HF - Heart FailureNCT07636759
AI ECG Algorithm for Detecting LV Systolic Dysfunction
An observational study of HF - Heart Failure, run by Ajou University School of Medicine at 1 site in South Korea.
Trial summary
This prospective observational cohort study aims to evaluate the clinical performance of a deep learning-based electrocardiography (ECG) algorithm (DeepECG LVSD) for detecting left ventricular systolic dysfunction (LVSD), defined as left ventricular ejection fraction (LVEF) ≤40%, using transthoracic echocardiography… Read more about this study
- Who is it for?
- Ages 19 years and older
- Conditions the study is for
- HF - Heart Failure
- Could I receive a placebo?
- Yes, some participants may receive a placebo
- How many people
- 1,500 participants
- Where
- 1 siteSouth Korea
- Study dates
- 1 Jan 2026to 31 Dec 2027
- How long will I take part?
- 1 Month
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 19 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Adult patients aged 19 years or older undergoing routine clinical care at Ajou University Hospital who have both transthoracic echocardiography and 12-lead electrocardiography (ECG) performed within 30 days. Participants may be recruited from outpatient clinics, inpatient wards, or the emergency department.
Who may be able to join
- Adults aged ≥19 years.
- Patients who underwent both transthoracic echocardiography and 12-lead electrocardiography (ECG) at Ajou University Hospital in the outpatient, inpatient, or emergency department setting.
- ECG and echocardiography performed within 30 days of each other.
Who cannot join
- Interval between ECG and echocardiography greater than 30 days.
- Missing or corrupted original ECG waveform data (XML or HL7 format).
- Presence of an implanted cardiac device, including a permanent pacemaker, implantable cardioverter-defibrillator (ICD), or cardiac resynchronization therapy (CRT) device.
- Missing age, sex, or left ventricular ejection fraction (LVEF) data.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
South Korea1 site, 1 recruiting
- Recruiting
Ajou University School of Medicine
Suwon, Gyeonggi-do, 16499
MOONSEUNG SOH, MD, +82-31-219-5111, mssoh7701@gmail.com
About this study
Official title: Prospective Observational Cohort Study of Deep Learning-based ECG Algorithm for Detecting Left Ventricular Systolic Dysfunction
This prospective observational cohort study aims to evaluate the clinical performance of a deep learning-based electrocardiography (ECG) algorithm (DeepECG LVSD) for detecting left ventricular systolic dysfunction (LVSD), defined as left ventricular ejection fraction (LVEF) ≤40%, using transthoracic echocardiography as the reference standard. Approximately 15,000 adult patients undergoing both ECG and echocardiography within 30 days at Ajou University Hospital will be enrolled. Diagnostic performance will be assessed using the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, positive predictive value, negative predictive value, and accuracy. Secondary analyses will evaluate the association between AI-predicted LVSD and 30-day clinical outcomes, including all-cause mortality, emergency department visits, and heart failure rehospitalization.
Read the detailed description
Left ventricular systolic dysfunction (LVSD) is associated with an increased risk of heart failure, hospitalization, and mortality. Although transthoracic echocardiography is the standard method for assessing left ventricular ejection fraction (LVEF), its widespread use as a screening tool is limited by availability, cost, and the need for specialized personnel. Artificial intelligence (AI)-based electrocardiography (ECG) algorithms have emerged as promising tools for identifying patients with reduced LVEF using routinely acquired ECG signals.
DeepECG LVSD is a deep learning-based ECG algorithm developed to detect LVSD (LVEF ≤40%) from standard 12-lead ECG recordings. Previous retrospective validation studies demonstrated high diagnostic performance; however, prospective clinical validation in real-world practice remains limited.
The purpose of this prospective observational cohort study is to evaluate the diagnostic performance and clinical utility of DeepECG LVSD in adult patients undergoing both ECG and transthoracic echocardiography at Ajou University Hospital. Approximately 15,000 patients aged 19 years or older who have undergone ECG and echocardiography within 30 days will be enrolled.
The primary objective is to assess the accuracy of the AI algorithm for detecting LVSD using echocardiographic LVEF as the reference standard. Diagnostic performance will be evaluated using the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy.
Secondary objectives include evaluating the association between AI-predicted LVSD and short-term clinical outcomes, including 30-day all-cause mortality, emergency department visits, and heart failure rehospitalization. Exploratory subgroup analyses will assess algorithm performance according to demographic and clinical characteristics, including age, sex, heart failure status, chronic kidney disease, hypertension, diabetes mellitus, and the interval between ECG and echocardiography.
This study is designed as a minimal-risk observational study and will provide prospective evidence regarding the effectiveness of AI-enabled ECG screening for LVSD in routine clinical practice. Findings from this study may support broader implementation of AI-based ECG tools for the early identification of patients at risk for heart failure and reduced left ventricular systolic function.
- Conditions
- HF - Heart Failure
- Being tested
- None-placebo
- Keywords
- Heart failure, ECG, Artificial Intelligence, Deep Learning
- Medical subject headings
- Heart Failure
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
None-placeboOther
There is no intervention group
| Study group | Role | What they receive |
|---|---|---|
| Adults aged ≥19 years with ECG and echocardiography performed within 30 days | Adult patients aged 19 years or older who underwent both transthoracic echocardiography and electrocardiography (ECG) within 30 days of each other Receives: Other: None-placebo |
What is being measured
The primary outcome is the main question the study is designed to answer.
AUROC for detection of LVSD (LVEF ≤40%)
Diagnostic performance including AUROC, sensitivity, specificity, positive predictive value, negative predictive value, and accuracy.
Measured: During procedure
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted9 Jun 2026
- Study start1 Jan 2026
- Primary completion31 Dec 2027 (estimated)
- Study completion31 Dec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (3)Papers the sponsor has linked to this study.
- Other trials for HF - Heart FailureStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 1,500 participants (estimated)
- Target duration
- 1 Month
Sponsor and investigators
- Lead sponsor
- Ajou University School of Medicine
- Sponsor type
- University, hospital or other
- Collaborators
- VUNO Inc.
- Responsible party
- Principal investigator, Moon-Seung Soh, Ajou University School of Medicine
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07636759
- Sponsor's protocol ID
- AJOUIRB-OB-2026-001
- Status last verified
- Jun 2026
- First submitted
- 4 Jun 2026
- Last updated
- 7 Aug 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Individual participant data will not be publicly shared due to patient privacy and institutional data protection policies.
Publications
Lopez-Jimenez F, Alger HM, Attia ZI, Barry B, Chatterjee R, Dolor R, Friedman PA, Greene SJ, Greenwood J, Gundurao V, Hackett S, Jain P, Kinaszczuk A, Mehta K, O'Grady J, Pandey A, Pullins C, Puranik AR, Ranganathan MK, Rushlow D, Stampehl M, Subramanian V, Vassor K, Zhu X, Awasthi S. A multicenter pragmatic implementation study of AI-ECG-based clinical decision support software to identify low LVEF: Clinical trial design and methods. Am Heart J Plus. 2025 Mar 21;54:100528. doi: 10.1016/j.ahjo.2025.100528. eCollection 2025 Jun.
PubMed 40276542Choi J, Lee S, Chang M, Lee Y, Oh GC, Lee HY. Author Correction: Deep learning of ECG waveforms for diagnosis of heart failure with a reduced left ventricular ejection fraction. Sci Rep. 2022 Oct 13;12(1):17191. doi: 10.1038/s41598-022-22012-7. No abstract available.
PubMed 36229570Attia ZI, Kapa S, Lopez-Jimenez F, McKie PM, Ladewig DJ, Satam G, Pellikka PA, Enriquez-Sarano M, Noseworthy PA, Munger TM, Asirvatham SJ, Scott CG, Carter RE, Friedman PA. Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram. Nat Med. 2019 Jan;25(1):70-74. doi: 10.1038/s41591-018-0240-2. Epub 2019 Jan 7.
PubMed 30617318
Study at a glance
From the registry record, last updated by the sponsor on 7 Aug 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Prospective Observational Cohort Study of Deep Learning-based ECG Algorithm for Detecting Left Ventricular Systolic Dysfunction
- ClinicalTrials.gov ID
- NCT07636759
- Acronym / sponsor ID
- AJOUIRB-OB-2026-001
- Conditions
- HF - Heart Failure
- Keywords
- Heart failure, ECG, Artificial Intelligence, Deep Learning
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 1,500 participants (estimated)
- Lead sponsor
- Ajou University School of MedicineUniversity, hospital or other
- Collaborators
- VUNO Inc.
- Study start
- 1 Jan 2026 (actual)
- Primary completion
- 31 Dec 2027 (estimated)
- Study completion
- 31 Dec 2027 (estimated)
- First posted
- 9 Jun 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
This prospective observational cohort study aims to evaluate the clinical performance of a deep learning-based electrocardiography (ECG) algorithm (DeepECG LVSD) for detecting left ventricular systolic dysfunction (LVSD), defined as left ventricular ejection fraction (LVEF) ≤40%, using transthoracic echocardiography as the reference standard. Approximately 15,000 adult patients undergoing both ECG and echocardiography within 30 days at Ajou University Hospital will be enrolled. Diagnostic performance will be assessed using the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, positive predictive value, negative predictive value, and accuracy. Secondary analyses will evaluate the association between AI-predicted LVSD and 30-day clinical outcomes, including all-cause mortality, emergency department visits, and heart failure rehospitalization.
Detailed description
Left ventricular systolic dysfunction (LVSD) is associated with an increased risk of heart failure, hospitalization, and mortality. Although transthoracic echocardiography is the standard method for assessing left ventricular ejection fraction (LVEF), its widespread use as a screening tool is limited by availability, cost, and the need for specialized personnel. Artificial intelligence (AI)-based electrocardiography (ECG) algorithms have emerged as promising tools for identifying patients with reduced LVEF using routinely acquired ECG signals.
DeepECG LVSD is a deep learning-based ECG algorithm developed to detect LVSD (LVEF ≤40%) from standard 12-lead ECG recordings. Previous retrospective validation studies demonstrated high diagnostic performance; however, prospective clinical validation in real-world practice remains limited.
The purpose of this prospective observational cohort study is to evaluate the diagnostic performance and clinical utility of DeepECG LVSD in adult patients undergoing both ECG and transthoracic echocardiography at Ajou University Hospital. Approximately 15,000 patients aged 19 years or older who have undergone ECG and echocardiography within 30 days will be enrolled.
The primary objective is to assess the accuracy of the AI algorithm for detecting LVSD using echocardiographic LVEF as the reference standard. Diagnostic performance will be evaluated using the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy.
Secondary objectives include evaluating the association between AI-predicted LVSD and short-term clinical outcomes, including 30-day all-cause mortality, emergency department visits, and heart failure rehospitalization. Exploratory subgroup analyses will assess algorithm performance according to demographic and clinical characteristics, including age, sex, heart failure status, chronic kidney disease, hypertension, diabetes mellitus, and the interval between ECG and echocardiography.
This study is designed as a minimal-risk observational study and will provide prospective evidence regarding the effectiveness of AI-enabled ECG screening for LVSD in routine clinical practice. Findings from this study may support broader implementation of AI-based ECG tools for the early identification of patients at risk for heart failure and reduced left ventricular systolic function.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 1 Month
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Adults aged ≥19 years with ECG and echocardiography performed within 30 days | – | Other: None-placeboAdult patients aged 19 years or older who underwent both transthoracic echocardiography and electrocardiography (ECG) within 30 days of each other |
Who may qualify
- Ages
- 19 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Adult patients aged 19 years or older undergoing routine clinical care at Ajou University Hospital who have both transthoracic echocardiography and 12-lead electrocardiography (ECG) performed within 30 days. Participants may be recruited from outpatient clinics, inpatient wards, or the emergency department.
Key inclusion criteria (3 listed)
- Adults aged ≥19 years.
- Patients who underwent both transthoracic echocardiography and 12-lead electrocardiography (ECG) at Ajou University Hospital in the outpatient, inpatient, or emergency department setting.
- ECG and echocardiography performed within 30 days of each other.
Key exclusion criteria (4 listed)
- Interval between ECG and echocardiography greater than 30 days.
- Missing or corrupted original ECG waveform data (XML or HL7 format).
- Presence of an implanted cardiac device, including a permanent pacemaker, implantable cardioverter-defibrillator (ICD), or cardiac resynchronization therapy (CRT) device.
- Missing age, sex, or left ventricular ejection fraction (LVEF) data.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| None-placeboThere is no intervention group | Other | Adults aged ≥19 years with ECG and echocardiography performed within 30 days |
Primary outcome
AUROC for detection of LVSD (LVEF ≤40%)
Time frame: During procedure
Diagnostic performance including AUROC, sensitivity, specificity, positive predictive value, negative predictive value, and accuracy.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 7 Aug 2026.
- Results publication: PubMed 40276542Lopez-Jimenez F, Alger HM, Attia ZI, Barry B, Chatterjee R, Dolor R, Friedman PA, Greene SJ, Greenwood J, Gundurao V, Hackett S, Jain P, Kinaszczuk A, Mehta K, O'Grady J, Pandey A, Pullins C, Puranik AR, Ranganathan MK, Rushlow D, Stampehl M, Subramanian V, Vassor K, Zhu X, Awasthi S. A multicenter pragmatic implementation study of AI-ECG-based clinical decision support software to identify low LVEF: Clinical trial design and methods. Am Heart J Plus. 2025 Mar 21;54:100528. doi: 10.1016/j.ahjo.2025.100528. eCollection 2025 Jun.
- Results publication: PubMed 36229570Choi J, Lee S, Chang M, Lee Y, Oh GC, Lee HY. Author Correction: Deep learning of ECG waveforms for diagnosis of heart failure with a reduced left ventricular ejection fraction. Sci Rep. 2022 Oct 13;12(1):17191. doi: 10.1038/s41598-022-22012-7. No abstract available.
- Results publication: PubMed 30617318Attia ZI, Kapa S, Lopez-Jimenez F, McKie PM, Ladewig DJ, Satam G, Pellikka PA, Enriquez-Sarano M, Noseworthy PA, Munger TM, Asirvatham SJ, Scott CG, Carter RE, Friedman PA. Screening for cardiac contractile dysfunction using an artificial intelligence-enabled electrocardiogram. Nat Med. 2019 Jan;25(1):70-74. doi: 10.1038/s41591-018-0240-2. Epub 2019 Jan 7.
- Individual participant dataSharing planned: No. Individual participant data will not be publicly shared due to patient privacy and institutional data protection policies.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of HF - Heart Failure, run by Ajou University School of Medicine at 1 site in South Korea.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 7 Aug 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in South Korea. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact MOONSEUNG SOH, MD (+82-31-219-5111, mssoh7701@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
AUROC for detection of LVSD (LVEF ≤40%) (time frame: During procedure). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07636759, which also lists 3 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 7 Aug 2026.