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Artificial Intelligence-enabled Large-scale Electrocardiogram Feature Extraction and Exploring Association Between the Extracted Features and Mortality, Stroke or Various Health Outcome of Interest

NCT06179849 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 3000000

Last updated 2024-01-02

No results posted yet for this study

Summary

* In this study, large-scale ECG data (Electrocardiogram data of all patients stored in the MUSE system by measuring standard 12-guided ECG at Severance Health Checkup at Severance Hospital from November 1, 2005 to October 31, 2022) are combined with electronic medical records, National Health Insurance Corporation data, and National Statistical Office death cause data, and the artificial intelligence algorithm is used to extract ECG features to analyze the association between death, stroke, and various health conditions, and to conduct external verification or transfer learning using public databases (e.g., UK Biobank data).
* Intended to use a web-based artificial intelligence platform to distribute computational loads generated during large-scale data processing and improve analysis accuracy and efficiency.

Conditions

  • Health Status(Death, Stroke Etc)

Sponsors & Collaborators

  • Yonsei University

    lead OTHER

Principal Investigators

  • Hui-Nam Pak · Yonsei University

Eligibility

Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2023-12-31
Primary Completion
2025-12-31
Completion
2025-12-31

Countries

  • South Korea

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06179849 on ClinicalTrials.gov