CI

At a glance

ClinicalIndex Comparison Record
N/AActive· 150,000 target
Drug / intervention
Deep learning approach of ECG for AMI detectionother
Likely dose
Not stated in record
Structured eligibility isn't available for this trial yet — see the full criteria in the Eligibility tab below.

Standardized by ClinicalIndex from the ClinicalTrials.gov record · verify against the source.

Search/NCT07163767
NCT07163767N/AActiveOn TrackUpdated 6mo ago
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Acute Myocardial Infarction Prediction Using Artificial Intelligence Applied to Electrocardiogram Images

Guangdong Provincial People's Hospital·observational·Posted Sep 9, 2025·Updated Dec 18, 2025

In Brief

An observational study evaluating Deep learning approach of ECG for AMI detection for Acute Myocardial Infarction (AMI) and 2 related conditions. Active but no longer recruiting, targeting 150,000 participants across 1 site.

Detailed 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?

Study Details

Study Typeobservational
Allocation--
Masking--
Primary Purpose--
ConditionsAcute Myocardial Infarction (AMI), Electrocardiography, Artifical Intelligence
CountriesChina
Collaborators--

Timeline

N/AActive
2026202720282029
First PostedSep 9, 2025
Enrollment StartAug 1, 2025
Primary CompletionDec 31, 2028
TodayJul 2, 2026
Enrollment to primary: 3.4 yearsPosted 10 months agoPrimary completion in 2.5 years

Interventions

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