
- Royal United Hospitals Bath NHS Foundation Trust
Electrocardiography (ECG), Clinical Competence
Gamification Versus E-Learning for Electrocardiogram Education
This randomized controlled study aims to compare the effectiveness of a gamification educational method and an e-learning educational method for electrocardiogram (ECG) education.
- Trial name
- NCT07646236
- Testing
- Gamification Educational Method, E-learning Educational Method
- Where
- Taiwan
- Volunteers
- 297 people, healthy volunteers welcome
- Sponsor
- Taiwan Municipal An-Nan Hospital-China Medical University
Electrocardiography, Clinical Competence
Storyline-Based Gamified Blended Learning for ECG Interpretation in Nursing Students
The goal of this randomized controlled trial is to determine whether a Storyline-based gamified blended learning (SGBL) intervention improves electrocardiogram (ECG) interpretation competence and self-reported clinical…
- Trial name
- SGBL-ECG (NCT07857369)
- Testing
- Storyline-based Gamified Blended Learning, Conventional Face-to-Face Instruction
- Where
- Iran
- Volunteers
- 66 people, healthy volunteers welcome
- Sponsor
- Qom University of Medical Sciences
Epilepsy, Temporal Lobe, Drug Resistant Epilepsy
Studying Heart Rhythms With a Wearable Sensor to Detect and Predict Seizures in People With Epilepsy
The goal of this observational study is to understand heart rhythm changes in adults with epilepsy. It will also explore whether computer programs can use heart recordings to detect or predict seizures.
- Trial name
- EpHR-DP (NCT07835594)
- Testing
- Textile-based wearable electrocardiography
- Where
- Turkey (Türkiye)
- Volunteers
- 40 people, healthy volunteers welcome
- Sponsor
- Acibadem University
Nursing Students, Nursing Skills
The Effect of Simulation-Based ECG Training on ECG Reading Skills of Nursing Students
Accurately analyzing ECG rhythms is a critical competency for nursing students. However, many students fail to achieve an adequate level of knowledge and skill in this area.
- Conditions
- Nursing Students, Nursing Skills, Simulation Based Learning, Simulation Training
- Trial name
- ECGNURSİNG (NCT07533838)
- Testing
- Simulation-based ECG training, Training
- Where
- Turkey (Türkiye)
- Volunteers
- 31 people, healthy volunteers welcome
- Sponsor
- Bartın Unıversity