Ignite Creation Date:
2025-12-25 @ 2:16 AM
Ignite Modification Date:
2025-12-26 @ 12:46 AM
Study NCT ID:
NCT07139860
Status:
RECRUITING
Last Update Posted:
2025-08-24
First Post:
2025-08-22
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Artificial Intelligence System for Early Warning of Adverse Events in Acute Myocardial Infarction
Sponsor:
Hui Chen
Organization:
Study Overview
Official Title:
Artificial Intelligence System for Early Warning of Adverse Events in Acute Myocardial Infarction
Status:
RECRUITING
Status Verified Date:
2025-08
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
AIEWAEAMI
Brief Summary:
The goal of this observational study is to learn about the effectiveness of an artificial intelligence-based early warning system for predicting adverse events in patients with acute myocardial infarction (AMI). The main question it aims to answer is:
Does an AI-based early warning system improve the assessment and prediction of adverse events across the full course of AMI care (from prevention to diagnosis, treatment, and rehabilitation)?
Participants who are receiving routine medical care for AMI in tertiary hospitals will have their multimodal medical data (clinical records, diagnostic tests, imaging, treatment pathways) collected and analyzed. Data will be integrated using innovative cross-modal representation methods and predictive models. The study will follow patients during their hospital stay and subsequent clinical follow-up to evaluate the feasibility, accuracy, and clinical value of the AI-based early warning system.
Does an AI-based early warning system improve the assessment and prediction of adverse events across the full course of AMI care (from prevention to diagnosis, treatment, and rehabilitation)?
Participants who are receiving routine medical care for AMI in tertiary hospitals will have their multimodal medical data (clinical records, diagnostic tests, imaging, treatment pathways) collected and analyzed. Data will be integrated using innovative cross-modal representation methods and predictive models. The study will follow patients during their hospital stay and subsequent clinical follow-up to evaluate the feasibility, accuracy, and clinical value of the AI-based early warning system.
Detailed Description:
None
Study Oversight
Has Oversight DMC:
False
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?: