Acute Myocardial Infarction (AMI)NCT07163767
Acute Myocardial Infarction Prediction Using Artificial Intelligence Applied to Electrocardiogram Images
An observational study of Acute Myocardial Infarction (AMI) and Electrocardiography, run by Guangdong Provincial People's Hospital at 1 site in China.
Trial summary
The goal of this observational study is to develop and validate an artificial intelligence(AI)-based prediction model for new-onset acute myocardial infarction(AMI) using electrocardiogram(ECG) data. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Acute Myocardial Infarction (AMI), Electrocardiography, Artifical Intelligence
- What is being tested?
- Deep learning approach of ECG for AMI detection
- How many people
- 150,000 participants
- Where
- 1 siteChina
- Study dates
- 1 Aug 2025to 31 Dec 2028
- 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 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: In-hospital patients with ECG records
Who may be able to join
- Hospitalized in cardiology department with myocardial injury marker testing (troponin T/I).
- In-hospital patients with ECG records.
Who cannot join
- First ECG obtained in emergency department.
- ACS diagnosis within 1 month of first ECG.
Trial locations and contacts
1 site in 1 country.
About this study
The goal of this observational study is to develop and validate an artificial intelligence(AI)-based prediction model for new-onset acute myocardial infarction(AMI) using electrocardiogram(ECG) data. The main question it aims to answer is whether the AI-based ECG accurately forecast new-onset AMI by previous ECG data with 'normal' diagnosis?
Read the detailed description
Myocardial infarction (MI), as one of the most critical acute clinical events in cardiovascular diseases, has high morbidity and mortality, imposing a significant burden on public health and medical resources. Traditional risk assessment for MI relies on clinical indicators (e.g., lipids, blood pressure, diabetes status, family history) and cardiac imaging, but these methods often suffer from invasive procedure, high cost, or limited predictive accuracy. Electrocardiography (ECG), a non-invasive, widely available, and low-cost modality, captures rich electrophysiological signals reflecting cardiac function. However, conventional analysis methods struggle to detect subtle, progressive patterns in ECG signals, leading to sub-optimal sensitivity and specificity in predicting new-onset MI.
Recent advancements in deep learning and big data have enabled significant progress in ECG-based prediction models. For example, deep convolutional neural networks (CNNs) for automatic feature extraction and pattern recognition from 12-lead ECGs have demonstrated promise in predicting cardiovascular events such as atrial fibrillation, left ventricular hypertrophy, and heart failure re-hospitalization. AI algorithms have also been shown to extract subtle information from ECGs that traditional methods miss, such as dynamic changes in ventricular electrical activity and early signs of micro-myocardial injury, enabling early risk warning of cardiac events. While numerous ECG-based AI models exist for predicting arrhythmia , heart failure, and other cardiovascular outcomes, research on predicting new-onset MI-particularly using non-invasive ECG data and deep learning to extract latent predictive markers-remains in its infancy. Traditional risk models, though successful in MI prevention, lack precision in individual-level prediction and early intervention.
This study aims to leverage large-scale electronic health records and ECG datasets with advanced deep learning to explore the quantitative relationship between fine-grained ECG signal features and MI incidence, thereby developing a clinical tool for early risk assessment. Inspirations also derive from recent attempts to build multi-modal prediction models combining ECG with physiological, genetic, and biochemical markers. Additionally, studies have highlighted ECG's unique advantages in evaluating myocardial compensatory mechanisms and early injury. Despite existing ECG-AI applications, direct prediction of new-onset MI remains a critical unmet need and a key direction for precision medicine using AI.
This is a multi-center observational cohort study. Large-scale in-hospital ECG data will be integrated to develop a deep learning model for MI prediction using an end-to-end deep neural network approach, with the goal of deriving a high-performance model for new-onset MI prediction. The ECG data from 5 multicenter Cardiorenal ImprovemeNt II (CIN-II) sites between 2010-2023 will be assessed.
- Being tested
- Deep learning approach of ECG for AMI detection
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Deep learning approach of ECG for AMI detectionOther
AMIdECG was trained to perform AMI detection in a supervised manner as a classification task. And the classification labels of AMI subtypes (" STEMI "or" NSTEMI ") or non-AMI states used during the training phase are real-world diagnostic results
| Study group | Role | What they receive |
|---|---|---|
| Cardiorenal ImprovemeNt II (CIN-II) | This is a multi-center, retrospective observation study collecting data on 184855 coronary angiography patients from January 2000 to Decemeber 2020. Receives: Other: Deep learning approach of ECG for AMI detection |
What is being measured
The primary outcome is the main question the study is designed to answer.
Acute myocardial infarction (AMI)
AMI was defined as STEMI, NSTEMI using ICD-10.
Measured: From June 2025 to April 2026
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted9 Sep 2025
- Study start1 Aug 2025
- Primary completion31 Dec 2028 (estimated)
- Study completion31 Dec 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (33)Papers the sponsor has linked to this study.
- Other trials for Acute Myocardial Infarction (AMI)Studies 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
- Retrospective
- Enrolment
- 150,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Guangdong Provincial People's Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Liu yong, Guangdong Provincial People's Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07163767
- Sponsor's protocol ID
- KY2025-514
- Status last verified
- May 2025
- First submitted
- 2 Sep 2025
- Last updated
- 18 Dec 2025
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Sadasivuni S, Saha M, Bhatia N, Banerjee I, Sanyal A. Fusion of fully integrated analog machine learning classifier with electronic medical records for real-time prediction of sepsis onset. Sci Rep. 2022 Apr 5;12(1):5711. doi: 10.1038/s41598-022-09712-w.
PubMed 35383233Jawade P, Khillare KM, Mangudkar S, Palange A, Dhadwad J, Deshmukh M. A Comparative Study of Ischemia-Modified Albumin: A Promising Biomarker for Early Detection of Acute Coronary Syndrome (ACS). Cureus. 2023 Aug 30;15(8):e44357. doi: 10.7759/cureus.44357. eCollection 2023 Aug.
PubMed 37779796Swenne CA, Ter Haar CC. Context-independent identification of myocardial ischemia in the prehospital ECG of chest pain patients. J Electrocardiol. 2024 Jan-Feb;82:34-41. doi: 10.1016/j.jelectrocard.2023.10.009. Epub 2023 Nov 7.
PubMed 38006762Akbilgic O, Butler L, Karabayir I, Chang PP, Kitzman DW, Alonso A, Chen LY, Soliman EZ. ECG-AI: electrocardiographic artificial intelligence model for prediction of heart failure. Eur Heart J Digit Health. 2021 Oct 9;2(4):626-634. doi: 10.1093/ehjdh/ztab080. eCollection 2021 Dec.
PubMed 34993487Aufiero S, Bleijendaal H, Robyns T, Vandenberk B, Krijger C, Bezzina C, Zwinderman AH, Wilde AAM, Pinto YM. A deep learning approach identifies new ECG features in congenital long QT syndrome. BMC Med. 2022 May 3;20(1):162. doi: 10.1186/s12916-022-02350-z.
PubMed 35501785Serhal H, Abdallah N, Marion JM, Chauvet P, Oueidat M, Humeau-Heurtier A. Overview on prediction, detection, and classification of atrial fibrillation using wavelets and AI on ECG. Comput Biol Med. 2022 Mar;142:105168. doi: 10.1016/j.compbiomed.2021.105168. Epub 2022 Jan 1.
PubMed 35033876Elul Y, Rosenberg AA, Schuster A, Bronstein AM, Yaniv Y. Meeting the unmet needs of clinicians from AI systems showcased for cardiology with deep-learning-based ECG analysis. Proc Natl Acad Sci U S A. 2021 Jun 15;118(24):e2020620118. doi: 10.1073/pnas.2020620118.
PubMed 34099565Liu YL, Lin CS, Cheng CC, Lin C. A Deep Learning Algorithm for Detecting Acute Pericarditis by Electrocardiogram. J Pers Med. 2022 Jul 15;12(7):1150. doi: 10.3390/jpm12071150.
PubMed 35887647Pignolo RJ. AI-ECG and the Prediction of Accelerated Aging. Mayo Clin Proc. 2023 Apr;98(4):502-503. doi: 10.1016/j.mayocp.2023.02.016. No abstract available.
PubMed 37019510Wang X, Khurshid S, Choi SH, Friedman S, Weng LC, Reeder C, Pirruccello JP, Singh P, Lau ES, Venn R, Diamant N, Di Achille P, Philippakis A, Anderson CD, Ho JE, Ellinor PT, Batra P, Lubitz SA. Genetic Susceptibility to Atrial Fibrillation Identified via Deep Learning of 12-Lead Electrocardiograms. Circ Genom Precis Med. 2023 Aug;16(4):340-349. doi: 10.1161/CIRCGEN.122.003808. Epub 2023 Jun 6.
PubMed 37278238Attia ZI, Friedman PA. Explainable AI for ECG-based prediction of cardiac resynchronization therapy outcomes: learning from machine learning? Eur Heart J. 2023 Feb 21;44(8):693-695. doi: 10.1093/eurheartj/ehac733. No abstract available.
PubMed 36546617Martinez-Selles M, Marina-Breysse M. Current and Future Use of Artificial Intelligence in Electrocardiography. J Cardiovasc Dev Dis. 2023 Apr 17;10(4):175. doi: 10.3390/jcdd10040175.
PubMed 37103054Butler L, Ivanov A, Celik T, Karabayir I, Chinthala L, Tootooni MS, Jaeger BC, Patterson LT, Doerr AJ, McManus DD, Davis RL, Herrington D, Akbilgic O. Time-Dependent ECG-AI Prediction of Fatal Coronary Heart Disease: A Retrospective Study. J Cardiovasc Dev Dis. 2024 Dec 8;11(12):395. doi: 10.3390/jcdd11120395.
PubMed 39728285Sekhri N, Feder GS, Junghans C, Hemingway H, Timmis AD. How effective are rapid access chest pain clinics? Prognosis of incident angina and non-cardiac chest pain in 8762 consecutive patients. Heart. 2007 Apr;93(4):458-63. doi: 10.1136/hrt.2006.090894. Epub 2006 Jun 21.
PubMed 16790531Stubbs P, Collinson P, Moseley D, Greenwood T, Noble M. Prospective study of the role of cardiac troponin T in patients admitted with unstable angina. BMJ. 1996 Aug 3;313(7052):262-4. doi: 10.1136/bmj.313.7052.262.
PubMed 8704534
Study at a glance
From the registry record, last updated by the sponsor on 18 Dec 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Acute Myocardial Infarction Prediction Using Artificial Intelligence Applied to Electrocardiogram Images
- ClinicalTrials.gov ID
- NCT07163767
- Acronym / sponsor ID
- KY2025-514
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 150,000 participants (estimated)
- Lead sponsor
- Guangdong Provincial People's HospitalUniversity, hospital or other
- Study start
- 1 Aug 2025 (actual)
- Primary completion
- 31 Dec 2028 (estimated)
- Study completion
- 31 Dec 2028 (estimated)
- First posted
- 9 Sep 2025 (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.
The goal of this observational study is to develop and validate an artificial intelligence(AI)-based prediction model for new-onset acute myocardial infarction(AMI) using electrocardiogram(ECG) data. The main question it aims to answer is whether the AI-based ECG accurately forecast new-onset AMI by previous ECG data with 'normal' diagnosis?
Detailed description
Myocardial infarction (MI), as one of the most critical acute clinical events in cardiovascular diseases, has high morbidity and mortality, imposing a significant burden on public health and medical resources. Traditional risk assessment for MI relies on clinical indicators (e.g., lipids, blood pressure, diabetes status, family history) and cardiac imaging, but these methods often suffer from invasive procedure, high cost, or limited predictive accuracy. Electrocardiography (ECG), a non-invasive, widely available, and low-cost modality, captures rich electrophysiological signals reflecting cardiac function. However, conventional analysis methods struggle to detect subtle, progressive patterns in ECG signals, leading to sub-optimal sensitivity and specificity in predicting new-onset MI.
Recent advancements in deep learning and big data have enabled significant progress in ECG-based prediction models. For example, deep convolutional neural networks (CNNs) for automatic feature extraction and pattern recognition from 12-lead ECGs have demonstrated promise in predicting cardiovascular events such as atrial fibrillation, left ventricular hypertrophy, and heart failure re-hospitalization. AI algorithms have also been shown to extract subtle information from ECGs that traditional methods miss, such as dynamic changes in ventricular electrical activity and early signs of micro-myocardial injury, enabling early risk warning of cardiac events. While numerous ECG-based AI models exist for predicting arrhythmia , heart failure, and other cardiovascular outcomes, research on predicting new-onset MI-particularly using non-invasive ECG data and deep learning to extract latent predictive markers-remains in its infancy. Traditional risk models, though successful in MI prevention, lack precision in individual-level prediction and early intervention.
This study aims to leverage large-scale electronic health records and ECG datasets with advanced deep learning to explore the quantitative relationship between fine-grained ECG signal features and MI incidence, thereby developing a clinical tool for early risk assessment. Inspirations also derive from recent attempts to build multi-modal prediction models combining ECG with physiological, genetic, and biochemical markers. Additionally, studies have highlighted ECG's unique advantages in evaluating myocardial compensatory mechanisms and early injury. Despite existing ECG-AI applications, direct prediction of new-onset MI remains a critical unmet need and a key direction for precision medicine using AI.
This is a multi-center observational cohort study. Large-scale in-hospital ECG data will be integrated to develop a deep learning model for MI prediction using an end-to-end deep neural network approach, with the goal of deriving a high-performance model for new-onset MI prediction. The ECG data from 5 multicenter Cardiorenal ImprovemeNt II (CIN-II) sites between 2010-2023 will be assessed.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Cardiorenal ImprovemeNt II (CIN-II) | – | Other: Deep learning approach of ECG for AMI detectionThis is a multi-center, retrospective observation study collecting data on 184855 coronary angiography patients from January 2000 to Decemeber 2020. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- In-hospital patients with ECG records
Key inclusion criteria (2 listed)
- Hospitalized in cardiology department with myocardial injury marker testing (troponin T/I).
- In-hospital patients with ECG records.
Key exclusion criteria (2 listed)
- First ECG obtained in emergency department.
- ACS diagnosis within 1 month of first ECG.
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 |
|---|---|---|
| Deep learning approach of ECG for AMI detectionAMIdECG was trained to perform AMI detection in a supervised manner as a classification task. And the classification labels of AMI subtypes (" STEMI "or" NSTEMI ") or non-AMI states used during the training phase are real-world diagnostic results | Other | Cardiorenal ImprovemeNt II (CIN-II) |
Primary outcome
Acute myocardial infarction (AMI)
Time frame: From June 2025 to April 2026
AMI was defined as STEMI, NSTEMI using ICD-10.
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 18 Dec 2025.
- Background: PubMed 35383233Sadasivuni S, Saha M, Bhatia N, Banerjee I, Sanyal A. Fusion of fully integrated analog machine learning classifier with electronic medical records for real-time prediction of sepsis onset. Sci Rep. 2022 Apr 5;12(1):5711. doi: 10.1038/s41598-022-09712-w.
- Background: PubMed 37779796Jawade P, Khillare KM, Mangudkar S, Palange A, Dhadwad J, Deshmukh M. A Comparative Study of Ischemia-Modified Albumin: A Promising Biomarker for Early Detection of Acute Coronary Syndrome (ACS). Cureus. 2023 Aug 30;15(8):e44357. doi: 10.7759/cureus.44357. eCollection 2023 Aug.
- Background: PubMed 38006762Swenne CA, Ter Haar CC. Context-independent identification of myocardial ischemia in the prehospital ECG of chest pain patients. J Electrocardiol. 2024 Jan-Feb;82:34-41. doi: 10.1016/j.jelectrocard.2023.10.009. Epub 2023 Nov 7.
- Background: PubMed 34993487Akbilgic O, Butler L, Karabayir I, Chang PP, Kitzman DW, Alonso A, Chen LY, Soliman EZ. ECG-AI: electrocardiographic artificial intelligence model for prediction of heart failure. Eur Heart J Digit Health. 2021 Oct 9;2(4):626-634. doi: 10.1093/ehjdh/ztab080. eCollection 2021 Dec.
- Background: PubMed 35501785Aufiero S, Bleijendaal H, Robyns T, Vandenberk B, Krijger C, Bezzina C, Zwinderman AH, Wilde AAM, Pinto YM. A deep learning approach identifies new ECG features in congenital long QT syndrome. BMC Med. 2022 May 3;20(1):162. doi: 10.1186/s12916-022-02350-z.
- Background: PubMed 35033876Serhal H, Abdallah N, Marion JM, Chauvet P, Oueidat M, Humeau-Heurtier A. Overview on prediction, detection, and classification of atrial fibrillation using wavelets and AI on ECG. Comput Biol Med. 2022 Mar;142:105168. doi: 10.1016/j.compbiomed.2021.105168. Epub 2022 Jan 1.
- Background: PubMed 34099565Elul Y, Rosenberg AA, Schuster A, Bronstein AM, Yaniv Y. Meeting the unmet needs of clinicians from AI systems showcased for cardiology with deep-learning-based ECG analysis. Proc Natl Acad Sci U S A. 2021 Jun 15;118(24):e2020620118. doi: 10.1073/pnas.2020620118.
- Background: PubMed 35887647Liu YL, Lin CS, Cheng CC, Lin C. A Deep Learning Algorithm for Detecting Acute Pericarditis by Electrocardiogram. J Pers Med. 2022 Jul 15;12(7):1150. doi: 10.3390/jpm12071150.
- Background: PubMed 37019510Pignolo RJ. AI-ECG and the Prediction of Accelerated Aging. Mayo Clin Proc. 2023 Apr;98(4):502-503. doi: 10.1016/j.mayocp.2023.02.016. No abstract available.
- Background: PubMed 37278238Wang X, Khurshid S, Choi SH, Friedman S, Weng LC, Reeder C, Pirruccello JP, Singh P, Lau ES, Venn R, Diamant N, Di Achille P, Philippakis A, Anderson CD, Ho JE, Ellinor PT, Batra P, Lubitz SA. Genetic Susceptibility to Atrial Fibrillation Identified via Deep Learning of 12-Lead Electrocardiograms. Circ Genom Precis Med. 2023 Aug;16(4):340-349. doi: 10.1161/CIRCGEN.122.003808. Epub 2023 Jun 6.
- Background: PubMed 36546617Attia ZI, Friedman PA. Explainable AI for ECG-based prediction of cardiac resynchronization therapy outcomes: learning from machine learning? Eur Heart J. 2023 Feb 21;44(8):693-695. doi: 10.1093/eurheartj/ehac733. No abstract available.
- Background: PubMed 37103054Martinez-Selles M, Marina-Breysse M. Current and Future Use of Artificial Intelligence in Electrocardiography. J Cardiovasc Dev Dis. 2023 Apr 17;10(4):175. doi: 10.3390/jcdd10040175.
- Individual participant dataSharing planned: No
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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. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
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 Acute Myocardial Infarction (AMI) and Electrocardiography, run by Guangdong Provincial People's Hospital at 1 site in China.
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 2 inclusion and 2 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: under way, not looking for participants, as of the sponsor's last update on 18 Dec 2025. 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 China. 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?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Acute myocardial infarction (AMI) (time frame: From June 2025 to April 2026). 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 NCT07163767, which also lists 33 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 18 Dec 2025.