Acute Myocardial Infarction Prediction Using Artificial Intelligence Applied to Electrocardiogram Images

NCT07163767 | Active Not Recruiting DMC

• 1 other identifier: KY2025-514
• Study Type: observational
• Target: 150,000
• Locations: 1 country
• Sites: 1

Brief 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. 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?

Conditions

Acute Myocardial Infarction (AMI); Electrocardiography; Artifical Intelligence

Outcome Measures

Primary Outcomes (1)

• Acute myocardial infarction (AMI)

  AMI was defined as STEMI, NSTEMI using ICD-10.

  From June 2025 to April 2026

Study Arms (1)

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.

Other: Deep learning approach of ECG for AMI detection

Interventions

Deep learning approach of ECG for AMI detection OTHER

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

Cardiorenal ImprovemeNt II (CIN-II)

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

In-hospital patients with ECG records

You may qualify if:

• Hospitalized in cardiology department with myocardial injury marker testing (troponin T/I).
• In-hospital patients with ECG records.

You may not qualify if:

• First ECG obtained in emergency department.
• ACS diagnosis within 1 month of first ECG.

Sponsors & Collaborators

• Guangdong Provincial People's Hospital: lead

Study Sites (1)

Guangdong Provincial People's Hospital

Guangzhou, Guangdong, 510080, China

Location

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Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: September 2, 2025
• First Posted: September 9, 2025
• Study Start: August 1, 2025
• Primary Completion (Estimated): December 31, 2028
• Study Completion (Estimated): December 31, 2028
• Last Updated: December 18, 2025

Record last verified: 2025-05

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)